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Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (1): 179-193.doi: 10.1007/s11390-020-9651-x
• Special Section on Applications • Previous Articles Next Articles
Wen-Li Zhang1, Member, CCF, ACM, IEEE, Ke Liu1, Member, CCF, Yi-Fan Shen1,2, Ya-Zhu Lan1, Member, CCF, Hui Song1, Member, CCF, Ming-Yu Chen1,2,3, Member, CCF, ACM, IEEE, Yuan-Fei Chen1,4, Member, CCF
[1] Gubbi J, Buyya R, Marusic S et al. Internet of Things (IoT):A vision, architectural elements, and future directions. Future Generation Computer Systems, 2013, 29(7):1645-1660. [2] Botta A, De Donato W, Persico V et al. Integration of cloud computing and Internet of Things:A survey. Future Generation Computer Systems, 2016, 56:684-700. [3] Mohammadi M, Al-Fuqaha A, Sorour S et al. Deep learning for IoT big data and streaming analytics:A survey. IEEE Communications Surveys & Tutorials, 2018, 20(4):2923-2960. [4] Dean J, Barroso L A. The tail at scale. Communications of the ACM, 2013, 56(2):74-80. [5] Zats D, Das T, Mohan P, Borthakur D, Katz R. DeTail:Reducing the flow completion time tail in datacenter networks. ACM SIGCOMM Comput. Commun. Rev., 2012, 42:139-150. [6] Li J, Sharma N K, Ports D R et al. Tales of the tail:Hardware, OS, and application-level sources of tail latency. In Proc. the ACM Symposium on Cloud Computing, November 2014, Article No. 9. [7] 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. [8] Thekkath C A, Nguyen T D, Moy E et al. Implementing network protocols at user level. IEEE/ACM Transactions on Networking, 1993, 1(5):554-565. [9] Zhang W, Liu K, Song H et al. Labeled network stack:A codesigned stack for low tail-latency and high concurrency in datacenter services. In Proc. the 15th IFIP WG 10.3 International Conference on Network and Parallel Computing, November 2018, pp.132-136. [10] Wu W, Feng X, Zhang W, Chen M. MCC:A predictable and scalable massive client load generator. In Proc. the 2019 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing, Nov. 2019. [11] Song H, Zhang W, Liu K et al. HCMonitor:An accurate measurement system for high concurrent network services. In Proc. the 2019 IEEE International Conference on Networking, Architecture and Storage, August 2019, Article No. 2. [12] Xu Z W, Li C D. Low-entropy cloud computing systems. SCIENTIA SINICA Informationis, 2017, 47(9):1149-1163. [13] Nowlan M F, Tiwari N, Iyengar J et al. Fitting square pegs through round pipes:Unordered delivery wire-compatible with TCP and TLS. In Proc. the 9th USENIX Symposium on Networked Systems Design and Implementation, April 2012, pp.383-398. [14] Moritz P, Nishihara R, Wang S et al. Ray:A distributed framework for emerging AI applications. In Proc. the 13th USENIX Symposium on Operating Systems Design and Implementation, October 2018, pp.561-577. [15] Nguyen M, Li Z, Duan F et al. The tail at scale:How to predict it? In Proc. the 8th USENIX Workshop on Hot Topics in Cloud Computing, June 2016, Article No. 17. [16] Delimitrou C, Kozyrakis C. Amdahl's law for tail latency. Communications of the ACM, 2018, 61(8):65-72. [17] Xu Y, Musgrave Z, Noble B et al. Bobtail:Avoiding long tails in the cloud. In Proc. the 10th USENIX Symposium on Networked Systems Design & Implementation, April 2013, pp.329-342. [18] Lai Z, Cui Y, Li M et al. TailCutter:Wisely cutting tail latency in cloud CDN under cost constraints. In Proc. the 35th Annual IEEE International Conference on Computer Communications, April 2016. [19] Suresh L, Canini M, Schmid S et al. C3:Cutting tail latency in cloud data stores via adaptive replica selection. In Proc. the 12th USENIX Conference on Networked Systems Design & Implementation, May 2015, pp.513-527. [20] 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. [21] Cerrato I, Annarumma M, Risso F. Supporting fine-grained network functions through Intel DPDK. In Proc. the 3rd European Workshop on Software Defined Networks, September 2014, pp.1-6. [22] Shanmugalingam S, Ksentini A, Bertin P. DPDK Open vSwitch performance validation with mirroring feature. In Proc. the 23rd International Conference on Telecommunications, May 2016, Article No. 45. [23] Marinos I, Watson R N M, Handley M. Network stack specialization for performance. ACM SIGCOMM Computer Communication Review, 2014, 44(4):175-186. [24] Ousterhout A, Fried J, Behrens J et al. Shenango:Achieving high CPU efficiency for latency-sensitive datacenter workloads. In Proc. the 16th USENIX Symposium on Networked Systems Design and Implementation, February 2019, pp.361-378. [25] Kaffes K, Chong T, Humphries J T et al. Shinjuku:Preemptive scheduling for μ second-scale tail latency. In Proc. the 16th USENIX Symposium on Networked Systems Design and Implementation, February 2019, pp.345-360. [26] Jeong E, Woo S, Jamshed M, Jeong H, Ihm S, Han D, Park K. mTCP:A highly scalable user-level TCP stack for multicore systems. In Proc. the 11th USENIX Symposium on Networked Systems Design and Implementation, April 2014, pp.489-502. [27] Belay A, Prekas G, Klimovic A et al. IX:A protected data plane operating system for high throughput and low latency. In Proc. the 11th USENIX Symposium on Operating Systems Design and Implementation, Oct. 2014, pp.49-65. [28] Dragojevic A, Narayanan D, Hodson O, Castro M. FaRM:Fast remote memory. In Proc. the 11th Symposium on Networked Systems Design and Implementation, April 2014, pp.401-414. [29] Jose J, Subramoni H, Luo M et al. Memcached design on high performance RDMA capable interconnects. In Proc. the 2011 International Conference on Parallel Processing, September 2011, pp.743-752. [30] Mitchell C, Geng Y, Li J. Using one-sided RDMA reads to build a fast, CPU-efficient key value store. In Proc. the 2013 USENIX Annual Technical Conference, June 2013, pp.103-114. [31] Ongaro D, Rumble S M, Stutsman R, Ousterhout J K, Rosenblum M. Fast crash recovery in RAMCloud. In Proc. the 23rd ACM Symposium on Operating Systems Principles, October 2011, pp.29-41. [32] Nishtala R, Fugal H, Grimm S et al. Scaling Memcache at Facebook. In Proc. the 10th Symposium on Networked Systems Design and Implementation, April 2013, pp.385-398. [33] Han S, Marshall S, Chun B G, Ratnasamy S. MegaPipe:A new programming interface for scalable network I/O. In Proc. the 10th USENIX Symposium on Operating System Design and Implementation, October 2012, pp.135-148. [34] Bao Y G, Wang S. Labeled von Neumann architecture for software-defined cloud. J. Comput. Sci. Technol., 2017, 32(2):219-223. [35] Ma J, Sui X, Sun N H et al. Supporting differentiated services in computers via programmable architecture for resourcing-on-demand (PARD). In Proc. the 20th International Conference on Architectural Support for Programming Languages and Operating Systems, March 2015, pp.131-143. [36] Marian T, Lee K S, Weatherspoon H. NetSlices:Scalable multi-core packet processing in user-space. In Proc. the 8th ACM/IEEE Symposium on Architectures for Networking and Communication Systems, October 2012, pp.27-38. |
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