Design and Implementation of File Storage and Sharing Using Various Cloud Simulators in Cloud Environment

Transcription

1 Suresh Gyan Vihar University, Jaipur International Journal of Converging Technologies and Management (IJCTM) Volume 1, Issue 1, 2015, pp $$ ISSN :XXXX-XXXX ISSN: Design and Implementation of File Storage and Sharing Using Various Cloud Simulators in Cloud Environment Mr. Bhaskar Kumawat 1 Mr. Ravi Shankar Sharma 2 1 M.Tech. Scholar, Department of Computer Science & Engineering, 2 Assoc. Professor, Department of Computer Science & Engineering, 1,2 Suresh Gyan Vihar University, Jaipur, Rajasthan, India 1 er.ravishankarsharma@gmail.com Abstract- Cloud service provider serves these services to cloud service consumer and charges for the processing power and bandwidth that is actually used. Cloud computing is a new cost-efficient computing paradigm in which information and computer power can be accessed from a Web browser by customers. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Several cloud simulators have been specifically developed for performance analysis of cloud computing environments including CloudSim, SPECI, CDOSIM and DCSim. In this paper, we review the existing cloud computing simulators. Furthermore, we indicate that there exist two types of cloud computing simulators, that is, simulators just based on software and simulators based on both software and hardware. Analysis and comparison of various features of the cloud computing simulators are identified. Index Terms: Cloud Computing, Deployment Models, Service Models, IT infrastructure, Cloud Simulators: CloudSim, CDOSim, SimJava, TeachCloud, icancloud, SPECI, GroudSim, DCSim I. INTRODUCTION A Cloud is a kind of comparable and scattered system consisting of a group of inter-connected and virtual computers that are animatedly provisioned and presented as one or more combined computing resource(s) based on service-level agreement established through mediation between the service supplier and customers. With cloud computing we have access to IT resources and services with remarkable convenience and speed. Cloud computing is a model for enabling convenient and on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal service provider interaction. Cloud computing can be viewed from two different perspectives: cloud application and cloud infrastructure as the building block for the cloud application. Now a days, most organizations focuses on adopting cloud computing model so that they can cut capital expenditure, efforts and control operating costs. These reasons trigger aggressive

2 growth for cloud adoption in business. Some of the traditional Cloud-based application services include social networking, web hosting, content delivery and real time instrumented data processing, which has different composition, configuration, and deployment requirements. Quantifying the performance of scheduling and allocation policies in a real Cloud computing environment for different application models is extremely challenging. The use of real infrastructures for benchmarking the application performance under variable conditions is often constrained by the rigidity of the infrastructure. Thus, it is not possible to perform benchmarking experiments in repeatable, dependable, and scalable environments using real world Cloud environments. A more viable alternative is the use of cloud simulation tools. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. They enable performance analysts to analyze system behavior by focusing on quality issues of specific component under different scenarios. These tools open up the possibility of evaluating the hypothesis in a controlled environment where one can easily reproduce results. Software as a service (SAAS) is a software delivery model in which the software is hosted by someone else s system and delivered via web, on customer s demand and pay as they use. Simulation-based approaches offer significant benefits to IT companies by allowing them to test their services in repeatable and controllable environment and experiment with different workload mix and resource performance scenarios on simulated infrastructures for developing and testing adaptive application provisioning techniques. None of the current distributed system simulators offer the environment that can be directly used for modeling Cloud computing environments But CloudSim which is generalized and extensible simulation framework that allows seamless modeling, simulation, and experimentation of emerging Cloud computing infrastructures and application services. This paper first gives background about various Simulators available. Section 3 define and explores various Cloud simulators such as CloudSim, CDOSIM, TeachCloud, icancloud, SPECI and DCSIM. II. CLOUD STORAGE Cloud Storage is a service where data is remotely maintained, managed, and backed up. The service is available to users over a network, which is usually the internet. It allows the user to store files online so that the user can access them from any location via the internet. The provider company makes them available to the user online by keeping the uploaded files on an external server. This gives companies using cloud storage services ease and convenience, but can potentially be costly. Users should also be aware that backing up their data is still required when using cloud storage services, because recovering data from cloud storage is much slower than local backup. A. Online Data Storage Online data storage is a virtual storage approach that allows users to use the Internet to store recorded data in a remote network. This data storage method may be either a cloud service component or used with other options not requiring on-site data backup. Online data storage is generally defined in contrast to physical data storage, where recorded data is stored on a hard disk or local drive, or, alternately, a server or device connected to a local network. Online data storage usually involves a contract with a third-party

