IMPROVING HIGH PERFORMANCE UTILIZATION OF INFRASTRUCTURE-AS-A- SERVICE CLOUDS

Size: px
Start display at page:

Download "IMPROVING HIGH PERFORMANCE UTILIZATION OF INFRASTRUCTURE-AS-A- SERVICE CLOUDS"

Transcription

1 International Journal Of Engineering And Computer Science ISSN: Volume 2 Issue 8 August, 2013 Page No IMPROVING HIGH PERFORMANCE UTILIZATION OF INFRASTRUCTURE-AS-A- SERVICE CLOUDS M.Anitha, Mr.Veera Prasad,Mrs.T.HimaBindu Ramachandra College Of Engineering Vatluru,Eluru anitha3harshak@gmail.com Ramachandra College Of Engineering Vatluru,Eluru veeru.nes@gmail.com Ramachandra College Of Engineering Vatluru,Eluru himagopal@gmail.com Abstract: An Infrastructure-as-a-Service (IaaS) cloud is provided that users on-demand access to resources. Infrastructure as a Service is a stipulation model in which an organization outsources the equipment used to support operations, including storage, hardware, servers and networking components. The service provider owns the equipment and is conscientious for housing, running and maintaining it. The client typically pays on a per-use basis. However, to provide on-demand access, cloud providers must either significantly overprovision their infrastructure or reject a large proportion of user requests. At the same time, not all users require truly on-demand access to resources. Many applications and workflows are designed for recoverable systems where interruptions in service are expected. We propose a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of cycles from idle cloud nodes to other processes. Infrastructure-as-a-Service (IaaS) represents a new consumption model for the use of IT resources. An IaaS provider offers customers bandwidth, storage and compute power on an elastic, on-demand basis, over the Internet. Key words: Cloud computing, Infrastructure-as-a-Service, High Throughput Computing, service, On-demand, Resources 1. Introduction Infrastructure-as-a-Service (IaaS) provides simple provisioning of processing, storage, networks, and other fundamental computing resources over a network.infrastructure-as-a-service (IaaS) cloud computing is emerging as a viable platform for scientific computing workloads and has the potential to augment traditional highperformance computing resource providers such as supercomputer centers because of its infinite capacity and compelling cost structure provided by the extreme economies of scale involved. Infrastructure-as-a-Service (IaaS) cloud computing has emerged as an attractive alternative to the acquisition and management of physical resources. The on-demand M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2454

2 provisioning it supports allows users to elastically expand and contract the resource base available to them based on an immediate need a pattern that enables a quick turnaround setup, it greatly limits both the speed of the VMs and the flexibility available in choosing software components for the system. time when dealing with emergencies, working towards Second, open-source frameworks, unlike commercial deadlines, or growing an institutional resource base. This frameworks, must be flexible enough to work with many pattern makes it convenient for institutions to configure underlying systems. However, such private cloud private clouds that allow their users a seamless or near seamless transition to community or commercial clouds supporting compatible Virtual Machines(VM) images and cloud interfaces. Such private clouds are typically configured using open source IaaS implementations such as Nimbus or Eucalyptus. Eucalyptus, OpenNebula and Nimbus are three major open-source cloud-computing software platforms. The overall function of these systems is to manage the provisioning of virtual machines for a cloud providing installations also face a utilization problem. In order to ensure on-demand availability a provider needs to overprovision: keep a large proportion of nodes idle so that they can be used to satisfy an on-demand request, which could come at any time. The need to keep all these nodes idle leads to low utilization. The only way to improve it is to keep fewer nodes idle. But this means potentially rejecting a higher proportion of requests to a point at which a provider no longer provides on-demand computing. This situation is particularly hard to accept in the world of infrastructure-as-a-service.these various open-source scientific computing where the use of batch schedulers projects provide an important alternative for those who do not wish to use a commercially provided cloud.the layout of the different parts of a generic cloud computing system. typically ensures high utilization and thus much better resource amortization. Thus, potential low utilization constitutes a significant potential obstacle to the adoption of cloud computing in the scientific world. At the same time, while the on-demand capabilities of IaaS clouds are ideal for many scientific use cases, there are others that do not necessarily require on-demand access to resources.many systems, specifically volunteer computing systems such as SETI@home and Folding@home. SETI@home ("SETI at home") is an Internet-based public volunteer computing project employing the BOINC software platform BOINC (Berkeley Open Infrastructure for Network Computing) is a software system that makes it easy for scientists to create and operate publicresource computing projects. It supports diverse Figure 1: Abstract Cloud Architecture applications, including those with large storage or In a generic open-source cloud computing system, we can identify six basic components. First, we hardware and operating systems that are on the various physical machines in the system. While these must be set up properly for any software system, we make especial note of the physical hardware and the base communication requirements. PC owners can participate in multiple BOINC projects, and can specify how their resources are allocated among these projects. The two original goals of SETI@home were: To do useful scientific work by supporting an observational analysis to detect intelligent life outside Earth, and operating system for two reasons. First, if the processors of the physical nodes do not have the needed hardware extensions to run pure virtualization, this limits the system 1. To prove the viability and practicality of the 'volunteer computing' concept. to par virtualization only. While such a cloud system can be M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2455

3 The current BOINC environment, a development of the original SETI@home, is providing support for many computationally intensive projects in a wide range of disciplines. Folding@home (FAH or F@h) is a distributed computing project for disease research that simulates protein folding, computational drug design, and other types of dynamics. These are capable of taking advantage of resources available opportunistically and are also preemptible, i.e., designed as failure resilient systems where interruptions in service can be handled without compromising the integrity of computation. One example in the scientific community is the use of high throughput computing (HTC), as implemented by e.g., the Condor system where users employ HTC-enabled resources to process their workloads.htc is an approach to distributed computing that focuses on long-term throughput, not instantaneous computing power. The key to HTC is effective management and exploitation of all available computing resources. Since the computing needs of most scientists can be satisfied these days by commodity CPUs and memory, high efficiency is not playing a major role in a HTC environment. The main challenge a typical HTC environment faces is how to maximize the amount of resources accessible to its customers. Distributed ownership of computing resources is the major obstacle such an environment has to overcome in order to expand the pool of resources it can draw from. Recent trends in the cost/performance ratio of computer hardware have placed the control (ownership) over powerful computing resources in the hands of individuals and small groups. These distributed owners will be willing to include their resources in a HTC environment only after they are convinced that their needs will be addressed and their rights protected. These applications are designed to scavenge unused resource cycles: for example, when a user stops using their desktop, the screensaver might use the resource to run a volunteer computing program. The job may then be preempted when the resource becomes unavailable (i.e., the user is using it again), in which case the job is typically requeued and rescheduled on another available resource by the HTC system that manages it. We propose a cloud infrastructure that combines on demand allocation of resources with opportunistic provisioning of cycles from idle cloud nodes to other processes, such as HTC, by deploying backfill VMs. Backfill VMs are deployed on idle cloud nodes and can be configured to perform any desired function. A backfill VM is terminated when the resource is needed to satisfy an on-demand request. If we can ensure that the computation occurring in backfill VMs is resilient to such sudden termination, the time that would otherwise be idle can be profitably spent. Furthermore, cycles via backfill VMs can be provided to users at a lower cost than ondemand VMs because of the cloud providers ability to terminate the instances when needed, thus for users that work with HTC resources and possibly expect such behavior already, backfill VMs would provide a less expensive option when moving their workloads to the cloud. Overall, this design achieves two goals: for cloud providers, it offers a path to higher utilized clouds; for cloud users, it offers another type of resource lease, potentially cheaper than on-demand, nonpreemptible resource. In our work we extend the Nimbus toolkit to deploy backfill VMs on idle Virtual Machine Monitor (VMM) nodes. Nimbus is an open source toolkit for deploying IaaS clouds, designed with extensibility in mind, which makes it particularly suitable for projects such as the one described here. To illustrate how the system works, we configure the backfill VMs as Condor workers that integrate with a Condor pool to process HTC jobs. We evaluate the ability of the system to increase utilization of the IaaS cloud infrastructure without sacrificing the ability of the IaaS cloud to provision resources on-demand. We also evaluate the ability of the system to contribute cycles that would otherwise be idle to processing HTC jobs. We find that during certain portions of our experimental evaluation backfill VMs contribute to an increase in the utilization of the IaaS cloud infrastructure from 37.5% to 100%with only 6.39% overhead cost for processing the HTC workload. Additionally, backfill VMs process the entire Condor workload using what would have otherwise been idle cycles. 2. Related Work Backfilling has been originally introduced to extend the First Come First Served (FCFS) approach in order to increase the M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2456

