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Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments

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Abstract

A hybrid cloud integrates private clouds and public clouds into one unified environment. For the economy and the efficiency reasons, the hybrid cloud environment should be able to automatically maximize the utilization rate of the private cloud and minimize the cost of the public cloud when users submit their computing jobs to the environment. In this paper, we propose the Adaptive-Scheduling-with-QoS-Satisfaction algorithm, namely AsQ, for the hybrid cloud environment to raise the resource utilization rate of the private cloud and to diminish task response time as much as possible. We exploit runtime estimation and several fast scheduling strategies for near-optimal resource allocation, which results in high resource utilization rate and low execution time in the private cloud. Moreover, the near-optimal allocation in the private cloud can reduce the amount of tasks that need to be executed on the public cloud to satisfy their deadline. For the tasks that have to be dispatched to the public cloud, we choose the minimal cost strategy to reduce the cost of using public clouds based on the characteristics of tasks such as workload size and data size. Therefore, the AsQ can achieve a total optimization regarding cost and deadline constraints. Many experiments have been conducted to evaluate the performance of the proposed AsQ. The results show that the performance of the proposed AsQ is superior to recent similar algorithms in terms of task waiting time, task execution time and task finish time. The results also show that the proposed algorithm achieves a better QoS satisfaction rate than other similar studies.

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Notes

  1. Amazon EC2, http://aws.amazon.com/ec2/.

  2. Google App Engine, http://code.gooogle.com/aooengine/.

  3. Microsoft Azure, http://msdn.microsoft.com/windowsazure.

  4. Yahoo! Video, http://video.yahoo.com/.

  5. http://www.nist.gov/index.html.

  6. http://www.itri.org.tw/chi/ccma/.

  7. http://www.f5.com/pdf/solution-center/vmware-vcloud-director.pdf.

  8. http://www-01.ibm.com/software/tivoli/products/hybrid-cloud/.

  9. Hadoop MapReduce, http://hadoop.apache.org/.

  10. Fair Scheduler, http://hadoop.apache.org/common/docs/r0.20.2/fair_scheduler.html.

  11. Capacity Scheduler, http://hadoop.apache.org/common/docs/r0.19.2/capacity_scheduler.html.

  12. MaxFS problem: Given an infeasible linear system AX P b, find a Maximum Feasible Subsystem, i.e., a feasible subsystem containing a maximum number of inequalities.

  13. http://risorse.dei.polimi.it/maxfs/.

  14. Job Scheduling in Hadoop, http://www.cloudera.com/blog/2008/11/job-scheduling-in-hadoop/.

  15. Fair Scheduler, http://hadoop.apache.org/common/docs/r0.20.2/fair_scheduler.html.

  16. Capacity Scheduler, http://hadoop.apache.org/common/docs/r0.19.2/capacity_scheduler.html.

  17. ClousSim, http://www.buyya.com/gridbus/cloudsim/.

  18. Java SE JDK 6u25, http://www.oracle.com/technetwork/java/javase/downloads/jdk-6u25-download-346242.html.

References

  1. Al Falasi A, Adel Serhani M (2011) A framework for SLA-based cloud services verification and composition. In: International conference on innovations in information technology (IIT), pp 25–27

    Google Scholar 

  2. Amaldi E, Kann V (1995) The complexity and approximability of finding maximum feasible subsystems of linear relations. Theor Comput Sci 147(1–2):181–210

    Article  MathSciNet  MATH  Google Scholar 

  3. Ardagna D, Pernici B (2007) Adaptive service composition in flexible processes. IEEE Trans Softw Eng 33(6):369–384

    Article  Google Scholar 

  4. Armbrust M et al (2010) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  5. Bittencourt LF, Madeira ERM (2011) HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds. J Internet Serv Appl 2(3):207–227

    Article  Google Scholar 

  6. Bossche R, Vanmechelen K, Broeckhove J (2010) Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: IEEE 3rd international conference on cloud computing, pp 228–235

    Google Scholar 

  7. Cao H, Feng X, Sun Y, Zhang Z, Wu Q (2007) A service selection model with multiple QoS constraints on the MMKP. In: Proceeding of the IFIP international conference on network and parallel computing workshops, September 2007, pp 584–589

    Google Scholar 

  8. Casola V, Rak M, Villano U (2010) Identify federation in cloud computing. In: 6th international conference on information assurance and security (IAS), pp 23–25

    Google Scholar 

  9. Celesti A, Tusa F, Villari M, Puliafito A (2010) Three-phase cross-cloud federation model: the cloud SSO authentication. In: 2nd international conference on advances in future Internet (AFIN), pp 94–101

