A novel index system describing program runtime characteristics for workload consolidation
Lin WANG, Depei QIAN, Rui WANG, Zhongzhi LUAN, Hailong YANG, Huaxiang ZHANG
A novel index system describing program runtime characteristics for workload consolidation
Workload consolidation is a common method to improve the resource utilization in clusters or data centers. In order to achieve efficient workload consolidation, the runtime characteristics of a program should be taken into consideration in scheduling. In this paper, we propose a novel index system for efficiently describing the program runtime characteristics. With the help of this index system, programs can be classified by the following runtime characteristics: 1) dependence to multi-dimensional resources including CPU, disk I/O, memory and network I/O; and 2) impact and vulnerability to resource sharing embodied by resource usage and resource sensitivity. In order to verify the effectiveness of this novel index system in workload consolidation, a scheduling strategy, Sche-index, using the new index system for workload consolidation is proposed. Experiment results show that compared with traditional least-loaded scheduling strategy, Sche-index can improve both program performance and system resource utilization significantly.
index system / runtime characteristics / workload consolidation / cluster scheduling
[1] |
Gmach D, Rolia J, Cherkasova L. Resource and virtualization costs up in the cloud: models and design choices. In: Proceedings of the 41st IEEE/IFIP International Conference on Dependable Systems & Networks. 2011, 395–402
CrossRef
Google scholar
|
[2] |
Ahmad R W, Gani A, Hamid S H A, Shiraz M, Yousafzai A, Xia F. A survey on virtual machine migration and server consolidation frameworks for cloud data centers. Journal of Network and Computer Applications, 2015, 52: 11–25
CrossRef
Google scholar
|
[3] |
Li X, Wang R, Luan Z, Liu Y, Qian D. Coordinating workload balancing and power switching in renewable energy powered data center. Frontiers of Computer Science, 2016, 10(3): 574–587
CrossRef
Google scholar
|
[4] |
Stansberry M, Kudritzki J. Uptime institute 2012 data center industry survey. Uptime Institute, 2012
|
[5] |
Zhuravlev S, Blagodurov S, Fedorova A. Addressing shared resource contention in multicore processors via scheduling. In: Proceedings of the 15th International Conference on Architectural Support for Programming Languages and Operating Systems. 2010, 129–141
CrossRef
Google scholar
|
[6] |
Moreto M, Cazorla F J, Ramirez A, Sakellariou R, Valero M. FlexDCP: a QoS framework for CMP architectures. ACM SIGOPS Operating Systems Review, 2009, 43(2): 86–96
CrossRef
Google scholar
|
[7] |
Dwyer T, Fedorova A, Blagodurov S, Roth M, Gaud F, Pei J. A practical method for estimating performance degradation on multicore processors, and its application to HPC workloads. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. 2012, 83–94
CrossRef
Google scholar
|
[8] |
Pacheco-Sanchez S, Casale G, Scotney B, McClean S, Parr G, Dawson S. Markovian workload characterization for QoS prediction in the cloud. In: Proceedings of the IEEE International Conference on Cloud Computing. 2011, 147–154
CrossRef
Google scholar
|
[9] |
Blagodurov S, Gmach D, Arlitt M, Chen Y, Hyser C, Fedorova A. Maximizing server utilization while meeting critical SLAs via weight-based collocation management. In: Proceedings of the 2013 IFIP/IEEE International Symposium on Integrated Network Management. 2013, 277–285
|
[10] |
Beloglazov A, Buyya R. Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science. 2010, 1–4
CrossRef
Google scholar
|
[11] |
Chen Q, Yang H, Mars J, Tang L. Baymax: QoS awareness and increased utilization for non-preemptive accelerators in warehouse scale computers. In: Proceedings of the 21st International Conference on Architectural Support for Programming Languages and Operating Systems. 2016, 681–696