4 efficiency of the resources usage. Backfilling improves resource utilization by allowing the first job J of the queue to reserve resources that are not available and by evaluating the possible execution of successive jobs in the submission queue Although our work utilizes backfill to achieve high utilization of an IaaS infrastructure, it is different from work that uses backfill scheduling to increase the utilization of large supercomputers. backfill scheduling algorithms Once the BACKFILL algorithm encounters a job step for which it cannot immediately find enough resources, that job step becomes known as a "top dog.define the maximum number of top dogs that the central manager will allocate. For each top dog, the BACKFILL algorithm will attempt to calculate the earliest time at which enough resources will become free to run the corresponding top dog. This is based on the assumption that each currently running job step will run until its hard wall clock limit is reached and that when a job step terminates, the resources which that step has been using will become available. The BACKFILL scheduling algorithm classifies job steps into distinct types: REGULAR, TOP DOG, and BACKFILL: The REGULAR job step is a job step for which enough resources are currently available and no top dogs have yet been allocated.the TOP DOG job step is a job step for which not enough resources are currently available, but enough resources are available at a future time and one of the following conditions is met: The TOP DOG job step is not expected to run at a time when any other top dog is expected to run.if the TOP DOG is expected to run at a time when some other top dogs are expected to run, then it cannot be using resources reserved by such top dogs.the BACKFILL job step is a job step for which enough resources are currently available and one of the following conditions is met:the BACKFILL job step is expected to complete before the future start times of all top dogs, based on the hard wall clock limit of the BACKFILL job step.if the BACKFILL job step is not expected to complete before the future start time of at least one top dog, then it cannot be using resources reserved by the top dogs that are expected to start before BACKFILL job step is expected to complete. Scheduling on supercomputers does not typically assume that backfill jobs will be preempted by an on demand request, seeking to immediately access the resources, while our work assumes this to be the default case. Instead, these backfill scheduling algorithms only attempt to backfill unused resources with requests that match the available slots both in their resource needs as well as their expected runtime.there are, however, preemption based backfill solutions that share many similar characteristics to our work. The major exception is their focus on queue-based supercomputers and our focus on IaaS cloud infrastructures. Volunteer computing systems, such as BOINC, harvest cycles from idle systems distributed across the Internet. Major examples of volunteer applications include SETI@Home and Folding@Home. These applications are designed to accommodate interruptions in service since widely distributed computers, operated by a seemingly infinite number of disparate users, cannot provide any guarantee of service. In the case of volunteer computing systems interruptions in service are usually the results of users returning to their systems to do work, systems crashing, or systems becoming disconnected from the Internet. Much research on volunteer computing focuses on the usefulness, efficiency, and failure prediction of these volatile environments.our work focuses on providing cycles within an IaaS infrastructure that would have otherwise been idle to other processes, such as HTC or volunteer computing, where the services may be interrupted by the arrival of requests for on-demand VMs. Applications that leverage volunteer computing systems would be ideal candidates for backfill VMs because of their ability to handle unexpected failures in service.in we also leverage recovery techniques, specifically suspending and resuming VMs, to achieve high utilization of IaaS cloud infrastructures. While the goal of maintaining high utilization via introducing different types of leases is the same as the work described here, the leases themselves as well as the recovery technique used, specifically that of suspending and resuming VMs, is different from the focus in our work. Instead of using suspend/resume to support advanced reservations we leverage a recovery system that uses resubmission (Condor) to ensure that high utilization is M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2457

5 achieved and no work is lost.another area that shares related A compute infrastructure cloud operates by allowing a user themes to our work is spot pricing, as exemplified by to make leases against its pool of resources; an infrastructure Amazon With spot pricing users place bids for instances and lease makes a resource available to the user based on set of the cloud provider periodically adjusts the price of spot lease terms defining the availability, capacity and general instances, terminating the spot instances with bids that fall conditions of a lease. In our system we focus on below the new spot price and launching instances that meet investigating two types of leases: or exceed the spot price. Our work uses the current demand On-demand, non-preemptible and flexible leases give a for on-demand user VMs to determine the availability for user access to a resource within interactive time of making backfill VMs while Amazon bases availability of spot the request and make the resource available for an agreedupon period of time. The user can deploy any instances on a spot price. VM 3. Approach Cloud computing can be the ability to use applications on the Internet that store and protect data while providing a service. Cloud computing builds on established trends for driving the cost out of the delivery of services while increasing the speed and agility with which services are deployed. Cloud computing incorporates virtualization, on-demand deployment, Internet delivery of services, and open source software. Distributed file system which spreads over multiple hard disks and machines. It provides redundancy, high speed and reliability. Data is never stored in one place only and when one unit fails; its place is taken automatically by another one. For the reader it might be important to know that user disk space is allocated on the distributed file system. While this technology ensures high reliability for the user's files, it is more expensive than a standard solution without data replication. Algorithm for resource allocation. Cloud computing is a complex distributed environment and it relies heavily on strong algorithms for allocating properly CPU, RAM and hard disk operations to end users and core processes in a mutual and shared system. Here comes the matter of resource accounting and there are two distinct alternatives. The first one is strictly usage-oriented where you have a limited number of units. Such units can be connected to CPU and/or Memory usage, time or they can be a compound indicator. This covers generally the idea of utility computing. As a whole it gives some flexibility but it is more expensive in the long term. compatible with the system. Opportunistic, preemptible and pre-set leases give a user access to a resource at an indeterminate time and make the resource available to the user for an indeterminate amount of time. Further, this resource is pre-defined for the user by the cloud administrator, i.e. the user cannot provide his or her own VM. An on-demand useris a user that requests on-demand VMs/leases from an IaaS cloud. An on-demand VM is an IaaS VM that has been provisioned via on on-demand lease for a specific user. A backfill VM is a VM that has been deployed automatically by the IaaS cloud manager on an idle IaaS node using a preemptible lease. An IaaS cloud administrator is the person or persons responsible for configuring and managing the IaaS cloud resource. An HTC user is a user that submits jobs to an HTC queue and an HTC workeris a system that process jobs from the HTC queue.in the context of IaaS clouds, backfill VMs are generic VMs deployed on IaaS resources using a preemptible lease that may be configured to perform any function. Backfill VMs have two major constraints. First, backfill VMs may be terminated suddenly in order to free up space for the IaaS cloud manager to service an on-demand lease. Second, because of the unpredictable timing of ondemand leases(from any number of unique users), a variable number of backfill VMs may be available at any given time. Thus, we assume that applications executing inside backfill VMs are designed to handle environments that contain a variable number of workers that may join or leave the system at any time.certain applications are not well suited for these volatile environments, for example, parallel applications that require all processes to be present for the duration of the application s execution and lack M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2458

