Author: Hang Yuan and C.-C. Jay Kuo

Large-scale video-sharing services, such as YouTube, have come to dominate the Internet traffic. To meet the rapid growth in both data and user demand in VSS, multi-layer infrastructures with parallel architectures have been deployed. In parallel video servers, each video file is divided into a number of blocks and spread across different disks. By breaking a relatively long and continuous video workload into smaller tasks, the system can serve more concurrent requests. The use of massive data centers for large-scale VSS has led to ever-increasing energy cost. In particular, video servers rely on the storage and bandwidth of the parallel disk system, which is among the heaviest energy consumer. It has been reported that such a storage system typically accounts for 27% of the total energy consumption in data centers.

My research focuses on energy management in this kind of parallel storage systems. In particular, we study how to make the best use of the low power modes in disks and how to optimize the usage of memory cache to improve energy efficiency. The goal is to not only minimize energy consumption but also control the impact of energy saving techniques on service delays. We developed a model that efficiently facilitates the selection of power modes for disks, and extended it to optimize cache utilization. Using the model, we can effectively minimize the energy consumption under different service delay constraints.

Our work can be extended in several areas. First, we are in the process of designing better data placement policies that can improve the performance of the algorithm. Second, we only optimized energy consumption for the idle periods of the disk, which prevented us from achieving more energy saving especially under high server load. We can investigate the use of more efficient caching algorithms or full-DRPM to improve our scheme along this direction. Third, we have yet to examine the energy consumption of memory cache. We are interested in extending our EDO framework to include memory energy. Finally, we plan to consider the storage of video with multiple layers (as in scalable video coding) and error resilience information.

• [1] Gurumurthi, S. and Sivasubramaniam, A. and Kandemir, M. and Franke, H. DRPM: dynamic speed control for power management in server class disks. Computer Architecture, 2003. Proceedings. 30th Annual International Symposium on, pages 169 – 179, 2003.
• [2] Hang Yuan, C.-C. Jay Kuo and Ishfaq Ahmad, “Energy Efficiency in Data Centers and Cloud-Based Multimedia Services”, 1st IGCC, Chicago, IL, Aug. 2010.
• [3] Hang Yuan, C.-C. Jay Kuo and Ishfaq Ahmad, “Design of Energy-Efficient Video-Sharing Servers with Delay Constraints”, 18th IEEE ICPADS, Singapore, Dec. 2012
• [4] Garg, Rajat and Son, Seung Woo and Kandemir, Mahmut and Raghavan, Padma and Prabhakar, Ramya. Markov Model Based Disk Power Management for Data Intensive Workloads. Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 76–83, Washington, DC, USA, 2009. IEEE Computer Society.
• [5] Zhu, Q. and Zhou, Y. Power-aware storage cache management. Computers, IEEE Transactions on, 54(5):587 – 602, 2005.