The Experts below are selected from a list of 43884 Experts worldwide ranked by ideXlab platform
Massoud Pedram - One of the best experts on this subject based on the ideXlab platform.
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a nested two stage game based optimization framework in mobile Cloud Computing System
Service Oriented Software Engineering, 2013Co-Authors: Yanzhi Wang, Xue Lin, Massoud PedramAbstract:The rapidly developing Cloud Computing and virtualization techniques provide mobile devices with battery energy saving opportunities by allowing them to offload computation and execute applications remotely. A mobile device should judiciously decide whether to offload computation and which portion of application should be offloaded to the Cloud. In this paper, we consider a mobile Cloud Computing (MCC) interaction System consisting of multiple mobile devices and the Cloud Computing facilities. We provide a nested two stage game formulation for the MCC interaction System. In the first stage, each mobile device determines the portion of its service requests for remote processing in the Cloud. In the second stage, the Cloud Computing facilities allocate a portion of its total resources for service request processing depending on the request arrival rate from all the mobile devices. The objective of each mobile device is to minimize its power consumption as well as the service request response time. The objective of the Cloud Computing controller is to maximize its own profit. Based on the backward induction principle, we derive the optimal or near-optimal strategy for all the mobile devices as well as the Cloud Computing controller in the nested two stage game using convex optimization technique. Experimental results demonstrate the effectiveness of the proposed nested two stage game-based optimization framework on the MCC interaction System. The mobile devices can achieve simultaneous reduction in average power consumption and average service request response time, by 21.8% and 31.9%, respectively, compared with baseline methods.
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IEEE Cloud - Hierarchical Virtual Machine Consolidation in a Cloud Computing System
2013 IEEE Sixth International Conference on Cloud Computing, 2013Co-Authors: Inkwon Hwang, Massoud PedramAbstract:Improving the energy efficiency of Cloud Computing Systems has become an important issue because the electric energy bill for 24/7 operation of these Systems can be quite large. The focus of this paper is on the virtual machine (VM) consolidation in a Cloud Computing System as a way of lowering daily energy consumption of the System. In contrast to the existing works that assume resource demands of VMs are known and given as scalar variables, this paper treats these demands as random variables with known means and standard deviations. These random variables may be correlated with one another, and there are several kinds of resources which can be performance bottlenecks. Therefore, both the correlation and multiple resource type should be considered. The VM consolidation problem is then formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we propose a heuristic method to solve the problem efficiently. The simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.
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CCGRID - SLA-based Optimization of Power and Migration Cost in Cloud Computing
2012 12th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (ccgrid 2012), 2012Co-Authors: Hadi Goudarzi, Mohammad Ghasemazar, Massoud PedramAbstract:Cloud Computing Systems (or hosting datacenters) have attracted a lot of attention in recent years. Utility Computing, reliable data storage, and infrastructure-independent Computing are example applications of such Systems. Electrical energy cost of a Cloud Computing System is a strong function of the consolidation and migration techniques used to assign incoming clients to existing servers. Moreover, each client typically has a service level agreement (SLA), which specifies constraints on performance and/or quality of service that it receives from the System. These constraints result in a basic trade-off between the total energy cost and client satisfaction in the System. In this paper, a resource allocation problem is considered that aims to minimize the total energy cost of Cloud Computing System while meeting the specified client-level SLAs in a probabilistic sense. The Cloud Computing System pays penalty for the percentage of a client's requests that do not meet a specified upper bound on their service time. An efficient heuristic algorithm based on convex optimization and dynamic programming is presented to solve the aforesaid resource allocation problem. Simulation results demonstrate the effectiveness of the proposed algorithm compared to previous work.
Dmitrii Zagorodnov - One of the best experts on this subject based on the ideXlab platform.
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The eucalyptus open-source Cloud-Computing System
2009 9th IEEE ACM International Symposium on Cluster Computing and the Grid CCGRID 2009, 2009Co-Authors: Daniel Nurmi, Chris Grzegorczyk, Graziano Obertelli, Sunil Soman, Lamia Youseff, Rich Wolski, Dmitrii ZagorodnovAbstract:Cloud Computing Systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming Systems. These types of Systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most Cloud Computing Systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by Systems researchers. In this work, we present Eucalyptus - an open-source software framework for Cloud Computing that implements what is commonly referred to as infrastructure as a service (IaaS); Systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the System, and discuss architectural trade-offs that we have made in order to allow EUCALYPTUS to be portable, modular and simple to use on infrastructure commonly found within academic settings. Finally, we provide evidence that EUCALYPTUS enables users familiar with existing grid and HPC Systems to explore new Cloud Computing functionality while maintaining access to existing, familiar application development software and grid middleware.
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CCGRID - The Eucalyptus Open-Source Cloud-Computing System
2009 9th IEEE ACM International Symposium on Cluster Computing and the Grid, 2009Co-Authors: Daniel Nurmi, Chris Grzegorczyk, Graziano Obertelli, Sunil Soman, Lamia Youseff, Rich Wolski, Dmitrii ZagorodnovAbstract:Cloud Computing Systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming Systems. These types of Systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most Cloud Computing Systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by Systems researchers. In this work, we present Eucalyptus -- an open-source software framework for Cloud Computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); Systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the System, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings. Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC Systems to explore new Cloud Computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.