    Google Scholar 

  10. Chinneck JW (2001) Fast heuristics for the maximum feasible subsystem problem. INFORMS J Comput 13(3):210–223

    Article  MathSciNet  MATH  Google Scholar 

  11. Video C Metrix report: US viewers watched an average of 3 hours of online video in July. http://www.comscore.com/press/release.asp?press=1687

  12. Dillon T, Wu C, Chang E (2010) Cloud computing: issues and challenges. In: 24th IEEE international conference on advanced information networking and applications, Perth, WA, 20–23 April 2010, pp 27–33

    Chapter  Google Scholar 

  13. Fan CT, Chang YS, Wang WJ, Yuan SM (2012) Execution time prediction using rough set theory in hybrid cloud. In: The 9th IEEE international conference on ubiquitous intelligence and computing (UIC 2012), Fukuoka, Japan, 4–7 September, pp 729–734

    Google Scholar 

  14. Frincu M, Craciun C (2011) Multi-objective meta-heuristics for scheduling applications with high availability requirements and cost constraints in multi-cloud environments. In: 2011 fourth IEEE international conference on utility and cloud computing, pp 267–274

    Chapter  Google Scholar 

  15. Funahashi M, Yoshikawa S (2011) Fujitsu’s approach to hybrid cloud systems. Fujitsu Sci Tech J 47(3):285–292

    Google Scholar 

  16. Ge Y, Wei G (2010) GA-based task scheduler for the cloud computing systems. In: International conference on web information systems and mining (WISM), pp 181–186

    Google Scholar 

  17. Goiri I, Guitart J, Torres J (2010) Characterizing cloud federation for enhancing providers’ profit. In: IEEE 3rd international conference on cloud computing (CLOUD), pp 5–10

    Google Scholar 

  18. Grounds NG, Antonio JK, Muehring J (2009) Cost-minimizing scheduling of workloads on a cloud of memory managed multicore machines. In: The 1st international conference on cloud computing, pp 435–450

    Chapter  Google Scholar 

  19. Kang C, Strong R, Fang H, Chen T, Rhodes J, Zhou R (2011) Complex service management in a hybrid cloud. In: Annual SRII global conference, pp 34–36

    Chapter  Google Scholar 

  20. Kang X, Zhang H, Jiang G, Chen H, Meng X, Yoshihira K (2008) Measurement, modeling, and analysis of Internet video sharing site workload: a case study. In: IEEE international conference on web services (ICWS), pp 278–285

    Google Scholar 

  21. Karypis G, Kumar V (1999) Multilevel k-way hypergraph partitioning. In: 36th ACM/IEEE conference on design automation (DAC), pp 343–348

    Google Scholar 

  22. Kc K, Anyanwu K (2010) Scheduling hadoop jobs to meet deadlines. In: 2010 IEEE second international conference on cloud computing technology and science (CloudCom), pp 388–392

    Chapter  Google Scholar 

  23. Knorr E, Gruman G (2009) What cloud computing really is. InfoWorld cloud computing deep dive (InfoWorld)

  24. Kübert R, Wesner S (2010) Service level agreements for job control in high-performance computing. In: International multiconference on computer science and information technology, pp 655–661

    Google Scholar 

  25. Lehman TJ (2011) We’ve looked at clouds from both sides now. In: 2011 annual SRII global conference, pp 342–348

    Chapter  Google Scholar 

  26. Li W, Tordsson J, Elmroth E (2011) Modeling for dynamic cloud scheduling via migration of virtual machines. In: 2011 third IEEE international conference on cloud computing technology and science, pp 163–171

    Chapter  Google Scholar 

  27. Liang H, Huang D, Lin C, Shen X, Peng D (2011) Resource allocation for security services in mobile cloud computing. In: IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 10–15

    Google Scholar 

  28. Lin CF, Sheu RK, Chang YS, Yuan SM (2011) A relaxable service selection algorithm for QoS-based web service composition. Inf Softw Technol 53(12):1370–1381

    Article  Google Scholar 

  29. Linthicum D (2010) Selecting the right cloud: a step-by-step guide. InfoWorld cloud computing deep dive (InfoWorld). Accessed from http://www.infoworld.com/d/cloud-computing/selecting-the-right-cloud-step-step-guide-692

  30. Martello S, Toth P (1990) Knapsack problems: algorithms and computer interpretations. Wiley-Interscience, New York. MR1086874, ISBN 0-471-92420-2

