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Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (1): 209-220.doi: 10.1007/s11390-020-9732-x
• Special Section on Applications • Previous Articles Next Articles
Sa Wang1,2,3, Member, CCF, ACM, Yan-Hai Zhu4,*, Shan-Pei Chen4, Tian-Ze Wu1,2, Member, CCF, IEEE, Wen-Jie Li1,2, Xu-Sheng Zhan1,2, Hai-Yang Ding4, Wei-Song Shi5, Fellow, IEEE, Yun-Gang Bao1,2,3, Senior Member, CCF, Member, ACM, IEEE
[1] Reiss C, Tumanov A, Ganger G R, Katz R H, Kozuch M A. Heterogeneity and dynamicity of clouds at scale:Google trace analysis. In Proc. the 3rd ACM Symposium on Cloud Computing, October 2012, Article No. 7. [2] Liu H. A measurement study of server utilization in public clouds. In Proc. the 9th IEEE International Conference on Dependable, Autonomic and Secure Computing, December 2011, pp.435-442. [3] Delimitrou C, Kozyrakis C. Quasar:Resource-efficient and QoS-aware cluster management. ACM SIGPLAN Notices, 2014, 49(4):127-144. [4] Cortez E, Bonde A, Muzio A, Russinovich M, Fontoura M, Bianchini R. Resource central:Understanding and predicting workloads for improved resource management in large cloud platforms. In Proc. the 26th Symposium on Operating Systems Principles, October 2017, pp.153-167. [5] Lo D, Cheng L Q, Govindaraju R, Ranganathan P, Kozyrakis C. Heracles:Improving resource efficiency at scale. ACM SIGARCH Computer Architecture News, 2015, 43:450-462. [6] Chen S, Delimitrou C, Martínez J F. PARTIES:QoS-aware resource partitioning for multiple interactive services. In Proc. the 24th International Conference on Architectural Support for Programming Languages and Operating Systems, April 2019, pp.107-120. [7] Zhuravlev S, Blagodurov S, Fedorova A. Addressing shared resource contention in multicore processors via scheduling. ACM SIGPLAN Notices, 2010, 45:129-142. [8] Zhang X, Tune E, Hagmann R et al. CPI2:CPU performance isolation for shared compute clusters. In Proc. the 8th ACM European Conference on Computer Systems, April 2013, pp.379-391. [9] Yasin A. A top-down method for performance analysis and counters architecture. In Proc. the 2014 IEEE International Symposium on Performance Analysis of Systems and Software, March 2014, pp.35-44. [10] Kasture H, Sanchez D. Tailbench:A benchmark suite and evaluation methodology for latency-critical applications. In Proc. the 2016 IEEE International Symposium on Workload Characterization, September 2016, pp.3-12. [11] Henning J L. SPEC CPU2006 benchmark descriptions. SIGARCH Comput. Archit. News, 2006, 34(4):1-17. [12] Verma A, Pedrosa L, Korupolu M, Oppenheimer D, Tune E, Wilkes J. Large-scale cluster management at Google with Borg. In Proc. the 10th European Conference on Computer Systems, April 2015, Article No. 18. [13] Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph A D, Katz R H, Shenker S, Stoica I. Mesos:A platform for fine-grained resource sharing in the data center. In Proc. the 8th USENIX Symposium on Networked Systems Design and Implementation, March 2011, Article No. 4. [14] Schwarzkopf M, Konwinski A, Abd-El-Malek M, Wilkes J. Omega:Flexible, scalable schedulers for large compute clusters. In Proc. the 8th ACM European Conference on Computer Systems, April 2013, pp.351-364. [15] Ousterhout K, Wendell P, Zaharia M, Stoica I. Sparrow:Distributed, low latency scheduling. In Proc. the 24th ACM Symposium on Operating Systems Principles, November 2013, pp.69-84. [16] Zhang Z, Li C, Tao Y Y, Yang R Y, Tang H, Xu J. Fuxi:A fault-tolerant resource management and job scheduling system at Internet scale. Proceedings of the VLDB Endowment, 2014, 7(13):1393-1404. [17] Guo J, Chang Z H, Wang S, Ding H Y, Feng Y H, Mao L, Bao Y G. Who limits the resource efficiency of my datacenter:An analysis of Alibaba datacenter traces. In Proc. the International Symposium on Quality of Service, June 2019, Article No. 39. [18] Herdrich A, Verplanke E, Autee P, Illikkal R, Gianos C, Singhal R, Iyer R. Cache QoS:From concept to reality in the intelr Xeonr processor E5-2600 v3 product family. In Proc. the 2016 IEEE International Symposium on High Performance Computer Architecture, March 2016, pp.657-668. |
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