Co-management of Power and Performance in Virtualized Distributed Environments.
ABSTRACT Rapid growth of large-scale applications and their widespread use in research and industry has led to dramatic increases in
energy consumption in enterprise data centers and large-scale distributed systems such as Grids. Any attempt at reducing the
energy consumption without concern for performance can be destructive and deteriorate the overall efficiency of data centers
and large-scale distributed systems running such applications. In this paper, we present an optimization model for resource
management in virtualized distributed systems to minimize power costs automatically while satisfying performance constraints.
The objective of our model is to keep the utilization of servers near to an optimum point to prevent performance degradation.
The model includes two objective functions, one for power costs and another for performance. Using the objective functions,
we present a scheduling algorithm to place a set of virtual machines on a set of servers dynamically so that to integrate
power management with performance management. We show experimentally that the proposed scheduler consumes approximately 24%
less energy than static power management techniques while maintaining comparable performance.
- SourceAvailable from: Aman Kansal[show abstract] [hide abstract]
ABSTRACT: Consolidation of applications in cloud computing environments presents a significant opportunity for energy optimization. As a first step toward enabling energy efficient consolidation, we study the inter-relationships between energy consumption, resource utilization, and performance of consolidated workloads. The study reveals the energy performance trade-offs for consolidation and shows that optimal operating points exist. We model the consolidation problem as a modified bin packing problem and illustrate it with an example. Finally, we outline the challenges in finding effective solutions to the consolidation problem.Cluster Computing - CLUSTER. 01/2008;
Conference Proceeding: Shares and utilities based power consolidation in virtualized server environments[show abstract] [hide abstract]
ABSTRACT: Virtualization technologies like VMware and Xen provide features to specify the minimum and maximum amount of resources that can be allocated to a virtual machine (VM) and a shares based mechanism for the hypervisor to distribute spare resources among contending VMs. However much of the existing work on VM placement and power consolidation in data centers fails to take advantage of these features. One of our experiments on a real testbed shows that leveraging such features can improve the overall utility of the data center by 47% or even higher. Motivated by these, we present a novel suite of techniques for placement and power consolidation of VMs in data centers taking advantage of the min-max and shares features inherent in virtualization technologies. Our techniques provide a smooth mechanism for power-performance tradeoffs in modern data centers running heterogeneous applications, wherein the amount of resources allocated to a VM can be adjusted based on available resources, power costs, and application utilities. We evaluate our techniques on a range of large synthetic data center setups and a small real data center testbed comprising of VMware ESX servers. Our experiments confirm the end-to-end validity of our approach and demonstrate that our final candidate algorithm, PowerExpandMinMax, consistently yields the best overall utility across a broad spectrum of inputs - varying VM sizes and utilities, varying server capacities and varying power costs - thus providing a practical solution for administrators.Integrated Network Management, 2009. IM '09. IFIP/IEEE International Symposium on; 07/2009
- [show abstract] [hide abstract]
ABSTRACT: The energy consumption of under-utilized resources, particularly in a cloud environment, accounts for a substantial amount of the actual energy use. Inherently, a resource allocation strategy that takes into account resource utilization would lead to a better energy efficiency; this, in clouds, extends further with virtualization technologies in that tasks can be easily consolidated. Task consolidation is an effective method to increase resource utilization and in turn reduces energy consumption. Recent studies identified that server energy consumption scales linearly with (processor) resource utilization. This encouraging fact further highlights the significant contribution of task consolidation to the reduction in energy consumption. However, task consolidation can also lead to the freeing up of resources that can sit idling yet still drawing power. There have been some notable efforts to reduce idle power draw, typically by putting computer resources into some form of sleep/power-saving mode. In this paper, we present two energy-conscious task consolidation heuristics, which aim to maximize resource utilization and explicitly take into account both active and idle energy consumption. Our heuristics assign each task to the resource on which the energy consumption for executing the task is explicitly or implicitly minimized without the performance degradation of that task. Based on our experimental results, our heuristics demonstrate their promising energy-saving capability. KeywordsCloud computing–Energy aware computing–Load balancing–SchedulingThe Journal of Supercomputing 60(2):268-280. · 0.92 Impact Factor