Advanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads
|
|
- Conrad Martin
- 8 years ago
- Views:
Transcription
1 Advanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads G. Suganthi (Member, IEEE), K. N. Vimal Shankar, Department of Computer Science and Engineering, V.S.B. Engineering College, Karur Abstract - A distributed system is a collection of independent computers that appears to its users as a single coherent system. The main goal of distributed system is to make it easy for users and applications to access remote resources and to share them in a control and efficient way. To reduce the cost of infrastructure and electrical energy, enterprise datacenters consolidate workloads on the same physical hardware. It allows integrated management of heterogeneous workloads composed of transactional applications and long-running jobs, dynamically placing the workloads in such a way as to equalize their satisfaction. It also leverages virtualization control mechanisms to perform online system reconfiguration. For enterprise datacenters and cloud computing infrastructures, the resource utilization is a critical goal even in presence of heterogeneous workloads. To provide the better heterogeneous services in the distributed environment, the service-level agreements through multi issue negotiation for transactional and batch workloads are established and the load balancing mechanism is implemented. The use of live VM migration has enabled more effective sharing of system resources in a physical server. This project helps us to maximize mixed workload performance and increases overall system resource utilization. Keywords - Virtual Machine (VM), Quality of Service (QoS), Performance management, Workload management, Resource management and cloud computing. 1. INTRODUCTION Transactional applications and batch jobs are widely used by many organizations to deliver services to their customers and partners. Due to intrinsic differences between these workloads, the physical server gets imbalanced, which contributes to resource underutilization and management complexity. Integrated performance management of mixed workloads is a challenging problem. First, the performance goals for different workloads tend to be of different types. For interactive workloads, goals are typically defined in terms of average or percentile response time or throughput over a short time interval, while goals for non interactive workloads concern the performance (e.g., completion time) of individual jobs. Second, due to the nature of their goals and the short duration of individual requests, interactive workloads lend themselves to management at short control cycles, whereas non interactive workloads typically require calculation of a schedule for an extended period of time. In addition, different types of workload require different control mechanisms for management. Transactional workloads are managed using flow control, load balancing, and application placement. Non interactive workloads need scheduling and resource control. Traditionally, these have been addressed separately. Compute clouds are commonly used by many different users that rely on the existing computing infrastructure to deploy their workloads. As a result, heterogeneous workloads run on the same physical nodes and pose extraordinary challenges on a cloud management middleware. Our technique relies on load balancing mechanisms to manage workloads. The use of live VM migration has enabled more effective sharing of system resources in a physical server. Different QoS parameters such as Maximum delay for a request, Minimum number of parallel request handled per second and Deadline for completion of each jobs request are used for finding the feasible host. Then predict queuing delay and execution time on feasible hosts. If utility ISSN: Page 1431
2 value of each path is less than threshold of utility value of each path then predict QoS and allocate it best host based on the resources needs. 2. RELATED WORK 2.1. Server Virtualization in Autonomic Management of Heterogeneous Workloads Server virtualization opens up a range of new possibilities for autonomic datacenter management, through the availability of new automation mechanisms that can be exploited to control and monitor tasks running within virtual machines. This offers not only new and more flexible control to the operator using a management console, but also more powerful and flexible autonomic control, through management software that maintains the system in a desired state in the face of changing workload and demand. This paper explores in particular the use of server virtualization technology in the autonomic management of data centers running a heterogeneous mix of workloads Managing SLAs of Heterogeneous Workloads using Dynamic Application Placement This paper deals with the problem of managing heterogeneous workloads in a virtualized data center. Consider two different workloads: transactional applications and long-running jobs. This technique permits collocation of these workload types on the same physical hardware. It dynamically modifies workload placement by leveraging control mechanisms such as suspension and migration, and strives to optimally trade off resource allocation among these workloads in spite of their differing characteristics and performance objectives. This paper extends our framework with the capability to manage long-running workloads. This is achieved by using utility functions, which permits to compare the performance