Time-Aware Multi-Application Task Scheduling with Guaranteed Delay Constraints in Green Data Center

Abstract

A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks' delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners - This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling.