Controlling the Variability of Capacity Allocations Using Service Deferrals
Ensuring predictability is a crucial goal for service systems. Traditionally, research has focused on designing systems that ensure predictable performance for service requests. Motivated by applications in cloud computing and electricity markets, this article focuses on a different form of predictability: predictable allocations of service capacity. The focus of the article is a new model where service capacity can be scaled dynamically and service deferrals (subject to deadline constraints) can be used to control the variability of the active service capacity. Four natural policies for the joint problem of scheduling and managing the active service capacity are considered. For each, the variability of service capacity and the likelihood of deadline misses are derived. Further, the paper illustrates how pricing can be used to provide incentives for jobs to reveal deadlines and thus enable the possibility of service deferral in systems where the flexibility of jobs is not known to the system a priori.
© 2017 IEEE. Received November 2016; accepted April 2017. The research in this paper has been partially supported by IADB–Ministerio de Industria y Energıa–Uruguay ATN/KF 13883 UR (Component 3) and ANII–Uruguay under grant FSE_1_2014_1_102426. Additional funding for this work was provided by the NSF through grants CNS-1319820, CNS-1518941, and CPS-1545096 as part of the NSF/DHS/DOT/NASA/NIH Cyber-Physical Systems Program.