    Google Scholar 

  31. Pallis G (2010) Cloud computing: the new frontier of Internet computing. IEEE Internet Comput 14(5):70–73

    Article  Google Scholar 

  32. Parra-Hernández R, Dimopoulos NJ (2005) A new heuristic for solving the multichoice multidimensional knapsack problem. IEEE Trans Syst Man Cybern, Part A, Syst Hum 35(5):708–717

    Article  Google Scholar 

  33. Prahalad HA, Talukder A, Pardeshi S, Tamsekar S, Krishna RH, Chandrashekar MA, Niket B, Gandhem S (2010) Phoenix: system for implement private and hybrid cloud for OMIC sciences applications. In: 7th international conference on wireless and optical communications networks (WOCN), pp 6–8

    Google Scholar 

  34. Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput 13(5):14–22

    Article  Google Scholar 

  35. Subramanian K (2011) Hybrid clouds. Access from http://emea.trendmicro.com/imperia/md/content/uk/cloud-security/wp01_hybridcloud-krish_110624us.pdf

  36. Tao Q, Chang H, Yi Y, Gu C, Yang Y (2009) QoS constrained grid workflow scheduling optimization based on a novel PSO algorithm. In: 8th international conference on grid and cooperative computing (GCC), pp 153–159

    Google Scholar 

  37. Tasi WT, Zhong P, Balasooriya J, Chen Y, Bai X, Elston J (2011) An approach for service composition and testing for cloud computing. In: 10th international symposium on autonomous decentralized systems (ISADS), pp 23–27

    Google Scholar 

  38. Tsai MY, Chiang PF, Chang YJ, Wang WJ (2012) Heuristic scheduling strategies for linear-dependent and independent jobs on heterogeneous grids. Commun Comput Inf Sci 261:496–505

    Article  Google Scholar 

  39. Vouk MA (2008) Cloud computing—issues, research and implementations. CIT, J Comput Inf Technol 16(4):235–246

    Google Scholar 

  40. Wang WJ, Lo YM, Chen SJ, Chang YS (2012) Intelligent application migration within a self-provisioned hybrid cloud environment. In: Lecture notes in electrical engineering, vol 114, pp 295–303, part 1

    Google Scholar 

  41. Wang YA, Cheng H, Ross KW (2011) Estimating the performance of hypothetical cloud service deployments: a measurement-based approach. In: IEEE INFOCOM, pp 10–15

    Google Scholar 

  42. Wu Z, Luo J (2006) The measurement model of grid QoS. In: 10th international conference on computer supported cooperative work in design (CSCWD), 3–5 May, pp 1–6

    Google Scholar 

  43. Xue Q, Li Y, Zhong L, Zheng M (2011) Study on key techniques for 3G mobile learning platform based on cloud service. In: International conference on consumer electronics, communications and networks (CECNet), pp 16–18

    Google Scholar 

  44. Yu T, Zhang Y, Lin KJ (2007) Efficient algorithm for web services selection with end-to-end QoS constraints. ACM Trans Web 1(1):6

    Article  Google Scholar 

  45. Zeng L, Benatallah B (2004) QoS-aware middleware for web services composition. IEEE Trans Softw Eng 30(5):311–327

    Article  Google Scholar 

  46. Zhang H, Jiang G, Yoshihira K, Chen H, Saxena A (2009) Intelligent workload factoring for a hybrid cloud computing model. In: World conference on services I, pp 701–708

    Google Scholar 

  47. Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1:7–18

    Article  Google Scholar 

  48. Zhang S, Zhang S, Chen X, Wu S (2010) Analysis and research of cloud computing system instance. In: Second international conference on future networks (ICFN), pp 88–92

    Google Scholar 

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Acknowledgement

This work was supported by the Nation Science Council of Republic of China under Grant No. 101-2221-E-305-009.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Central University, 300, Jhongda Rd., Jhongli City, Taoyuan County, 32001, Taiwan, R.O.C.

    Wei-Jen Wang

  2. Software Research Center, National Central University, 300, Jhongda Rd., Jhongli City, Taoyuan County, 32001, Taiwan, R.O.C.

    Wei-Jen Wang

  3. Department of Computer Science and Information Engineering, National Taipei University, 151, University Road, Sanhsia Distinct, New Taipei City, 237, Taiwan, R.O.C.

    Yue-Shan Chang & Yi-Kang Lee

  4. Department of Computer Science, Tunghai University, 181, Section 3, Taichung port Road, Taichung City, 40704, Taiwan, R.O.C.

    Win-Tsung Lo

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  1. Wei-Jen Wang
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Correspondence to Yue-Shan Chang.

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Wang, WJ., Chang, YS., Lo, WT. et al. Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments. J Supercomput 66, 783–811 (2013). https://doi.org/10.1007/s11227-013-0890-2

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