Integrated vector instruction translator and offloading framework for mobile cloud computing / Junaid Shuja - Junaid , Shuja (2017)
Mobile Cloud Computing (MCC) facilitates energy efficient operations of mobile devices through computational offload. The mobile devices offload computations to nearby cloud servers while limiting energy consumption in the low-power wait mode. The MCC offloading frameworks are enabled by system virtualization, application virtualization, and native code migration techniques to address the heterogeneous computing architectures. The existing MCC offloading techniques suffer from either computational or communicational overheads leading to higher execution time and energy consumption on the cloud server. This research work addresses the overhead of conventional MCC offloading frameworks while focusing on vectorized applications based on Single Instruction Multiple Data (SIMD). We propose SIMDOM, a framework for SIMD instruction translation and offloading in heterogeneous MCC architectures. The SIMD translator utilizes re-compilation of SIMD instructions of the mobile device (ARM architecture) that are translated to corresponding cloud server instructions (x86 architecture). Based on inputs from the application, network, and mobile device energy profilers, the offloader module decides upon the feasibility of code offload. The SIMD translator is analyzed for its accuracy and translation overhead. The impact of code offload size, application partition, and device sleep time is investigated on the energy and time efficiency of the mobile applications. The lower feasibility bounds for server speed and application partition are derived from the system model. The SIMDOM framework prototype is implemented on a cloudlet and a cloud server. Results show that SIMDOM framework provides 85.66% energy and 3.93% time efficiency compared to MCC-disabled execution. Comparison with state-ofthe- art code offloading framework reveals that SIMDOM provides 55.99% energy and 57.30% time efficiency. The SIMDOM framework provides 31.10% higher energy efficiency while translating SIMD instructions as compared to existing MCC offloading frameworks. The improvement in energy and time efficiency increases the usability of MCC offloading frameworks for vectorized applications.