About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
CLOUD 2021
Short paper
Rightsizing Clusters for Time-Limited Tasks
Abstract
Cluster rightsizing facilitates cost-performance trade-off in resource-constrained clouds. Multidimensional bin-packing algorithms can address this rightsizing problem, but these assume that every task on the cluster is always active. In contrast, real-world tasks may be active only during specific time-periods, which allows reusing resources via time sharing and optimal packing. This motivates our generalized problem of rightsizing for time-limited tasks: given a timeline, time-periods and resource demands for tasks, the objective is to place the tasks on a minimum cost cluster of nodes without violating node capacities at any time instance. We design a baseline two-phase algorithm that performs penalty-based mapping of task to node-Type and then, solves each node-Type independently. We prove that the algorithm has an approximation ratio of O(D. min(m, T)), where D, m and T are the number of resources, node-Types and timeslots, respectively, We then present an improved linear programming based mapping strategy, enhanced further with a cross-node-Type filling mechanism. Our experiments on synthetic and real-world cluster traces show significant cost reduction by LP-based mapping compared to the baseline, and the filling mechanism improves further to produce solutions within 20% of (a lower-bound to) the optimal solution.