Zhiping Peng - One of the best experts on this subject based on the ideXlab platform.
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delay optimal scheduling of vms in a queueing Cloud Computing System with heterogeneous workloads
IEEE Transactions on Services Computing, 2020Co-Authors: Mian Guo, Quansheng Guan, Weiqi Chen, Zhiping PengAbstract:This paper studies virtual machine (VM) scheduling in a queueing Cloud Computing System with stochastical arrivals of heterogeneous jobs by considering jobs' delay requirements. The delay-optimal VM scheduling in such a Cloud Computing System is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queueing model that buffers the same type of VM jobs in one virtual queue. The queueing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. A min-min best fit (MM-BF) policy is used to schedule the jobs in different queues to minimize the remaining System resources, while a shortest-job-first (SJF) policy is used to buffer the job requests in each queue based on their job lengths in an ascending order. To avoid job starvation for the long-duration jobs in SJF-MMBF, we further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. Simulation results show that, SJF-MMBF and SJF-QMW achieve low delay performance in terms of average job completion time and high throughput performance in terms of job hosting ratio.
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random task scheduling scheme based on reinforcement learning in Cloud Computing
Cluster Computing, 2015Co-Authors: Zhiping Peng, Delong Cui, Jinglong Zuo, Weiwei LinAbstract:Task scheduling is a necessary prerequisite for performance optimization and resource management in the Cloud Computing System. Focusing on accurate scaled Cloud Computing environment and efficient task scheduling under resource constraints problems, we introduce fine-grained Cloud Computing System model and optimization task scheduling scheme in this paper. The System model is comprised of clearly defined separate submodels including task schedule submodel, task execute submodel and task transmission submodel, so that they can be accurately analyzed in the order of processing of user requests. Moreover the submodels are scalable enough to capture the flexibility of the Cloud Computing paradigm. By analyzing the submodels, where results are repeated to obtain sufficient accuracy, we design a novel task scheduling scheme based on reinforcement learning and queuing theory to optimize task scheduling under the resource constraints, and the state aggregation technologies is employed to accelerate the learning progress. Our results, on the one hand, demonstrate the efficiency of the task scheduling scheme and, on the other hand, reveal the relationship between the arrival rate, server rate, number of VMs and the number of buffer size.
Weiwei Lin - One of the best experts on this subject based on the ideXlab platform.
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random task scheduling scheme based on reinforcement learning in Cloud Computing
Cluster Computing, 2015Co-Authors: Zhiping Peng, Delong Cui, Jinglong Zuo, Weiwei LinAbstract:Task scheduling is a necessary prerequisite for performance optimization and resource management in the Cloud Computing System. Focusing on accurate scaled Cloud Computing environment and efficient task scheduling under resource constraints problems, we introduce fine-grained Cloud Computing System model and optimization task scheduling scheme in this paper. The System model is comprised of clearly defined separate submodels including task schedule submodel, task execute submodel and task transmission submodel, so that they can be accurately analyzed in the order of processing of user requests. Moreover the submodels are scalable enough to capture the flexibility of the Cloud Computing paradigm. By analyzing the submodels, where results are repeated to obtain sufficient accuracy, we design a novel task scheduling scheme based on reinforcement learning and queuing theory to optimize task scheduling under the resource constraints, and the state aggregation technologies is employed to accelerate the learning progress. Our results, on the one hand, demonstrate the efficiency of the task scheduling scheme and, on the other hand, reveal the relationship between the arrival rate, server rate, number of VMs and the number of buffer size.
Daniel Nurmi - One of the best experts on this subject based on the ideXlab platform.
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The eucalyptus open-source Cloud-Computing System
2009 9th IEEE ACM International Symposium on Cluster Computing and the Grid CCGRID 2009, 2009Co-Authors: Daniel Nurmi, Chris Grzegorczyk, Graziano Obertelli, Sunil Soman, Lamia Youseff, Rich Wolski, Dmitrii ZagorodnovAbstract:Cloud Computing Systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming Systems. These types of Systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most Cloud Computing Systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by Systems researchers. In this work, we present Eucalyptus - an open-source software framework for Cloud Computing that implements what is commonly referred to as infrastructure as a service (IaaS); Systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the System, and discuss architectural trade-offs that we have made in order to allow EUCALYPTUS to be portable, modular and simple to use on infrastructure commonly found within academic settings. Finally, we provide evidence that EUCALYPTUS enables users familiar with existing grid and HPC Systems to explore new Cloud Computing functionality while maintaining access to existing, familiar application development software and grid middleware.
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CCGRID - The Eucalyptus Open-Source Cloud-Computing System
2009 9th IEEE ACM International Symposium on Cluster Computing and the Grid, 2009Co-Authors: Daniel Nurmi, Chris Grzegorczyk, Graziano Obertelli, Sunil Soman, Lamia Youseff, Rich Wolski, Dmitrii ZagorodnovAbstract:Cloud Computing Systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming Systems. These types of Systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most Cloud Computing Systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by Systems researchers. In this work, we present Eucalyptus -- an open-source software framework for Cloud Computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); Systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the System, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings. Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC Systems to explore new Cloud Computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.