SCCOF: smart cooperative computation offloading framework for mobile cloud computing services

Virtual reality games and image processing Apps are examples of mobile cloud computing services (MCCS) common on Smartphones (SPs) nowadays, requiring intensive processing and/or wireless networking. The consequences are slow execution and huge battery consumption. Offloading the intensive computations of such Apps to a cloud based server can overcome such consequences. However, such offloading will introduce time delay and communication overheads. This paper proposes to do the offloading to nearby computing resources in a cooperative computation sharing network via short-range wireless connectivity. The proposed SCCOF reduces offloading response time and energy consumption overheads. SCCOF is supported by an intelligent cloud located controller that will form the cooperative resource sharing network on the go when needed, based on available devices in the vicinity, and will use the cloud if necessary. Upon the initiation of the MCCS service via the App, our controller will devise the offloaded VMs as well as the offloading network. A study test scenario was performed to evaluate the performance of SCCOF, resulting in saving of up to 16.2x in execution time and 57.25% energy

Enabling Multi-Hop Remote Method Invocation in Device-To-Device Networks

To avoid shrinking down the performance and preserve energy, low-end mobile devices can collaborate with the nearby ones by offloading computation intensive code. However, despite the long research history, code offloading is dilatory and unfit for applications that require rapidly consecutive requests per short period. Even though Remote Procedure Call (RPC) is apparently one possible approach that can address this problem, the RPC-based or message queue-based techniques are obsolete or unwieldy for mobile platforms. Moreover, the need of accessibility beyond the limit reach of the device-to-device (D2D) networks originates another problem. This article introduces a new software framework to overcome these shortcomings by enabling routing RPC architecture on multiple group device-to-device networks. Our framework provides annotations for declaring distribution decision and out-of-box components that enable peer-to-peer offloading, even when a client app and the service provider do not have a direct network link or Internet connectivity. This article also discusses the two typical mobile applications that built on top of the framework for chatting and remote browsing services, as well as the empirical experiments with actual test-bed devices to unveil the low overhead conduct and similar performance as RPC in reality

Mobile-to-mobile opportunistic task splitting and offloading

With the advent of wearable computing and the resulting growth in mobile application market, we investigate mobile opportunistic cloud computing where mobile devices leverage nearby computational resources in order to save execution time and consumed energy. Our goal is to enable generic computation offloading to heterogeneous devices forming a mobile-to-mobile opportunistic computing platform. In this paper, we adopt (1) an analytical approach and (2) an experimental approach to highlight the gain given by mobile-to-mobile opportunistic offloading compared to local execution. We also investigate multiple offloading strategies with regards to both computation time and energy consumption. We propose an auto-splitting and offloading algorithms that computes the optimal chunks sizes that could be offloaded remotely to neighboring mobile device. We show that our splitting and offloading algorithm succeeds in picking the optimal chunk sizes and distribution with up to 99.7% efficiency. In addition, the offloader device saves up to 80% energy while offloading the task remotely. For instance if the offloader device is running out of battery, offloading is the ultimate solution to increase its lifetime