Stochastic and Robust Scheduling in the Cloud
Presented at the International Workshop on Approximation Algorithms for Combinatorial Optimization
Users of cloud computing services are offered rapid access to computing resources via the Internet. Cloud providers use different pricing options such as (i) time slot reservation in advance at a fixed price and (ii) on-demand service at a (hourly) pay-as-used basis. Choosing the best combination of pricing options is a challenging task for users, in particular, when the instantiation of computing jobs underlies uncertainty. We propose a natural model for two-stage scheduling under uncertainty that captures such resource provisioning and scheduling problem in the cloud. Reserving a time unit for processing jobs incurs some cost, which depends on when the reservation is made: a priori decisions, based only on distributional information, are much cheaper than on-demand decisions when the actual scenario is known. We consider both stochastic and robust versions of scheduling unrelated machines with objectives of minimizing the sum of weighted completion times Pj wjCj and the makespan maxj Cj . Our main contribution is an (8+)-approximation algorithm for the min-sum objective for the stochastic polynomial-scenario model. The same technique gives a (7.11 + )- approximation for minimizing the makespan. The key ingredient is an LP-based separation of jobs and time slots to be considered in either the first or the second stage only, and then approximately solving the separated problems. At the expense of another our results hold for any arbitrary scenario distribution given by means of a black-box. Our techniques also yield approximation algorithms for robust two-stage scheduling.
|International Workshop on Approximation Algorithms for Combinatorial Optimization|
|Organisation||Life Sciences and Health|
Chen, L, Megow, N, Rischke, R, & Stougie, L. (2015). Stochastic and Robust Scheduling in the Cloud. In Proceedings of International Workshop on Approximation Algorithms for Combinatorial Optimization 2015 (APPROX 18) (pp. 175–186).