|
Journal of Computer Science and Technology ›› 2022, Vol. 37 ›› Issue (4): 852-868.doi: 10.1007/s11390-021-1488-4
Special Issue: Computer Networks and Distributed Computing
• Special Section of MASS 2020-2021 • Previous Articles Next Articles
Yubin Duan1 (段钰斌), Student Member, IEEE, Ning Wang2 (王宁), Member, IEEE, and Jie Wu1,*, Fellow, IEEE
[1] Duan Y, Wang N, Wu J. Reducing makespans of DAG scheduling through interleaving overlapping resource utilization. In Proc. the 17th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, December 2020, pp.392-400. DOI: 10.1109/MASS50613.2020.00055. [2] Isard M, Prabhakaran V, Currey J, Wieder U, Talwar K, Goldberg A. Quincy: Fair scheduling for distributed computing clusters. In Proc. the 22nd ACM SIGOPS Symposium on Operating Systems Principles, October 2009, pp.261-276. DOI: 10.1145/1629575.1629601. [3] Grandl R, Ananthanarayanan G, Kandula S, Rao S, Akella A. Multi-resource packing for cluster schedulers. ACM SIGCOMM Computer Communication Review, 2014, 44(4): 455-466. DOI: 10.1145/2740070.2626334. [4] Zhang Z, Li C, Tao Y, Yang R, Tang H, Xu J. Fuxi: A faulttolerant resource management and job scheduling system at Internet scale. Proc. the VLDB Endowment, 2014, 7(13): 1393-1404. DOI: 10.14778/2733004.2733012. [5] Vulimiri A, Curino C, Godfrey P B, Jungblut T, Padhye J, Varghese G. Global analytics in the face of bandwidth and regulatory constraints. In Proc. the 12th USENIX Symposium on Networked Systems Design and Implementation, May 2015, pp.323-336. [6] Grandl R, Kandula S, Rao S, Akella A, Kulkarni J. GRAPHENE: Packing and dependency-aware scheduling for data-parallel clusters. In Proc. the 12th USENIX Symposium on Operating Systems Design and Implementation, November 2016, pp.81-97. [7] Hu Z, Li B, Chen C, Ke X. FlowTime: Dynamic scheduling of deadline-aware workflows ad-hoc jobs. In Proc. the 38th IEEE International Conference on Distributed Computing Systems, July 2018, pp.929-938. DOI: 10.1109/ICDCS.2018.00094. [8] Brucker P. Scheduling Algorithms (5th edition). Springer, 2007. [9] Wang H, Sinnen O. List-scheduling versus cluster-scheduling. IEEE Transactions on Parallel, Distributed Systems, 2018, 29(8): 1736-1749. DOI: 10.1109/TPDS.2018.2808959. [10] Johnson S M. Optimal two-and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, 1954, 1(1): 61-68. DOI: 10.1002/nav.3800010110. [11] Amdahl G M. Validity of the single processor approach to achieving large scale computing capabilities. In Proc. the AFIPS ’67 Spring Joint Computer Conference, April 1967, pp.483-485. DOI: 10.1145/1465482.1465560. [12] Mao H, Schwarzkopf M, Venkatakrishnan S B, Meng Z, Alizadeh M. Learning scheduling algorithms for data processing clusters. In Proc. the ACM Special Interest Group on Data Communication, August 2019, pp.270-288. DOI: 10.1145/3341302.3342080. [13] Zaharia M, Borthakur D, Sen S J, Elmeleegy K, Shenker S, Stoica I. Delay scheduling: A simple technique for achieving locality, fairness in cluster scheduling. In Proc. the 5th European Conference on Computer Systems, April 2010, pp.265-278. DOI: 10.1145/1755913.1755940. [14] Khalil E, Dai H, Zhang Y, Dilkina B, Song L. Learning combinatorial optimization algorithms over graphs. In Proc. the