6 checkpoint/restart capabilities. An example of a system that is well-suited for backfill VMs are High Throughput Computing (HTC) workloads. HTC workloads typically consist of a large number of jobs that eventually need to be IaaS cloud. The configuration is influenced by the characteristics of the underlying physical IaaS resources, the users of on-demand leases, and the users of preemptible leases.first, appropriate backfill applications and workflows processed. An assumption of HTC workloads is that they do should be identified. These applications should not have an immediate deadline and therefore do not need resources to be available at a particular time. 3.1 Architecture accommodate the constraints discussed previously, namely, they should be able to utilize a variable number of nodes that may join or leave the system at any time.second, if the IaaS Virtual Machine Monitor (VMM) nodes have multiple cores, the IaaS cloud administrator must determine the granularity with which to deploy backfill VMs. A virtual machine monitor (VMM) is a host program that allows a single computer to support multiple, identical execution environments. All the users see their systems as selfcontained computers isolated from other users, even though every user is served by the same machine. In this context, a virtual machine is an operating system (OS) that is managed by an underlying control program. One possible solution is to deploy a single backfill VM for each core on a Figure 2 : Example Backfill Deployment Figure 2 is a simple example deployment consisting of an IaaS Nimbus cloud using backfill VMs to run Condor HTC jobs in order to increase cloud infrastructure utilization. In this example the HTC user submits 3 individual tasks to the Condor master, which is able to schedule 1 task immediately on a local worker. However, because the remaining resources in site B s Condor pool are busy, Condor leverages the cycles provided by site A s backfill VMs to launch the other 2 tasks immediately.condor is an ideal candidate for such a deployment because of its original design as a cycle-scavenger where idle desktop machines execute jobs until the system s user returns, after which the job is either migrated to another system or prematurely terminated and re-launched on another system. A backfill deployment could be much more complex then the example depicted in Figure 2. For instance, backfill VMs could host a number of different backfill processes in addition to Condor workers. The Condor workers could also be configured to execute jobs from multiple Condor pools or sites instead of a single pool, as shown in the figure.iaas cloud administrators must consider a variety of different backfill configurations when deploying a backfill solution on an VMM node. This approach allows the IaaS cloud manager to have fine-grained control over VM deployment. However, the disadvantage of this approach is the increased overhead introduced by running additional VMs. An alternative solution is to deploy a single backfill VM per VMM node, utilizing all of the available cores in the backfill VM. This approach reduces the virtualization overhead on the VMM node since only a single backfill VM would be running, however, if the cloud receives an on-demand user request for a single core it is possible that the entire backfill VM will need to be terminated to satisfy the on-demand request. This would leave the additional VMM cores idle. Alternatively, the administrator may wish to configure VM deployments based on allocation of other resources, such as RAM, instead of (or in addition to)cpu cores. Third, the IaaS cloud administrator must determine the approximate size of the backfill deployment, relative to the size of the IaaS cloud. The administrator may allow the backfill deployment to utilize all available nodes, however, the administrator should consider the additional overhead required to terminate backfill nodes in order to fulfill ondemand user requests. Depending on the method used to terminate backfill VMs and the number of backfill nodes being terminated, this overhead may be significant. M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2459

7 Consequently, IaaS cloud administrators may configure backfill deployments to onlyutilize a percentage of idle nodes or never allow backfill deployments exceed a preset and static number of idle nodes. Finally, the IaaS cloud administrator must determine the backfill VM image deployment method. A fresh backfill VM image could potentially be transferred from the VM image repository for every backfill VM deployment. however, this introduces network contention and may slow the deployment of ondemand user VMs. Another solution is to propagate the backfill image to the node and cache it on the node, only propagating it again if the image is updated or is removed from the cache on the node. A third and simple solution is to manually place the backfill image on each VMM node. Thus, when a backfill VM boots, the image is already on the VMM node and doesn t require an image to be copied. This approach reduces network contention (since it is only performed at select times) and reduces launch time for the VM (since the backfill image doesn t need to be copied across the network). However, any changes to the backfill image require that the IaaS cloud administrator push it out to all VMM nodes. 3.2 Backfill Termination Policies Backfill VMs are deployed on idle VMM nodes and terminated whenever space is needed to service an ondemand lease. However, the specific backfill VMs that are selected for termination may impact the services, applications, or workflows executing inside those VMs. For this reason, ideally the backfill VM termination policies should consider the applications and services running inside those VMs as well as generic factors such as the need for clean shutdown, the ability of an application to checkpoint/restart, etc. We discuss below two simple policies that do not integrate such hints and highlight their shortcomings. One simple policy is to select the backfill VMs to terminate at random. This approach ignores a number of factors that could impact the services and applications running inside backfill VMs. In particular, if the random backfill VM selected for termination is the only backfill VM performing a useful task then its work may be lost while idle backfill VMs continue running. Another policy is to select the backfill VM that has been running for the least amount of time. This policy makes the assumption that the backfill VMs running the longest also run longrunning jobs that have performed the most work and therefore will lose the most work when terminated. The intuition behind this policy is that a workload consisting entirely of short running jobs (e.g. less then a few minutes) will only be slightly impacted by the termination of any backfill VM however, workloads that consist of long running jobs (or a combination of long and short jobs) will be impacted more by the termination of a VM that has been deployed for an extended period of time. In this case all of the VM's work will be lost unless the application supports checkpoint/restart or migration. It is, however, possible that the backfill VM running the longest will not always be the backfill VM with the most work to lose; without tighter integration between the HTC job manager and backfill termination policy this information is not readily accessible. More advanced backfill termination policies will require hints from the IaaS scheduler in the form of a more complete picture of cloud VM placement or integration with the applications and services running inside of the backfill VMs. For instance, if the backfill termination policy is aware of VM placement on individual VMM nodes, it may be able to select the fewest number of backfill VMs for termination in order to satisfy an on-demand lease instead of blindly terminating backfill VM s until the request can be fulfilled. Alternatively, if the backfill termination policy integrates with the applications and services running inside of backfill VMs then it may be able to identify which backfill VMs are performing useful tasks and avoid terminating those VMs. 3.3 Infrastructure-as-a-Service Infrastructure-as-a-Service (IaaS) represents a new consumption model for the use of IT resources. An IaaS provider offers customers bandwidth, storage and compute power on an elastic, on-demand basis, over the Internet. Companies reasons for choosing an IaaS environment differ, depending on the size of the organization and the nature of the business. Cost is often the primary reason. M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2460

8 For Small and Medium Businesses (SMBs) with a limited capital budget, IaaS shifts the capital requirement to an operational expense that tracks with the growth of the business. Even among large enterprises, infrastructure costs are a driving force for considering IaaS. IaaS other key benefits include improved cashflow, accommodation of widely inaccurate provision planning, and exceptional transparency in utilization and costs. Tata Communications has demonstrated its commitment to IaaS with the introduction of InstaCompute. Joining our suite of managed services that also includes colocation and managed hosting, InstaCompute adds a key component to the company s IT services road map. Evolution to Infrastructure-as-a-Service (IaaS) Traditionally, companies met their growing IT needs by investing in more capital equipment. Today, competitive pressures continue to demand improvements in quality of service despite growing numbers of users and applications. At the same time, the challenging economic environment has increased pressure on IT departments to keep costs down. The convergence of those trends, with other advances of the last several years, has made it possible to take infrastructure outsourcing to a new level. Building on the foundation of managed services such as colocation, hosting, and virtualization services, Infrastructure-as-a-Service (IaaS) has emerged as an easily deployed service that enables companies to flexibly and cost-effectively anticipate and evolve with their customers rapidly changing business requirements. IaaS provides simple provisioning of processing, storage, networks, and other fundamental computing resources over a network. With IaaS, IT services can be delivered as a subscription service, eliminating up-front costs and driving down ongoing support costs (enabling companies to make a strategic shift from a CAPEX to OPEX-based business model). As with managed hosting, IaaS providers keep costs low by pooling resources and giving customers access to a shared facility. But a major difference is that IaaS resources are elastic and available on a selfservice, on-demand basis.while IaaS providers often differ in their specific offerings, key features of all IaaS models include: Instant deployment Ability to rapidly scale Lower TCO Predictable uptime Software-as-a-Service (SaaS) Platform-as-a-Service (PaaS) Infrastructure-as-a-Service (IaaS) Virtualization Layer Physical Layer Figure 3: Cloud Computing Service Models One generally agreed-upon area in the industry is that there are three service models of cloud computing: Infrastructureas-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software- as-a-service (SaaS).PaaS is similar to IaaS, except that the service includes a specific set of programming languages and tools (the platform). Generally aimed at the developer community, PaaS is analogous to an on-premises application server, only with elasticity and other cloud-computing features. Since platform software is fixed with the service, PaaS places restrictions on the applications that can be built.saas is essentially the delivery of conventional IT applications to end users over the Internet. SaaS is analogous to a client/server model, except that the server is replaced by the SaaS provider s data center, the clients are simply web browsers on desktops (or mobile devices), and the service offers cloudcomputing benefits such as elasticity and pay-as-you-consume metering. SaaS gained a foothold with universal applications such as and Customer Relationship Management (CRM) applications like Salesforce.com. M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2461