3 service that will accept data routed through Internet Protocol (IP). In order to be considered cloud storage, a service must be sold on demand, provide elasticity (the user can have as much or as little as desired) and offer self-service capabilities. Many individuals and organizations use a mix of on-site and online storage capabilities. For example, they might use local storage for files they use frequently and online storage for backup or archive data. Or they might use local storage for personal data and online storage for files that they wish to share with others. B. Personal File Storage Personal file storage services are aimed at private individuals, offering a sort of "network storage" for personal backup, file access, or file distribution. Users can upload their files and share them publicly or keep them password-protected. Document-sharing services allow users to share and collaborate on document files, such as PDFs, word processor documents, and spreadsheets, but do not support storage of other types of files. C. File sync and sharing services: File syncing and sharing services are file hosting services which allow users to create special folders on each of their computers or mobile devices, which the service then synchronizes so that it appears to be the same folder regardless of which computer is used to view it. Files placed in this folder also are typically accessible through a website and mobile apps, and can be easily shared with other users for viewing or collaboration. Such services have become popular via consumer products such as Drop box and Google Drive III. LITERATURE REVIEW There have been many studies using simulation techniques to investigate behavior of large scale distributed systems and tools to support such research. Some of these simulators are GridSim, MicroGrid, GangSim, OptorSim, SimGrid and CloudSim. While the first three focuses on Grid computing systems. CloudSim is the only simulation framework for studying Cloud computing systems. However, grid simulators have been used to evaluate costs of executing distributed applications in Cloud infrastructures. GridSim is a java based event driven simulation toolkit and was developed to address the problem of performance evaluation of real large scaled distributed environments and heterogeneous Grid systems in a repeatable and controlled manner. CloudSim enables seamless modeling, simulation and experimenting on Cloud computing infrastructures. It is a self-contained platform that can be used to model data centers, service brokers, and scheduling and allocation policies of large scale Cloud platforms. CloudSim framework is built on top of GridSim toolkit. SimGrid is a generic framework for simulation of distributed applications in Grid platforms. GangSim is a Grid simulation toolkit that provides support for modeling of Grid-based virtual organizations and resources. In particular, there is no support in existing Grid simulation toolkits for modeling of on-demand virtualization enabled resource and application management. Further, Cloud infrastructure modeling and simulation toolkits must provide support for economic entities such as Cloud brokers and Cloud exchange for enabling real-time trading of services. Among the currently available simulators discussed, only GridSim offers support for

4 economic-driven resource management and application scheduling simulation. IV. CLOUD SIMULATORS While grid computing simulators have good but they cannot sufficiently model the cloud infrastructure. There are still only a few options for simulating cloud architecture, possibly because virtualization has enabled the deployment of virtual private clouds on small scale physical test beds. However, there have been some notable proposals for software simulation of clouds of very large scale. The CloudSim simulation framework is based on the SimJava discrete event simulation engine at the lowest layer, while the higher layers implement the GridSim toolkit for the modeling of the cluster, including networks, traffic profiles, resources, etc. CloudSim effectively extends the GridSim core functionalities by modeling storage, application services, resource provisioning between virtual machines, and data centre brokerage, and can even simulate federated clouds. A CloudSim is a new, generalized and extensible simulation toolkit and application which enables seamless modeling, simulation, and experimentation of emerging cloud computing system, infrastructures and application environments for single and internetworked clouds. The existing distributed system simulators were not applicable to the cloud computing environment due to evaluating the performance of cloud provisioning policies, services, application workload, models and resources under varying system, user configurations and requirements. To overcome this challenge, CloudSim can be used. In simple words, CloudSim is a development toolkit for simulation of Cloud scenarios. CloudSim is not a framework as it does not provide a ready to use environment for execution of a complete scenario with a specific input. Instead, users of CloudSim have to develop the Cloud scenario it wishes to evaluate, define the required output, and provide the input parameters. CloudSim is invented as CloudBus Project at the University of Melbourne, Australia and supports system and behavior modeling of cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited efforts. CloudSim helps the researchers to focus on specific system design issues without getting concerned about the low level details related to cloud-based infrastructures and services. A. CloudSim is an open source web application that launches preconfigured machines designed to run common open source robotic tools, robotics simulator Gazebo. B. SimJava is a toolkit for building working models of complex systems. It is based around a discrete event simulation kernel at the lowest level of CloudSim. It includes facilities for representing simulation objects as animated icons on screen. C. CDOSim is a cloud deployment option (CDO) Simulator which can simulate the response times, SLA violations and costs of a CDO. A CDO is a decisions concerning simulator which takes decision about the selection of a cloud provider, specific runtime adaptation strategies, components deployment of virtual machine and its instances configuration. Component deployment to virtual machine instances includes the possibility of forming new components of already existing components. Virtual machine instance s configuration, refer to the

