Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.

Efficient placement of multi-component applications in edge computing systems

Abstract: Mobile Edge Computing (MEC) is a new paradigm which has been introduced to solve the inefficiencies of mobile cloud computing technologies. The key idea behind MEC is to enhance the capabilities of mobile devices by forwarding the computation of applications to the edge of the network instead of to a cloud data-center. One of the main challenges in MEC is determining an efficient placement of the components of a mobile application on the edge servers that minimizes the cost incurred when running the application. In this paper, we address the problem of multi-component application placement in edge computing by designing an efficient heuristic on-line algorithm that solves it. We also present a Mixed Integer Linear Programming formulation of the multi-component application placement problem that takes into account the dynamic nature of users' location and the network capabilities. We perform extensive experiments to evaluate the performance of the proposed algorithm. Experimental results indicate that the proposed algorithm has very small execution time and obtains near optimal solutions.

Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0

Abstract: Industry 4.0 aims to create a modern industrial system by introducing technologies, such as cloud computing, intelligent robotics, and wireless sensor networks. In this article, we consider the multichannel access and task offloading problem in mobile-edge computing (MEC)-enabled industry 4.0 and describe this problem in multiagent environment. To solve this problem, we propose a novel multiagent deep reinforcement learning (MADRL) scheme. The solution enables edge devices (EDs) to cooperate with each other, which can significantly reduce the computation delay and improve the channel access success rate. Extensive simulation results with different system parameters reveal that the proposed scheme could reduce computation delay by 33.38% and increase the channel access success rate by 14.88% and channel utilization by 3.24% compared to the traditional single-agent reinforcement learning method.

FAAL: Fog computing-based patient monitoring system for ambient assisted living

Abstract: From the last few years, Wireless body area networks (WBANs) have attracted a lot of attention from both academia and industry due to an increase in real-time data capturing and processing for patient monitoring. This has become possible due to the technological advancements in which high computing and communication facilities are available for most of the modern handheld devices. In this environment, computing resources are available close to the proximity of the end users using the most popular technology called as Fog computing (devices used in the fog computing are called as fog devices). Most of the solutions reported in the literature for this purpose have used the traditional cloud-based infrastructure in which there may occur a long delay for getting the response even for data which is not of very huge amount which may cause a performance degradation for most of the implemented solutions (such as for treatment of neurological diseases where a real-time monitoring is required) in this environment. Hence, to cope up these issues, in this paper, we proposed a fog computing based patient monitoring system for ambient assisted living (FAAL). Data traces of the movement of the patients (for neurological diseases) are collected using sensor nodes using body area networks (BANs) and are passed using the fog gateways. To reduce the load on the communication infrastructure, an efficient clustering algorithm for data transmission is also presented in the paper. Performance of the proposed solution has been evaluated using the parameters such as-latency, and data overloading. Results obtained clearly show the superior performance of the proposed scheme as compared to the non-fog computing based environment.

Cost Aware cloudlet Placement for big data processing at the edge

Abstract: As accessing computing resources from the remote cloud for big data processing inherently incurs high end-to-end (E2E) delay for mobile users, cloudlets, which are deployed at the edge of networks, can potentially mitigate this problem. Although load offloading in cloudlet networks has been proposed, placing the cloudlets to minimize the deployment cost of cloudlet providers and E2E delay of user requests has not been addressed so far. The locations and number of cloudlets and their servers have a crucial impact on both the deployment cost and E2E delay of user requests. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing strategy (CAPABLE) to optimize the tradeoff between the deployment cost and E2E delay. When cloudlets are already placed in the network, we also design a load allocation scheme to minimize the E2E delay of user requests by assigning the workload of each region to the suitable cloudlets. The performance of CAPABLE is demonstrated by extensive simulation results.