Abstract
Energy efficiency is a fundamental problem due to the fact that numerous tasks are running on mobile devices with limited resources. Mobile cloud computing (MCC) technology can offload computation-intensive tasks from mobile devices onto powerful cloud servers, which can significantly reduce the energy consumption of mobile devices and thus enhance their capabilities. In MCC, mobile devices transmit data through the wireless channel. However, since the state of the channel is dynamic, offloading at a low transmission rate will result in the serious waste of time and energy, which further degrades the quality of service (QoS). To address this problem, this paper proposes an energy-efficient and deadline-aware task offloading strategy based on the channel constraint, with the goal of minimizing the energy consumption of mobile devices while satisfying the deadlines constraints of mobile cloud workflows. Specifically, we first formulate a task offloading decision model that combines the channel state with task attributes such as the workload and the size of the data transmission to determine whether the task needs to be offloaded or not. Afterward, we apply it to a new adaptive inertia weight-based particle swarm optimization (NAIWPSO) algorithm to create our channel constraint-based strategy (CC-NAIWPSO), which can obtain a near-optimal offloading plan that can consume less energy while meeting the deadlines. The experimental results show that our proposed task offloading strategy can outperform other strategies with respect to the energy consumption of mobile devices, the execution time of mobile cloud workflows, and the running time of algorithms.
Highlights
Mobile devices such as smart-phones and tablets have become an essential part of our daily lives due to their high convenience and efficiency
We propose an energy-efficient and deadline-aware task offloading strategy based on the channel constraint for mobile cloud workflows
Afterwards, we propose a new channel constraint based adaptive inertia weight based particle swarm optimization (CC-new adaptive inertia weight-based particle swarm optimization (NAIWPSO)) strategy to find the near-optimal offloading decision, which can prevent the premature convergence of the particle swarm optimization (PSO) algorithm and significantly reduce the energy consumption of the mobile device
Summary
Mobile devices such as smart-phones and tablets have become an essential part of our daily lives due to their high convenience and efficiency. Battery life is a critical factor affecting the Quality of Service (QoS) of mobile applications. Mobile cloud computing (MCC) [1] has become a promising way to enhance the capabilities of mobile devices. In MCC, task offloading technology [2] can migrate computation-intensive tasks to powerful servers in the cloud through a wireless network to reduce the energy consumption of mobile devices. Task offloading will inevitably increase the communication. Between the mobile device and the cloud and increases the cost on the energy consumption, communication time and bandwidth [3], [4]. The decision of task offloading is a decision on the tradeoff between the communication cost and the computation cost considering the QoS constrains of mobile workflow applications
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