20 research outputs found
Power Management Techniques for Data Centers: A Survey
With growing use of internet and exponential growth in amount of data to be
stored and processed (known as 'big data'), the size of data centers has
greatly increased. This, however, has resulted in significant increase in the
power consumption of the data centers. For this reason, managing power
consumption of data centers has become essential. In this paper, we highlight
the need of achieving energy efficiency in data centers and survey several
recent architectural techniques designed for power management of data centers.
We also present a classification of these techniques based on their
characteristics. This paper aims to provide insights into the techniques for
improving energy efficiency of data centers and encourage the designers to
invent novel solutions for managing the large power dissipation of data
centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy
Efficiency, Green Computing, DVFS, Server Consolidatio
Priority Based Power Management and Reduced Downtime in Data Centers
The project deals successfully with software that performs priority based power management and reduced downtime for virtual machines running in data centers. The software deals with power management only at the processor level. The software automatically performs load distribution among servers in data centers to save power. In addition, the software also lets administrator of data centers to mark certain virtual machines, which run user applications, as critical to minimize downtimes for these virtual machines. The software reveals that energy consumption can be minimized while maintaining high runtime availability for the mission critical applications. The software operates in Green mode and in regular mode while maintaining high runtime availability. The experimental results show that Green mode minimizes energy usage by as much as 35%
Elastic DVS Management in Processors with Discrete Voltage/Frequency Modes
Applying classical dynamic voltage scaling (DVS) techniques to real-time systems running on processors with discrete voltage/frequency modes causes a waste of computational resources. In fact, whenever the ideal speed level computed by the DVS algorithm is not available in the system, to guarantee the feasibility of the task set, the processor speed must be set to the nearest level greater than the optimal one, thus underutilizing the system. Whenever the task set allows a certain degree of flexibility in specifying timing constraints, rate adaptation techniques can be adopted to balance performance (which is a function of task rates) versus energy consumption (which is a function of the processor speed).
In this paper, we propose a new method that combines discrete DVS management with elastic scheduling to fully exploit the available computational resources. Depending on the application
requirements, the algorithm can be set to improve performance or reduce energy consumption, so enhancing the flexibility of the system. A reclaiming mechanism is also used to take advantage
of early completions. To make the proposed approach usable in real-world applications, the task model is enhanced to consider some of the real CPU characteristics, such as discrete voltage/frequency levels, switching overhead, task execution times nonlinear with the frequency, and tasks with different power consumption. Implementation issues and experimental results for the proposed algorithm are also discussed
Distributed Utilization Control for Real-time Clusters with Load Balancing
Recent years have seen rapid growth of online services that rely on large-scale server clusters to handle high volume of requests. Such clusters must adaptively control the CPU utilizations of many processors in order to maintain desired soft real-time performance and prevent system overload in face of unpredictable workloads. This paper presents DUC-LB, a novel distributed utilization control algorithm for cluster-based soft real-time applications. Compared to earlier works on utilization control, a distinguishing feature of DUC-LB is its capability to handle system dynamics caused by load balancing, which is a common and essential component of most clusters today. Simulation results and control-theoretic analysis demonstrate that DUC-LB can provide robust utilization control and effective load balancing in large-scale clusters
Adaptive performance control of computing systems via distributed cooperative control: Application to power management in computing clusters
Proceedings of the 3rd International Conference on Autonomic Computing, ICAC 2006, pp. 165-174.Advanced control and optimization techniques offer
a theoretically sound basis to enable self-managing behavior
in distributed computing models such as utility computing.
To tractably solve the performance management problems of
interest, including resource allocation and provisioning in such
distributed computing environments, we develop a fully decentralized
control framework wherein the optimization problem
for the system is first decomposed into sub-problems, and each
sub-problem is solved separately by individual controllers to
achieve the overall performance objectives. Concepts from optimal
control theory are used to implement individual controllers.
The proposed framework is highly scalable, naturally tolerates
controller failures, and allows for the dynamic addition/removal
of controllers during system operation. As a case study, we
apply the control framework to minimize the power consumed
by a computing cluster subject to a dynamic workload while
satisfying the specified quality-of-service goals. Simulations using
real-world workload traces show that the proposed technique has
very low control overhead, and adapts quickly to both workload
variations and controller failures
A hierarchical optimization framework for autonomic performance management of distributed computing systems
26th IEEE International Conference on Distributed Computing Systems, ICDCS 2006: pp. 1648796-1 - 1648796-10.This paper develops a scalable online optimization
framework for the autonomic performance management
of distributed computing systems operating in
a dynamic environment to satisfy desired quality-ofservice
objectives. To efficiently solve the performance
management problems of interest in a distributed setting,
we develop a hierarchical structure where a highlevel
limited-lookahead controller manages interactions
between lower-level controllers using forecast operating
and environment parameters. We develop the overall
control structure, and as a case study, show how to
efficiently manage the power consumed by a computer
cluster. Using workload traces from the Soccer World
Cup 98 web site, we show via simulations that the proposed
method is scalable, has low run-time overhead,
and adapts quickly to time-varying workload patterns
POWER MANAGEMENT IN THE CLUSTER SYSTEM
With growing cost of electricity, the power management of server clusters has become an important problem. However, most previous researchers have only addressed the challenge in traditional homogeneous environments. Considering the increasing popularity of heterogeneous and virtualized systems, this thesis develops a series of efficient algorithms respectively for power management of heterogeneous soft real-time clusters and a virtualized cluster system. It is built on simple but effective mathematical models. When deployed to a new platform, the software incurs low configuration cost because no extensive performance measurements and profiling are required. Built upon optimization, queuing theory and control theory techniques, our approach achieves the design goal, where QoS is provided to a larger number of requests with a smaller amount of power consumption. To strive for efficiency, a threshold based approach is adopted in the first part of the thesis. Then we systematically study this approach and its design decisions. To deploy our mechanisms on the virtualized clusters, we extend the work by developing a novel power-efficient workload distribution algorithm.
Adviser: Ying L