of various workloads, and which are used to drive allocation decisions Efficient Resource Allocation and Power Saving in Multitierd Systems In this paper, a parameter-free algorithm for dynamic resource provisioning uses simple statistics to promptly distill information about changes in workload burstiness. This information, coupled with the application's end-to-end response times and system bottleneck characteristics, guide resource allocation that shows to be very effective under a broad variety of burstiness profiles and bottleneck scenarios Adaptive Data-aware Utility-based Scheduling in Resourceconstrained Systems This paper addresses the problem of dynamic scheduling of data-intensive multiprocessor jobs. Each job requires some number of CPUs and some amount of data that needs to be downloaded into a local storage space before starting the job. The completion of each job brings some benefit (utility) to the system, and the goal is to find the optimal scheduling policy that maximizes the average utility per unit of time obtained from all completed jobs. A co-evolutionary solution methodology is proposed, where the utility-based policies for managing local storage and for scheduling jobs onto the available CPUs mutually affect each other's environments, with both policies being adaptively tuned using the Reinforcement Learning methodology. Our simulation results demonstrate the feasibility of this approach and show that it performs better than the best heuristic scheduling policy we could find for this domain Quantifying Load Imbalance on Virtualized Enterprise Servers Virtualization has been shown to be an attractive path to increase overall system resource utilization. The use of live Virtual Machine (VM) migration has enabled more effective sharing of system resources across multiple physical servers, resulting in an increase in overall performance. The algorithm for balancing the load of virtualized enterprise servers follows a greedy approach, inductively predicting which VM migration will yield the greatest improvement of the imbalance metric in a particular step. Move VM 2 to Server 2 VM 1 VM 2 VM 3 VM 4 Physical Server 1 Physical Server 2 Virtualization Manager Fig.1. Live VM Migration Process ISSN: Page 1432
3 3. INTEGRATED MANAGEMENT OF HETEROGENEOUS WORKLOADS The goal of the technique introduced in this paper is to make placement decisions that involve applications of different nature, more specifically transactional applications and long running workloads. Given the different characteristics of each workload, that makes their performance hardly comparable, it leverages RPF to produce a normalized representation of their performance. RPFs are leveraged by the placement algorithm to make placement decisions, with the goal of maximizing the relative performance delivered by all the applications in the system Measuring the Job Properties Given a set of jobs. With each job m, the following information s are associated. Resource usage profile: A resource usage profile describes the resource requirements of a job and is given at job submission time in the real system, this profile comes from the job workload profiler. The profile is estimated based on historical data. Each job m consists of a sequence of N stages, S 1,.,SN m, where each stage S k is described by the following parameters: The amount of CPU cycles consumed in this stage, α k,m. The maximum speed with which the stage may run, w max k,m. The minimum speed with which the stage must run, whenever it runs, w min k,m. The memory requirement γ k,m Designing the Virtualization Control Mechanism The SLA objective for a job is expressed in terms of its desired completion time T m, which is the time by which the job must complete. Clearly, T m should be greater than the job s desired start time, T m start which itself is greater than or equal to the time when the job was submitted. The difference between the completion time goal and the desired start time, T m - T m start, is called the relative goal, and can be understood as the maximum acceptable job runtime. Notice that job runtime will depend on allocated resources to the Virtual Machine in which the job runs. RPF that maps actual job completion time tm to a measure of satisfaction from achieving it, u (t ). If job m completes at time t, then the relative distance of its completion time from the goal is the job s actual runtime normalized to its relative goal, which is expressed by the RPF of the following form: u m (t m ) = Runtime state. At runtime, we monitor and estimate the following properties for each job: current status, which may be either running, not started, suspended, or paused; and CPU time consumed thus far, α. Relative goal factor. For the purpose of easily controlling the tightness of SLA goals in our experiments, we introduce a relative goal factor which is defined as the ratio of the relative goal of the job to its execution time at the maximum speed, Load Balancing Mechanism through Improved Heuristics A system in which all jobs can be placed simultaneously, and in which the available CPU power may be arbitrarily finely allocated among the jobs. It requires a function that maps the system s CPU power to the relative performance achievable by jobs when placed on it. Let us consider job m. Based on its properties, estimate the completion time needed to achieve relative performance u. Then calculate the average speed with which the job must proceed over its remaining lifetime to achieve u, as follows: α, w (u) = t (u) t Let u 1 = -α, u 2,,u R =1, where R is a small constant, be a set of sampling points (target relative performance values from now on). Define matrices W and V as follows: W i,m = w (u ), if u u, w (u ), otherwise, ISSN: Page 1433
