Application Scheduling in Mobile Cloud Computing with Load Balancing

Abstract

Mobile cloud computing (MCC) enables the mobile devices to offload their applications to the cloud and thus greatly enriches the types of applications on mobile devices and enhances the quality of service of the applications. Under various circumstances, researchers have put forward several MCC architectures. However, how to reduce the response latency while efficiently utilizing the idle service capacities of the mobile devices still remains a challenge. In this paper, we firstly give a definition of MCC and divide the recently proposed architectures into four categories. Secondly, we present a Hybrid Local Mobile Cloud Model (HLMCM) by extending the Cloudlet architecture. Then, after formulating the application scheduling problems in HLMCM and bringing forward the Hybrid Ant Colony algorithm based Application Scheduling (HACAS) algorithm, we finally validate the efficiency of the HACAS algorithm by simulation experiments.

Energy efficient offloading for competing users on a shared communication channel

“…Mobile cloud computing (MCC) is evolving as a new computing paradigm that intends to augment resourceconstrained mobile devices, taking advantage of the abundant resources hosted by clouds. It brings powerful resources of the cloud centers for mobile devices and applications while having low cost, high scalability, and inheriting the robustness of cloud computing (Wei et al, 2013). Theoretically, Cloud computing can supply mobile devices with inexhaustible resources.…”

“…Performance results are given in , LTE, or Wi-Fi) that are inherently less reliable than their wired counterparts due to mobility requirements (Shiraz et al, 2013). Researchers have proposed various architectures for MCC, such as MobiCloud, MAUI, CloneCloud, Cloudlet, and Hyrax to support different types of applications (Wei et al, 2013). We divide these architectures into two main categories depending on where the users execute their tasks and how mobile devices and the cloud can be connected.…”

“…The rapid development of embedded systems, software, and high-speed wireless networks enables them to make calls and send short messages and emails. Moreover, it makes them capable of sensing the environment and making social contacts, healthcare, and mobile learning (Wei et al, 2013). Users can interact with other devices and social community without any time and space restriction because of the inherent mobility of the mobile devices.…”

Context-aware multi-objective resource allocation in mobile cloud

“…Although the experimental results show the scalability of Hyrax, the performance of the platform is poor for CPU-bound tasks. In [27], a Hybrid Local Mobile Cloud Model (HLMCM) is defined consisting of cloudlet and mobile devices where cloudlet plays the role of central broker while both cloudlet and neighboring mobile devices play the role of service provider. The objective of application scheduling is to maximize the profit as well as lifetime of HLMCM, while satisfying resource capacity constraint of service providers.…”

“…In our proposed method, this shortcoming is regarded in parallel with lifetime of the mobile cloud. In addition, in both [26,27], central server runs the scheduling algorithm and manages the offloading process which is a bottleneck while in our proposed method, resource allocation decision and execution of offloading process are performed individually on each mobile device.…”