Improving Accuracy of Virtual Machine Power Model by Relative-PMC Based Heuristic Scheduling

Conventional utilization-based power model is effective for measuring the power consumption of physical machines. However, in virtualized environments its accuracy cannot be guaranteed because of the recursive resource accessing among multiple virtual machines. In this paper, we present a novel virtual machine scheduling algorithm, which uses Performance-Monitor-Counter as heuristic information to compensate the recursive power consumption. Theoretical analysis indicates that the error of virtual machine power model can be quantitative bounded when using the proposed scheduling algorithm. Extensive experiments based on standard benchmarks show that the error of virtual machine power measurements can be significantly reduced comparing with the classic credit-based scheduling algorithm

System Support for Distributed Energy Management in Modular Operating Systems

This thesis proposes a novel approach for managing energy in modular operating systems. Our approach enables energy awareness if the resource-management subsystem is distributed among multiple operating-system modules. There are four key achievements: a model for modularization-aware energy management; the support for exposed and distributed energy accounting and allocation; the use of different energy-management interaction protocols; and, finally, the support virtualization of energy effects

A Survey of Research on Power Management Techniques for High Performance Systems

This paper surveys the research on power management techniques for high performance systems. These include both commercial high performance clusters and scientific high performance computing (HPC) systems. Power consumption has rapidly risen to an intolerable scale. This results in both high operating costs and high failure rates so it is now a major cause for concern. It is imposed new challenges to the development of high performance systems. In this paper, we first review the basic mechanisms that underlie power management techniques. Then we survey two fundamental techniques for power management: metrics and profiling. After that, we review the research for the two major types of high performance systems: commercial clusters and supercomputers. Based on this, we discuss the new opportunities and problems presented by the recent adoption of virtualization techniques, and again we present the most recent research on this. Finally, we summarise and discuss future research directions

A survey on energy efficiency in information systems

Concerns about energy and sustainability are growing everyday involving a wide range of fields. Even Information Systems (ISs) are being influenced by the issue of reducing pollution and energy consumption and new fields are rising dealing with this topic. One of these fields is Green Information Technology (IT), which deals with energy efficiency with a focus on IT. Researchers have faced this problem according to several points of view. The purpose of this paper is to understand the trends and the future development of Green IT by analyzing the state-of-the-art and classifying existing approaches to understand which are the components that have an impact on energy efficiency in ISs and how this impact can be reduced. At first, we explore some guidelines that can help to understand the efficiency level of an organization and of an IS. Then, we discuss measurement and estimation of energy efficiency and identify which are the components that mainly contribute to energy waste and how it is possible to improve energy efficiency, both at the hardware and at the software level