4 V i,m = u, if u u, u, otherwise. W i,m contains the average speed with which application m should execute starting from t now to achieve relative performance u i, and cell V i,m contains the relative performance value u i if it is possible for application m to achieve this performance level u i. If relative performance u i is not achievable by application m, these cells instead contain the average speed with which the application should execute starting from t now to achieve its maximum achievable relative performance, and the value of the maximum relative performance, respectively. For a given w g, there exist two values k and k þ 1 such that: 4. Calculate current utility value of each path and select negotiable paths based on the minimum utility value 5. Select application, ai, in the ranked list of recommended actions and test feasibility of allocating the application ai, on the host, Hj. 6. If utility value of each path is less than threshold of utility value of each path then predict QoS and allocate it best host and exit 7. Else allocate the violated application to the host that has the minimum utility value 4. SIMULATION EXPERIMENTS This section presents the experimental results of the proposed algorithm for balancing the load across different types of workloads. Quality of Service negotiation approach is used for finding the best available host based on resources needs. W, w W, Allocating a CPU power to all jobs will result in a relative performance um for each job m in the range V, u V,.That corresponds to a hypothetical CPU allocation w m in the range W, w W,. At some point the algorithm needs to know the relative performance that each application will achieve (um) if it decides to allocate a CPU power of w g to all applications combined. We must find values w m and um for each application m such that (7) is satisfied and that fall within the ranges described above. The chosen values must also satisfy w = w. Fig.2. Construction of cloud architecture 3.4. Quality of Service Negotiation for Distributed Systems This algorithm is used for finding the feasible host based on the Quality of Service (QoS) parameters and it allocates the best host for the different types of workloads. The steps are: 1. Find a feasible host corresponding to resource needs 2. If a host is feasible, then predict queuing delay and execution time on feasible hosts 3. Else send QoS negotiation requests to all QoS managers Fig.3. Measuring transaction job requirements ISSN: Page 1434
5 Fig.4. Setting Performance goal Fig.5. Job assigned to cloud 5. CONCLUSION In this paper, we present a technique that allows integrated management of heterogeneous workloads composed of transactional applications and long-running jobs, dynamically placing the workloads in such a way as to equalize their satisfaction. We use relative performance functions to make the satisfaction and performance of both workloads comparable. The heuristics used by the algorithm is improved, which resulted in a significant reduction in its computational complexity. The use of QoS negotiation has enabled more effective sharing of system resources in a physical server. It maximizes mixed workload performance while providing service differentiation based on high-level performance goals. The system introduces several novel features. First, it allows heterogeneous workloads to be collocated on any server machine, thus reducing the granularity of resource allocation. Second, the resource utilization is increased. Third, the placement problems can be reduced. REFERENCES [1] David Carrera, Malgorzata Steinder, Ian Whalley, Jordi Torres,and Eduard Ayguade, Autonomic Placement of Mixed Batch and Transactional Workloads, IEEE Transactions on Parallel and Distributed Systems 23, (2012). [2] E. Arzuaga and D.R. Kaeli, Quantifying Load Imbalance on Virtualized Enterprise Servers, Proc.First Joint WOSP/SIPEW Int l Conf.Performance Eng. (WOSP/SIPEW 10), pp , [3] M. Steinder, I. Whalley, D. Carrera, I. Gaweda, and D. Chess, Server Virtualization in Autonomic Management of Heterogeneous Workloads, Proc. IEEE/IFIP 10th Symp. Integrated Management (IM 07), [4] D. Carrera, M. Steinder, I. Whalley, J. Torres, and E. Ayguade, Managing SLAs of Heterogeneous Workloads Using Dynamic Application Placement, Technical Report RC 24469, IBM Research, Jan [5] A. Caniff, L. Lu, N. Mi, L. Cherkasova, and E. Smirni, Efficient Resource Allocation and Power Saving in Multi-Tiered Systems, Proc. 19th Int l Conf. World Wide Web (WWW 10), pp , [6] D.M. David Vengerov, L. Mastroleon, and N. Bambos, Adaptive Data- Aware Utility-Based Scheduling in Resource-Constrained Systems, Technical Report TR , Sun Microsystems, Apr [7] P. Bodı k, R. Griffith, C. Sutton, A. Fox, M.I. Jordan, and D.A. Patterson, Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters, Proc. Conf. Hot Topics in Cloud Computing (HotCloud 09), [8] M. Cardosa, M.R. Korupolu, and A. Singh, Shares and Utilities Based Power Consolidation in Virtualized Server Environments, Proc. IFIP/IEEE Int l Symp. Integrated Network Management (IM 09), pp , [9] X. Wang, D. Lan, G. Wang, X. Fang, M. Ye, Y. Chen, and Q. Wang, Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center, Proc. Fourth Int l Conf. Autonomic Computing (ICAC 07), p. 29, [10] J. Xu, M. Zhao, J. Fortes, R. Carpenter, and M. Yousif, On the Use of Fuzzy Modeling in Virtualized Data Center Management, Proc. Fourth Int l Conf. Autonomic Computing (ICAC 07), p. 25, June Fig.6. Performance comparison ISSN: Page 1435