5 instance type of virtual machine instances. CDOSim can simulate cloud deployments of software systems that were reverse engineered to KDM models. CDOSim has ability to represent the user s rather than the provider s perspective. CDOSim is a simulator that allows the integration of fine-grained models. CDOSim is best example for comparing runtime reconfiguration plans or for determining the trade-off between costs and performance.cdosim is designed to address the major shortcomings of other existing cloud simulators such as: 1. Consequently oriented towards the cloud user perspective instead of exposing fine-grained internals of a cloud platform. 2. Mitigates the cloud user s lack of knowledge and control concerning a cloud platform structure. 3. Simulation is independent of concrete programming languages in the case appropriate KDM extractors exist for a particular language. 4. Workload profiles from production monitoring data can be used to replay actual user behavior for simulating CDOs. D. TeachCloud is a cloud simulator which is specially made for education purposes. TeachCloud provides a simple graphical interface through which students and scholars can modify a cloud s configuration and perform simple experiments. TeachCloud uses CloudSim as the basic design platform and introduces many new enhancements on top of it such as: 1. Developing a GUI toolkit, 2. Adding the cloud workload generator to the CloudSim simulator, 3. Adding new modules related to SLA and BPM, 4. Adding new cloud network models such as VL2, BCube, Portland and DCell, 5. Introducing a monitoring outlet for most of the cloud system component, 6. Adding an action module that enables students to reconfigure the cloud system and study the impact of such changes on the total system performance. E. icancloud is a cloud simulator which is based on SIMCAN. In simple words, icancloud is a software simulation framework for large storage networks. icancloud can predict the trade-off between costs and performance of a particular application in a specific hardware in order to inform the users about the costs involved. It focuses on policies which charge users in a pay-as-you-go manner.icancloud has a full graphical user interface from which experiments can be designed and run, but existing software systems can only be modeled manually. It also allows parallel execution of one experiment over several machines. F. SPECI Simulation Program for Elastic Cloud Infrastructures (SPECI) is a simulation tool which allows analyzing and exploration of scaling properties of large data center behavior under the size and design policy of the middleware as inputs. SPECI is a simulation tool which allows exploration of aspects of scaling as well as performance properties of future Data Centers. The aim of SPECI is to simulate the performance and behavior of data centers, given the size and middleware design policy

6 as input. Discrete event simulations (DES) are a type of simulation where events are ordered in time maintained in a queue of events by the simulator and each processed at given simulation time [8, 9]. SPECI uses an existing package for DES in Java. SPECI is intended to give us insights into the expected performance of DCs when they are designed, and before they are built. The size of data centers that provide cloud computing services is increasing, and some middleware properties that manage these data centers will not scale linearly with the number of components. SPECI is composed of two packages: data center layout and topology, and the components for experiment execution and measuring. The experiment part of the simulator builds upon SimKit, which offers event scheduling as well as random distribution drawing applications by integrating GroudSim into the ASKALON environment. GroudSim provides a comprehensive set of features for complex simulation scenarios such as simple job executions on leased computing resources, calculation of costs, and background load on resources. Simulations can be parameterized and are easily extendable by probability distribution packages for failures which normally occur in complex environments. Experimental results demonstrate the improved scalability of GroudSim compared to a related process- based approach. H. DCSimDataCenter Simulator is concentrated on virtualized data center which offers IaaS to Multiple tenants, in order to achieve a simulator to evaluate and develop data center management techniques. Data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operational expenses of data centers have skyrocketed with the increase in computing capacity. V. VARIOUS CLOUDSIM SIMULATORS The users could analyze specific system problems through CloudSim, without considering the low level details related to Cloud based Infrastructures and services. Several works have been done from then on to improve CloudSim as are described briefly below: G. GroudSim is an event based simulator that needs one simulation thread for scientific applications on grid and cloud environments based on a scalable simulation independent discreteevent core. It is mainly concentrated on the IaaS, but it is easily extendable to support additional models such as PaaS, DaaS and TaaS. The user to simulate their experiments from the same environment used for real Figure 2: CloudSim Components