CrossRef
Google scholar
|
[12] |
Liu M, Li T. Optimizing virtual machine consolidation performance on NUMA server architecture for cloud workloads. In: Proceedings of the 41st International Symposium on Computer Architecture. 2014, 325–336
CrossRef
Google scholar
|
[13] |
Mayer-Schönberger V, Cukier K. Big Data: A Revolution That will Transform How We Live, Work, and Think. Boston: Houghton Mifflin Harcourt, 2013
|
[14] |
Di S, Kondo D, Cappello F. Characterizing cloud applications on a Google data center. In: Proceedings of the 42nd International Conference on Parallel Processing. 2013, 468–473
CrossRef
Google scholar
|
[15] |
Schwarzkopf M, Konwinski A, Abd-El-Malek M, Wilkes J. Omega: flexible, scalable schedulers for large compute clusters. In: Proceedings of the 8th ACM European Conference on Computer Systems. 2013, 351–364
CrossRef
Google scholar
|
[16] |
Wang J, Wen J, Han Y, Zhang J, Li C, Xiong Z. Achieving high throughput and TCP Reno fairness in delay-based TCP over large networks. Frontiers of Computer Science, 2014, 8(3): 426–439
CrossRef
Google scholar
|
[17] |
Henning J L. SPEC CPU2006 benchmark descriptions. ACM SIGARCH Computer Architecture News, 2006, 34(4): 1–17
CrossRef
Google scholar
|
[18] |
Bienia C, Kumar S, Singh J P, Li K. The PARSEC benchmark suite: characterization and architectural implications. In: Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques. 2008, 72–81
CrossRef
Google scholar
|
[19] |
Ferdman M, Adileh A, Kocberber O. Clearing the clouds: a study of emerging scale-out workloads on modern hardware. ACM SIGPLAN Notices, 2012, 47(4): 37–48
|
[20] |
Mars J, Tang L. Whare-map: heterogeneity in homogeneous warehouse-scale computers. ACM SIGARCH Computer Architecture News, 2013, 41(3): 619–630
CrossRef
Google scholar
|
[21] |
Bailey D H, Barszcz E, Barton J T. The NAS parallel benchmarks. The International Journal of Supercomputing Applications, 1991, 5(3): 63–73
CrossRef
Google scholar
|
[22] |
Kopytov A. SysBench manual. MySQL AB, 2012, 2–3
|
[23] |
Delimitrou C, Kozyrakis C. Paragon: QoS-aware scheduling for heterogeneous datacenters. ACM SIGPLAN Notices, 2013, 48(4): 77–88
CrossRef
Google scholar
|
[24] |
Mars J, Tang L, Hundt R, Skadron K, Soffa M L. Bubble-up: increasing utilization in modern warehouse scale computers via sensible colocations. In: Proceedings of the 44th Annual IEEE/ACMInternational Symposium on Microarchitecture. 2011, 248–259
CrossRef
Google scholar
|
[25] |
Delimitrou C, Kozyrakis C. Quasar: resource efficient and QoS-aware cluster management. ACM SIGPLAN Notices, 2014, 49(4): 127–144
|
[26] |
Delimitrou C, Sanchez D, Kozyrakis C. Tarcil: reconciling scheduling speed and quality in large shared clusters. In: Proceedings of the 6th ACM Symposium on Cloud Computing. 2015, 97–110
CrossRef
Google scholar
|
[27] |
Lo D, Cheng L, Govindaraju R, Ranganathan P, Kozyrakis C. Heracles: improving resource efficiency at scale. ACM SIGARCH Computer Architecture News, 2015, 43(3): 450–462
CrossRef
Google scholar
|
[28] |
Han J, Jeon S, Choi Y, Huh J. Interference management for distributed parallel applications in consolidated clusters. In: Proceedings of the 21st International Conference on Architectural Support for Programming Languages and Operating Systems. 2016, 443–456
CrossRef
Google scholar
|
[29] |
Mars J, Vachharajani N, Hundt R, Soffa M L. Contention aware execution: online contention detection and response. In: Proceedings of the 8th Annual IEEE/ACM International Symposium on Code Generation and Optimization. 2010, 257–265
CrossRef
Google scholar
|
[30] |
Tang L, Mars J, Soffa M L. Contentiousness vs. sensitivity: improving contention aware runtime systems on multicore architectures. In: Proceedings of the 1st International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era. 2011, 12–21
CrossRef
Google scholar
|
/
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