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

Power Analysis and Optimization Techniques for Energy Efficient Computer Systems

Reducing power consumption has become a major challenge in the design and operation of to-day’s computer systems. This chapter describes different techniques addressing this challenge at different levels of system hardware, such as CPU, memory, and internal interconnection network, as well as at different levels of software components, such as compiler, operating system and user applications. These techniques can be broadly categorized into two types: Design time power analysis versus run-time dynamic power management. Mechanisms in the first category use ana-lytical energy models that are integrated into existing simulators to measure the system’s power consumption and thus help engineers to test power-conscious hardware and software during de-sign time. On the other hand, dynamic power management techniques are applied during run-time, and are used to monitor system workload and adapt the system’s behavior dynamically to save energy

Chapter One – An Overview of Architecture-Level Power- and Energy-Efficient Design Techniques

Power dissipation and energy consumption became the primary design constraint for almost all computer systems in the last 15 years. Both computer architects and circuit designers intent to reduce power and energy (without a performance degradation) at all design levels, as it is currently the main obstacle to continue with further scaling according to Moore's law. The aim of this survey is to provide a comprehensive overview of power- and energy-efficient “state-of-the-art” techniques. We classify techniques by component where they apply to, which is the most natural way from a designer point of view. We further divide the techniques by the component of power/energy they optimize (static or dynamic), covering in that way complete low-power design flow at the architectural level. At the end, we conclude that only a holistic approach that assumes optimizations at all design levels can lead to significant savings.Peer ReviewedPostprint (published version

Energy-Aware Scheduling for Streaming Applications

Streaming applications have become increasingly important and widespread,with application domains ranging from embedded devices to server systems.Traditionally, researchers have been focusing on improving the performanceof streaming applications to achieve high throughput and low response time.However, increasingly more attention is being shifted topower/performance trade-offbecause power consumption has become a limiting factor on system designas integrated circuits enter the realm of nanometer technology.This work addresses the problem of scheduling a streaming application(represented by a task graph)with the goal of minimizing its energy consumptionwhile satisfying its two quality of service (QoS) requirements,namely, throughput and response time.The available power management mechanisms are dynamic voltage scaling (DVS),which has been shown to be effective in reducing dynamic power consumption, andvary-on/vary-off, which turns processors on and off to save static power consumption.Scheduling algorithms are proposed for different computing platforms (uniprocessor and multiprocessor systems),different characteristics of workload (deterministic and stochastic workload),and different types of task graphs (singleton and general task graphs).Both continuous and discrete processor power models are considered.The highlights are a unified approach for obtaining optimal (or provably close to optimal)uniprocessor DVS schemes for various DVS strategies anda novel multiprocessor scheduling algorithm that exploits the differencebetween the two QoS requirements to perform processor allocation,task mapping, and task speedscheduling simultaneously

A dynamic power-aware partitioner with task migration for multicore embedded systems