7 A. CloudAnalyst CloudAnalyst was derived from CloudSim and extends some of its capabilities and features proposed.cloudanalyst separates the simulation experimentation exercise from a programming exercise. It also enables a modeler to repeatedly perform simulations and to conduct a series of simulation experiments with slight parameters variations in a quick and easy manner. CloudAnalyst can be applied to examining behavior of large scaled Internet application in a cloud environment fashion. Moreover, GreenCloud offers a thorough investigation of workload distributions. In particular, a special focus is devoted to accurately capture communication patterns of currently deployed and future data center architectures. GreenCloud can act as Cloud Bridge. In simple words, GreenCloud is the practice of designing, manufacturing, using and disposing computing resources with minimal environmental damage. The Green Cloud is a supercomputing project under active development at the University of Notre Dame. Green Cloud provides a virtual computing platform by using grid heating which reduces cluster upkeep costs. Figure 3: Cloud Analyst Figure 4: Green Cloud B. GreenCloud GreenCloud is a CloudSim that have green cloud computing approach with confidently, painlessly, and successfully. In other words, GreenCloud is developed as an advanced packet level cloud network simulator with concentration on cloud communication. GreenCloud extracts, aggregates and makes fine grained information about the energy consumed by computing and communication elements of the data center equipment such as computing servers, network switches and communication links available in an unprecedented GreenCloud Aim: 1. To develop high-end computing systems such as Clusters, Data Centers, and Clouds that allocate resources to applications hosting Internet services to meet users' quality of service requirements 2. To minimize consumption of electric power by improving power management, dynamically managing and configuring power-aware ability of system devices. 3. To Provide a detailed simulators

8 4. To analyze energy efficiency and measure cloud performance. GreenCloud can reduce Data Center Power Consumption by: 1. Workload consolidation via DC virtualization. 2. By statistical multiplexing Incentives leading to aggressively lowering OpEx. 3. By improving sustainability by reducing host count. C. Network CloudSim Network CloudSim is an extension of CloudSim as a simulation framework which supports generalized applications such as high performance computing applications, workflows and ecommerce. Network CloudSim uses Network Topology class which implements network layer in CloudSim, reads a BRITE file and generates a topological network. In network CloudSim, the topology file contains nodes, number of entities in the simulation which allows users to increase scale of simulation without changing the topology file. Each CloudSim entity must be mapped to one BRITE node to allow proper work of the network simulation. Each BRITE node can be mapped to only one entity at a time. Network CloudSim allows for modeling of Cloud data centers utilizing bandwidth sharing and latencies to enable scalable and fast simulations. Network CloudSim structure supports designing of the real Cloud data centers and mapping different strategies. Information of network CloudSim is used to simulate latency in network traffic of CloudSim. D. EMUSIM EMUSIM is an integrated architecture to anticipate service s behavior on cloud platforms to a higher standard. EMUSIM combines emulation and simulation to extract information automatically from the application behavior via emulation and uses this information to generate the corresponding simulation model. Such a simulation model is then used to build a simulated scenario that is closer to the actual computing resources and request patterns. Information that is typically not disclosed by platform owners, such as location of virtual machines and number of virtual machines per host in a given time, is not required by EMUSIM. EMUSIM is built on top of two software systems: Automated Emulation Framework (AEF) for emulation and CloudSim for simulation. E. MDC Sim MDCSim is a commercial discrete event simulator developed at the Pennsylvania State University. It helps the analyzer to model unique hardware characteristics of different components of a data center such as servers, communication links and switches which are collected from different dealers and allows estimation of power consumption. MDCSim is the most prominent tool to beused as it has low simulation overhead and moreover its network package maintains a data center topology in the form of directed graph. CONCLUSION Cloud computing has been one of the fastest growing parts in IT industry. Cloud computing is a new and

