Novel Mobile Computation Offloading Framework for Android Devices

The thesis implements an offloading framework for GoogleTM AndroidTM based on mobile devices. Today, the full potential for smartphones may be constrained by certain technical limits such as battery endurance and computational performance. Modern mobile applications own more powerful functions but need larger computation and faster frame rate, which consume more battery energy. Using the proposed offloading framework, mobile devices can offload computational intensive workload to servers to save battery energy consumption and reduce the execution time. The framework can also enable software developers to easily build and deploy services on the servers to support mobile devices to run computationally intensive jobs. Compared with other offloading schemes for android cell phones, the scheme enables developers to choose which parts of the codes are potentially offloading. As developers fully understand the data flow models of the apps, they are considered most capable of making offloading decisions. Developers can minimize communication overhead brought by offloading by carefully partitioning source code by data dependency. Experiment results and data showed that the proposed offloading scheme could significantly reduce computational time and battery energy consumption

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein

Towards Multi-Criteria Heuristic Optimization for Computational Offloading in Multi-Access Edge Computing

In recent years, there has been considerable interest in computational offloading algorithms. The interest is mainly driven by the potential savings that offloading offers in task completion time and mobile device energy consumption. This paper builds on authors' previous work on computational offloading and describes a multi-objective optimization model that optimizes time and energy in a network with multiple Multi-Access Edge Computing servers (MECs) and Mobile Devices (MDs). Each MD has multiple computational jobs to process, and each task can be processed locally or offloaded to one of the MEC servers. Several heuristic offloading policies are proposed and tested with an objective function with a range of weightings for optimizing time and energy. The approaches are illustrated with the help of three test cases of varying complexity. The objective function shows a continuous variation as the emphasis is placed on either time or energy saving by the weighting factors. The numerical tests demonstrate that the proposed heuristic algorithms produce near-optimal computational offloading solutions while considering a combined weighted score for schedule task completion time and energy

Mirroring Mobile Phone in the Clouds

This paper presents a framework of Mirroring Mobile Phone in the Clouds (MMPC) to speed up data/computing intensive applications on a mobile phone by taking full advantage of the super computing power of the clouds. An application on the mobile phone is dynamically partitioned in such a way that the heavy-weighted part is always running on a mirrored server in the clouds while the light-weighted part remains on the mobile phone. A performance improvement (an energy consumption reduction of 70% and a speed-up of 15x) is achieved at the cost of the communication overhead between the mobile phone and the clouds (to transfer the application codes and intermediate results) of a desired application. Our original contributions include a dynamic profiler and a dynamic partitioning algorithm compared with traditional approaches of either statically partitioning a mobile application or modifying a mobile application to support the required partitioning