Annual Conference on Neural Information Processing Systems, December 2017, pp.6348-6358. [15] Williams R J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 1992, 8(3/4): 229-256. DOI: 10.1007/BF00992696. [16] Weaver L, Tao N. The optimal reward baseline for gradient-based reinforcement learning. arXiv:1301.2315, 2013. https://arxiv.org/abs/1301.2315, Jan. 2022. [17] Shao W, Xu F, Chen L, Zheng H, Liu F. Stage delay scheduling: Speeding up DAG-style data analytics jobs with resource interleaving. In Proc. the 48th International Conference on Parallel Processing, August 2019, Article No. 8. DOI: 10.1145/3337821.3337872. [18] Hu Z, Li B, Qin Z, Goh R S M. Job scheduling without prior information in big data processing systems. In Proc. the 37th IEEE International Conference on Distributed Computing Systems, June 2017, pp.572-582. DOI: 10.1109/ICDCS.2017.105. [19] Liu S, Wang H, Li B. Optimizing shuffle in wide-area data analytics. In Proc. the 37th IEEE International Conference on Distributed Computing Systems, June 2017, pp.560-571. DOI: 10.1109/ICDCS.2017.131. [20] Delimitrou C, Kozyrakis C. Paragon: QoS-aware scheduling for heterogeneous datacenters. ACM SIGPLAN Notices, 2013, 48(4): 77-88. DOI: 10.1145/2499368.2451125. [21] Vavilapalli V K, Murthy A C, Douglas C et al. Apache Hadoop YARN: Yet another resource negotiator. In Proc. the 4th Annual Symposium on Cloud Computing, October 2013, Article No. 5. DOI: 10.1145/2523616.2523633 [22] Delimitrou C, Kozyrakis C. Quasar: Resource-efficient, QoS-aware cluster management. ACM SIGARCH Computer Architecture News, 2014, 42(1): 127-144. DOI: 10.1145/2654822.2541941. [23] Zhang W, Zheng N, Chen Q, Yang Y, Song Z, Ma T, Leng J, Guo M. URSA: Precise capacity planning, fair scheduling based on low-level statistics for public clouds. In Proc. the 49th International Conference on Parallel Processing, August 2020, Article No. 73. DOI: 10.1145/3404397.3404451. [24] Ousterhout K, Canel C, Ratnasamy S, Shenker S. Monotasks: Architecting for performance clarity in data analytics frameworks. In Proc. the 26th Symposium on Operating Systems Principles, October 2017, pp.184-200. DOI: 10.1145/3132747.3132766. [25] Agrawal K, Li J, Lu K, Moseley B. Scheduling parallel DAG jobs online to minimize average flow time. In Proc. the 27th Annual ACM-SIAM Symposium on Discrete Algorithms, January 2016, pp.176-189. DOI: 10.1137/1.9781611974331.ch14. [26] Chekuri C, Goel A, Khanna S, Kumar A. Multi-processor scheduling to minimize flow time with ε resource augmentation. In Proc. the 36th Annual ACM Symposium on Theory of Computing, June 2004, pp.363-372. DOI: 10.1145/1007352.1007411. [27] Mastrolilli M, Svensson O. (Acyclic) job shops are hard to approximate. In Proc. the 49th Annual IEEE Symposium on Foundations of Computer Science, October 2008, pp.583-592. DOI: 10.1109/FOCS.2008.36. [28] Shmoys D B, Stein C, Wein J. Improved approximation algorithms for shop scheduling problems. SIAM Journal on Computing, 1994, 23(3): 617-632. DOI: 10.1137/S009753979222676X. [29] Zheng H, Wu J. Joint scheduling of overlapping MapReduce phases: Pair jobs for optimization. IEEE Transactions on Services Computing, 2021, 14(5): 1453-1463. DOI: 10.1109/TSC.2018.2875698. [30] Zheng