Page 140 - 《软件学报》2020年第10期

3116 Journal of Software 软件学报 Vol.31, No.10, October 2020

[12] Zhang X, Tune E, Hagmann R, et al. CPI 2: CPU performance isolation for shared compute clusters. In: Proc. of the 8th ACM
European Conf. on Computer Systems. ACM, 2013.
[13] Lo D, Cheng L, Govindaraju R, et al. Heracles: Improving resource efficiency at scale. ACM SIGARCH Computer Architecture
News, 2015,43(3):450–462.
[14] Mars J, Tang L, Hundt R, et al. Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations. In:
Proc. of the 44th Annual IEEE/ACM Int’l Symp. on Microarchitecture. ACM, 2011. 248–259.
[15] Zhang SG, et al. Tail amplification in n-tier systems: A study of transient cross-resource contention attacks. In: Proc. of the 39th
IEEE Int’l Conf. on Distributed Computing Systems (ICDCS). IEEE, 2019
[16] Barroso LA, Dean J, Holzle U. Web search for a planet: The Google cluster architecture. IEEE Micro, 2003,23(2):22–28.
[17] Kasture H, Sanchez D. Tailbench: A benchmark suite and evaluation methodology for latency-critical applications. In: Proc. of the
2016 IEEE Int’l Symp. on Workload Characterization (IISWC). IEEE, 2016. 1–10.
[18] Garefalakis P, Karanasos K, Pietzuch P, et al. Medea: Scheduling of long running applications in shared production clusters. In:
Proc. of the 13th EuroSys Conf. 2018. 1–13.
[19] Xu M, Buyya R. Brownout approach for adaptive management of resources and applications in cloud computing systems: A
taxonomy and future directions. ACM Computing Surveys (CSUR), 2019,52(1):1–27.
[20] Amazon Blog. https://glinden.blogspot.jp/2006/11/marissa-mayer-at-web-20.html
[21] Card SK, Robertson GG, Mackinlay JD. The information visualizer: An information workspace. In: Proc. of the ACM SIGCHI Conf.
on Human Factors in Computing Systems. New York: ACM Press, 1991. 181–188.
[22] Reiss C, Tumanov A, Ganger GR, et al. Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proc. of the 3rd
ACM Symp. on Cloud Computing. 2012. 1–13.
[23] Garg SK, Lakshmi J. Workload performance and interference on containers. In: Proc. of the 2017 IEEE SmartWorld, Ubiquitous
Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing,
Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2017. 1–6.
[24] Reiss C, Tumanov A, Ganger GR, et al. Towards understanding heterogeneous clouds at scale: Google trace analysis. Technical
Report, Intel Science and Technology Center for Cloud Computing, 2012. 84.
[25] Cheng Y, Chai Z, Anwar A. Characterizing co-located datacenter workloads: An Alibaba case study. In: Proc. of the 9th Asia-
Pacific Workshop on Systems. 2018. 1–3.
[26] Kozyrakis C. Resource efficient computing for warehouse-scale datacenters. In: Proc. of the 2013 Design, Automation & Test in
Europe Conf. & Exhibition (DATE). IEEE, 2013. 1351–1356.
[27] Ghodsi A, Zaharia M, Hindman B, et al. Dominant resource fairness: Fair allocation of multiple resource types. NSDI, 2011,11
(2011):24–24.
[28] Isard M, Prabhakaran V, Currey J, et al. Quincy: Fair scheduling for distributed computing clusters. In: Proc. of the ACM SIGOPS
22nd Symp. on Operating Systems Principles. 2009. 261–276.
[29] Ousterhout K, Wendell P, Zaharia M, et al. Sparrow: Distributed, low latency scheduling. In: Proc. of the 24th ACM Symp. on
Operating Systems Principles. 2013. 69–84.
[30] Liu Q, Yu Z. The elasticity and plasticity in semi-containerized co-locating cloud workload: A view from Alibaba trace. In: Proc. of
the ACM Symp. on Cloud Computing. 2018. 347–360.
[31] Barve YD, Shekhar S, Chhokra A, et al. FECBench: A holistic interference-aware approach for application performance modeling.
In: Proc. of the 2019 IEEE Int’l Conf. on Cloud Engineering (IC2E). IEEE, 2019. 211–221.
[32] Zhao JC, Cui HM, Feng XB. Analyzing cross-core performance interference on multi-core processors based on statistical learning.
Ruan Jian Xue Bao/Journal of Software, 2013,24(11):2558−2570 (in Chinese with English abstract). http://www.jos.org.cn/1000-
9825/4482.htm [doi: 10.3724/SP.J.1001.2013.04482]
[33] Zhao J, Cui H, Xue J, et al. Predicting cross-core performance interference on multicore processors with regression analysis. IEEE
Trans. on Parallel and Distributed Systems, 2015,27(5):1443–1456.
[34] Chen Q, Yang H, Guo M, et al. Prophet: Precise QoS prediction on non-preemptive accelerators to improve utilization in
warehouse-scale computers. ACM SIGOPS Operating Systems Review, 2017,51(2):17–32.
[35] Yang H, Breslow A, Mars J, et al. Bubble-flux: Precise online QoS management for increased utilization in warehouse scale
computers. ACM SIGARCH Computer Architecture News, 2013,41(3):607–618.