Multi-User Task Offloading to Heterogeneous Processors With Communication Delay and Budget Constraints

We study task scheduling and offloading in a cloud computing system with multiple users where tasks have different processing times, release times, communication times, and weights. Each user may schedule a task locally or offload it to a shared cloud with heterogeneous processors by paying a price for the resource usage. We consider four different models in this article: (i) zero task release and communication times; (ii) non-zero task release times and zero communication times; (iii) non-zero task release times and fixed communication times; and (iv) non-zero task release times and sequence-dependent communication times. Our article aims at identifying a task scheduling decision that minimizes the weighted sum completion time of all tasks, while satisfying the users' budget constraints. We propose an efficient solution framework for this NP-hard problem. As a first step, we use a relaxation and a rounding technique to obtain an integer solution that is a constant factor approximation to the minimum weighted sum completion time. This solution violates the budget constraints, but the average budget violation decreases as the number of users increases. Thus, we develop a scalable algorithm termed Single-Task Unload for Budget Resolution (STUBR), which resolves budget violations and orders the tasks to obtain robust solutions. We prove performance bounds for the rounded solution as well as for the budget-resolved solution, for all four models considered. Via extensive trace-driven simulation for both chess and compute-intensive applications, we observe that STUBR exhibits robust performance under practical scenarios and outperforms existing alternatives. We also use simulation to study the scalability of STUBR algorithm as the number of tasks and the number of users in the system increases.