The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Nagarajan Kandasamy - One of the best experts on this subject based on the ideXlab platform.
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Combined Power and Performance Management of Virtualized Computing Environments Serving Session-Based Workloads
IEEE Transactions on Network and Service Management, 2011Co-Authors: Dara Marie Kusic, Nagarajan Kandasamy, Guofei JiangAbstract:This paper develops an online resource provisioning framework for combined power and performance management in a Virtualized Computing environment serving session-based workloads. We pose this management problem as one of sequential optimization under uncertainty and solve it using limited lookahead control (LLC), a form of model-predictive control. The approach accounts for the switching costs incurred when provisioning virtual machines and explicitly encodes the risk of provisioning resources in an uncertain and dynamic operating environment. We experimentally validate the control framework on a server cluster supporting three online services. When managed using LLC, our cluster setup saves, on average, 41% in power-consumption costs over a twenty-four hour period when compared to a system operating without dynamic control. Finally, we use trace-based simulations to analyze LLC performance on server clusters larger than our testbed and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
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a distributed control framework for performance management of Virtualized Computing environments
International Conference on Autonomic Computing, 2010Co-Authors: Rui Wang, Dara Marie Kusic, Nagarajan KandasamyAbstract:This paper develops a distributed cooperative control framework to manage the performance of Virtualized Computing environments. We consider a server cluster hosting multiple enterprise applications on a set of virtual machines (VMs) in which the system must dynamically optimize the CPU capacity provided to each VM in response to incoming workload intensity such that desired response times are satisfied. We solve the overall control/optimization problem by decomposing it into a set of smaller subproblems that can be solved cooperatively by individual controllers. Model-predictive controllers, implemented locally within each server, independently decide the CPU capacity to allocate to VMs under their control such that the overall system's performance goals are satisfied. We experimentally validate the proposed framework on a server cluster supporting three online services, showing that our scheme is highly scalable, naturally tolerates server failures, and allows for the dynamic addition/removal of servers during system operation without requiring changes to the overall control architecture.
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ICAC - A distributed control framework for performance management of Virtualized Computing environments
Proceeding of the 7th international conference on Autonomic computing - ICAC '10, 2010Co-Authors: Rui Wang, Dara Marie Kusic, Nagarajan KandasamyAbstract:This paper develops a distributed cooperative control framework to manage the performance of Virtualized Computing environments. We consider a server cluster hosting multiple enterprise applications on a set of virtual machines (VMs) in which the system must dynamically optimize the CPU capacity provided to each VM in response to incoming workload intensity such that desired response times are satisfied. We solve the overall control/optimization problem by decomposing it into a set of smaller subproblems that can be solved cooperatively by individual controllers. Model-predictive controllers, implemented locally within each server, independently decide the CPU capacity to allocate to VMs under their control such that the overall system's performance goals are satisfied. We experimentally validate the proposed framework on a server cluster supporting three online services, showing that our scheme is highly scalable, naturally tolerates server failures, and allows for the dynamic addition/removal of servers during system operation without requiring changes to the overall control architecture.
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A distributed control framework for performance management of Virtualized Computing environments
Proceeding of the 7th international conference on Autonomic computing - ICAC '10, 2010Co-Authors: Rui Wang, Dara Marie Kusic, Nagarajan KandasamyAbstract:There is growing incentive to reduce the power consumed by data centers. Virtualization is a promising approach to con- solidating multiple online services onto a smaller number of Computing resources. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain desired service-level agreements with end users while achiev- ing higher server utilization and energy efficiency. This pa- per proposes a distributed cooperative control framework for the power and performance management of Virtualized com- puting environments, and presents some preliminary results aimed at establishing the feasibility of this approach.
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Power and performance management of Virtualized Computing environments via lookahead control
Cluster Computing, 2009Co-Authors: Dara Marie Kusic, Nagarajan Kandasamy, Jeffrey O. Kephart, James E. Hanson, Guofei JiangAbstract:There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of Computing resources. A Virtualized server environment allows Computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for Virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
Dara Marie Kusic - One of the best experts on this subject based on the ideXlab platform.
