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Does Live Migration of Virtual Machines cost Energy?
"... Abstract—Live migration, the process of moving a virtual machine (VM) interruption-free between physical hosts is a core concept in modern data centers. Power management strategies use live migration to consolidate services in a cluster environment and switch off underutilized machines to save power ..."
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Abstract—Live migration, the process of moving a virtual machine (VM) interruption-free between physical hosts is a core concept in modern data centers. Power management strategies use live migration to consolidate services in a cluster environment and switch off underutilized machines to save power. However, most migration models do not consider the energy cost of migration. This paper experimentally investigates the power consumption and the duration of virtual machine migration. We use the KVM platform for our experiment and show that live migration entails an energy overhead and the size of this overhead varies with the size of the virtual machine and the available network bandwidth. I.
Analysis of the power consumption of a multimedia server under different dvfs policies
- In IEEE CLOUD
, 2012
"... Abstract—Dynamic voltage and frequency scaling (DVFS) has been a useful power management strategy in embedded systems, mobile devices, and wireless sensor networks. Recently, it has also been proposed for servers and data centers in conjunction with service consolidation and optimal resource-pool si ..."
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Abstract—Dynamic voltage and frequency scaling (DVFS) has been a useful power management strategy in embedded systems, mobile devices, and wireless sensor networks. Recently, it has also been proposed for servers and data centers in conjunction with service consolidation and optimal resource-pool sizing. In this paper, we experimentally investigate the scope and usefulness of DVFS in a server environment. We set up a multimedia server which will be used in two different scenarios. In the first scenario, the server will host requests to download video files of known and available formats. In the second scenario, videos of unavailable formats can be accepted; in which case the server employs a transcoder to convert between AVI, MPEG and SLV formats before the videos are downloaded. The workload we generate has a uniform arrival rate and an exponentially distributed video size. We use four dynamic scaling policies which are widely used with existing mainstream Linux operating systems. Our observation is that while the gain of DVFS is clear in the first scenario (in which a predominantly IO-bound application is used), its use in the second scenario is rather counterproductive. Index Terms—Energy consumption of servers, dynamic voltage and frequency scaling, power consumption analysis, dynamic power management, energy consumption I.
Investigation into the Energy Cost of Live Migration of Virtual Machines
"... Abstract—One of the mechanisms to achieve energy efficiency in virtualized environments is to consolidate the workload (virtual machines) of underutilized servers and to switch-off these servers all together. Similarly,the workloads of overloaded servers can be distributed onto other servers for a l ..."
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Abstract—One of the mechanisms to achieve energy efficiency in virtualized environments is to consolidate the workload (virtual machines) of underutilized servers and to switch-off these servers all together. Similarly,the workloads of overloaded servers can be distributed onto other servers for a load balancing reason. Central to this approach is the migration of virtual machines at runtime,which may introduce its own overhead in terms of energy consumption and service execution latency. This paper experimentally investigates the magnitude of this overhead. We use the Kernel-based Virtual Machine (KVM) hypervisor and a custom-made benchmark for our experiments. We will demonstrate that the workload of a virtual machine does not have any bearing on the power consumption of the destination server during migration but it has on the source server. Moreover,the available network bandwidth and the size of the virtual machine do indeed introduce a non-negligible energy overhead and migration latency on both the source and the destination server. Index Terms—virtual machine,live virtual machine migration,migration time,migration cost,power consumption,energy overhead,workload types,energyefficient computing. I.
Automated analysis of performance and energy consumption for cloud applications
- In ICPE
, 2014
"... In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning ..."
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In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.
1Power Consumption Estimation Models for Processors, Virtual Machines, and Servers
"... Abstract—The power consumption of presently available Inter-net servers and data centers is not proportional to the work they accomplish. The scientific community is attempting to address this problem in a number of ways, for example, by employing dynamic voltage and frequency scaling, selectively s ..."
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Abstract—The power consumption of presently available Inter-net servers and data centers is not proportional to the work they accomplish. The scientific community is attempting to address this problem in a number of ways, for example, by employing dynamic voltage and frequency scaling, selectively switching off idle or underutilized servers, and employing energy-aware task scheduling. Central to these approaches is the accurate estimation of the power consumption of the various subsystems of a server, particularly, the processor. We distinguish between power consumption measurement techniques and power con-sumption estimation models. The techniques refer to the art of instrumenting a system to measure its actual power consumption whereas the estimation models deal with indirect evidences (such as information pertaining to CPU utilization or events captured by hardware performance counters) to reason about the power consumption of a system under consideration. The paper provides a comprehensive survey of existing or proposed approaches to estimate the power consumption of single-core as well as multicore processors, virtual machines, and an entire server. Index Terms—Power consumption models, energy-efficiency, server’s power consumption, processor’s power consumption, virtual machine’s power consumption, power consumption es-timation I.