9 promising paradigm delivering ITservices as computing utilities. A secure storage system is implemented where the user can upload his files in a safe manner with the help of the fully homomorphism encryption scheme and download the file too. Simulation based approaches become popular in industry and academia to evaluate cloud computing systems, application behaviors and their security. Several simulators have been specifically developed for performance analysis of cloud computing environments including CloudSim, SPECI, GroudSim and DCSim but the number of simulation environments for cloud computing data centers available for public use is limited. The CloudSim simulator is probably the most sophisticated among the simulators overviewed. REFERENCES [1] D. Bruneo, S. Distefano, F. Longo, A. Puliafito, M. Scarpa,Workload-Based software rejuvenation in cloud systems,2013. [2] R. Buyya, J. Broberg, A.Goscinski, Cloud Computing: principles and paradigms, New York, USA: Wiley Press,2013. [3] Dr. Rahul Malhotra and Prince Jain, An EMUSIM techniques and its components in a cloud computing environment, IJCTT, August, [4] R. N. Calheiros et al., CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and Experience, Vol.41, No.1, pp.23 50, [5] Xiaoying Bai et al., Cloud Testing Tools, Proceedings of The 6th IEEE International Symposium on Service Oriented System Engineering, SOSE, [6] I. Foster and C. Kesselman, The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann, [7] R. N. Calheiros et al., CloudSim: a novel framework for modeling and simulation of cloud computing infrastructure and services, Technical Report of GRIDS Laboratory, The University of Melbourne, Australia, [8] R. Buyya, R. Ranjan, and R. N. Calheiros, Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: challenges and opportunities, The International Conference on High Performance Computing and Simulation, pp.1 11, [9] B. Wickremasinghe, CloudAnalyst: a CloudSim based tool for modeling and analysis of large scale cloud computing environments, MEDC Project Report, [10] Wei Zhao et al., Modeling and Simulation of Cloud Computing: A Review, 2012 IEEE Asia Pacific Cloud Computing Congress (APCloudCC), IEEE, [11] Collin Bennett, Robert Grossman and Jonathan Seidman, Malstone: A Benchmark for Data Intensive Computing, Open Cloud Consortium Technical Report TR , 14 April 2009 Revised 1 June 2009 [12] C. Bennett, R. L. Grossman, D. Locke, J. Seidman, and S. Vejcik, MalStone: Towards a Benchmark for Analytics on Large Data Clouds, in Proceedings of the 16th ACM International Conference on Knowledge Discovery and Data mining (SIGKDD 10), 2010, pp [13] S. Ostermann, K. Plankensteiner, and D. Bodner, Integration of an event-based simulation-framework into a scientific workflow execution environment for grids and clouds, ServiceWave 2011, pp.1-13, [14] IlangoSriram, SPECI, a simulation tool exploring cloud-scale data centre s, CloudCom 2009, LNCS

10 5931, pp , 2009, M.G. Jaatun, G. Zhao, and C. Rong(Eds.), Springer-Verlag Berlin Heidelberg, 2009 [15] Simon Ostermann, KassianPlankensteiner, RaduProdan, and Thomas Fahringer, GroudSim: An Event Based Simulation Framework for Computational Grids and Clouds, M.R. Guarracino et al. (Eds.): Euro-Par 2010 Workshops, pp , Springer- Verlag Berlin Heidelberg, 2011 [16] S. Ostermann, K. Plankensteiner, and D. Bodner, Integration of an event-- based simulationframework into a scientific workflow execution environment for grids and clouds, ServiceWave 2011, LNCS 6994, pp.1-13, [17] T. Fahringer, R. Prodan, R. Duan, et al., ASKALON: a grid application development and computing environment, 6th IEEE/ACM International Conference on Grid Computing, pp , IEEE, 2005 [18] K. Popover and Z. Hocenski, 2010, Cloud computing security issues and challenges, in 2010Proceedings of the 33rd International Convention MIPRO, pp [19] K. Puffers, T. Tuunanen, et.al, 2007, A Design Science Research Methodology for Information Systems Research, Journal of Management Information Systems", vol. 24, no. 3, pp [20] Arif Mohamed; A History of Cloud Computing Available at: com/articles/2009/06/10/235429/a-history-of-cloudcomputing.htm [Accessed 30 April 2014] [21] Kurtz, R. L., & Vines, R. D. (2010). Cloud security: a comprehensive guide to secure cloud computing. Indianapolis, IN, Wiley