Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

Abstract

Nowadays, although the data processing capabilities of the modern mobile devices are developed in a fast speed, the resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular the computationally intensive ones, such as multimedia and gaming, often require more computational resources than a mobile device can afford. One way to address such a problem is that the mobile device can o oad those tasks to the centralized cloud with data centers, the nearby cloudlet or ad hoc mobile cloud. In this paper, we propose a data o oading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device (MD) who has computational tasks can access resources from nearby slave devices (SDs) or the cloudlet, instead of the centralized cloud, to share the workload, in order to reduce the energy consumption and computational cost. A two-stage Stackelberg game is then formulated where the SDs determine the amount of data execution units that they are willing to provide, while the MD who has the data and tasks to o oad sets the price strategies for di erent SDs accordingly. By using the backward induction method, the Stackelberg equilibrium is derived. Extensive simulations are conducted to demonstrate the e ectiveness of the proposed scheme.

CFRO: Cloudlet Federation for Resource Optimization

“…The approaches in [40,41] aims to minimize the cost in terms of energy and resource consumption. The objective of cost minimization is achieved through a cloning based scheme where each mobile device has a software clone in the cloud to offload compute intensive data.…”

Balanced Cloud Edge Resource Allocation Based on Conflict Conditions

“…Guo et al [11] combine game theory with cloudlet and propose a balanced task scheduling scheme, which reduces the workload of the centralized cloud and thus reduces energy consumption and computing cost.The strategy considers three attributes, namely response time, energy efficiency and service delay,but does not consider load balancing of nodes.However, if cluster nodes can reach the equilibrium state, the efficiency of edge nodes can be improved effectively.…”

A Communication Channels Dynamic Switching Model for Always-Connected Availability of Service Oriented Mobile Applications

“…Mobility offloading decision can be altered due to unstable connectivity of mobile networks therefore a novel and robust solution is proposed by [Deng, et al, 2015] to solve the problem of mobile computation-offloading due to workflows mobile services that occurred to fulfil their complex requirements; [Rego, et al, 2016] presents a framework to support a method-based offloading technique for applications of different mobile platforms; to reduce the energy consumption and computational cost, [Guo, et al, 2016] propose a data offloading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device who has computational tasks can access resources from nearby slave devices or the cloudlet; [Singh and Madan, 2016] proposed a system based upon the user's moving path mobility, which will assume the user's region to finish the process and reduce the response time as well as improve the load balancing; [Reiter, et al, 2016] shows a solution that enables a dynamic use of external resources and assures that security-critical computations are offloaded to trusted resources only; [Kaur and Makkar, 2016] implemented a distributed application processing to leverage the limitations of resources on mobile devices by outsourcing the application processing load to cloud server nodes entirely or partially; finally is proposed by [Liu, et al, 2016] an iterative decoupling algorithm with high efficiency to obtain near-optimal offloading decisions for energy saving.…”