9 The three cloud computing service models can be viewed as a stack, with each layer increasing in specificity, while decreasing control of the underlying resources. The three layers sit above a virtualization layer, which itself sits above the physical servers, storages, and network hardware. Provides applications via the Internet and web browser Targeted at end users Offers an application-centric environment Targeted at software developers Allocates bandwidth, storage, and compute resources to customers on demand Targeted at medium to large businesses 4. Conclusion In this paper we propose a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of cycles from idle cloud nodes to other processes, such as HTC, by deploying backfill VMs. Additionally, backfill VMs make available cycles that would have otherwise been idle to assist in processing HTC jobs.we extend the open source Nimbus IaaS toolkit to deploy backfill VMs on idle cloud nodes. We evaluate the backfill solution using an on-demand user workload and an HTC workload. BOINC now uses storage only on the disk or filesystem on which it is installed.this mechanism must allow user to specify preferences for whether and how alternate disks are used, and it must deal with situations where hosts share network-attached storage.we find backfill VMs contribute to an increase of the utilization of the IaaS cloud infrastructure from 37.5% to 100% during a portion of the evaluation trace but result in only 6.39% additional overhead. Although our supercomputer systems will almost certainlyremain allocated by committee instead of billed directly, we intend to support the cloud computing IaaS approach on our future systems. Even if these systems system cannot provide unlimited resources with no delay, it will most certainly be able to provide limited resources to a limited user community with no delay. By adding cloud support, HPC projects can continue to run using familiar interactive and Grid interfaces,and projects that could be run on commercial clouds can be migrated to virtual machine platforms by their developers overtime and at their convenience. 5. References 1. Amazon Web Services. Amazon.Com, Inc. [Online]. Retreived December 6, 2010, From: 2. Larson SM, Snow CD, Shirts M, Pande VS. Folding@Home And Genome@Home: Using Distributed Computing To Tackle Previously Intractable Problems In Computational Biology. Computational Genomics, Horizon Press, Lefe`Vre, L. And Orgerie, AC. Designing And Evaluating An Energy Efficient Cloud, The Journal Of Supercomputing, Vol. 51, Pp , 2010, 4. L. Youseff, M. Butrico, And D. Da Silva, Toward A Unified Ontology Of Cloud Computing, In Proceedings Of The Grid Computing Environments Workshop, (GCE08), November Desai, Cobalt: Component-Based Lightweight Toolkit (HPC JobManagementSuite) ojects/cobalt. 5. S.Ostermann, A. Iosup, N. Yigitbasi, R. Prodan, T. Fahringer, And D. Epema, A Performance Analysis Of EC2 Cloud Computing Services For Scientific Computing, In Cloudcomp 2009: Proceedings Of Cloudcomp 2009, October Amazon Elastic Compute Cloud (Amazon EC2) BOINC: A System For Public-Resource Computing And Storage David P. Anderson Space Sciences Laboratory University Of California At Berkeley Davea@Ssl.Berkeley.Edu 8. A Survey Of Mobile Cloud Computing: Architecture, Applications, And Approaches M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2462

10 Hoang T. Dinh, Chonho Lee, Dusit Niyato, And Ping Wang. 9. M. Satyanarayanan, P. Bahl, R. Caceres, And N. Davies, The Case For VM-Based Cloudlets In Mobile Computing, IEEE Pervasive Computing, Vol. 8, No. 4, Pp , October K. Keahey, M. Tsugawa, A. Matsunaga, And J. Fortes, Sky Computing, IEEE Internet Computing Magazine, Vol. 13, No. 5, Pp. 43, September 2009 M.Anitha, IJECS Volume 2 Issue 8 August, 2013 Page No Page 2463

Storage Way of Utilization the Infrastructure Clouds

Storage Way of Utilization the Infrastructure Clouds Storage Way of Utilization the Infrastructure Clouds Badi.Rajani 1, Dr.Venkateshwarla Rama Raju 2 Associate Professor, Dept. of CSE, CMRCET (Autonomous), Hyderabad, India Professor, Dept. of CSE, CMRCET

More information

Improving Utilization of Infrastructure Clouds

Improving Utilization of Infrastructure Clouds Improving Utilization of Infrastructure Clouds Paul Marshall Department of Computer Science University of Colorado at Boulder Boulder, CO USA paul.marshall@colorado.edu Kate Keahey 1,2 and Tim Freeman

More information

Civilizing Exploitation of Communication Clouds

Civilizing Exploitation of Communication Clouds Civilizing Exploitation of Communication Clouds Janga Santhosh, Puram Pradeep kumar, Majoju Sridhar kumar Computer science and Eng. Department, JNT University,Karimnagar, Andhra Pradesh, India Abstract

More information

A Comparative Study of cloud and mcloud Computing

A Comparative Study of cloud and mcloud Computing A Comparative Study of cloud and mcloud Computing Ms.S.Gowri* Ms.S.Latha* Ms.A.Nirmala Devi* * Department of Computer Science, K.S.Rangasamy College of Arts and Science, Tiruchengode. s.gowri@ksrcas.edu

More information

Infrastructure as a Service (IaaS)

Infrastructure as a Service (IaaS) Infrastructure as a Service (IaaS) (ENCS 691K Chapter 4) Roch Glitho, PhD Associate Professor and Canada Research Chair My URL - http://users.encs.concordia.ca/~glitho/ References 1. R. Moreno et al.,

More information

ABSTRACT. KEYWORDS: Cloud Computing, Load Balancing, Scheduling Algorithms, FCFS, Group-Based Scheduling Algorithm

ABSTRACT. KEYWORDS: Cloud Computing, Load Balancing, Scheduling Algorithms, FCFS, Group-Based Scheduling Algorithm A REVIEW OF THE LOAD BALANCING TECHNIQUES AT CLOUD SERVER Kiran Bala, Sahil Vashist, Rajwinder Singh, Gagandeep Singh Department of Computer Science & Engineering, Chandigarh Engineering College, Landran(Pb),

More information

Li Sheng. lsheng1@uci.edu. Nowadays, with the booming development of network-based computing, more and more

Li Sheng. lsheng1@uci.edu. Nowadays, with the booming development of network-based computing, more and more 36326584 Li Sheng Virtual Machine Technology for Cloud Computing Li Sheng lsheng1@uci.edu Abstract: Nowadays, with the booming development of network-based computing, more and more Internet service vendors

More information

PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM

PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM Akmal Basha 1 Krishna Sagar 2 1 PG Student,Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, India. 2 Associate

More information

Dynamic Resource Distribution Across Clouds

Dynamic Resource Distribution Across Clouds University of Victoria Faculty of Engineering Winter 2010 Work Term Report Dynamic Resource Distribution Across Clouds Department of Physics University of Victoria Victoria, BC Michael Paterson V00214440

More information

Permanent Link: http://espace.library.curtin.edu.au/r?func=dbin-jump-full&local_base=gen01-era02&object_id=154091

Permanent Link: http://espace.library.curtin.edu.au/r?func=dbin-jump-full&local_base=gen01-era02&object_id=154091 Citation: Alhamad, Mohammed and Dillon, Tharam S. and Wu, Chen and Chang, Elizabeth. 2010. Response time for cloud computing providers, in Kotsis, G. and Taniar, D. and Pardede, E. and Saleh, I. and Khalil,