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Combined Power and Performance Management of Virtualized Computing Environments Serving Session-Based Workloads
IEEE Transactions on Network and Service Management, 2011Co-Authors: Dara Marie Kusic, Nagarajan Kandasamy, Guofei JiangAbstract:This paper develops an online resource provisioning framework for combined power and performance management in a Virtualized Computing environment serving session-based workloads. We pose this management problem as one of sequential optimization under uncertainty and solve it using limited lookahead control (LLC), a form of model-predictive control. The approach accounts for the switching costs incurred when provisioning virtual machines and explicitly encodes the risk of provisioning resources in an uncertain and dynamic operating environment. We experimentally validate the control framework on a server cluster supporting three online services. When managed using LLC, our cluster setup saves, on average, 41% in power-consumption costs over a twenty-four hour period when compared to a system operating without dynamic control. Finally, we use trace-based simulations to analyze LLC performance on server clusters larger than our testbed and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
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a distributed control framework for performance management of Virtualized Computing environments
International Conference on Autonomic Computing, 2010Co-Authors: Rui Wang, Dara Marie Kusic, Nagarajan KandasamyAbstract:This paper develops a distributed cooperative control framework to manage the performance of Virtualized Computing environments. We consider a server cluster hosting multiple enterprise applications on a set of virtual machines (VMs) in which the system must dynamically optimize the CPU capacity provided to each VM in response to incoming workload intensity such that desired response times are satisfied. We solve the overall control/optimization problem by decomposing it into a set of smaller subproblems that can be solved cooperatively by individual controllers. Model-predictive controllers, implemented locally within each server, independently decide the CPU capacity to allocate to VMs under their control such that the overall system's performance goals are satisfied. We experimentally validate the proposed framework on a server cluster supporting three online services, showing that our scheme is highly scalable, naturally tolerates server failures, and allows for the dynamic addition/removal of servers during system operation without requiring changes to the overall control architecture.
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ICAC - A distributed control framework for performance management of Virtualized Computing environments
Proceeding of the 7th international conference on Autonomic computing - ICAC '10, 2010Co-Authors: Rui Wang, Dara Marie Kusic, Nagarajan KandasamyAbstract:This paper develops a distributed cooperative control framework to manage the performance of Virtualized Computing environments. We consider a server cluster hosting multiple enterprise applications on a set of virtual machines (VMs) in which the system must dynamically optimize the CPU capacity provided to each VM in response to incoming workload intensity such that desired response times are satisfied. We solve the overall control/optimization problem by decomposing it into a set of smaller subproblems that can be solved cooperatively by individual controllers. Model-predictive controllers, implemented locally within each server, independently decide the CPU capacity to allocate to VMs under their control such that the overall system's performance goals are satisfied. We experimentally validate the proposed framework on a server cluster supporting three online services, showing that our scheme is highly scalable, naturally tolerates server failures, and allows for the dynamic addition/removal of servers during system operation without requiring changes to the overall control architecture.
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A distributed control framework for performance management of Virtualized Computing environments
Proceeding of the 7th international conference on Autonomic computing - ICAC '10, 2010Co-Authors: Rui Wang, Dara Marie Kusic, Nagarajan KandasamyAbstract:There is growing incentive to reduce the power consumed by data centers. Virtualization is a promising approach to con- solidating multiple online services onto a smaller number of Computing resources. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain desired service-level agreements with end users while achiev- ing higher server utilization and energy efficiency. This pa- per proposes a distributed cooperative control framework for the power and performance management of Virtualized com- puting environments, and presents some preliminary results aimed at establishing the feasibility of this approach.
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Power and performance management of Virtualized Computing environments via lookahead control
Cluster Computing, 2009Co-Authors: Dara Marie Kusic, Nagarajan Kandasamy, Jeffrey O. Kephart, James E. Hanson, Guofei JiangAbstract:There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of Computing resources. A Virtualized server environment allows Computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for Virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
Guofei Jiang - One of the best experts on this subject based on the ideXlab platform.
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Combined Power and Performance Management of Virtualized Computing Environments Serving Session-Based Workloads
IEEE Transactions on Network and Service Management, 2011Co-Authors: Dara Marie Kusic, Nagarajan Kandasamy, Guofei JiangAbstract:This paper develops an online resource provisioning framework for combined power and performance management in a Virtualized Computing environment serving session-based workloads. We pose this management problem as one of sequential optimization under uncertainty and solve it using limited lookahead control (LLC), a form of model-predictive control. The approach accounts for the switching costs incurred when provisioning virtual machines and explicitly encodes the risk of provisioning resources in an uncertain and dynamic operating environment. We experimentally validate the control framework on a server cluster supporting three online services. When managed using LLC, our cluster setup saves, on average, 41% in power-consumption costs over a twenty-four hour period when compared to a system operating without dynamic control. Finally, we use trace-based simulations to analyze LLC performance on server clusters larger than our testbed and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
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Power and performance management of Virtualized Computing environments via lookahead control
Cluster Computing, 2009Co-Authors: Dara Marie Kusic, Nagarajan Kandasamy, Jeffrey O. Kephart, James E. Hanson, Guofei JiangAbstract:There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of Computing resources. A Virtualized server environment allows Computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for Virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
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Power and Performance Management of Virtualized Computing Environments Via Lookahead Control
2008 International Conference on Autonomic Computing, 2008Co-Authors: Dara Marie Kusic, Nagarajan Kandasamy, Jeffrey O. Kephart, James E. Hanson, Guofei JiangAbstract:There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of Computing resources. A Virtualized server environment allows Computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for Virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 26% of the power required by a system without dynamic control while still maintaining QoS goals.
Yifen Mu - One of the best experts on this subject based on the ideXlab platform.
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power and performance management in nonlinear Virtualized Computing systems via predictive control
PLOS ONE, 2015Co-Authors: Yifen MuAbstract:The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in Virtualized Computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller.