International Journal of Scientific & Engineering Research, Volume 4, Issue 5, May-2013 617 ISSN 2229-5518
International Journal of Scientific & Engineering Research, Volume 4, Issue 5, May-2013 617 Load Distribution & Resource Scheduling for Mixed Workloads in Cloud Environment 1 V. Sindhu Shri II ME (Software
More informationHeterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing
Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing Deep Mann ME (Software Engineering) Computer Science and Engineering Department Thapar University Patiala-147004
More informationAvoiding Overload Using Virtual Machine in Cloud Data Centre
Avoiding Overload Using Virtual Machine in Cloud Data Centre Ms.S.Indumathi 1, Mr. P. Ranjithkumar 2 M.E II year, Department of CSE, Sri Subramanya College of Engineering and Technology, Palani, Dindigul,
More informationExploring Resource Provisioning Cost Models in Cloud Computing
Exploring Resource Provisioning Cost Models in Cloud Computing P.Aradhya #1, K.Shivaranjani *2 #1 M.Tech, CSE, SR Engineering College, Warangal, Andhra Pradesh, India # Assistant Professor, Department
More informationSurvey on Models to Investigate Data Center Performance and QoS in Cloud Computing Infrastructure
Survey on Models to Investigate Data Center Performance and QoS in Cloud Computing Infrastructure Chandrakala Department of Computer Science and Engineering Srinivas School of Engineering, Mukka Mangalore,
More informationSLA-Based Resource Provisioning for Heterogeneous Workloads in a Virtualized Cloud Datacenter
SLA-Based Resource Provisioning for Heterogeneous Workloads in a Virtualized Cloud Datacenter Saurabh Kumar Garg, Srinivasa K. Gopalaiyengar, and Rajkumar Buyya Cloud Computing and Distributed Systems
More informationCost Effective Automated Scaling of Web Applications for Multi Cloud Services
Cost Effective Automated Scaling of Web Applications for Multi Cloud Services SANTHOSH.A 1, D.VINOTHA 2, BOOPATHY.P 3 1,2,3 Computer Science and Engineering PRIST University India Abstract - Resource allocation
More informationMonitoring Performances of Quality of Service in Cloud with System of Systems
Monitoring Performances of Quality of Service in Cloud with System of Systems Helen Anderson Akpan 1, M. R. Sudha 2 1 MSc Student, Department of Information Technology, 2 Assistant Professor, Department
More informationEnhancing the Scalability of Virtual Machines in Cloud
Enhancing the Scalability of Virtual Machines in Cloud Chippy.A #1, Ashok Kumar.P #2, Deepak.S #3, Ananthi.S #4 # Department of Computer Science and Engineering, SNS College of Technology Coimbatore, Tamil
More informationEnergy Constrained Resource Scheduling for Cloud Environment
Energy Constrained Resource Scheduling for Cloud Environment 1 R.Selvi, 2 S.Russia, 3 V.K.Anitha 1 2 nd Year M.E.(Software Engineering), 2 Assistant Professor Department of IT KSR Institute for Engineering
More informationInfrastructure 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 informationDynamic Resource Allocation in Software Defined and Virtual Networks: A Comparative Analysis
Dynamic Resource Allocation in Software Defined and Virtual Networks: A Comparative Analysis Felipe Augusto Nunes de Oliveira - GRR20112021 João Victor Tozatti Risso - GRR20120726 Abstract. The increasing
More informationINCREASING SERVER UTILIZATION AND ACHIEVING GREEN COMPUTING IN CLOUD
INCREASING SERVER UTILIZATION AND ACHIEVING GREEN COMPUTING IN CLOUD M.Rajeswari 1, M.Savuri Raja 2, M.Suganthy 3 1 Master of Technology, Department of Computer Science & Engineering, Dr. S.J.S Paul Memorial
More informationDynamic 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 informationAn Enhanced Cost Optimization of Heterogeneous Workload Management in Cloud Computing
An Enhanced Cost Optimization of Heterogeneous Workload Management in Cloud Computing 1 Sudha.C Assistant Professor/Dept of CSE, Muthayammal College of Engineering,Rasipuram, Tamilnadu, India Abstract:
More informationLoad Balancing on a Non-dedicated Heterogeneous Network of Workstations
Load Balancing on a Non-dedicated Heterogeneous Network of Workstations Dr. Maurice Eggen Nathan Franklin Department of Computer Science Trinity University San Antonio, Texas 78212 Dr. Roger Eggen Department
More informationAchieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services
Achieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services Ms. M. Subha #1, Mr. K. Saravanan *2 # Student, * Assistant Professor Department of Computer Science and Engineering Regional
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015
RESEARCH ARTICLE OPEN ACCESS Ensuring Reliability and High Availability in Cloud by Employing a Fault Tolerance Enabled Load Balancing Algorithm G.Gayathri [1], N.Prabakaran [2] Department of Computer
More informationMultifaceted 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@IJMTER-2015, All rights Reserved 355
e-issn: 2349-9745 p-issn: 2393-8161 Scientific Journal Impact Factor (SJIF): 1.711 International Journal of Modern Trends in Engineering and Research www.ijmter.com A Model for load balancing for the Public
More information5 Performance Management for Web Services. Rolf Stadler School of Electrical Engineering KTH Royal Institute of Technology. stadler@ee.kth.