More information

INTRODUCTION TO CLOUD COMPUTING CEN483 PARALLEL AND DISTRIBUTED SYSTEMS

INTRODUCTION TO CLOUD COMPUTING CEN483 PARALLEL AND DISTRIBUTED SYSTEMS INTRODUCTION TO CLOUD COMPUTING CEN483 PARALLEL AND DISTRIBUTED SYSTEMS CLOUD COMPUTING Cloud computing is a model for enabling convenient, ondemand network access to a shared pool of configurable computing

More information

International Journal of Advanced Research in Computer Science and Software Engineering

International Journal of Advanced Research in Computer Science and Software Engineering Volume 2, Issue 12, December 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Designs of

More information

Towards a New Model for the Infrastructure Grid

Towards a New Model for the Infrastructure Grid INTERNATIONAL ADVANCED RESEARCH WORKSHOP ON HIGH PERFORMANCE COMPUTING AND GRIDS Cetraro (Italy), June 30 - July 4, 2008 Panel: From Grids to Cloud Services Towards a New Model for the Infrastructure Grid

More information

Ecole des Mines de Nantes. Journée Thématique Emergente "aspects énergétiques du calcul"

Ecole des Mines de Nantes. Journée Thématique Emergente aspects énergétiques du calcul Ecole des Mines de Nantes Entropy Journée Thématique Emergente "aspects énergétiques du calcul" Fabien Hermenier, Adrien Lèbre, Jean Marc Menaud menaud@mines-nantes.fr Outline Motivation Entropy project

More information

An Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform

An Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform An Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform A B M Moniruzzaman 1, Kawser Wazed Nafi 2, Prof. Syed Akhter Hossain 1 and Prof. M. M. A. Hashem 1 Department

More information

Keywords Distributed Computing, On Demand Resources, Cloud Computing, Virtualization, Server Consolidation, Load Balancing

Keywords Distributed Computing, On Demand Resources, Cloud Computing, Virtualization, Server Consolidation, Load Balancing Volume 5, Issue 1, January 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Survey on Load

More information

CHAPTER 8 CLOUD COMPUTING

CHAPTER 8 CLOUD COMPUTING CHAPTER 8 CLOUD COMPUTING SE 458 SERVICE ORIENTED ARCHITECTURE Assist. Prof. Dr. Volkan TUNALI Faculty of Engineering and Natural Sciences / Maltepe University Topics 2 Cloud Computing Essential Characteristics

More information

Security Considerations for Public Mobile Cloud Computing

Security Considerations for Public Mobile Cloud Computing Security Considerations for Public Mobile Cloud Computing Ronnie D. Caytiles 1 and Sunguk Lee 2* 1 Society of Science and Engineering Research Support, Korea rdcaytiles@gmail.com 2 Research Institute of

More information

Auto-Scaling Model for Cloud Computing System

Auto-Scaling Model for Cloud Computing System Auto-Scaling Model for Cloud Computing System Che-Lun Hung 1*, Yu-Chen Hu 2 and Kuan-Ching Li 3 1 Dept. of Computer Science & Communication Engineering, Providence University 2 Dept. of Computer Science

More information

Sistemi Operativi e Reti. Cloud Computing

Sistemi Operativi e Reti. Cloud Computing 1 Sistemi Operativi e Reti Cloud Computing Facoltà di Scienze Matematiche Fisiche e Naturali Corso di Laurea Magistrale in Informatica Osvaldo Gervasi ogervasi@computer.org 2 Introduction Technologies

More information

Cloud Infrastructure Pattern

Cloud Infrastructure Pattern 1 st LACCEI International Symposium on Software Architecture and Patterns (LACCEI-ISAP-MiniPLoP 2012), July 23-27, 2012, Panama City, Panama. Cloud Infrastructure Pattern Keiko Hashizume Florida Atlantic

More information

Mobile Hybrid Cloud Computing Issues and Solutions

Mobile Hybrid Cloud Computing Issues and Solutions , pp.341-345 http://dx.doi.org/10.14257/astl.2013.29.72 Mobile Hybrid Cloud Computing Issues and Solutions Yvette E. Gelogo *1 and Haeng-Kon Kim 1 1 School of Information Technology, Catholic University

More information

Cloud Computing: The Next Computing Paradigm

Cloud Computing: The Next Computing Paradigm Cloud Computing: The Next Computing Paradigm Ronnie D. Caytiles 1, Sunguk Lee and Byungjoo Park 1 * 1 Department of Multimedia Engineering, Hannam University 133 Ojeongdong, Daeduk-gu, Daejeon, Korea rdcaytiles@gmail.com,

More information

Tamanna Roy Rayat & Bahra Institute of Engineering & Technology, Punjab, India talk2tamanna@gmail.com

Tamanna Roy Rayat & Bahra Institute of Engineering & Technology, Punjab, India talk2tamanna@gmail.com IJCSIT, Volume 1, Issue 5 (October, 2014) e-issn: 1694-2329 p-issn: 1694-2345 A STUDY OF CLOUD COMPUTING MODELS AND ITS FUTURE Tamanna Roy Rayat & Bahra Institute of Engineering & Technology, Punjab, India

More information

VIRTUAL RESOURCE MANAGEMENT FOR DATA INTENSIVE APPLICATIONS IN CLOUD INFRASTRUCTURES

VIRTUAL RESOURCE MANAGEMENT FOR DATA INTENSIVE APPLICATIONS IN CLOUD INFRASTRUCTURES U.P.B. Sci. Bull., Series C, Vol. 76, Iss. 2, 2014 ISSN 2286-3540 VIRTUAL RESOURCE MANAGEMENT FOR DATA INTENSIVE APPLICATIONS IN CLOUD INFRASTRUCTURES Elena Apostol 1, Valentin Cristea 2 Cloud computing

More information

In a dynamic economic environment, your company s survival

In a dynamic economic environment, your company s survival Chapter 1 Cloud Computing Defined In This Chapter Examining the reasons for cloud Understanding cloud types Defining the elements of cloud computing Comparing private and public clouds In a dynamic economic

More information

Prof. Luiz Fernando Bittencourt MO809L. Tópicos em Sistemas Distribuídos 1 semestre, 2015

Prof. Luiz Fernando Bittencourt MO809L. Tópicos em Sistemas Distribuídos 1 semestre, 2015 MO809L Tópicos em Sistemas Distribuídos 1 semestre, 2015 Introduction to Cloud Computing IT Challenges 70% of the budget to keep IT running, 30% available to create new value that needs to be inverted

More information

An Approach to Load Balancing In Cloud Computing

An Approach to Load Balancing In Cloud Computing An Approach to Load Balancing In Cloud Computing Radha Ramani Malladi Visiting Faculty, Martins Academy, Bangalore, India ABSTRACT: Cloud computing is a structured model that defines computing services,

More information

A Gentle Introduction to Cloud Computing

A Gentle Introduction to Cloud Computing A Gentle Introduction to Cloud Computing Source: Wikipedia Platform Computing, Inc. Platform Clusters, Grids, Clouds, Whatever Computing The leader in managing large scale shared environments o 18 years

More information

Competitive Comparison Between Microsoft and VMware Cloud Computing Solutions

Competitive Comparison Between Microsoft and VMware Cloud Computing Solutions Competitive Comparison Between Microsoft and VMware Cloud Computing Solutions Introduction As organizations evaluate how cloud computing can help them improve business agility, reduce management complexity

More information

Toward a Unified Ontology of Cloud Computing

Toward a Unified Ontology of Cloud Computing Toward a Unified Ontology of Cloud Computing Lamia Youseff University of California, Santa Barbara Maria Butrico, Dilma Da Silva IBM T.J. Watson Research Center 1 In the Cloud Several Public Cloud Computing

More information

Cyberinfrastructure Education and Hands-on Training Using the CH3D-GTM Virtual Appliance on SURAGrid

Cyberinfrastructure Education and Hands-on Training Using the CH3D-GTM Virtual Appliance on SURAGrid Cyberinfrastructure Education and Hands-on Training Using the CH3D-GTM Virtual Appliance on SURAGrid Renato Figueiredo http://grid-appliance.org J. Davis, J. Fortes, P. Sheng, V. Paramygin, B. Tutak, D.

