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FTLLS: A fault tolerant, low latency, distributed scheduling approach based on sparrow

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Abstract

Big data processing systems are developing towards larger degrees of parallelism and shorter task durations in order to achieve lower response time. Scheduling highly parallel tasks that complete in sub-seconds poses a great challenge to traditional centralized schedulers. Taking the challenge, researchers turn to distributed scheduling approaches to avoid the throughput limitation of centralized schedulers, among which Sparrow is a leading design. However, little effort is devoted to the fault tolerance of Sparrow and there are problems with Sparrow’s sample-based techniques, which gives rise to incomplete jobs and large scheduling latency. We then present Fault Tolerant, Low Latency Sparrow (FTLLS). It extends Sparrow with an assistant machine to handle worker failures and to make better scheduling decisions. Through simulations, it is proved that FTLLS can detect worker failures more quickly than a naive timeout approach and make better scheduling decisions than native Sparrow. Through implementation, the results show that FTLLS guarantees no incomplete jobs at the presence of worker failures and reduces scheduling latencies by over 1.5 × when compared to native Sparrow. In addition, the simplicity of the idea adopted by FTLLS makes it applicable to a wide variety of distributed scheduling approaches.

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References

  1. Amazon ec2. http://aws.amazon.com/ec2

  2. Sparrow scheduling platform. https://github.com/radlab/sparrow

  3. Borthakur D (2008) Hdfs architecture guide. HADOOP APACHE PROJECT http://hadoop.apache.org/common/docs/current/hdfsdesign.pdf p 39

  4. Boutin E, Ekanayake J, Lin W, Shi B, Zhou J, Qian Z, Wu M, Zhou L (2014) Apollo: scalable and coordinated scheduling for cloud-scale computing 11th USENIX symposium on operating systems design and implementation (OSDI 14), pp 285–300

    Google Scholar 

  5. Bryant R, Katz RH, Lazowska ED (2008) Big-data computing: creating revolutionary breakthroughs in commerce, science and society

  6. Chen W, Toueg S, Aguilera MK (2002) On the quality of service of failure detectors. IEEE Trans Comput 51(5):561–580

    Article  MathSciNet  Google Scholar 

  7. Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51 (1):107–113

    Article  Google Scholar 

  8. Demers A, Keshav S, Shenker S (1989) Analysis and simulation of a fair queueing algorithm ACM SIGCOMM Computer communication review, vol 19. ACM, pp 1–12

  9. Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph AD, Katz RH, Shenker S, Stoica I (2011) Mesos: a platform for fine-grained resource sharing in the data center NSDI, vol 11, pp 22–22

    Google Scholar 

  10. Ibrahim S, Jin H, Lu L, He B, Antoniu G, Wu S (2013) Handling partitioning skew in mapreduce using leen. Peer-to-Peer Networking and Applications 6(4):409–424

    Article  Google Scholar 

  11. Kornacker M, Behm A, Bittorf V, Bobrovytsky T, Ching C, Choi A, Erickson J, Grund M, Hecht D, Jacobs M et al (2015) Impala: a modern, open-source sql engine for hadoop CIDR

    Google Scholar 

  12. Li W, Lin C (2014) Design and analysis of fault tolerance mechanism for sparrow. In: 2014 IEEE international performance computing and communications conference (IPCCC). IEEE, pp 1–7

  13. Mitzenmacher M (2001) The power of two choices in randomized load balancing. IEEE Trans Parallel Distrib Syst 12(10):1094–1104

    Article  Google Scholar 

  14. Ousterhout K, Wendell P, Zaharia M, Stoica I (2012) Batch sampling: Low overhead scheduling for sub-second prallel jobs. University of California, Berkeley

    Google Scholar 

  15. Ousterhout K, Wendell P, Zaharia M, Stoica I (2013) Sparrow: distributed, low latency scheduling. In: Proceedings of the twenty-fourth ACM symposium on operating systems principles. ACM, pp 69–84

  16. Rasley J, Karanasos K, Kandula S, Fonseca R, Vojnovic M, Rao S (2016) Efficient queue management for cluster scheduling. In: Proceedings of the Eleventh european conference on computer systems. ACM, p 36

  17. Ren X, Ananthanarayanan G, Wierman A, Yu M (2015) Hopper: decentralized speculation-aware cluster scheduling at scale. ACM SIGCOMM Computer Communication Review 45(4):379–392

    Article  Google Scholar 

  18. Schwarzkopf M, Konwinski A, Abd-El-Malek M, Wilkes J (2013) Omega: flexible, scalable schedulers for large compute clusters. In: Proceedings of the 8th ACM european conference on computer systems. ACM, pp 351–364

  19. Toshniwal A, Taneja S, Shukla A, Ramasamy K, Patel JM, Kulkarni S, Jackson J, Gade K, Fu M, Donham J et al (2014) Storm@ twitter. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data. ACM, pp 147–156

  20. Vavilapalli VK, Murthy AC, Douglas C, Agarwal S, Konar M, Evans R, Graves T, Lowe J, Shah H, Seth S et al (2013) Apache hadoop yarn: Yet another resource negotiator. In: Proceedings of the 4th annual symposium on cloud computing. ACM, p 5

  21. Xiong N, Vasilakos A, Yang Y et al (2010) An effective failure detector based on general traffic-feature analysis in fault-tolerant networks. Tech. rep., Technical Report

  22. Xiong N, Vasilakos AV, Wu J, Yang YR, Rindos A, Pan Y A class of practical self-tuning failure detection schemes for distributed networks

  23. Yu X, Qiao C, Liu Y (2004) Tcp implementations and false time out detection in obs networks. In: INFOCOM 2004. Twenty-Third annualjoint conference of the IEEE computer and communications societies, vol 2. IEEE, pp 774–784

  24. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I (2012) Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX conference on networked systems design and implementation. USENIX association, pp 2–2

  25. Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. HotCloud 10:10–10

    Google Scholar 

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Acknowledgements

It is my first time to submit my paper to a journal and I gain a lot. The research is supported in part by Professor Lin for insightful guidance. Also I am indebted to Yin Li for helpful comments on several drafts of this paper and for help with simulations of our mechanism. They are enthusiastic to offer me help and suggestions when I encountered problems in this research, which keeps me improving. I wish to thank all my teachers and seniors in my research lab for providing such valuable suggestions.

This work was supported by the National Natural Science Foundation of China (No. 61472199); the National Basic Research Program of China (973 Program) under grants 2010CB328105; Tsinghua University Initiative Scientific Research Program (No.20121087999).

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Authors and Affiliations

  1. Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, 30 Shuangqing Rd, Haidian Qu, Beijing Shi, China

    Wenzhuo Li & Chuang Lin

  2. Department of Computer Science and Technology, Tsinghua University, 30 Shuangqing Rd, Haidian Qu, Beijing Shi, China

    Wenzhuo Li & Chuang Lin

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  1. Wenzhuo Li
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  2. Chuang Lin
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Correspondence to Wenzhuo Li.

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This article is part of the Topical Collection: Special Issue on Big Data Networking

Guest Editors: Xiaofei Liao, Song Guo, Deze Zeng, and Kun Wang

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Li, W., Lin, C. FTLLS: A fault tolerant, low latency, distributed scheduling approach based on sparrow. Peer-to-Peer Netw. Appl. 11, 1129–1140 (2018). https://doi.org/10.1007/s12083-017-0590-4

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