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2018 | OriginalPaper | Buchkapitel

Adaptive Allocation of Multi-class Tasks in the Cloud

verfasst von : Lan Wang

Erschienen in: Computer and Information Sciences

Verlag: Springer International Publishing

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Abstract

Cloud computing enables the accommodation of an increasing number of applications in shared infrastructures. The routing for the incoming jobs in the cloud has become a real challenge due to the heterogeneity in both workload and machine hardware and the changes of load conditions over time. The present paper design and investigate the adaptive dynamic allocation algorithms that take decisions based on on-line and up-to-date measurements, and make fast online decisions to achieve both desirable QoS levels and high resource utilization. The Task allocation platform (TAP) is implemented as a practical system to accommodate the allocation algorithms and perform online measurement. The paper studies the potential of our proposed algorithms to deal with multi-class tasks in heterogeneous cloud environments and the experimental evaluations are also presented.

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Chen, W., Zhang, J.: An ant colony optimization approach to a grid workflow scheduling problem with various qos requirements. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 39(1), 29–43 (2009). https://doi.org/10.1109/TSMCC.2008.2001722CrossRef

Delimitrou, C., Kozyrakis, C.: QoS-aware scheduling in heterogeneous datacenters with paragon. ACM Trans. Comput. Syst. 31(4), 12:1–12:34 (2013). https://doi.org/10.1145/2556583CrossRef

Gelenbe, E., Fourneau, J.: Random neural networks with multiple classes of signals. Neural Comput. 11(4), 953–963 (1999)CrossRef

Gelenbe, E.: Sensible decisions based on QoS. Comput. Manag. Sci. 1(1), 1–14 (2003)CrossRef

Gelenbe, E., Lent, R.: Trade-offs between energy and quality of service. In: Sustainable Internet and ICT for Sustainability (SustainIT), pp. 1–5. IEEE (2012)

Gelenbe, E., Lent, R.: Optimising server energy consumption and response time. Theor. Appl. Inform. 4, 257–270 (2013). https://doi.org/10.2478/v10179-012-0016-1CrossRef

Gelenbe, E., Timotheou, S., Nicholson, D.: Fast distributed near-optimum assignment of assets to tasks. Comput. J. 53(9), 1360–1369 (2010). https://doi.org/10.1093/comjnl/bxq010CrossRef

Gelenbe, E., Wang, L.: Tap: A task allocation platform for the EU FP7 PANACEA project. In: The proceedings of the EU projects track, September 2015

Hou, E., Ansari, N., Ren, H.: A genetic algorithm for multiprocessor scheduling. IEEE Trans. Parallel Distrib. Syst. 5(2), 113–120 (1994). https://doi.org/10.1109/71.265940CrossRef

10.

Iosup, A., Ostermann, S., Yigitbasi, M., Prodan, R., Fahringer, T., Epema, D.H.J.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011). https://doi.org/10.1109/TPDS.2011.66CrossRef

11.

Kwok, Y.K., Ahmad, I.: Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors. IEEE Trans. Parallel Distrib. Syst. 7(5), 506–521 (1996). https://doi.org/10.1109/71.503776CrossRef

12.

Moreno, I.S., Garraghan, P., Townend, P., Xu, J.: Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. IEEE Trans. Cloud Comput. PP(99), 1–1 (2014). https://doi.org/10.1109/TCC.2014.2314661CrossRef

13.

Pandey, S., Linlin, W., Guru, S., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 400–407, April 2010. https://doi.org/10.1109/AINA.2010.31

14.

Topcuouglu, H.: Hariri, S., you Wu, M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002). https://doi.org/10.1109/71.993206CrossRef

15.

Wang, L.: Online work distribution to clouds. In: 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 295–300, September 2016. https://doi.org/10.1109/MASCOTS.2016.64

16.

Wang, L., Brun, O., Gelenbe, E.: Adaptive workload distribution for local and remote clouds. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 003984–003988, October 2016. https://doi.org/10.1109/SMC.2016.7844856

17.

Zaman, S., Grosu, D.: A combinatorial auction-based dynamic vm provisioning and allocation in clouds. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom), pp. 107–114, November 2011. https://doi.org/10.1109/CloudCom.2011.24

18.

Zhan, J., Wang, L., Li, X., Shi, W., Weng, C., Zhang, W., Zang, X.: Cost-aware cooperative resource provisioning for heterogeneous workloads in data centers. IEEE Trans. Comput. 62(11), 2155–2168 (2013). https://doi.org/10.1109/TC.2012.103MathSciNetCrossRefMATH

19.

Zhang, Q., Zhani, M., Boutaba, R., Hellerstein, J.: Dynamic heterogeneity-aware resource provisioning in the cloud. IEEE Trans. Cloud Comput. 2(1), 14–28 (2014). https://doi.org/10.1109/TCC.2014.2306427CrossRef

20.

Zhuravlev, S., Blagodurov, S., Fedorova, A.: Addressing shared resource contention in multicore processors via scheduling. SIGPLAN Not. 45(3), 129–142 (2010). https://doi.org/10.1145/1735971.1736036CrossRef

Titel: Adaptive Allocation of Multi-class Tasks in the Cloud
verfasst von: Lan Wang
Verlag: Springer International Publishing
Buch: Computer and Information Sciences
Print ISBN: 978-3-030-00839-0

Electronic ISBN: 978-3-030-00840-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00840-6_2

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