More information

Outline. What is cloud computing? History Cloud service models Cloud deployment forms Advantages/disadvantages

Outline. What is cloud computing? History Cloud service models Cloud deployment forms Advantages/disadvantages Ivan Zapevalov 2 Outline What is cloud computing? History Cloud service models Cloud deployment forms Advantages/disadvantages 3 What is cloud computing? 4 What is cloud computing? Cloud computing is the

More information

Cloud computing and SAP

Cloud computing and SAP Cloud computing and SAP Next Generation SAP Technologies Volume 1 of 2010 Table of contents Document history 1 Overview 2 SAP Landscape challenges 3 Infrastructure as a Service (IaaS) 4 Public, Private,

More information

Lecture 02a Cloud Computing I

Lecture 02a Cloud Computing I Mobile Cloud Computing Lecture 02a Cloud Computing I 吳 秀 陽 Shiow-yang Wu What is Cloud Computing? Computing with cloud? Mobile Cloud Computing Cloud Computing I 2 Note 1 What is Cloud Computing? Walking

More information

Enterprise Cloud Solutions

Enterprise Cloud Solutions IT(O) IT Outsourcing Options Enterprise Cloud Solutions CloudAgile Select Partner PDF v2.2 9/11/12 Cloud Computing with Latisys With the Latisys Cloud, your Enterprise can: Achieve unprecedented control,

More information

Getting Familiar with Cloud Terminology. Cloud Dictionary

Getting Familiar with Cloud Terminology. Cloud Dictionary Getting Familiar with Cloud Terminology Cloud computing is a hot topic in today s IT industry. However, the technology brings with it new terminology that can be confusing. Although you don t have to know

More information

Amazon Web Services Primer. William Strickland COP 6938 Fall 2012 University of Central Florida

Amazon Web Services Primer. William Strickland COP 6938 Fall 2012 University of Central Florida Amazon Web Services Primer William Strickland COP 6938 Fall 2012 University of Central Florida AWS Overview Amazon Web Services (AWS) is a collection of varying remote computing provided by Amazon.com.

More information

Understanding Microsoft Storage Spaces

Understanding Microsoft Storage Spaces S T O R A G E Understanding Microsoft Storage Spaces A critical look at its key features and value proposition for storage administrators A Microsoft s Storage Spaces solution offers storage administrators

More information

GETTING THE MOST FROM THE CLOUD. A White Paper presented by

GETTING THE MOST FROM THE CLOUD. A White Paper presented by GETTING THE MOST FROM THE CLOUD A White Paper presented by Why Move to the Cloud? CLOUD COMPUTING the latest evolution of IT services delivery is a scenario under which common business applications are

More information

A Study of Infrastructure Clouds

A Study of Infrastructure Clouds A Study of Infrastructure Clouds Pothamsetty Nagaraju 1, K.R.R.M.Rao 2 1 Pursuing M.Tech(CSE), Nalanda Institute of Engineering & Technology,Siddharth Nagar, Sattenapalli, Guntur., Affiliated to JNTUK,

More information

Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load

Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load Pooja.B. Jewargi Prof. Jyoti.Patil Department of computer science and engineering,

More information

21/09/11. Introduction to Cloud Computing. First: do not be scared! Request for contributors. ToDO list. Revision history

21/09/11. Introduction to Cloud Computing. First: do not be scared! Request for contributors. ToDO list. Revision history Request for contributors Introduction to Cloud Computing https://portal.futuregrid.org/contrib/cloud-computing-class by various contributors (see last slide) Hi and thanks for your contribution! If you

More information

Datacenters and Cloud Computing. Jia Rao Assistant Professor in CS http://cs.uccs.edu/~jrao/cs5540/spring2014/index.html

Datacenters and Cloud Computing. Jia Rao Assistant Professor in CS http://cs.uccs.edu/~jrao/cs5540/spring2014/index.html Datacenters and Cloud Computing Jia Rao Assistant Professor in CS http://cs.uccs.edu/~jrao/cs5540/spring2014/index.html What is Cloud Computing? A model for enabling ubiquitous, convenient, ondemand network

More information

Capacity Leasing in Cloud Systems using the OpenNebula Engine

Capacity Leasing in Cloud Systems using the OpenNebula Engine Capacity Leasing in Cloud Systems using the OpenNebula Engine Borja Sotomayor 1,2, Rubén Santiago Montero 1, Ignacio Martín Llorente 1, and Ian Foster 2,3 1 Facultad de Informática, Universidad Complutense

More information

Multifaceted Resource Management for Dealing with Heterogeneous Workloads in Virtualized Data Centers

Multifaceted Resource Management for Dealing with Heterogeneous Workloads in Virtualized Data Centers Multifaceted Resource Management for Dealing with Heterogeneous Workloads in Virtualized Data Centers Íñigo Goiri, J. Oriol Fitó, Ferran Julià, Ramón Nou, Josep Ll. Berral, Jordi Guitart and Jordi Torres

More information

Optimizing the Cost for Resource Subscription Policy in IaaS Cloud

Optimizing the Cost for Resource Subscription Policy in IaaS Cloud Optimizing the Cost for Resource Subscription Policy in IaaS Cloud Ms.M.Uthaya Banu #1, Mr.K.Saravanan *2 # Student, * Assistant Professor Department of Computer Science and Engineering Regional Centre

More information

RESOURCE MANAGEMENT IN CLOUD COMPUTING ENVIRONMENT

RESOURCE MANAGEMENT IN CLOUD COMPUTING ENVIRONMENT RESOURCE MANAGEMENT IN CLOUD COMPUTING ENVIRONMENT A.Chermaraj 1, Dr.P.Marikkannu 2 1 PG Scholar, 2 Assistant Professor, Department of IT, Anna University Regional Centre Coimbatore, Tamilnadu (India)

More information

In Cloud, Do MTC or HTC Service Providers Benefit from the Economies of Scale?

In Cloud, Do MTC or HTC Service Providers Benefit from the Economies of Scale? In Cloud, Do MTC or HTC Service Providers Benefit from the Economies of Scale? Lei Wang, Jianfeng Zhan, Weisong Shi, Yi Liang, Lin Yuan Institute of Computing Technology, Chinese Academy of Sciences Department

More information

Cloud and Virtualization to Support Grid Infrastructures

Cloud and Virtualization to Support Grid Infrastructures ESAC GRID Workshop '08 ESAC, Villafranca del Castillo, Spain 11-12 December 2008 Cloud and Virtualization to Support Grid Infrastructures Distributed Systems Architecture Research Group Universidad Complutense

More information

Managing Traditional Workloads Together with Cloud Computing Workloads

Managing Traditional Workloads Together with Cloud Computing Workloads Managing Traditional Workloads Together with Cloud Computing Workloads Table of Contents Introduction... 3 Cloud Management Challenges... 3 Re-thinking of Cloud Management Solution... 4 Teraproc Cloud

More information

Deploying Business Virtual Appliances on Open Source Cloud Computing

Deploying Business Virtual Appliances on Open Source Cloud Computing International Journal of Computer Science and Telecommunications [Volume 3, Issue 4, April 2012] 26 ISSN 2047-3338 Deploying Business Virtual Appliances on Open Source Cloud Computing Tran Van Lang 1 and

More information

How To Compare Cloud Computing To Cloud Platforms And Cloud Computing

How To Compare Cloud Computing To Cloud Platforms And Cloud Computing Volume 3, Issue 11, November 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Cloud Platforms

More information

Navigating Among the Clouds. Evaluating Public, Private and Hybrid Cloud Computing Approaches

