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Issue No.01 - January-June (2013 vol.12)
pp: 29-32
The hundreds of thousands of servers in modern warehouse-scale systems make performance and efficiency optimizations pressing design challenges. These systems are traditionally considered homogeneous. However, that is not typically the case. Multiple server generations compose a heterogeneous environment, whose performance opportunities have not been fully explored since techniques that account for platform heterogeneity typically do not scale to the tens of thousands of applications hosted in large-scale cloud providers. We present ADSM, a scalable and efficient recommendation system for application-to-server mapping in large-scale datacenters (DCs) that is QoS-aware. ADSM overcomes the drawbacks of previous techniques, by leveraging robust and computationally efficient analytical methods to scale to tens of thousands of applications with minimal overheads. It is also QoS-aware, mapping applications to platforms while enforcing strict QoS guarantees. ADSM is derived from validated analytical models, has low and bounded prediction errors, is simple to implement and scales to thousands of applications without significant changes to the system. Over 390 real DC workloads, ADSM improves performance by 16% on average and up to 2.5x and efficiency by 22% in a DC with 10 different server configurations.
Data centers, Large-scale systems, Computer architecture, Scheduling, Data centers, Multiprocessing systems,simulation of multiple-processor systems, Computer Systems Organization, Computer System Implementation, Large and Medium (“Mainframe”) Computers, Super (very large) computers, Computer Systems Organization, Processor Architectures, Other Architecture Styles, Heterogeneous (hybrid) systems, Computer Systems Organization, Processor Architectures, Parallel Architectures, Scheduling and task partitioning, Computer Systems Organization, Performance of Systems, Design studies, Computer Systems Organization, Special-Purpose and Application-Based Systems, Application studies resulting in better multiple-processor systems, Computer Systems Organization, Performance of Systems, Measurement, evaluation, modeling
C. Delimitrou, C. Kozyrakis, "The Netflix Challenge: Datacenter Edition", IEEE Computer Architecture Letters, vol.12, no. 1, pp. 29-32, January-June 2013, doi:10.1109/L-CA.2012.10
1. L. A. Barroso., “Warehouse-Scale Computing: Entering the Teenage Decade”. ISCA Keynote, SJ, June 2011.
2. L. A. Barroso,U. Holzle., “The Datacenter as a Computer”. Synthesis Series on Computer Architecture, May 2009.
3. R. M. Bell., Y. Koren,C. Volinsky., “The BellKor 2008 Solution to the Netflix Prize”. Technical report, AT&T Labs — Research, October 2007.
4. C. Bienia,S. Kumar, et al. “, The PARSEC benchmark suite: Characterization and architectural implications”. In Proc. of PACT, 2008.
5. Amazon Elastic Compute Cloud-EC2.
6. Google AppEngine.
7. J.R. Hamilton., “Cost of Power in Large-Scale Data Centers”. TR, 2008.
8. A. Jaleel,M. Mattina,B. Jacob., “Last Level Cache (LLC) Performance of Data Mining Workloads On a CMP — A Case Study of Parallel Bioinformatics Workloads”. In Proc. of 12th HPCA, TX, 2006.
9. J. Mars,L. Tang et al. “Heterogeneity in “Homogeneous” Warehouse-Scale Computers: A Performance Opportunity”. In IEEE CAL, 2011.
10. J. Mars,L. Tang, et al. “Bubble-Up: Increasing Utilization in Modern Warehouse Scale Computers via Sensible Co-locations”. In Proc. of MICRO-44, Sao Paolo, 2011.
11. R. Narayanan,B. Ozisikyilmaz, et al. “MineBench: A Benchmark Suite for DataMining Workloads”. In Proc. of IISWC, CA, 2006.
12. R. Nathuji,C. Isci,, and E. Gorbatov., “Exploiting platform heterogeneity for power efficient data centers”. In Proc. of ICAC'07, FL, 2007.
13. Vantage: Scalable and Efficient Fine-Grain Cache Partitioning. Daniel Sanchez, Christos Kozyrakis. In Proc. of the 38th ISCA, CA, 2011.
14. T. Wenisch,R. Wunderlich et al. “SimFlex: Statistical Sampling of Computer System Simulation.” In IEEE Micro, July 2006.
15. Windows Azure, http:/
16. S. Woo,M. Ohara, et al. , The SPLASH-2 Programs: Characterization and Methodological Considerations. In Proc. of the 22nd ISCA, 1995.
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