Self-similarity: Behind workload reshaping and prediction

Energy efficiency has become one of the most important challenges in designing future computing systems. Workload reshaping and prediction have a significant impact on the energy conservation of computer components. In this paper, by using three HTTP traffic traces, we perform various evaluations to explore the possible reasons and indications which impact the workload reshaping and prediction. The experimental results indicate that the principles of reshaping workload to improve prediction quality are constructing or improving the self-similarity of the reshaped workloads.

[1]  Ahmed Amer,et al.  Adapting Predictions and Workloads for Power Management , 2006, 14th IEEE International Symposium on Modeling, Analysis, and Simulation.

[2]  Yuhui Deng,et al.  What is the future of disk drives, death or rebirth? , 2011, ACM Comput. Surv..

[3]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[4]  Ahmed Amer,et al.  Predicting when not to predict , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..

[5]  Luca Benini,et al.  A survey of design techniques for system-level dynamic power management , 2000, IEEE Trans. Very Large Scale Integr. Syst..

[6]  Mahmut T. Kandemir,et al.  Reducing Disk Power Consumption in Servers with DRPM , 2003, Computer.

[7]  Yuhui Deng,et al.  Conserving disk energy in virtual machine based environments by amplifying bursts , 2010, Computing.

[8]  Hui Lei,et al.  An analytical approach to file prefetching , 1997 .

[9]  Laura Carrington,et al.  A performance prediction framework for scientific applications , 2003, Future Gener. Comput. Syst..

[10]  P. Krishnan,et al.  Thwarting the Power-Hungry Disk , 1994, USENIX Winter.

[11]  Allen C.-H. Wu,et al.  A predictive system shutdown method for energy saving of event-driven computation , 1997, 1997 Proceedings of IEEE International Conference on Computer Aided Design (ICCAD).

[12]  Darrell D. E. Long,et al.  Adaptive disk spin‐down for mobile computers , 2000, Mob. Networks Appl..

[13]  Dirk Grunwald,et al.  Massive Arrays of Idle Disks For Storage Archives , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[14]  David C. Levy,et al.  Task profiling model for load profile prediction , 2011, Future Gener. Comput. Syst..

[15]  Walter Willinger,et al.  On the self-similar nature of Ethernet traffic , 1993, SIGCOMM '93.

[16]  Michalis Faloutsos,et al.  A user-friendly self-similarity analysis tool , 2003, CCRV.

[17]  Y. Charlie Hu,et al.  Program counter-based prediction techniques for dynamic power management , 2006, IEEE Transactions on Computers.

[18]  Daniel A. Menascé.  Web Performance Modeling Issues , 2000, Int. J. High Perform. Comput. Appl..

[19]  Alma Riska,et al.  Long-Range Dependence at the Disk Drive Level , 2006, Third International Conference on the Quantitative Evaluation of Systems - (QEST'06).

[20]  Daniel A. Reed,et al.  Automatic ARIMA time series modeling for adaptive I/O prefetching , 2004, IEEE Transactions on Parallel and Distributed Systems.

[21]  Bu-Sung Lee,et al.  A model to predict the optimal performance of the Hierarchical Data Grid , 2010, Future Gener. Comput. Syst..

[22]  P. Krishnan,et al.  Optimal prefetching via data compression , 1996, JACM.

[23]  Eric A. Brewer,et al.  Self-similarity in file systems , 1998, SIGMETRICS '98/PERFORMANCE '98.

[24]  Yuhui Deng,et al.  EED: Energy Efficient Disk drive architecture , 2008, Inf. Sci..

[25]  Alan Jay Smith,et al.  Characteristics of I/O traffic in personal computer and server workloads , 2002, IBM Syst. J..