Semi-online task assignment policies for workload consolidation in cloud computing systems

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Armant, Vincent
De Cauwer, Milan
Brown, Kenneth N.
O'Sullivan, Barry
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Elsevier B.V.
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Satisfying on-demand access to cloud computing infrastructures under quality-of-service constraints while minimising the wastage of resources is an important challenge in data centre resource management. In this paper we tackle this challenge in a semi-online workload management system allocating tasks with uncertain duration to physical servers. Our semi-online framework, based on a bin packing approach, allows us to gather information on incoming tasks during a short time window before deciding on their assignments. Our contributions are as follows: (i) we propose a formal framework capturing the semi-online consolidation problem; (ii) we propose a new dynamic and real-time allocation algorithm based on the incremental merging of bins; and (iii) an adaptation of standard bin packing heuristics with a local search algorithm for the semi-online context considered here. We provide a systematic study of the impact of varying time-period size and varying the degrees of uncertainty on the duration of incoming tasks. The policies are compared in terms of solution quality and solving time on a data-set extracted from a real-world cluster trace. Our results show that, around periods of high demand, our best policy saves up to 40% of the resources compared to the other polices, and is robust to uncertainty in the task durations. Finally, we show that small increases in the allowable time window allows a significant improvement, but that larger time windows do not necessarily improve resource usage for real world data sets.
Cloud computing , Workload consolidation , Semi-online policies , Stochastic task duration
Armant, V., De Cauwer, M. Brown, K. N. and O'Sullivan, B. (2018) 'Semi-online task assignment policies for workload consolidation in cloud computing systems', Future Generation Computer Systems, 82, pp. 89-103. doi:10.1016/j.future.2017.12.035