5 Performance Management for Web Services Rolf Stadler School of Electrical Engineering KTH Royal Institute of Technology stadler@ee.kth.se April 2008 Overview Service Management Performance Mgt QoS Mgt
More informationAdaptive Task Scheduling for Multi Job MapReduce
Adaptive Task Scheduling for MultiJob MapReduce Environments Jordà Polo, David de Nadal, David Carrera, Yolanda Becerra, Vicenç Beltran, Jordi Torres and Eduard Ayguadé Barcelona Supercomputing Center
More informationSCHEDULING IN CLOUD COMPUTING
SCHEDULING IN CLOUD COMPUTING Lipsa Tripathy, Rasmi Ranjan Patra CSA,CPGS,OUAT,Bhubaneswar,Odisha Abstract Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
More informationA Review of Load Balancing Algorithms for Cloud Computing
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume - 3 Issue -9 September, 2014 Page No. 8297-8302 A Review of Load Balancing Algorithms for Cloud Computing Dr.G.N.K.Sureshbabu
More informationLoad Balancing for Improved Quality of Service in the Cloud
Load Balancing for Improved Quality of Service in the Cloud AMAL ZAOUCH Mathématique informatique et traitement de l information Faculté des Sciences Ben M SIK CASABLANCA, MORROCO FAOUZIA BENABBOU Mathématique
More informationFigure 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 informationWORKFLOW ENGINE FOR CLOUDS
WORKFLOW ENGINE FOR CLOUDS By SURAJ PANDEY, DILEBAN KARUNAMOORTHY, and RAJKUMAR BUYYA Prepared by: Dr. Faramarz Safi Islamic Azad University, Najafabad Branch, Esfahan, Iran. Workflow Engine for clouds
More informationCost Effective Selection of Data Center in Cloud Environment
Cost Effective Selection of Data Center in Cloud Environment Manoranjan Dash 1, Amitav Mahapatra 2 & Narayan Ranjan Chakraborty 3 1 Institute of Business & Computer Studies, Siksha O Anusandhan University,
More informationInternational Journal of Advance Research in Computer Science and Management Studies
Volume 3, Issue 6, June 2015 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
More informationA Study on Analysis and Implementation of a Cloud Computing Framework for Multimedia Convergence Services
A Study on Analysis and Implementation of a Cloud Computing Framework for Multimedia Convergence Services Ronnie D. Caytiles and Byungjoo Park * Department of Multimedia Engineering, Hannam University
More informationA Comparative Study of Load Balancing Algorithms in Cloud Computing
A Comparative Study of Load Balancing Algorithms in Cloud Computing Reena Panwar M.Tech CSE Scholar Department of CSE, Galgotias College of Engineering and Technology, Greater Noida, India Bhawna Mallick,
More informationAN 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 informationAgent Based Framework for Scalability in Cloud Computing
Agent Based Framework for Scalability in Computing Aarti Singh 1, Manisha Malhotra 2 1 Associate Prof., MMICT & BM, MMU, Mullana 2 Lecturer, MMICT & BM, MMU, Mullana 1 Introduction: Abstract: computing
More informationGroup Based Load Balancing Algorithm in Cloud Computing Virtualization
Group Based Load Balancing Algorithm in Cloud Computing Virtualization Rishi Bhardwaj, 2 Sangeeta Mittal, Student, 2 Assistant Professor, Department of Computer Science, Jaypee Institute of Information
More informationDynamic Load Balancing of Virtual Machines using QEMU-KVM
Dynamic Load Balancing of Virtual Machines using QEMU-KVM Akshay Chandak Krishnakant Jaju Technology, College of Engineering, Pune. Maharashtra, India. Akshay Kanfade Pushkar Lohiya Technology, College
More informationVirtualization Technology using Virtual Machines for Cloud Computing
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Virtualization Technology using Virtual Machines for Cloud Computing T. Kamalakar Raju 1, A. Lavanya 2, Dr. M. Rajanikanth 2 1,
More informationLoad balancing model for Cloud Data Center ABSTRACT:
Load balancing model for Cloud Data Center ABSTRACT: Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to
More informationKeywords 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 informationEnergetic Resource Allocation Framework Using Virtualization in Cloud
Energetic Resource Allocation Framework Using Virtualization in Ms.K.Guna *1, Ms.P.Saranya M.E *2 1 (II M.E(CSE)) Student Department of Computer Science and Engineering, 2 Assistant Professor Department
More informationTask Scheduling for Efficient Resource Utilization in Cloud
Summer 2014 Task Scheduling for Efficient Resource Utilization in Cloud A Project Report for course COEN 241 Under the guidance of, Dr.Ming Hwa Wang Submitted by : Najuka Sankhe Nikitha Karkala Nimisha
More informationResource Allocation Avoiding SLA Violations in Cloud Framework for SaaS
Resource Allocation Avoiding SLA Violations in Cloud Framework for SaaS Shantanu Sasane Abhilash Bari Kaustubh Memane Aniket Pathak Prof. A. A.Deshmukh University of Pune University of Pune University
More informationEfficient Resources Allocation and Reduce Energy Using Virtual Machines for Cloud Environment
