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Towards big data processing in clouds: An online cost-minimization approach

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  • Due to its elastic and on-demand nature of resource provisioning, cloud computing provides a cost effective and powerful technology for the processing of big data. Under this paradigm, Data Service Provider (DSP) may rent geographically distributed datacenters to process their large amount of data. As the data are dynamically generated and the resource pricing varies over time, moving the data from differently geographic locations to different datacenters while provisioning adequate computation resource to process them is an essential task to achieve cost effectiveness for DSP. In this paper, a joint online approach is proposed to address this task. We formulate the problem into a joint stochastic optimization problem, which is then decoupled into two independent subproblems via the Lyapunov framework. Our method is able to minimize the long-term time average cost including computing cost, storage cost, bandwidth cost and latency cost. Theoretical analysis shows that our online algorithm can produce a solution within an upper bound to the optimal solution achieved through offline computing and guarantee that the data processing can be completed with preset delays.
    Mathematics Subject Classification: Primary: 90B15, 90B22; Secondary: 68W15, 68W27.


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  • [1]

    P. Barham, B. Dragovic and K. Fraser, Xen and the art of virtualization, SIGOPS Operating Systems Review, 37 (2003), 164-177.doi: 10.1145/945445.945462.


    B. Cho and I. Gupta, New algorithms for planning bulk transfer via internet and shipping networks, in Proc. IEEE ICDCS, (2010), 305-314.doi: 10.1109/ICDCS.2010.59.


    B. Cho and I. Gupta, Budget-constrained bulk data transfer via internet and shipping networks, in Proc. ACM ICAC, (2011), 71-80.doi: 10.1145/1998582.1998595.


    J. Dean and S. Ghemawat, MapReduce: Simplified data processing on large clusters, Communications of the ACM, 51 (2008), 107-113.


    Y. Feng, B. Li and B. Li, Airlift: Video conferencing as a cloud service using inter-datacenter networks, in Proceedings of the IEEE International Conference on Network Protocols(ICNP'12), (2012), 1-11.doi: 10.1109/ICNP.2012.6459966.


    L. Georgiadis, M. J. Neely and L. Tassiulas, Resource allocation and cross-layer control in wireless networks, Foundations and Trends in Networking, 1 (2006), 1-144.doi: 10.1561/1300000001.


    Z. Huang, C. Mei, L. Li and T. Woo, CloudStream: Delivering high-quality streaming videos through a cloud-based SVC proxy, in Proceedings of the IEEE INFOCOM, (2011), 201-205.doi: 10.1109/INFCOM.2011.5935009.


    F. Liu, Z. Zhou, H. Jin, B. Li, B. Li and H. Jiang, On arbitrating the power-performance tradeoff in SaaS clouds, IEEE Transactions on Parallel and Distributed Systems, 25 (2014), 2648-2658.doi: 10.1109/TPDS.2013.208.


    X. Mo and H. Wang, Asynchronous index strategy for high performance real-time big data stream storage, in Network Infrastructure and Digital Content (IC-NIDC), (2012), 232-236.doi: 10.1109/ICNIDC.2012.6418750.


    X. Nan, Y. He and L. Guan, Optimal resource allocation for multimedia cloud based on queuing model, in Proc. of IEEE MMSP Workshop, (2011), 1-6.doi: 10.1109/MMSP.2011.6093813.


    M. J. Neely, Stochastic Network Optimization with Application to Communication and Queueing Systems, Morgan and Claypool, 2010.doi: 10.2200/S00271ED1V01Y201006CNT007.


    M. J. Neely, Opportunistic scheduling with worst case delay guarantees in single and multi-hop networks, in Proc. of INFOCOM, (2011), 1728-1736.doi: 10.1109/INFCOM.2011.5934971.


    E. E. Schadt, M. D. Linderman, J. Sorenson, L. Lee and G. P. Nolan, Computational solutions to large-scale data management and analysis, Nat Rev Genet, 11 (2010), 647-657.doi: 10.1038/nrg2857.


