Page 142 - 《软件学报》2020年第10期
P. 142

3118                                  Journal of Software  软件学报 Vol.31, No.10, October 2020

         [61]    Hong CY,  Caesar  M,  Godfrey PB. Finishing flows quickly  with preemptive scheduling.  ACM SIGCOMM  Computer
             Communication Review, 2012,42(4):127–138.
         [62]    Zats D, Das T,  Mohan  P,  et al.  DeTail:  Reducing the flow  completion time tail in datacenter networks.  In:  Proc. of  the  ACM
             SIGCOMM 2012 Conf. on Applications, Technologies, Architectures, and Protocols for Computer Communication. 2012. 139–150.
         [63]    Cascade Lake—Microarchitectures. https://en.wikichip.org/wiki/intel/microarchitectures/cascade_lake
         [64]    Xiang Y, Ye C,  Wang X,  et al. EMBA: Efficient memory  bandwidth allocation  to improve  performance on  Intel commodity
             processor. In: Proc. of the 48th Int’l Conf. on Parallel Processing. 2019. 1–12.
         [65]    Park J, Park S, Han M, et al. Hypart: A hybrid technique for practical memory bandwidth partitioning on commodity servers. In:
             Proc. of the 27th Int’l Conf. on Parallel Architectures and Compilation Techniques. 2018. 1–14.
         [66]    Hashemi M, Swersky K, Smith JA, et al. Learning memory access patterns. arXiv Preprint arXiv: 1803.02329, 2018.
         [67]    Yun H,  Yao G, Pellizzoni  R,  et al. Memguard:  Memory bandwidth reservation  system  for  efficient performance isolation in
             multi-core platforms. In: Proc. of the 19th IEEE Real-time and Embedded Technology and Applications Symposium (RTAS). IEEE,
             2013. 55–64.
         [68]    Subramanian L, Lee D, Seshadri V, et al. BLISS: Balancing performance, fairness and complexity in memory access scheduling.
             IEEE Trans. on Parallel and Distributed Systems, 2016,27(10):3071–3087.
         [69]    Hennessy JL, Patterson DA. Computer Architecture: A Quantitative Approach. Elsevier, 2011.
         [70]    Intel CAT. https://github.com/intel/intel-cmt-cat
         [71]    Herdrich A, Verplanke E, Autee P, et al. Cache QoS: From concept to reality in the Intel® Xeon® processor E5-2600 v3 product
             family. In: Proc. of the 2016 IEEE Int’l Symp. on High Performance Computer Architecture (HPCA). IEEE, 2016. 657–668.
         [72]    Chen Q, Yang H, Mars J, et al. Baymax: QoS awareness and increased utilization for non-preemptive accelerators in warehouse
             scale computers. Computer Architecture News, 2016,44(2):681–696.
         [73]    Charles J, Jassi P, Ananth NS, Sadat A, Fedorova A. Evaluation of the Intel® core i7 turbo boost feature. In: Proc. of the IEEE Int’l
             Symp. on Workload Characterization. 2009.
         [74]    Iorgulescu C, Azimi R, Kwon Y, et al. Perfiso: Performance isolation for commercial latency-sensitive services. In: Proc. of the
             2018 {USENIX} Annual Technical Conf. ({USENIX}{ATC} 18). 2018. 519–532.
         [75]    Funaro  L, Ben-Yehuda  OA, Schuster  A. Ginseng: Market-driven {LLC}  allocation.  In: Proc. of the 2016 {USENIX} Annual
             Technical Conf. ({USENIX}{ATC} 16). 2016. 295–308.
         [76]    Nishtala R, Petrucci V, Carpenter P, et al. TWIG: Multiagent task management for colocated latency-critical cloud services. In:
             Proc. of the Int’l Symp. High-performance Computer Architecture. New York: ACM, 2020.
         [77]    Nguyen H, Shen Z, Gu X, et al. {AGILE}: Elastic distributed resource scaling for infrastructure-as-a-service. In: Proc. of the 10th
             Int’l Conf. on Autonomic Computing ({ICAC} 13). 2013. 69–82.
         [78]    Google Trace Data. 2015. https://github.com/google/cluster-data
         [79]    Alibaba Cluster Data. 2018. https://github.com/alibaba/clusterdata
         [80]    Matrix Warehouse Computer OS. https://myslide.cn/slides/570
         [81]    Alibaba Inc.  Evolution of  Alibaba  large-scale  colocation technology. 2018. https://www.alibabacloud.com/blog/evolution-of-
             alibaba-large-scale-colocation-technology_594172
         [82]    Tencent Yard. https://myslide.cn/slides/9806
         [83]    Guo J, Chang Z, Wang S, et al. Who limits the resource efficiency of my datacenter: An analysis of Alibaba datacenter traces. In:
             Proc. of the Int’l Symp. on Quality of Service. 2019. 1–10.
         [84]    Jiang C, Han G, Lin J, et al. Characteristics of co-allocated online services and batch jobs in internet data centers: A case study from
             Alibaba cloud. IEEE Access, 2019,7:22495–22508.
         [85]    Hauswald JMA, Zhang LY, Li C, Rovinski A, Khurana A, Dreslinski R, Mudge T, Petrucci V, Tang L, Mars J. Sirius: An open
             end-to-end voice and vision personal assistant and its implications for future warehouse scale computers. In: Proc. of the 20th Int’l
             Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS). New York: ACM, 2015. 223–238.
         [86]    Dragoni N, Giallorenzo S, Lafuente AL, et al. Microservices: Yesterday, today, and tomorrow. In: Present and Ulterior Software
             Engineering. Cham: Springer-Verlag, 2017. 195–216.
         [87]    Jamshidi P, Pahl C, Mendonça N C, et al. Microservices: The journey so far and challenges ahead. IEEE Software, 2018,35(3):
             2435.
         [88]    Kannan RS, Subramanian L, Raju A, et al. Grandslam: Guaranteeing slas for jobs in microservices execution frameworks. In: Proc.
             of the 14th EuroSys Conf. 2019. 2019. 1–16.
   137   138   139   140   141   142   143   144   145   146   147