Towards Understanding Uncertainty in Cloud Computing Resource Provisioning

In spite of extensive research of uncertainty issues in different fields ranging from computational biology to decision making in economics, a study of uncertainty for cloud computing systems is limited. Most of works examine uncertainty phenomena in users perceptions of the qualities, intentions and actions of cloud providers, privacy, security and availability. But the role of uncertainty in the resource and service provisioning, programming models, etc. have not yet been adequately addressed in the scientific literature. There are numerous types of uncertainties associated with cloud computing, and one should to account for aspects of uncertainty in assessing the efficient service provisioning. In this paper, we tackle the research question: what is the role of uncertainty in cloud computing service and resource provisioning? We review main sources of uncertainty, fundamental approaches for scheduling under uncertainty such as reactive, stochastic, fuzzy, robust, etc. We also discuss potentials of these approaches for scheduling cloud computing activities under uncertainty, and address methods for mitigating job execution time uncertainty in the resource provisioning.

[1]  Andrei Tchernykh,et al.  Idle regulation in non-clairvoyant scheduling of parallel jobs , 2009, Discret. Appl. Math..

[2]  Saeed Jalili,et al.  Predicting Job Wait Time in Grid Environment by Applying Machine Learning Methods on Historical Information , 2012 .

[3]  Uwe Schwiegelshohn,et al.  Bi-objective online scheduling with quality of service for IaaS clouds , 2014, 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet).

[4]  Lianmin Zhang,et al.  Preemptive stochastic online scheduling on uniform machines with bounded speed ratios , 2011, ICSSSM11.

[5]  Tjark Vredeveld Stochastic online scheduling , 2011, Computer Science - Research and Development.

[6]  P. Zieliński,et al.  MINMAX (REGRET) SCHEDULING PROBLEMS , 2013 .

[7]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[8]  Daniel J. Veit,et al.  The Role Of Uncertainty In Cloud Computing Continuance: Antecedents, Mitigators, And Consequences , 2013, ECIS.

[9]  Warren Smith,et al.  Predicting Application Run Times Using Historical Information , 1998, JSSPP.

[10]  Uwe Schwiegelshohn,et al.  Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids , 2011, Journal of Grid Computing.

[12]  Allen B. Downey Predicting queue times on space-sharing parallel computers , 1997, Proceedings 11th International Parallel Processing Symposium.

[13]  Andrei Tchernykh,et al.  Performance Evaluation of Infrastructure as Service Clouds with SLA Constraints , 2013 .

[14]  Emmanuel Jeannot,et al.  Evaluation and Optimization of the Robustness of DAG Schedules in Heterogeneous Environments , 2010, IEEE Transactions on Parallel and Distributed Systems.

[15]  Sathish S. Vadhiyar,et al.  Identifying Quick Starters: Towards an Integrated Framework for Efficient Predictions of Queue Waiting Times of Batch Parallel Jobs , 2012, JSSPP.

[16]  Andrei Tchernykh,et al.  Load Balancing for Parallel Computations with the Finite Element Method , 2013 .

[17]  Andrei Tchernykh,et al.  Algorithms for dynamic scheduling of unit execution time tasks , 2003, Eur. J. Oper. Res..

[18]  Nicole Megow,et al.  Approximation in Preemptive Stochastic Online Scheduling , 2006, ESA.

[19]  Albert Y. Zomaya,et al.  CA-DAG: Communication-Aware Directed Acyclic Graphs for Modeling Cloud Computing Applications , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[20]  Albert Y. Zomaya,et al.  CA-DAG: Modeling Communication-Aware Applications for Scheduling in Cloud Computing , 2015, Journal of Grid Computing.

[21]  Füsun Özgüner,et al.  Run-time statistical estimation of task execution times for heterogeneous distributed computing , 1996, Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing.

[22]  Lianmin Zhang,et al.  Scheduling with stochastic approaches , 2014 .

[23]  Nicole Megow,et al.  Models and Algorithms for Stochastic Online Scheduling , 2006, Math. Oper. Res..

[24]  Uwe Schwiegelshohn,et al.  Online Hierarchical Job Scheduling on Grids , 2008 .

[25]  Yuri N. Sotskov,et al.  Book review Sequencing and Scheduling with Inaccurate Data , 2014 .

[26]  Uwe Schwiegelshohn,et al.  Online Scheduling for Cloud Computing and Different Service Levels , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[27]  Andrei Tchernykh,et al.  Adaptive energy efficient scheduling in Peer-to-Peer desktop grids , 2014, Future Gener. Comput. Syst..