H, Wan Z, Wu J. Optimizing MapReduce framework through joint scheduling of overlapping phases. In Proc. the 25th IEEE International Conference on Computer Communication and Networks, August 2016. DOI: 10.1109/ICCCN.2016.7568555. [31] Grandl R, Chowdhury M, Akella A, Ananthanarayanan G. Altruistic scheduling in multi-resource clusters. In Proc. the 12th USENIX Symposium on Operating Systems Design and Implementation, November 2016, pp.65-80. [32] Ferguson R D, Bodı́k P, Kandula S, Boutin E, Fonseca R. Jockey: Guaranteed job latency in data parallel clusters. In Proc. the 7th EuroSys Conference on Computer Systems, April 2012, pp.99-112. DOI: 10.1145/2168836.2168847. [33] Im S, Kell N, Kulkarni J, Panigrahi D. Tight bounds for online vector scheduling. In Proc. the 56th IEEE Annual Symposium on Foundations of Computer Science, October 2015, pp.525-544. DOI: 10.1109/FOCS.2015.39. [34] Tan H, Han Z, Li X Y, Lau F C M. Online job dispatching, scheduling in edge-clouds. In Proc. the IEEE Conference on Computer Communications, May 2017. DOI: 10.1109/INFOCOM.2017.8057116. [35] Marchetti-Spaccamela A, Megow N, Schlöter J, Skutella M, Stougie L. On the complexity of conditional DAG scheduling in multiprocessor systems. In Proc. the IEEE International Parallel and Distributed Processing Symposium, May 2020, pp.1061-1070. DOI: 10.1109/IPDPS47924.2020.00112. [36] Luo J H, Zhou Y F, Li X J, Yuan M X, Yao J G, Zeng J. Learning to optimize DAG scheduling in heterogeneous environment. arXiv:2103.06980, 2021. https://arxiv.org/abs/2103.06980, March 2022. |
[1] | Jian Liu, Jia-Liang Sun, Yong-Zhuang Liu. Effective Identification and Annotation of Fungal Genomes [J]. Journal of Computer Science and Technology, 2021, 36(2): 248-260. |
[2] | Yu Zhang, Yu-Fen Yu, Hui-Fang Cao, Jian-Kang Chen, Qi-Liang Zhang. CHAUS:Scalable VM-Based Channels for Unbounded Streaming [J]. , 2017, 32(6): 1288-1304. |
[3] | Xu-Meng Wang, Tian-Ye Zhang, Yu-Xin Ma, Jing Xia, Wei Chen. A Survey of Visual Analytic Pipelines [J]. , 2016, 31(4): 787-804. |
[4] | Yu Zhang, Zhao-Peng Li, Hui-Fang Cao. System-Enforced Deterministic Streaming for Efficient Pipeline Parallelism [J]. , 2015, 30(1): 57-73. |
[5] | Hong-Guang Ren (任洪广), Zhi-Ying Wang (王志英), Senior Member, CCF, Member, ACM, IEEE and Doug Edwards, Member, ACM, IEEE. Structure-Based Deadlock Checking of Asynchronous Circuits [J]. , 2011, 26(6): 1031-1040. |
[6] | Jun Yao (姚骏), Member, IEEE, Shinobu Miwa, Hajime Shimada, and Shinji Tomita, Member, ACM, IEEE. A Fine-Grained Runtime Power/Performance Optimization Method for Processors with Adaptive Pipeline Depth [J]. , 2011, 26(2): 292-301. |
[7] | Wei-Wu Hu, Ji-Ye Zhao, Shi-Qiang Zhong, Xu Yang, Elio Guidetti, and Chris Wu. Implementing a 1GHz Four-Issue Out-of-Order Execution Microprocessor in a Standard Cell ASIC Methodology [J]. , 2007, 22(1): 1-0. |
[8] | Wei-Wu Hu, Fu-Xin Zhang, and Zu-Song Li. Microarchitecture of the Godson-2 Processor [J]. , 2005, 20(2): 0-0. |
[9] | WU Jie(吴杰)and CHEN Xiao. Fault-Tlerant Tree-Based Multicasting in Mesh Multicomputers [J]. , 2001, 16(5): 0-0. |
[10] | Tang Zhimin; Xia Peisu;. A Maximum Time Difference Pipelined Arithmetic Unit Based on CMOS Gate Array [J]. , 1995, 10(2): 97-103. |
|