Efficient Resources Allocation and Reduce Energy Using Virtual Machines for Cloud Environment R.Giridharan M.E. Student, Department of CSE, Sri Eshwar College of Engineering, Anna University - Chennai,
More informationScheduling Data Intensive Workloads through Virtualization on MapReduce based Clouds
ABSTRACT Scheduling Data Intensive Workloads through Virtualization on MapReduce based Clouds 1 B.Thirumala Rao, 2 L.S.S.Reddy Department of Computer Science and Engineering, Lakireddy Bali Reddy College
More informationFair Scheduling Algorithm with Dynamic Load Balancing Using In Grid Computing
Research Inventy: International Journal Of Engineering And Science Vol.2, Issue 10 (April 2013), Pp 53-57 Issn(e): 2278-4721, Issn(p):2319-6483, Www.Researchinventy.Com Fair Scheduling Algorithm with Dynamic
More informationLoad Balancing in the Cloud Computing Using Virtual Machine Migration: A Review
Load Balancing in the Cloud Computing Using Virtual Machine Migration: A Review 1 Rukman Palta, 2 Rubal Jeet 1,2 Indo Global College Of Engineering, Abhipur, Punjab Technical University, jalandhar,india
More informationA Novel Method for Resource Allocation in Cloud Computing Using Virtual Machines
A Novel Method for Resource Allocation in Cloud Computing Using Virtual Machines Ch.Anusha M.Tech, Dr.K.Babu Rao, M.Tech, Ph.D Professor, MR. M.Srikanth Asst Professor & HOD, Abstract: Cloud computing
More informationImplementing Parameterized Dynamic Load Balancing Algorithm Using CPU and Memory
Implementing Parameterized Dynamic Balancing Algorithm Using CPU and Memory Pradip Wawge 1, Pritish Tijare 2 Master of Engineering, Information Technology, Sipna college of Engineering, Amravati, Maharashtra,
More informationAffinity Aware VM Colocation Mechanism for Cloud
Affinity Aware VM Colocation Mechanism for Cloud Nilesh Pachorkar 1* and Rajesh Ingle 2 Received: 24-December-2014; Revised: 12-January-2015; Accepted: 12-January-2015 2014 ACCENTS Abstract The most of
More informationCURTAIL THE EXPENDITURE OF BIG DATA PROCESSING USING MIXED INTEGER NON-LINEAR PROGRAMMING
Journal homepage: http://www.journalijar.com INTERNATIONAL JOURNAL OF ADVANCED RESEARCH RESEARCH ARTICLE CURTAIL THE EXPENDITURE OF BIG DATA PROCESSING USING MIXED INTEGER NON-LINEAR PROGRAMMING R.Kohila
More informationPower Management in Cloud Computing using Green Algorithm. -Kushal Mehta COP 6087 University of Central Florida
Power Management in Cloud Computing using Green Algorithm -Kushal Mehta COP 6087 University of Central Florida Motivation Global warming is the greatest environmental challenge today which is caused by
More informationThis is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12902
Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited
More informationPerformance 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 informationA Study on the Application of Existing Load Balancing Algorithms for Large, Dynamic, Heterogeneous Distributed Systems
A Study on the Application of Existing Load Balancing Algorithms for Large, Dynamic, Heterogeneous Distributed Systems RUPAM MUKHOPADHYAY, DIBYAJYOTI GHOSH AND NANDINI MUKHERJEE Department of Computer
More informationGrid Computing Approach for Dynamic Load Balancing
International Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Issue-1 E-ISSN: 2347-2693 Grid Computing Approach for Dynamic Load Balancing Kapil B. Morey 1*, Sachin B. Jadhav
More informationEfficient Qos Based Resource Scheduling Using PAPRIKA Method for Cloud Computing
Efficient Qos Based Resource Scheduling Using PAPRIKA Method for Cloud Computing Hilda Lawrance* Post Graduate Scholar Department of Information Technology, Karunya University Coimbatore, Tamilnadu, India
More informationDynamic Resource Allocation in Computing Clouds using Distributed Multiple Criteria Decision Analysis
Dynamic Resource Allocation in Computing Clouds using Distributed Multiple Criteria Decision Analysis Yağız Onat Yazır α, Chris Matthews α, Roozbeh Farahbod β Stephen Neville γ, Adel Guitouni β, Sudhakar
More informationIaaS 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 information1. Simulation of load balancing in a cloud computing environment using OMNET
Cloud Computing Cloud computing is a rapidly growing technology that allows users to share computer resources according to their need. It is expected that cloud computing will generate close to 13.8 million
More informationTask Placement in a Cloud with Case-based Reasoning
Task Placement in a Cloud with Case-based Reasoning Eric Schulte-Zurhausen and Mirjam Minor Institute of Informatik, Goethe University, Robert-Mayer-Str.10, Frankfurt am Main, Germany {eschulte, minor}@informatik.uni-frankfurt.de
More informationMigration of Virtual Machines for Better Performance in Cloud Computing Environment
Migration of Virtual Machines for Better Performance in Cloud Computing Environment J.Sreekanth 1, B.Santhosh Kumar 2 PG Scholar, Dept. of CSE, G Pulla Reddy Engineering College, Kurnool, Andhra Pradesh,