Nowadays, a key design issue in embedded systems is how to reduce the power consumption, since batteries have a limited energy budget. For this purpose, several techniques such as Dynamic Voltage Scaling (DVS) or task migration can be used. DVS allows reducing power by selecting the optimal voltage supply, while task migration achieves this effect by balancing the workload among cores. This paper first analyzes the impact on energy due to task migration in multicore embedded systems with DVS capability and using the well-known Worst Fit (WF) partitioning heuristic. To reduce overhead, migrations are only performed at the time that a task arrives to and/or leaves the system and, in such a case, only one migration is allowed. The huge potential on energy saving due to task migration, leads us to propose a new dynamic partitioner, namely DP, that migrates tasks in a more efficient way than typical partitioners. Unlike WF, the proposed algorithm examines which is the optimal target core before allowing a migration. Experimental results show that DP can improve energy consumption in a factor up to 2.74 over the typical WF algorithm. © 2011 Springer-Verlag.This work was supported by Spanish CICYT under Grant TIN2009-14475-C04-01, and by Consolider-Ingenio under Grant CSD2006-00046.March Cabrelles, JL.; Sahuquillo Borrás, J.; Petit Martí, SV.; Hassan Mohamed, H.; Duato Marín, JF. (2011). A dynamic power-aware partitioner with task migration for multicore embedded systems. En Euro-Par 2011 Parallel Processing. Springer Verlag (Germany). 2011(6852):218-229. https://doi.org/10.1007/978-3-642-23400-2_21S21822920116852AlEnawy, T.A., Aydin, H.: Energy-Aware Task Allocation for Rate Monotonic Scheduling. In: Proceedings of the 11th Real Time on Embedded Technology and Applications Symposium, March 7-10, pp. 213–223. IEEE Computer Society, San Francisco (2005)Aydin, H., Yang, Q.: Energy-Aware Partitioning for Multiprocessor Real-Time Systems. In: Proceedings of the 17th International Parallel and Distributed Processing Symposium, Workshop on Parallel and Distributed Real-Time Systems, April 22-26, p. 113. IEEE Computer Society, Nice (2003)Baker, T.P.: An Analysis of EDF schedulability on a multiprocessor. IEEE Transactions on Parallel and Distributed Systems 16(8), 760–768 (2005)Brandenburg, B.B., Calandrino, J.M., Anderson, J.H.: On the Scalability of Real-Time Scheduling Algorithms on Multicore Platforms: A Case Study. In: Proceedings of the 29th Real-Time Systems Symposium, November 30-December 3, pp. 157–169. IEEE Computer Society, Barcelona (2008)Brião, E., Barcelos, D., Wronski, F., Wagner, F.R.: Impact of Task Migration in NoC-based MPSoCs for Soft Real-time Applications. In: Proceedings of the International Conference on VLSI, October 15-17, pp. 296–299. IEEE Computer Society, Atlanta (2007)Cazorla, F., Knijnenburg, P., Sakellariou, R., Fernández, E., Ramirez, A., Valero, M.: Predictable Performance in SMT Processors: Synergy between the OS and SMTs. IEEE Transactions on Computers 55(7), 785–799 (2006)Donald, J., Martonosi, M.: Techniques for Multicore Thermal Management: Classification and New Exploration. In: Proceedings of the 33rd Annual International Symposium on Computer Architecture, June 17-21, pp. 78–88. IEEE Computer Society, Boston (2006)El-Haj-Mahmoud, A., AL-Zawawi, A., Anantaraman, A., Rotenberg, E.: Virtual Multiprocessor: An Analyzable, High-Performance Architecture for Real-Time Computing. In: Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded Systems, September 24-27, pp. 213–224. ACM Press, San Francisco (2005)Hung, C., Chen, J., Kuo, T.: Energy-Efficient Real-Time Task Scheduling for a DVS System with a Non-DVS Processing Element. In: Proceedings of the 27th Real-Time Systems Symposium, December 5-8, pp. 303–312. IEEE Computer Society, Rio de Janeiro (2006)Kalla, R., Sinharoy, B., Tendler, J.M.: IBM Power5 Chip: A Dual-Core Multithreaded Processor. IEEE Micro 24(2), 40–47 (2004)Kato, S., Yamasaki, N.: Global EDF-based Scheduling with Efficient Priority Promotion. In: Proceedings of the 14th International Conference on Embedded and Real-Time Computing Systems and Applications, August 25-27, pp. 197–206. IEEE Computer Society, Kaohisung (2008)Malardalen Real-Time Research Center, Vasteras, Sweden: WCET Analysis Project. WCET Benchmark Programs (2006), [Online], http://www.mrtc.mdh.se/projects/wcet/March, J., Sahuquillo, J., Hassan, H., Petit, S., Duato, J.: A New Energy-Aware Dynamic Task Set Partitioning Algorithm for Soft and Hard Embedded Real-Time Systems. To be published on The Computer Journal (2011)McNairy, C., Bhatia, R.: Montecito: A Dual-Core, Dual-Thread Itanium Processor. IEEE Micro 25(2), 10–20 (2005)Seo, E., Jeong, J., Park, S., Lee, J.: Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors. IEEE Transactions on Parallel and Distributed Systems 19(11), 1540–1552 (2008)Shah, A.: Arm plans to add multithreading to chip design. ITworld (2010), [Online], http://www.itworld.com/hardware/122383/arm-plans-add-multithreading-chip-designUbal, R., Sahuquillo, J., Petit, S., López, P.: Multi2Sim: A Simulation Framework to Evaluate Multicore-Multithreaded Processors. In: Proceedings of the 19th International Symposium on Computer Architecture and High Performance Computing, October 24-27, pp. 62–68. IEEE Computer Society, Gramado (2007)Watanabe, R., Kondo, M., Imai, M., Nakamura, H., Nanya, T.: Task Scheduling under Performance Constraints for Reducing the Energy Consumption of the GALS Multi-Processor SoC. In: Proceedings of the Design Automation and Test in Europe, April 16-20, pp. 797–802. 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Design and Analysis of an Adaptive Asynchronous System Architecture for Energy Efficiency

Power has become a critical design parameter for digital CMOS integrated circuits. With performance still garnering much concern, a central idea has emerged: minimizing power consumption while maintaining performance. The use of dynamic voltage scaling (DVS) with parallelism has shown to be an effective way of saving power while maintaining performance. However, the potency of DVS and parallelism in traditional, clocked synchronous systems is limited because of the strict timing requirements such systems must comply with. Delay-insensitive (DI) asynchronous systems have the potential to benefit more from these techniques due to their flexible timing requirements and high modularity. This dissertation presents the design and analysis of a real-time adaptive DVS architecture for paralleled Multi-Threshold NULL Convention Logic (MTNCL) systems. Results show that energy-efficient systems with low area overhead can be created using this approach