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dynamic power saving via least square self tuning regulator in the Virtualized Computing systems
Journal of Systems Science & Complexity, 2015Co-Authors: Xiang Long, Yifen MuAbstract:In recent years, power saving problem has become more and more important in many fields and attracted a lot of research interests. In this paper, the authors consider the power saving problem in the Virtualized Computing system. Since there are multiple objectives in the system as well as many factors influencing the objectives, the problem is complex and hard. The authors will formulate the problem as an optimization problem of power consumption with a prior requirement on performance, which is taken as the response time in the paper. To solve the problem, the authors design the adaptive controller based on least-square self-tuning regulator to dynamically regulate the Computing resource so as to track a given reasonable reference performance and then minimize the power consumption using the tracking result supplied by the controller at each time. Simulation is implemented based on the data collected from real machines and the time delay of turning on/off the machine is included in the process. The results show that this method based on adaptive control theory can save power consumption greatly with satisfying the performance requirement at the same time, thus it is suitable and effective to solve the problem.
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adaptive controller for dynamic power and performance management in the Virtualized Computing systems
PLOS ONE, 2013Co-Authors: Xiang Long, Yifen MuAbstract:Power and performance management problem in large scale Computing systems like data centers has attracted a lot of interests from both enterprises and academic researchers as power saving has become more and more important in many fields. Because of the multiple objectives, multiple influential factors and hierarchical structure in the system, the problem is indeed complex and hard. In this paper, the problem will be investigated in a Virtualized Computing system. Specifically, it is formulated as a power optimization problem with some constraints on performance. Then, the adaptive controller based on least-square self-tuning regulator(LS-STR) is designed to track performance in the first step; and the resource solved by the controller is allocated in order to minimize the power consumption as the second step. Some simulations are designed to test the effectiveness of this method and to compare it with some other controllers. The simulation results show that the adaptive controller is generally effective: it is applicable for different performance metrics, for different workloads, and for single and multiple workloads; it can track the performance requirement effectively and save the power consumption significantly.
Anand Raghunathan - One of the best experts on this subject based on the ideXlab platform.
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A Trusted Virtual Machine in an Untrusted Management Environment
IEEE Transactions on Services Computing, 2012Co-Authors: Chunxiao Li, Anand RaghunathanAbstract:Virtualization is a rapidly evolving technology that can be used to provide a range of benefits to Computing systems, including improved resource utilization, software portability, and reliability. Virtualization also has the potential to enhance security by providing isolated execution environments for different applications that require different levels of security. For security-critical applications, it is highly desirable to have a small trusted Computing base (TCB), since it minimizes the surface of attacks that could jeopardize the security of the entire system. In traditional virtualization architectures, the TCB for an application includes not only the hardware and the virtual machine monitor (VMM), but also the whole management operating system (OS) that contains the device drivers and virtual machine (VM) management functionality. For many applications, it is not acceptable to trust this management OS, due to its large code base and abundance of vulnerabilities. For example, consider the "Computing-as-a-service” scenario where remote users execute a guest OS and applications inside a VM on a remote Computing platform. It would be preferable for many users to utilize such a Computing service without being forced to trust the management OS on the remote platform. In this paper, we address the problem of providing a secure execution environment on a Virtualized Computing platform under the assumption of an untrusted management OS. We propose a secure virtualization architecture that provides a secure runtime environment, network interface, and secondary storage for a guest VM. The proposed architecture significantly reduces the TCB of security-critical guest VMs, leading to improved security in an untrusted management environment. We have implemented a prototype of the proposed approach using the Xen virtualization system, and demonstrated how it can be used to facilitate secure remote Computing services. We evaluate the performance penalties incurred by the proposed architecture, and demonstrate that the penalties are minimal.
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Secure Virtual Machine Execution under an Untrusted Management OS
2010 IEEE 3rd International Conference on Cloud Computing, 2010Co-Authors: Chunxiao Li, Anand RaghunathanAbstract:Virtualization is a rapidly evolving technology that can be used to provide a range of benefits to Computing systems, including improved resource utilization, software portability, and reliability. For security-critical applications, it is highly desirable to have a small trusted Computing base (TCB), since it minimizes the surface of attacks that could jeopardize the security of the entire system. In traditional virtualization architectures, the TCB for an application includes not only the hardware and the virtual machine monitor (VMM), but also the whole management operating system (OS) that contains the device drivers and virtual machine (VM) management functionality. For many applications, it is not acceptable to trust this management OS, due to its large code base and abundance of vulnerabilities. In this paper, we address the problem of providing a secure execution environment on a Virtualized Computing platform under the assumption of an untrusted management OS. We propose a secure virtualization architecture that provides a secure run-time environment, network interface, and secondary storage for a guest VM. The proposed architecture significantly reduces the TCB of security-critical guest VMs, leading to improved security in an untrusted management environment. We have implemented a prototype of the proposed approach using the Xen virtualization system, and demonstrated how it can be used to facilitate secure remote Computing services. We evaluate the performance penalties incurred by the proposed architecture, and demonstrate that the penalties are minimal.