Navigating Among the Clouds. Evaluating Public, Private and Hybrid Cloud Computing Approaches Navigating Among the Clouds Evaluating Public, Private and Hybrid Cloud Computing Approaches June 2012 Much like the winds of change that continue to alter the cloud landscape in the skies above, a powerful

More information

Cloud Computing Services on Provisioning Cost Approach

Cloud Computing Services on Provisioning Cost Approach Cloud Computing Services on Provisioning Cost Approach 1 Sasidevi Puppala, 2 P.Radha Krishna, 3 Srilakshmi Aluri 1, 3 Student, Nova College of Engineering & Technology, Jupudi, Ibrahimpatnm. 2 Associate

More information

Performance Management for Cloudbased STC 2012

Performance Management for Cloudbased STC 2012 Performance Management for Cloudbased Applications STC 2012 1 Agenda Context Problem Statement Cloud Architecture Need for Performance in Cloud Performance Challenges in Cloud Generic IaaS / PaaS / SaaS

More information

An exploration of cloud service delivery models in a hybrid environment A new depiction to corporate computing

An exploration of cloud service delivery models in a hybrid environment A new depiction to corporate computing Proc. of World Cong. on Multimedia and Computer Science An exploration of cloud service delivery models in a hybrid environment A new depiction to corporate computing C. Vijayalakshmi, M.C.A., M.Phil Lecturer,

More information

White Paper: Optimizing the Cloud Infrastructure for Enterprise Applications

White Paper: Optimizing the Cloud Infrastructure for Enterprise Applications White Paper: Optimizing the Cloud Infrastructure for Enterprise Applications 2010 Ashton, Metzler, & Associates. All rights reserved. Executive Summary Given the technological and organizational risks

More information

Efficient Scheduling Of On-line Services in Cloud Computing Based on Task Migration

Efficient Scheduling Of On-line Services in Cloud Computing Based on Task Migration Efficient Scheduling Of On-line Services in Cloud Computing Based on Task Migration 1 Harish H G, 2 Dr. R Girisha 1 PG Student, 2 Professor, Department of CSE, PESCE Mandya (An Autonomous Institution under

More information

An Introduction to Cloud Computing Concepts

An Introduction to Cloud Computing Concepts Software Engineering Competence Center TUTORIAL An Introduction to Cloud Computing Concepts Practical Steps for Using Amazon EC2 IaaS Technology Ahmed Mohamed Gamaleldin Senior R&D Engineer-SECC ahmed.gamal.eldin@itida.gov.eg

More information

Scheduler in Cloud Computing using Open Source Technologies

Scheduler in Cloud Computing using Open Source Technologies Scheduler in Cloud Computing using Open Source Technologies Darshan Upadhyay Prof. Chirag Patel Student of M.E.I.T Asst. Prof. Computer Department S. S. Engineering College, Bhavnagar L. D. College of

More information

The Cisco Powered Network Cloud: An Exciting Managed Services Opportunity

The Cisco Powered Network Cloud: An Exciting Managed Services Opportunity . White Paper The Cisco Powered Network Cloud: An Exciting Managed Services Opportunity The cloud computing phenomenon is generating a lot of interest worldwide because of its potential to offer services

More information

Overview. The Cloud. Characteristics and usage of the cloud Realities and risks of the cloud

Overview. The Cloud. Characteristics and usage of the cloud Realities and risks of the cloud Overview The purpose of this paper is to introduce the reader to the basics of cloud computing or the cloud with the aim of introducing the following aspects: Characteristics and usage of the cloud Realities

More information

White Paper on CLOUD COMPUTING

White Paper on CLOUD COMPUTING White Paper on CLOUD COMPUTING INDEX 1. Introduction 2. Features of Cloud Computing 3. Benefits of Cloud computing 4. Service models of Cloud Computing 5. Deployment models of Cloud Computing 6. Examples

More information

Cloud Computing with Red Hat Solutions. Sivaram Shunmugam Red Hat Asia Pacific Pte Ltd. sivaram@redhat.com

Cloud Computing with Red Hat Solutions. Sivaram Shunmugam Red Hat Asia Pacific Pte Ltd. sivaram@redhat.com Cloud Computing with Red Hat Solutions Sivaram Shunmugam Red Hat Asia Pacific Pte Ltd sivaram@redhat.com Linux Automation Details Red Hat's Linux Automation strategy for next-generation IT infrastructure

More information

Cloud Computing: IaaS & PaaS

Cloud Computing: IaaS & PaaS Cloud Computing: IaaS & PaaS Thomas Kurian Executive Vice President Product Development 62 Safe Harbor Statement "Safe Harbor" Statement: Statements in this presentation relating to Oracle's future plans,

More information

An Efficient Checkpointing Scheme Using Price History of Spot Instances in Cloud Computing Environment

An Efficient Checkpointing Scheme Using Price History of Spot Instances in Cloud Computing Environment An Efficient Checkpointing Scheme Using Price History of Spot Instances in Cloud Computing Environment Daeyong Jung 1, SungHo Chin 1, KwangSik Chung 2, HeonChang Yu 1, JoonMin Gil 3 * 1 Dept. of Computer

More information

Infrastructureas-a-Service

Infrastructureas-a-Service Infrastructureas-a-Service Fulfilling the Promise of Cloud Computing A White Paper www.tatacommunications.com 4 Executive Summary 5 Introduction 6 IaaS Defined 10 Economies of Scale 10 Why IaaS? 12 Key

More information

Future of Cloud Computing. Irena Bojanova, Ph.D. UMUC, NIST

Future of Cloud Computing. Irena Bojanova, Ph.D. UMUC, NIST Future of Cloud Computing Irena Bojanova, Ph.D. UMUC, NIST No Longer On The Horizon Essential Characteristics On-demand Self-Service Broad Network Access Resource Pooling Rapid Elasticity Measured Service

More information

Figure 1. The cloud scales: Amazon EC2 growth [2].

Figure 1. The cloud scales: Amazon EC2 growth [2]. - Chung-Cheng Li and Kuochen Wang Department of Computer Science National Chiao Tung University Hsinchu, Taiwan 300 shinji10343@hotmail.com, kwang@cs.nctu.edu.tw Abstract One of the most important issues

More information

International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015 36 ISSN 2229-5518

International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015 36 ISSN 2229-5518 International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015 36 An Efficient Approach for Load Balancing in Cloud Environment Balasundaram Ananthakrishnan Abstract Cloud computing

More information

2) Xen Hypervisor 3) UEC

2) Xen Hypervisor 3) UEC 5. Implementation Implementation of the trust model requires first preparing a test bed. It is a cloud computing environment that is required as the first step towards the implementation. Various tools

More information

Emerging Technology for the Next Decade

Emerging Technology for the Next Decade Emerging Technology for the Next Decade Cloud Computing Keynote Presented by Charles Liang, President & CEO Super Micro Computer, Inc. What is Cloud Computing? Cloud computing is Internet-based computing,

More information

SURVEY OF ADAPTING CLOUD COMPUTING IN HEALTHCARE

SURVEY OF ADAPTING CLOUD COMPUTING IN HEALTHCARE SURVEY OF ADAPTING CLOUD COMPUTING IN HEALTHCARE H.Madhusudhana Rao* Md. Rahmathulla** Dr. B Rambhupal Reddy*** Abstract: This paper targets on the productivity of cloud computing technology in healthcare

More information

Planning the Migration of Enterprise Applications to the Cloud

Planning the Migration of Enterprise Applications to the Cloud Planning the Migration of Enterprise Applications to the Cloud A Guide to Your Migration Options: Private and Public Clouds, Application Evaluation Criteria, and Application Migration Best Practices Introduction

More information

Clearing the Clouds. Understanding cloud computing. Ali Khajeh-Hosseini ST ANDREWS CLOUD COMPUTING CO-LABORATORY. Cloud computing

