Computation Offloading Mechanism Research Articles

Computational Offloading in Android Devices Using Cloud Computing Capabilities

Mobile devices have witnessed several stages as phone adoption has increased rapidly across the globe, with the results that the manipulations of smartphones are increasing thrice compared to Personal Computers. The recent developments in smartphone technology have led users into a new way of mobile computing. Portable communication devices such as smartphones and other hand held devices have replaced the existing computing technology. Smart mobile devices are equipped with low processing capacity computing processors and limited power batteries as compared to computers, which instead have a high-level hardware configuration. Hence it becomes onerous to execute any applications on these compact devices, resulting in a performance lacking and high power consumption. The offloading computation mechanism proves out to be an uncompromisable solution to this situation, which may result in high performance efficiency and improved battery life in these devices. As this solution involves cloud computing, hence uploading data to cloud service engines and retrieving back the results come out to be a time and energy-consuming factor. Therefore, this investigation tries to put its focus on the behavior of time and energy consumption in mobile devices by using Computation offloading mechanism and by implementing a decision-making framework termed as Ternary decision maker, which can outcome the above factor.

ThriftyEdge: Resource-Efficient Edge Computing for Intelligent IoT Applications

In this article we propose a new paradigm of resource-efficient edge computing for the emerging intelligent IoT applications such as flying ad hoc networks for precision agriculture, e-health, and smart homes. We devise a resource-efficient edge computing scheme such that an intelligent IoT device user can well support its computationally intensive task by proper task offloading across the local device, nearby helper device, and the edge cloud in proximity. Different from existing studies for mobile computation offloading, we explore the novel perspective of resource efficiency and devise an efficient computation offloading mechanism consisting of a delay-aware task graph partition algorithm and an optimal virtual machine selection method in order to minimize an intelligent IoT device's edge resource occupancy and meanwhile satisfy its QoS requirement. Performance evaluation corroborates the effectiveness and superior performance of the proposed resource-efficient edge computing scheme.

Collaborative Mobile Edge Computation Offloading for IoT over Fiber-Wireless Networks

Mobile edge computing is envisioned to be a promising paradigm to address the conflict between computationally intensive IoT applications and resource-constrained lightweight mobile devices. However, most existing research on mobile edge computation offloading has only taken the resource allocation between the mobile devices and the MEC servers into consideration, ignoring the huge computation resources in the centralized cloud computing center. To make full use of the centralized cloud and distributed MEC resources, designing a collaborative computation offloading mechanism becomes particularly important. Note that current MEC hosted networks, which mostly adopt the networking technology integrating cellular and core networks, face new challenges of single networking mode, long latency, poor reliability, high congestion, and high energy consumption. Hybrid fiber-wireless networks integrating both low-latency fiber optic and flexible wireless technologies should be a promising solution. Toward this end, we provide in this article a generic fiber-wireless architecture with coexistence of centralized cloud and distributed MEC for IoT connectivity. The problem of cloud-MEC collaborative computation offloading is defined, and a game-theoretic collaborative computation offloading scheme is proposed as our solution. Numerical results corroborate that our proposed scheme can achieve high energy efficiency and scales well as the number of mobile devices increases.

An efficient game theoretic based computation offloading framework for network optimisation in mobile cloud IoT systems

Mobile cloud computing (MCC) is visualised as an encouraging approach to increase the computational capabilities of mobile devices. With the advancement of internet of things (IoT) technologies and mobile cloud, a new concept is developed called as mobile cloud IoT (MCIoT) which is the convergence of IoT and mobile cloud. In this paper, we propose a nested game theoretic technique for computation offloading algorithms. Firstly we introduce a game theoretic approach for network selection based on strategic and repeated game theory. Then, for determining the portion of application to be offloaded we use bargaining game theory based on Rubinstein-Stahl's model. Then the selection of the best cloud is analysed using auction game theory and trading market theory. Numerical results show that the proposed scheme can achieve effective computation offloading mechanism under MCIoT systems.

An efficient game theoretic based computation offloading framework for network optimisation in mobile cloud IoT systems

Mobile cloud computing (MCC) is visualised as an encouraging approach to increase the computational capabilities of mobile devices. With the advancement of internet of things (IoT) technologies and mobile cloud, a new concept is developed called as mobile cloud IoT (MCIoT) which is the convergence of IoT and mobile cloud. In this paper, we propose a nested game theoretic technique for computation offloading algorithms. Firstly we introduce a game theoretic approach for network selection based on strategic and repeated game theory. Then, for determining the portion of application to be offloaded we use bargaining game theory based on Rubinstein-Stahl's model. Then the selection of the best cloud is analysed using auction game theory and trading market theory. Numerical results show that the proposed scheme can achieve effective computation offloading mechanism under MCIoT systems.

Decentralized Computation Offloading Game for Mobile Cloud Computing

Mobile cloud computing is envisioned as a promising approach to augment computation capabilities of mobile devices for emerging resource-hungry mobile applications. In this paper, we propose a game theoretic approach for achieving efficient computation offloading for mobile cloud computing. We formulate the decentralized computation offloading decision making problem among mobile device users as a decentralized computation offloading game. We analyze the structural property of the game and show that the game always admits a Nash equilibrium. We then design a decentralized computation offloading mechanism that can achieve a Nash equilibrium of the game and quantify its efficiency ratio over the centralized optimal solution. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.