    J. Tang, W. P. Tay and Y. Wen, Dynamic request redirection and elastic service scaling in cloud-centric media networks, IEEE Transactions on Multimedia, 16 (2014), 1434-1445.doi: 10.1109/TMM.2014.2308726.


    L. Tassiulas and A. Ephremides, Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks, IEEE Transactions on Automatic Control, 37 (1992), 1936-1948.doi: 10.1109/9.182479.


    C. Union, Homepage http://www.cloudunion.cn/.


    R. Urgaonkar, U. Kozat, K. Igarashi and M. J. Neely, Resource allocation and power management in virtualized data centers, in Proceedings of the IEEE Network Operations and Management Symp(NOMS'10), (2010), 479-486.doi: 10.1109/NOMS.2010.5488484.


    J. Wang, W. Bao, X. Zhu, L. T. Yang and Y. Xiang, FESTAL: Fault-tolerant elastic scheduling algorithm for real-time tasks in virtualized clouds, IEEE Transactions on Computers, 64 (2014), 2445-2558.doi: 10.1109/TC.2014.2366751.


    F. Wang, J. Liu and M. Chen, CALMS: Cloud-assisted live media streaming for globalized demands with time/ region diversities, in Proceedings of the IEEE INFOCOM, (2012), 199-207.doi: 10.1109/INFCOM.2012.6195578.


    D. Wu, Z. Xue and J. He, iCloudAccess: Cost-effective streaming of videogames from the cloud with low latency, IEEE Transactions on Circuits and Systems for Video Technology, 28 (2014), 1405-1416.doi: 10.1109/TCSVT.2014.2302543.


    Y. Wu, C. Wu, B. Li, X. Qiu and F.C.M. Lau , Cloudmedia: When cloud on demand meets video on demand, In Proc. of IEEE ICDCS, (2011), 268-277.doi: 10.1109/ICDCS.2011.50.


    Y. Wu, C. Wu, B. Li, L. Zhang, Z. Li and F. Lau, Scaling social media applications into geo-distributed clouds, in Proc. IEEE INFOCOM, (2012), 684-692.doi: 10.1109/INFCOM.2012.6195813.


    W. Xiao, W. Bao, X. Zhu, C. Wang, L. Chen and L. T. Yang, Dynamic request redirection and resource provisioning for cloud-based video services under heterogeneous environment, IEEE Transactions on Parallel and Distributed Systems, pp (2015), p1.doi: 10.1109/TPDS.2015.2470676.


    Y. Yao, L. Huang and A. B. Sharma, L. Golubchik and M. J. Neely, Power cost reduction in distributed data centers: A two-time-scale approach for delay tolerant workloads, IEEE Transactions On Parallel and Distributed Systems, 25 (2014), 200-211.doi: 10.1109/TPDS.2012.341.


    M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker and I. Stoica., Spark: cluster computing with working sets, In Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing(HotCloud'10), Berkeley, CA, USA, (2010), p10.


    L. Zhang, C. Wu, Z. Li, C. Guo, M. Chen and F. C. M. Lau, Moving big data to the cloud: An online cost-minimizing approach, IEEE Journal on Selected Areas in Communications, 31 (2013), 2710-2721.doi: 10.1109/JSAC.2013.131211.


    X. Zhu, C. Chen, L. T. Yang and Y. Xiang, ANGEL: Agent-based scheduling for real-time tasks in virtualized clouds, IEEE Transactions on Computers, pp (2015), p1.doi: 10.1109/TC.2015.2409864.


    X. Zhu, R. Ge, J. Sun and C. He, 3E: Energy-efficient elastic scheduling for independent tasks in heterogeneous computing system, Journal of Systems and Software, 86 (2012), 302-314.doi: 10.1016/j.jss.2012.08.017.


    X. Zhu, X. Qin and M. Qiu, QoS-aware fault-tolerant scheduling for real-time tasks on heterogeneous clusters, IEEE Transactions on Computers, 60 (2011), 800-812.doi: 10.1109/TC.2011.68.

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