Refining Service Delay Using Temporal Task Scheduling for Profit Maximization in Hybrid Cloud

Abstract

As cloud computing is becoming growingly popular, consumers’ tasks around the
world arrive in cloud data centers. A private cloud provider aims to achieve profit
maximization by intelligently scheduling tasks while guaranteeing the service delay bound
of delay-tolerant tasks. However, the aperiodicity of arrival tasks brings a challenging
problem of how to dynamically schedule all arrival tasks given the fact that the capacity of
a private cloud provider is limited. Previous works usually provide an admission control to
intelligently refuse some of arrival tasks. Nevertheless, this will decrease the throughput of
a private cloud, and cause revenue loss. This paper studies the problem of how to maximize
the profit of a private cloud in hybrid clouds while guaranteeing the service delay bound of
delay-tolerant tasks. We propose a profit maximization algorithm (PMA) to discover the
temporal variation of prices in hybrid clouds. The temporal task scheduling provided by
PMA can dynamically schedule all arrival tasks to execute in private and public clouds. The
sub problem in each iteration of PMA is solved by the proposed hybrid heuristic
optimization algorithm, simulated annealing particles warm optimization (SAPSO).
Besides, SAPSO is compared with existing baseline algorithms. Extensive simulation
experiments demonstrate that the proposed method can greatly increase the throughput
and the profit of a private cloud while guaranteeing the service delay bound