The filter cache: an energy efficient memory structure
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Buffer sizing for minimum energy-delay product by using an approximating polynomial
Proceedings of the 13th ACM Great Lakes symposium on VLSI
A scheduling model for reduced CPU energy
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Dimensioning Large Call Centers
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Dynamic Speed Scaling to Manage Energy and Temperature
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Algorithmic problems in power management
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Coordinated, distributed, formal energy management of chip multiprocessors
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Idletime scheduling with preemption intervals
Proceedings of the twentieth ACM symposium on Operating systems principles
Speed scaling to manage energy and temperature
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ACM Transactions on Algorithms (TALG)
Getting the best response for your erg
ACM Transactions on Algorithms (TALG)
Speed scaling with an arbitrary power function
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Feasibility regions: exploiting tradeoffs between power and performance in disk drives
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Online job-migration for reducing the electricity bill in the cloud
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
Sleep management on multiple machines for energy and flow time
ICALP'11 Proceedings of the 38th international colloquim conference on Automata, languages and programming - Volume Part I
Energy-aware capacity scaling in virtualized environments with performance guarantees
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Deferred maintenance of indexes and of materialized views
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Modeling and analyzing power management policies in server farms using stochastic Petri nets
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
Characterizing the impact of the workload on the value of dynamic resizing in data centers
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Energy-performance trade-off in dense WLANs: A queuing study
Computer Networks: The International Journal of Computer and Telecommunications Networking
Risk and Energy Consumption Tradeoffs in Cloud Computing Service via Stochastic Optimization Models
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Empirical evaluation of power saving policies for data centers
ACM SIGMETRICS Performance Evaluation Review
Optimizing service replication in clouds
Proceedings of the Winter Simulation Conference
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Exact analysis of the M/M/k/setup class of Markov chains via recursive renewal reward
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Nonclairvoyant sleep management and flow-time scheduling on multiple processors
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The Balance Mechanism of Power and Performance in the Virtualization
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Decoupled speed scaling: Analysis and evaluation
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IEEE/ACM Transactions on Networking (TON)
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A central question in designing server farms today is how to efficiently provision the number of servers to extract the best performance under unpredictable demand patterns while not wasting energy. While one would like to turn servers off when they become idle to save energy, the large setup cost (both, in terms of setup time and energy penalty) needed to switch the server back on can adversely affect performance. The problem is made more complex by the fact that today's servers provide multiple sleep or standby states which trade off the setup cost with the power consumed while the server is 'sleeping'. With so many controls, finding the optimal server farm management policy is an almost intractable problem - How many servers should be on at any given time, how many should be off, and how many should be in some sleep state? In this paper, we employ the popular metric of Energy-Response time Product (ERP) to capture the energy-performance trade-off, and present the first theoretical results on the optimality of server farm management policies. For a stationary demand pattern, we prove that there exists a very small, natural class of policies that always contains the optimal policy for a single server, and conjecture it to contain a near-optimal policy for multi-server systems. For time-varying demand patterns, we propose a simple, traffic-oblivious policy and provide analytical and empirical evidence for its near-optimality.