Abstract
The Internet of Things (IoT) is evolving rapidly and requires IoT devices to have more resources to meet the growing needs in diverse application domains. Despite increasing demands, modern IoT devices do not fully utilize somewhat over-provisioned computing resources. In this work, we introduce a new concept of IoT-assisted Edge Computing which makes use of consolidated idle resources in IoT devices for edge services through offloading edge tasks to nearby IoT devices. For the IoT-assisted edge computing be beneficent, two important conditions should be satisfied: 1) offloaded edge tasks to IoT devices do not hurt normal execution of local IoT tasks, and 2) computing resources in IoT devices should be effectively exploited to increase the throughput of edge services. To that end, we propose a collaborative task scheduling for IoT-assisted edge computing, in which an edge node determines where to offload edge tasks among participating IoT devices based on the offloaded execution time and energy consumption, and each IoT device determines when to execute the offloaded tasks considering local tasks execution. Experimental results show that the proposed scheme not only achieves near-optimal task throughput but also outperforms other scheduling algorithms in terms of deadline satisfaction ratio of time critical tasks, while guaranteeing deadlines of local tasks in IoT devices.
Highlights
R ECENTLY, a new computing paradigm of Edge Computing has emerged to exploit the computing resources located at the edge of the network, i.e., edge servers, for processing all or part of user services
A good example is the work presented by Clark et al [32], that proposed a concept of a virtual machine (VM)-based remote execution scheme in which the live migration of virtual machines enables an entire OS including all running applications to be moved to other virtual machine
As more and more IoT devices with abundant computational resources are emerged, the effective exploitation of unused resources in IoT devices for other meaningful work than the local IoT job is of increasing importance
Summary
R ECENTLY, a new computing paradigm of Edge Computing has emerged to exploit the computing resources located at the edge of the network, i.e., edge servers, for processing all or part of user services. A good example is the work presented by Clark et al [32], that proposed a concept of a virtual machine (VM)-based remote execution scheme in which the live migration of virtual machines enables an entire OS including all running applications to be moved to other virtual machine Based on this technique, CloneCloud [27] and Cloudlet [28] proposed methods to offload computation to cloud by executing tasks of mobile devices on remote VMs without programmers’ efforts. CloneCloud [27] and Cloudlet [28] proposed methods to offload computation to cloud by executing tasks of mobile devices on remote VMs without programmers’ efforts These studies focus on cloud offloading to overcome the low performance rather than to increase the energy-efficiency of mobile devices. GMS [15] suggests the greedy-based algorithms for distributing tasks from a mobile device (MD) to the mobile edge cloud consisting of multiple energy harvesting wireless access points (APs)
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