More informationPayment 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 informationManaged Virtualized Platforms: From Multicore Nodes to Distributed Cloud Infrastructures
Managed Virtualized Platforms: From Multicore Nodes to Distributed Cloud Infrastructures Ada Gavrilovska Karsten Schwan, Mukil Kesavan Sanjay Kumar, Ripal Nathuji, Adit Ranadive Center for Experimental
More informationLearn How to Leverage System z in Your Cloud
Learn How to Leverage System z in Your Cloud Mike Baskey IBM Thursday, February 7 th, 2013 Session 12790 Cloud implementations that include System z maximize Enterprise flexibility and increase cost savings
More informationReducing Wasted Resources to Help Achieve Green Data Centers
Reducing Wasted Resources to Help Achieve Green Data Centers Jordi Torres, David Carrera, Kevin Hogan, Ricard Gavaldà, Vicenç Beltran, Nicolás Poggi Barcelona Supercomputing Center (BSC) - Technical University
More informationA Virtual Machine Placement Algorithm in Mobile Cloud Computing Environment by Considering Network Features
A Virtual Machine Placement Algorithm in Mobile Cloud Computing Environment by Considering Network Features Chaitra Sathyampet M.E. Scholar Department of Computer Science & Engineering APPA Institute Of
More informationPower Aware Live Migration for Data Centers in Cloud using Dynamic Threshold
Richa Sinha et al, Int. J. Comp. Tech. Appl., Vol 2 (6), 2041-2046 Power Aware Live Migration for Data Centers in Cloud using Dynamic Richa Sinha, Information Technology L.D. College of Engineering, Ahmedabad,
More informationOptimizing 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 informationSolution Brief Availability and Recovery Options: Microsoft Exchange Solutions on VMware
Introduction By leveraging the inherent benefits of a virtualization based platform, a Microsoft Exchange Server 2007 deployment on VMware Infrastructure 3 offers a variety of availability and recovery
More informationCloud Management: Knowing is Half The Battle
Cloud Management: Knowing is Half The Battle Raouf BOUTABA David R. Cheriton School of Computer Science University of Waterloo Joint work with Qi Zhang, Faten Zhani (University of Waterloo) and Joseph
More informationAN ADAPTIVE DISTRIBUTED LOAD BALANCING TECHNIQUE FOR CLOUD COMPUTING
AN ADAPTIVE DISTRIBUTED LOAD BALANCING TECHNIQUE FOR CLOUD COMPUTING Gurpreet Singh M.Phil Research Scholar, Computer Science Dept. Punjabi University, Patiala gurpreet.msa@gmail.com Abstract: Cloud Computing
More informationAn Energy-efficient Scheduling Approach Based on Private Clouds
Journal of Information & Computational Science 8: 4 (211) 716 724 Available at http://www.joics.com An Energy-efficient Scheduling Approach Based on Private Clouds Jiandun Li a, Junjie Peng a,b,, Zhou
More informationNewsletter 4/2013 Oktober 2013. www.soug.ch
SWISS ORACLE US ER GRO UP www.soug.ch Newsletter 4/2013 Oktober 2013 Oracle 12c Consolidation Planer Data Redaction & Transparent Sensitive Data Protection Oracle Forms Migration Oracle 12c IDENTITY table
More informationA Service Revenue-oriented Task Scheduling Model of Cloud Computing
Journal of Information & Computational Science 10:10 (2013) 3153 3161 July 1, 2013 Available at http://www.joics.com A Service Revenue-oriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,
More informationScheduling Allowance Adaptability in Load Balancing technique for Distributed Systems
Scheduling Allowance Adaptability in Load Balancing technique for Distributed Systems G.Rajina #1, P.Nagaraju #2 #1 M.Tech, Computer Science Engineering, TallaPadmavathi Engineering College, Warangal,
More informationMaximizing Profit and Pricing in Cloud Environments
Maximizing Profit and Pricing in Cloud Environments FACULTY OF ENGINEERING & INFORMATION TECHNOLOGIES Albert Y. Zomaya Chair Professor of High Performance Computing & Networking Centre for Distributed
More informationSLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS
SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS Foued Jrad, Jie Tao and Achim Streit Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany {foued.jrad, jie.tao, achim.streit}@kit.edu
More informationCloud deployment model and cost analysis in Multicloud
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) ISSN: 2278-2834, ISBN: 2278-8735. Volume 4, Issue 3 (Nov-Dec. 2012), PP 25-31 Cloud deployment model and cost analysis in Multicloud
More informationEfficient 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 informationOracle Quality of Service Management - Meeting Availability and SLA Requirements in the Database Cloud
Oracle Quality of Service Management - Meeting Availability and SLA Requirements in the Database Cloud Mark V. Scardina Director of Product Management Oracle Quality of Service Management 1 Copyright 2013,
More informationTowards Online Performance Model Extraction in Virtualized Environments
Towards Online Performance Model Extraction in Virtualized Environments Simon Spinner 1, Samuel Kounev 1, Xiaoyun Zhu 2, and Mustafa Uysal 2 1 Karlsruhe Institute of Technology (KIT) {simon.spinner,kounev}@kit.edu