Clearing the Clouds. Understanding cloud computing. Ali Khajeh-Hosseini ST ANDREWS CLOUD COMPUTING CO-LABORATORY. Cloud computing Clearing the Clouds Understanding cloud computing Ali Khajeh-Hosseini ST ANDREWS CLOUD COMPUTING CO-LABORATORY Cloud computing There are many definitions and they all differ Simply put, cloud computing

More information

Self-Service Provisioning and the Private Cloud

Self-Service Provisioning and the Private Cloud Self-Service Provisioning and the Private Cloud Using Microsoft Server Virtualization and Dell Compellent Storage Virtualization to Improve Delivery of Infrastructure as a Service Solution Overview Published:

More information

Five Features Your Cloud Disaster Recovery Solution Should Have

Five Features Your Cloud Disaster Recovery Solution Should Have Five Features Your Cloud Disaster Recovery Solution Should Have Content Executive summary... 3 Problems with traditional disaster recovery... 3 Benefits Azure and AWS bring to the data center... 4 5 Features

More information

Dynamic Resource management with VM layer and Resource prediction algorithms in Cloud Architecture

Dynamic Resource management with VM layer and Resource prediction algorithms in Cloud Architecture Dynamic Resource management with VM layer and Resource prediction algorithms in Cloud Architecture 1 Shaik Fayaz, 2 Dr.V.N.Srinivasu, 3 Tata Venkateswarlu #1 M.Tech (CSE) from P.N.C & Vijai Institute of

More information

Copyright www.agileload.com 1

Copyright www.agileload.com 1 Copyright www.agileload.com 1 INTRODUCTION Performance testing is a complex activity where dozens of factors contribute to its success and effective usage of all those factors is necessary to get the accurate

More information

Introduction to Cloud Computing

Introduction to Cloud Computing Introduction to Cloud Computing Cloud Computing I (intro) 15 319, spring 2010 2 nd Lecture, Jan 14 th Majd F. Sakr Lecture Motivation General overview on cloud computing What is cloud computing Services

More information

Clouds for research computing

Clouds for research computing Clouds for research computing Randall Sobie Institute of Particle Physics University of Victoria Collaboration UVIC, NRC (Ottawa), NRC-HIA (Victoria) Randall Sobie IPP/University of Victoria 1 Research

More information

Cloud Computing Trends

Cloud Computing Trends UT DALLAS Erik Jonsson School of Engineering & Computer Science Cloud Computing Trends What is cloud computing? Cloud computing refers to the apps and services delivered over the internet. Software delivered

More information

Cloud Computing. Chapter 1 Introducing Cloud Computing

Cloud Computing. Chapter 1 Introducing Cloud Computing Cloud Computing Chapter 1 Introducing Cloud Computing Learning Objectives Understand the abstract nature of cloud computing. Describe evolutionary factors of computing that led to the cloud. Describe virtualization

More information

Storage XenMotion: Live Storage Migration with Citrix XenServer

Storage XenMotion: Live Storage Migration with Citrix XenServer Storage XenMotion: Live Storage Migration with Citrix XenServer Enabling cost effective storage migration and management strategies for enterprise and cloud datacenters www.citrix.com Table of Contents

More information

Private & Hybrid Cloud: Risk, Security and Audit. Scott Lowry, Hassan Javed VMware, Inc. March 2012

Private & Hybrid Cloud: Risk, Security and Audit. Scott Lowry, Hassan Javed VMware, Inc. March 2012 Private & Hybrid Cloud: Risk, Security and Audit Scott Lowry, Hassan Javed VMware, Inc. March 2012 Private and Hybrid Cloud - Risk, Security and Audit Objectives: Explain the technology and benefits behind

More information

OCRP Implementation to Optimize Resource Provisioning Cost in Cloud Computing

OCRP Implementation to Optimize Resource Provisioning Cost in Cloud Computing OCRP Implementation to Optimize Resource Provisioning Cost in Cloud Computing K. Satheeshkumar PG Scholar K. Senthilkumar PG Scholar A. Selvakumar Assistant Professor Abstract- Cloud computing is a large-scale

More information

Cloud computing: the state of the art and challenges. Jānis Kampars Riga Technical University

Cloud computing: the state of the art and challenges. Jānis Kampars Riga Technical University Cloud computing: the state of the art and challenges Jānis Kampars Riga Technical University Presentation structure Enabling technologies Cloud computing defined Dealing with load in cloud computing Service

More information

AN IMPLEMENTATION OF E- LEARNING SYSTEM IN PRIVATE CLOUD

AN IMPLEMENTATION OF E- LEARNING SYSTEM IN PRIVATE CLOUD AN IMPLEMENTATION OF E- LEARNING SYSTEM IN PRIVATE CLOUD M. Lawanya Shri 1, Dr. S. Subha 2 1 Assistant Professor,School of Information Technology and Engineering, Vellore Institute of Technology, Vellore-632014

More information

IaaS Cloud Architectures: Virtualized Data Centers to Federated Cloud Infrastructures

IaaS Cloud Architectures: Virtualized Data Centers to Federated Cloud Infrastructures IaaS Cloud Architectures: Virtualized Data Centers to Federated Cloud Infrastructures Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF Introduction

More information

Virtualizing Apache Hadoop. June, 2012

Virtualizing Apache Hadoop. June, 2012 June, 2012 Table of Contents EXECUTIVE SUMMARY... 3 INTRODUCTION... 3 VIRTUALIZING APACHE HADOOP... 4 INTRODUCTION TO VSPHERE TM... 4 USE CASES AND ADVANTAGES OF VIRTUALIZING HADOOP... 4 MYTHS ABOUT RUNNING

More information

The Definitive Guide to the Cloud and Kentico CMS THOMAS ROBBINS

The Definitive Guide to the Cloud and Kentico CMS THOMAS ROBBINS The Definitive Guide to the Cloud and Kentico CMS THOMAS ROBBINS Contents Introduction... 4 What is Cloud Computing?... 4 The Benefits of the Cloud... 6 Full Hardware Utilization... 6 Lower Power Costs...

More information

Reallocation and Allocation of Virtual Machines in Cloud Computing Manan D. Shah a, *, Harshad B. Prajapati b

Reallocation and Allocation of Virtual Machines in Cloud Computing Manan D. Shah a, *, Harshad B. Prajapati b Proceedings of International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA-14) Reallocation and Allocation of Virtual Machines in Cloud Computing Manan

More information

Computing Service Provision in P2P Clouds

Computing Service Provision in P2P Clouds Computing Service Provision in P2P Clouds Ghislain FOUODJI TASSE Supervisor: DR. Karen BRADSHAW Department of Computer Science Rhodes University Research Statement Leverage advantages of cloud computing

More information

Data Centers and Cloud Computing

Data Centers and Cloud Computing Data Centers and Cloud Computing CS377 Guest Lecture Tian Guo 1 Data Centers and Cloud Computing Intro. to Data centers Virtualization Basics Intro. to Cloud Computing Case Study: Amazon EC2 2 Data Centers

More information

Multilevel Communication Aware Approach for Load Balancing

Multilevel Communication Aware Approach for Load Balancing Multilevel Communication Aware Approach for Load Balancing 1 Dipti Patel, 2 Ashil Patel Department of Information Technology, L.D. College of Engineering, Gujarat Technological University, Ahmedabad 1

More information

The Lattice Project: A Multi-Model Grid Computing System. Center for Bioinformatics and Computational Biology University of Maryland

The Lattice Project: A Multi-Model Grid Computing System. Center for Bioinformatics and Computational Biology University of Maryland The Lattice Project: A Multi-Model Grid Computing System Center for Bioinformatics and Computational Biology University of Maryland Parallel Computing PARALLEL COMPUTING a form of computation in which

More information

A Survey on Resource Provisioning in Cloud

A Survey on Resource Provisioning in Cloud RESEARCH ARTICLE OPEN ACCESS A Survey on Resource in Cloud M.Uthaya Banu*, M.Subha** *,**(Department of Computer Science and Engineering, Regional Centre of Anna University, Tirunelveli) ABSTRACT Cloud

More information