Code Offloading Research Articles

Tradeoff between execution speedup and reliability for compute-intensive code offloading in mobile device cloud

With the advent of different mobile computing technologies, mobile devices have opened up a plethora of computational infrastructure to provide improved performance for compute-intensive applications to the end users. Mobile Device Cloud (MDC) technology brings the code offloading mechanism from distant cloud to neighbor mobile devices. However, the major challenges of code offloading in MDC systems include maximization of computation speedup and reliability; unfortunately, these two performance parameters often oppose each other. In this paper, an optimization framework, namely TESAR, has been devised to tradeoff between application execution speedup and reliability while maintaining device energy within a predefined range. We also provide an algorithm for developing a dependency tree among the modules of an application so as to allow higher number of parallel executions, wherever and whenever it is possible. The emulation results of the proposed algorithm outperform the relevant state-of-the-art works in terms of application completion time, communication latency and rescheduling overhead.

Supporting mobility-aware computational offloading in mobile cloud environment

Mobile Cloud Computing (MCC) enables resource-constrained smartphones to run high processing and storage intensive applications through public clouds or cloudlet code offloading. However, handover support in the MCC context has not been thoroughly explored, since most works do not address the devices mobility during the offloading operation. This paper presents the Mobile Offloading System (MOSys), which supports seamless offloading operations during user mobility between wireless networks. To this end, MOSys benefits from the software-defined networking (SDN) paradigm for mobility management and remote caching techniques to reduce the offloading response time. Also, middleware is responsible for ensuring code and data offloading, profiling service, cloud discovery and application deployment. To evaluate the proposed system and to analyze mobility impact on the offloading performance, several experiments were conducted using different smartphones categories and benchmark applications, on three scenarios. Our results have shown that the MOSys’ mobility management application is energy efficient, especially considering the low-cost smartphone category, while remote caching proved to be an attractive alternative for reducing the offloading response time.

SIMDOM: A framework for SIMD instruction translation and offloading in heterogeneous mobile architectures

AbstractFog and mobile edge computing is a paradigm that augments resource‐scarce mobile devices with resource‐rich network servers to enable ubiquitous computing. Smartphone applications rely on code offloading techniques to leverage high‐performance computing opportunities available on edge and cloud servers for compute‐intensive applications. Mobile (ARM) and edge/cloud (x86) architectures are heterogeneous and necessitate dynamic binary translation for compiled code migration that increases the application execution time. The application execution time and energy consumption should be lesser on the edge/cloud server as compared with the local mobile execution for optimal offload. Multimedia‐based applications contain a large set of single instruction multiple data (SIMD) instructions that are compute and resource intensive. However, dynamic binary translation techniques of SIMD instructions lose the parallelism and optimization because of inefficient vector‐to‐scalar translation. We present a framework for SIMD instruction translation and offloading for mobile devices (SIMDOM) in cloud and edge environments. The SIMDOM framework reduces the execution overhead of migrated vectorized multimedia application by using vector‐to‐vector instruction mappings. The framework maps and translates ARM SIMD intrinsic instructions to x86 SIMD intrinsic instructions such that an application programmed for the mobile platform can be executed on the cloud server without any modification. The offload decision is based on inputs from the device energy, network, and application profilers. Experiments show that SIMDOM framework provides 84.78%, 3.41%, and 79.93% energy, time, and performance efficiency, respectively, compared with local offload‐disabled execution. Compared with compiled code offloading, the SIMDOM framework provides 55.99%, 57.50%, and 96.23% energy, time, and performance efficiency, respectively.

Analysis of Vector Code Offloading Framework in Heterogeneous Cloud and Edge Architectures

Smartphones are computationally constrained compared with server devices due to their size and limited battery-based power. Compute-intensive tasks are often offloaded from smartphones to high-performance computing opportunities provided by nearby high-end cloud and edge servers. ARM architectures dominate smartphones, while x86 dominate server devices. The difference in architectures requires dynamic binary translation (DBT) of compiled code migration, which increases the task execution time on the cloud servers. Multimedia applications contain a large number of vector instructions (single instruction multiple data) that are compute and resource intensive. Vector instructions optimize application execution by parallel processing multiple data points in a single instruction. However, DBT of vector instructions losses the parallelism and optimization due to vector-scalar translations. We present and analyze a framework for pre-compiled vector instruction translation and offloading in heterogeneous compute architectures that avoids the execution overhead of compiled code offloading. The framework maps and translates ARM vector intrinsics to x86 vector intrinsics such that an application programmed for ARM architecture can be executed on the x86 architecture without any modification. We analyze the code offloading framework with static code analysis to determine the optimal compilers and corresponding compilation parameters. Moreover, we analyze the overhead of the vector instruction translator and application profiler. Furthermore, the comparative analysis based on increasing computational sizes reveals that our framework provides 78.8% energy efficiency as compared with existing code translation and offloading frameworks.

An Energy Efficient Code Offloading Approach for Mobile Cloud Computing

Objectives: This research work proposes a methodology for estimating the energy consumption of tasks by considering processor and memory usage. Methods: To facilitate energy efficiency in CPU, the mobile devices may be operated at different frequencies during the execution of tasks. This research work applies CPU frequency scaling as its base to achieve energy efficiency. Besides, it also considers energy consumption during memory access while making the offloading decision. Findings: The proposed approach uses energy consumption during computation as well as memory access as its metric to conceive the offloading decision. Additionally, the proposed energy model is simulated and the results are concluded that there is a considerable amount of energy saving in mobile devices due to computation offloading to nearby mobile devices or cloud resources. Applications: To save energy decide which application in mobile as energy consume considered as model. The models work as code offloading in MATLAB and determine by two level genetic algorithms. The efficiency of proposed model is evaluated by a simulation and average energy result can be concluded for a mobile device.

Mobile code offloading: from concept to practice and beyond

The emerging mobile cloud has expanded the horizon of application development and deployment with techniques such as code offloading. While offloading has been widely considered for saving energy and increasing responsiveness of mobile devices, the technique still faces many challenges pertaining to practical usage. In this article, we adopt a systemic approach for analyzing the components of a generic code offloading architecture. Based on theoretical and experimental analysis, we identify the key limitations for code offloading in practice and then propose solutions to mitigate these limitations. We develop a generic architecture to evaluate the proposed solutions. The results provide insights regarding the evolution and deployment of code offloading.

Just-in-Time Code Offloading for Wearable Computing

Wearable computing becomes an emerging computing paradigm for various recently developed wearable devices, such as Google Glass and the Samsung Galaxy Smartwatch, which have significantly changed our daily life with new functions. To magnify the applications on wearable devices with limited computational capability, storage, and battery capacity, in this paper, we propose a novel three-layer architecture consisting of wearable devices, mobile devices, and a remote cloud for code offloading. In particular, we offload a portion of computation tasks from wearable devices to local mobile devices or remote cloud such that even applications with a heavy computation load can still be upheld on wearable devices. Furthermore, considering the special characteristics and the requirements of wearable devices, we investigate a code offloading strategy with a novel just-in-time objective, i.e., maximizing the number of tasks that should be executed on wearable devices with guaranteed delay requirements. Because of the NP-hardness of this problem as we prove, we propose a fast heuristic algorithm based on the genetic algorithm to solve it. Finally, extensive simulations are conducted to show that our proposed algorithm significantly outperforms the other three offloading strategies.