More informationIntroducing Virtual Execution Environments for Application Lifecycle Management and SLA-Driven Resource Distribution within Service Providers
29 Eighth IEEE International Symposium on Network Computing and Applications Introducing Virtual Execution Environments for Application Lifecycle Management and SLA-Driven Resource Distribution within
More informationBACKFILLING STRATEGIES FOR SCHEDULING STREAMS OF JOBS ON COMPUTATIONAL FARMS
BACKFILLING STRATEGIES FOR SCHEDULING STREAMS OF JOBS ON COMPUTATIONAL FARMS A.D.Techiouba, G.Capannini, Ranieri Baraglia, D.Puppin, M.Pasquali ISTI,CNR Via Moruzzi, 1 Pisa, Italy techioub@cli.di.unipi.it
More informationFuture Generation Computer Systems. Energy-efficient and multifaceted resource management for profit-driven
Future Generation Computer Systems 28 (2012) 718 731 Contents lists available at SciVerse ScienceDirect Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs Energy-efficient
More informationAllocation of Datacenter Resources Based on Demands Using Virtualization Technology in Cloud
Allocation of Datacenter Resources Based on Demands Using Virtualization Technology in Cloud G.Rajesh L.Bobbian Naik K.Mounika Dr. K.Venkatesh Sharma Associate Professor, Abstract: Introduction: Cloud
More informationCloud Infrastructure Services for Service Providers VERYX TECHNOLOGIES
Cloud Infrastructure Services for Service Providers VERYX TECHNOLOGIES Meeting the 7 Challenges in Testing and Performance Management Introduction With advent of the cloud paradigm, organizations are transitioning
More informationConceptual Approach for Performance Isolation in Multi-Tenant Systems
Conceptual Approach for Performance Isolation in Multi-Tenant Systems Manuel Loesch 1 and Rouven Krebs 2 1 FZI Research Center for Information Technology, Karlsruhe, Germany 2 SAP AG, Global Research and
More informationThe Probabilistic Model of Cloud Computing
A probabilistic multi-tenant model for virtual machine mapping in cloud systems Zhuoyao Wang, Majeed M. Hayat, Nasir Ghani, and Khaled B. Shaban Department of Electrical and Computer Engineering, University
More informationLow Cost Quality Aware Multi-tier Application Hosting on the Amazon Cloud
Low Cost Quality Aware Multi-tier Application Hosting on the Amazon Cloud Waheed Iqbal, Matthew N. Dailey, David Carrera Punjab University College of Information Technology, University of the Punjab, Lahore,
More informationBSC vision on Big Data and extreme scale computing
BSC vision on Big Data and extreme scale computing Jesus Labarta, Eduard Ayguade,, Fabrizio Gagliardi, Rosa M. Badia, Toni Cortes, Jordi Torres, Adrian Cristal, Osman Unsal, David Carrera, Yolanda Becerra,
More informationEfficient DNS based Load Balancing for Bursty Web Application Traffic
ISSN Volume 1, No.1, September October 2012 International Journal of Science the and Internet. Applied However, Information this trend leads Technology to sudden burst of Available Online at http://warse.org/pdfs/ijmcis01112012.pdf
More informationCDBMS Physical Layer issue: Load Balancing
CDBMS Physical Layer issue: Load Balancing Shweta Mongia CSE, School of Engineering G D Goenka University, Sohna Shweta.mongia@gdgoenka.ac.in Shipra Kataria CSE, School of Engineering G D Goenka University,
More informationHow To Manage Cloud Service Provisioning And Maintenance
Managing Cloud Service Provisioning and SLA Enforcement via Holistic Monitoring Techniques Vincent C. Emeakaroha Matrikelnr: 0027525 vincent@infosys.tuwien.ac.at Supervisor: Univ.-Prof. Dr. Schahram Dustdar
More informationProfit Maximization and Power Management of Green Data Centers Supporting Multiple SLAs
Profit Maximization and Power Management of Green Data Centers Supporting Multiple SLAs Mahdi Ghamkhari and Hamed Mohsenian-Rad Department of Electrical Engineering University of California at Riverside,
More informationPERFORMANCE 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 informationAn 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 informationKeywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age.
Volume 3, Issue 10, October 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Load Measurement
More informationA Review on Load Balancing In Cloud Computing 1
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 4 Issue 6 June 2015, Page No. 12333-12339 A Review on Load Balancing In Cloud Computing 1 Peenaz Pathak, 2 Er.Kamna
More informationDynamic Round Robin for Load Balancing in a Cloud Computing
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 2, Issue. 6, June 2013, pg.274
More informationCollaborative & Integrated Network & Systems Management: Management Using Grid Technologies
2011 International Conference on Computer Communication and Management Proc.of CSIT vol.5 (2011) (2011) IACSIT Press, Singapore Collaborative & Integrated Network & Systems Management: Management Using
More information