Delay-Optimal Proactive Service Framework for Block-Stream as a Service

Abstract

Block-stream as a service (BaaS) has been considered as a promising service provisioning model for lightweight devices, in which the server streams code blocks partitioned from an APP to devices in an on-demand way. However, serving with code blocks makes the quality-of-services of BaaS more prone to the dynamics in users’ requests and wireless links. In this letter, we propose a proactive service framework to minimize the response delay while bounding the time-average energy consumption, by proactively streaming blocks before the requests. The framework is composed of three components, i.e., model estimation, dual learning, and block serving. The first two components achieve the statistics of the request arrivals and the optimal dual solution of the original stochastic problem, respectively. The last component determines whether to proactively serve blocks based only on the instantaneous and estimated system states.