مقاله انگلیسی رایانش در لبه های تلفن همراه برای برنامه های اینترنت اشیا (2019 الزویر)

Computation offloading is a protuberant elucidation for the resource-constrained mobile devices to accomplish the process demands high computation capability. The mobile cloud is the well-known existing offloading platform, which usually far-end network solution, to leverage computation of the resource-constrained mobile devices. Because of the far-end network solution, the user devices experience higher latency or network delay, which negatively affects the real-time mobile Internet of things (IoT) applications. Therefore, this paper proposed near-end network solution of computation offloading in mobile edge/fog. The mobility, heterogeneity and geographical distribution mobile devices through several challenges in computation offloading in mobile edge/fog. However, for handling the computation resource demand from the massive mobile devices, a deep Q-learning based autonomic management framework is proposed. The distributed edge/fog network controller (FNC) scavenging the available edge/fog resources i.e. processing, memory, network to enable edge/fog computation service. The randomness in the availability of resources and numerous options for allocating those resources for offloading computation fits the problem appropriate for modeling through Markov decision process (MDP) and solution through reinforcement learning. The proposed model is simulated through MATLAB considering oscillated resource demands and mobility of end user devices. The proposed autonomic deep Q-learning based method significantly improves the performance of the computation offloading through minimizing the latency of service computing. The total power consumption due to different offloading decisions is also studied for comparative study purpose which shows the proposed approach as energy efficient with respect to the state-of-the-art computation offloading solutions.

Introduction

The massive growth of mobile devices (e.g. smart phones, laptops, tablet pc’s, mobile IoT’s and automobiles) and their computation demands imposed a huge scarcity in communication network and computation resources. Some of the application services e.g. image processing and real-time translation services require extensive computation, the resource-constrained mobile devices are not the feasible domiciles to process those applications. Therefore, to meet the computation demands of such type of mobile devices and applications the outsourcing of computation is the demand in need. Computation offloading is a relocation mechanism of processes or modules of software applications or systems from resourceconstrained devices to the resource-rich platforms. Mobile cloud is the well-known platform for computation offloading of mobile devices. Mobile cloud computing is becoming a popular method for mobile services e.g. mobile video games, video streaming, education, social networking, messenger and mobile healthcare services [1]. However, the key barriers to offloading computation in mobile cloud are the network bandwidth and latency. Data travels a longer hazardous path from mobile device to the mobile cloud during offloading and thus consumes huge network bandwidth [2]. The bandwidth scarcity, and internet bottlenecks and traffic congestions are the catalysts for the higher latency of offloading computation. Real-time applications are highly latency sensitive and thus it requires to compute data in a close proximity of mobile devices or users. So, mobile fog can be the effective and suitable platform for offloading mobile computation. Fog computing [3] is introduced by Cisco Systems Inc. to extend the cloud computing paradigm to the edge of network especially for Internet of Things (IoT) services. Mobile Fog is the complementary model of fog computing especially prototyped for seamless and latency-aware mobile services [4]. However, the key research questions for offloading computation in mobile fog are (1) How to offload computation in the mobile fog? (2) Which module or process of mobile application should offload? (3) Where to offload the module or process for minimizing the latency of service computing? Moreover, the mobility, heterogeneity and geographical distribution mobile devices impose additional challenges of computation offloading in mobile fog.