ارائه روشي براي بخش‌بندي و برون‌سپاري اجراي كاربردهاي مبتني بر خدمات وب در سامانه‌هاي سيار با محدوديت تبادل داده

Design and Evaluation of a Method for Partitioning and Offloading Web-based Applications in Mobile Systems with Bandwidth Constraints

برون‌سپاري اجرا به ‌عنوان يك راه حل مهم براي اجراي برنامه‌هاي كاربردي سنگين روي سامانه‌هاي تلفن همراه است. در سامانه‌هاي تلفن همراه تغييرات پهناي‌باند در دسترس، به‌طور معمول اتفاق مي‌افتد كه روي بخش‌بندي بهينه تأثير مي‌گذارد. به‌منظور اجتناب از تكرار اين فرايند سنگين بخش‌بندي بايد به‌صورت تطبيقي و يك بار انجام شده و با تغييرات پهناي‌باند سازگار باشد. در اين مقاله با در‌نظر‌گرفتن تغييرات پهناي‌­باند و محدوديت تبادل ­داده، مسأله بخش‌بندي تطبيقي و برون‌سپاري اجراي كاربردهاي مبتني بر خدمات وب به‌صورت سه مدل جداگانه با اهداف متفاوت شامل بهينه‌­سازي زمان اجرا، بهينه‌سازي مصرف انرژي و بهينهسازي تركيب وزن­دار زمان اجرا و مصرف انرژي، فرموله شده و روشي ابتكاري مبتني بر الگوريتم ژنتيك براي حل هر مسأله بهينه‌سازي در زمان معقول ارائه شده است. نتايج ‌شبيه‌­سازي و ارزيابي الگوريتم پيشنهادي نشان مي­دهدكه در مقابل تغييرات پهناي­باند در دسترس سامانه سيار، عملكرد الگوريتم ارائه‌شده به‌نحو قابل ملاحظه‌­اي بهتر از كارهاي مشابه است.

Computation offloading is known to be among the effective solutions of running heavy applications on smart mobile devices. However, irregular changes of a mobile data rate have direct impacts on code partitioning when offloading is in progress. It is believed that once a rate-adaptive partitioning performed, the replication of such substantial processes due to bandwidth fluctuation can be avoided. Currently, a wide range of mobile applications are based on web services, which in turn influences the process of offloading and partitioning. As a result, mobile users are prone to face difficulties in data communications due to cost of preferences or connection quality. Taking into account the fluctuations of mobile connection bandwidth and thereby data rate constraints, the current paper proposes a method of adaptive partitioning and computation offloading in three forms. Accordingly, an optimization problem is primarily formulated to each of three main objectives under the investigation. These objectives include run time, energy consumption and the weighted composition of run time and energy consumption. Next, taking into consideration the time complexity of the optimization problems, a heuristic partitioning method based on Genetic Algorithm (GABP) is proposed to solve each of the three objectives and with the capability of acceptable performance maintenance in both dynamic and static partitionings. In order to evaluate and analyze the performance of the proposed approach, a simulation framework was built to run for random graphs of different sizes with the capability of setting specific bandwidth limits as target. The simulation results evidence improved performance against bandwidth fluctuations when compared to similar approaches. Moreover, it was also seen that once the problem circumstances are modified, the offloading can take place in the vicinity of the target node. Furthermore, we implemented the proposed method in form of an application on Android platform to conduct experiments on real applications. The experiments prove that those partitions of the applications requiring higher processing reqources rather than data rate are the best candidates for offloading.