Analysis of the Power and Hardware Resource Consumption of Servers under Different Load Balancing Policies
"... Abstract—Most Internet applications employ some kind of load balancing policies in a cluster setting to achieve reliable service provision as well as to deal with a resource bottleneck. However, these policies may not ensure the utilization of all of the hardware resources in a server equally effici ..."
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Abstract—Most Internet applications employ some kind of load balancing policies in a cluster setting to achieve reliable service provision as well as to deal with a resource bottleneck. However, these policies may not ensure the utilization of all of the hardware resources in a server equally efficiently. This paper experimentally investigates the relationship between the power consumption and resource utilization of a multimedia server cluster when different load balancing policies are used to distribute a workload. Our observations are the following: (1) A bottleneck on a single hardware resource can lead to a significant amount of underutilization of the entire system. (2) A ten times increment in the network bandwidth of the entire cluster can double the throughput of individual servers. The associated increment in power consumption of the individual servers is 1.2% only. (3) For TCP-based applications, session information is more useful than other types of status information to utilize power more efficiently. (4) The use of dynamic frequency scaling does not affect the overall throughput of IO-bound applications but reduces the power consumption of the servers; but this reduction is only 12 % of the overall power consumption. More power can be saved by avoiding a resource bottleneck or through service consolidation. Index Terms—Cluster computing, load balancing, power consumption, resource utilization, service consolidation I.
Situation recognition for service management systems using OWL 2 reasoners
- In Proc. of the 10th IEEE Workshop on Context Modeling and Reasoning (CoMoRea’13
, 2013
"... Abstract—For service management systems the early recog-nition of situations that necessitate a rebinding or a migration of services is an important task. To describe these situations on differing levels of detail and to allow their recognition even if only incomplete information is available, we em ..."
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Abstract—For service management systems the early recog-nition of situations that necessitate a rebinding or a migration of services is an important task. To describe these situations on differing levels of detail and to allow their recognition even if only incomplete information is available, we employ the ontology language OWL 2 and the reasoning services defined for it. In this paper we provide a case study on the performance of state of the art OWL 2 reasoning systems for answering class queries and conjunctive queries modeling the relevant situations for service rebinding or migration in the differing OWL 2 profiles. I.
scope and usefulness of Dynamic Voltage and Frequency Scaling
"... Abstract—In this paper, we experimentally investigate the ..."
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Mutual Influence of Application- and Platform-Level Adaptations on Energy-Efficient Computing
"... Abstract—We experimentally investigate the mutual influence of application- and platform-level adaptations in a virtualized cluster environment. At the application level, applications can adapt to a changing execution environment by dynamically ex-changing components that enable them to trade energy ..."
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Abstract—We experimentally investigate the mutual influence of application- and platform-level adaptations in a virtualized cluster environment. At the application level, applications can adapt to a changing execution environment by dynamically ex-changing components that enable them to trade energy for utility and vice versa. Likewise, at the platform level, virtual machine monitors can migrate virtual machines from one server to another either to consolidate workloads and switch-off underutilized servers or to distribute the workload of overloaded servers. Our experiment quantify impacts of various types of adaptations on QoS, power consumption, and energy-overhead. Keywords—Adaptation, cloud computing, energy-efficient com-puting, virtualization, virtual machines migration, migration costs I.
Estimation of the Cost of VM Migration
"... Abstract—One of the mechanisms to achieve energy efficiency in virtualized/cloud environments is consolidation of workloads on an optimal number of servers and switching-off of idle or underutilized servers. Central to this approach is the migration of virtual machines at runtime. In this paper we i ..."
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Abstract—One of the mechanisms to achieve energy efficiency in virtualized/cloud environments is consolidation of workloads on an optimal number of servers and switching-off of idle or underutilized servers. Central to this approach is the migration of virtual machines at runtime. In this paper we investigate the cost (migration time) of virtual machines migration. We shall show that migration time exponentially increases as the available network bandwidth decreases; migration time linearly increases as the RAM size of a virtual machine increases. Furthermore, the power consumption of both the destination and the source servers remain by and large the same for a fixed network bandwidth, regardless of the VM size. Interestingly, for the same combination of virtual machines, different orders of migrations resulted in different migration time. We observed that migrating resource intensive virtual machines first yields the shortest migration time. In general, the migration time should be modeled as a random variable since the factors that affect it cannot be known except in a probabilistic sense. Therefore, we propose a probabilistic approach to quantify the cost of virtual machines migration. Index Terms—Cloud computing, energy-efficient computing, migration cost, migration time, server consolidation, virtual machine migration, workload consolidation I.