Qos-aware fine-grained power management in networked computing systems

Power is a major design concern of today\u27s networked computing systems, from low-power battery-powered mobile and embedded systems to high-power enterprise servers. Embedded systems are required to be power efficiency because most embedded systems are powered by battery with limited capacity. Similar concern of power expenditure rises as well in enterprise server environments due to cooling requirement, power delivery limit, electricity costs as well as environment pollutions. The power consumption in networked computing systems includes that on circuit board and that for communication. In the context of networked real-time systems, the power dissipation on wireless communication is more significant than that on circuit board. We focus on packet scheduling for wireless real-time systems with renewable energy resources. In such a scenario, it is required to transmit data with higher level of importance periodically. We formulate this packet scheduling problem as an NP-hard reward maximization problem with time and energy constraints. An optimal solution with pseudo polynomial time complexity is presented. In addition, we propose a sub-optimal solution with polynomial time complexity. Circuit board, especially processor, power consumption is still the major source of system power consumption. We provide a general-purposed, practical and comprehensive power management middleware for networked computing systems to manage circuit board power consumption thus to affect system-level power consumption. It has the functionalities of power and performance monitoring, power management (PM) policy selection and PM control, as well as energy efficiency analysis. This middleware includes an extensible PM policy library. We implemented a prototype of this middleware on Base Band Units (BBUs) with three PM policies enclosed. These policies have been validated on different platforms, such as enterprise servers, virtual environments and BBUs. In enterprise environments, the power dissipation on circuit board dominates. Regulation on computing resources on board has a significant impact on power consumption. Dynamic Voltage and Frequency Scaling (DVFS) is an effective technique to conserve energy consumption. We investigate system-level power management in order to avoid system failures due to power capacity overload or overheating. This management needs to control the power consumption in an accurate and responsive manner, which cannot be achieve by the existing black-box feedback control. Thus we present a model-predictive feedback controller to regulate processor frequency so that power budget can be satisfied without significant loss on performance. In addition to providing power guarantee alone, performance with respect to service-level agreements (SLAs) is required to be guaranteed as well. The proliferation of virtualization technology imposes new challenges on power management due to resource sharing. It is hard to achieve optimization in both power and performance on shared infrastructures due to system dynamics. We propose vPnP, a feedback control based coordination approach providing guarantee on application-level performance and underlying physical host power consumption in virtualized environments. This system can adapt gracefully to workload change. The preliminary results show its flexibility to achieve different levels of tradeoffs between power and performance as well as its robustness over a variety of workloads. It is desirable for improve energy efficiency of systems, such as BBUs, hosting soft-real time applications. We proposed a power management strategy for controlling delay and minimizing power consumption using DVFS. We use the Robbins-Monro (RM) stochastic approximation method to estimate delay quantile. We couple a fuzzy controller with the RM algorithm to scale CPU frequency that will maintain performance within the specified QoS

Power Modeling and Resource Optimization in Virtualized Environments

The provisioning of on-demand cloud services has revolutionized the IT industry. This emerging paradigm has drastically increased the growth of data centers (DCs) worldwide. Consequently, this rising number of DCs is contributing to a large amount of world total power consumption. This has directed the attention of researchers and service providers to investigate a power-aware solution for the deployment and management of these systems and networks. However, these solutions could be bene\ufb01cial only if derived from a precisely estimated power consumption at run-time. Accuracy in power estimation is a challenge in virtualized environments due to the lack of certainty of actual resources consumed by virtualized entities and of their impact on applications\u2019 performance. The heterogeneous cloud, composed of multi-tenancy architecture, has also raised several management challenges for both service providers and their clients. Task scheduling and resource allocation in such a system are considered as an NP-hard problem. The inappropriate allocation of resources causes the under-utilization of servers, hence reducing throughput and energy e\ufb03ciency. In this context, the cloud framework needs an e\ufb00ective management solution to maximize the use of available resources and capacity, and also to reduce the impact of their carbon footprint on the environment with reduced power consumption. This thesis addresses the issues of power measurement and resource utilization in virtualized environments as two primary objectives. At \ufb01rst, a survey on prior work of server power modeling and methods in virtualization architectures is carried out. This helps investigate the key challenges that elude the precision of power estimation when dealing with virtualized entities. A di\ufb00erent systematic approach is then presented to improve the prediction accuracy in these networks, considering the resource abstraction at di\ufb00erent architectural levels. Resource usage monitoring at the host and guest helps in identifying the di\ufb00erence in performance between the two. Using virtual Performance Monitoring Counters (vPMCs) at a guest level provides detailed information that helps in improving the prediction accuracy and can be further used for resource optimization, consolidation and load balancing. Later, the research also targets the critical issue of optimal resource utilization in cloud computing. This study seeks a generic, robust but simple approach to deal with resource allocation in cloud computing and networking. The inappropriate scheduling in the cloud causes under- and over- utilization of resources which in turn increases the power consumption and also degrades the system performance. This work \ufb01rst addresses some of the major challenges related to task scheduling in heterogeneous systems. After a critical analysis of existing approaches, this thesis presents a rather simple scheduling scheme based on the combination of heuristic solutions. Improved resource utilization with reduced processing time can be achieved using the proposed energy-e\ufb03cient scheduling algorithm