Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

The Internet of Things (IoT) has been envisioned as an enabler of the digital transformation that can enhance different features of people's daily lives, such as healthcare, home automation, and smart transportation. The vast amount of data generated by a massive number of devices in an IoT system could lead to a severe performance problem. Edge cloud computing and network function virtualization (NFV) technologies are potential approaches to improve the efficiency of resource use and the flexibility of responsive services in an IoT system. In this paper, we consider the joint optimization problem of gateway placement and multihop routing in the IoT layer, the problem of service placement in the edge and cloud layers of an NFV-enabled IoT system in edge cloud computing (NIoT). We propose three optimization models (i.e., GMO, SP1O, SP2O) that allow an IoT service provider to find the optimal deployment of gateways, the optimal resource allocation for service functions, and the optimal routing according to a cost function with a performance constraint in a NIoT system. We then develop three approximation algorithms (i.e., GMA, SP1A, SP2A) for tackling the problems in a large-scale NIoT system. The evaluation results under a set of scenarios with various topologies and parameters show that the approximation algorithms can obtain results close to the optimal solution with a significant reduction in computation time. We also derive new insights into the strategy for an IoT provider to optimize its objectives. Specifically, the results suggest that an IoT provider should select an appropriate service placement strategy with regard to a charging agreement with an NFV infrastructure provider, and only deploy service functions with a strict delay requirement on the edge of networks for optimizing its cost.

Section: Related Workmentioning

confidence: 99%

Optimization of Resource Management for NFV-Enabled IoT Systems in Edge Cloud Computing

Pham

Nguyen

2020

“…With the rapid development of smart devices, IoT (the Internet of Things) and 5G technologies, edge computing, as well as the multi-access edge computing (MEC) technology, gains more and more attraction. And device-enhanced MEC, which makes full use of the computing power and storage capacity of the terminal, was proposed as traditional edge computing nodes lacks of resource computing efficiency and service flexibility [44].…”

Section: Device-enhanced Mecmentioning

confidence: 99%

Efficient, Customizable and Edge-Based WebGIS System

He¹,

Zhu

2020

Nowadays there is a wide range of applications for WebGIS which can add great value to modern economic, and building WebGIS system for specific scenarios is the common requirement of the industry. While currently separate WebGIS systems are deployed at different sites and operated by different owners, each of which has the whole set of functionalities of WebGIS, and thus introduce high cost of development and maintenance, which is a waste of resources as most of the functionalities are the same or similar. An edge computing based WebGIS architecture is proposed in the paper to meet the need of customization by applying the idea of SaaS. In this distributed architecture, the resource load is reasonably balanced between the server and the browser, which improves the overall performance of the system. Also it utilizes edge computing to reduce the pressure on the server by sharing map tiles among WebGIS clients. The proposed WebGIS system can not only be well customized and personalized as it is edge computing base, but also be well usable for large number of visits due to its distributed feature. The experiments show 5 concurrent requests per second, as well as response speed increases by more than 38.6% against traditional deployment. INDEX TERMS edge computing, customizable, WebGIS, high performance, map tile sharing, deviceenhanced MEC.

“…When a helper is in cell n at time m , the amount of overload data after offloading can be written as follows. (16) When entering an underload cell, a mobile helper stores content for the overload cells in its movement path through path prediction by the helper and content prediction and overload estimation by neighboring cells.…”

Section: E Offloadingmentioning

confidence: 99%

“…In order to reduce the load of overload cells, one can use mobile edge caching (MEC) or wireless caching technology that stores content by installing a cache in a base station or on a mobile end device [9]- [12]. When a mobile end device requests video content and the content is in the base station's cache, the base station can provide the content to the device without using the backhaul [13]- [16]. If the content is in the cache of another end device, the content may be supplied using device-to-device (D2D) communication or through the base station as a relay [17]- [20].…”

Section: Introductionmentioning

confidence: 99%

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Peak-Hour Caching Schemes of Mobile Devices for Overload Cells in Wireless Caching Systems

Kang

2020

In the case of broadband wireless communication systems with high carrier frequencies, high-speed wireless communication is feasible, but if high-capacity backhaul is not supplied to all base stations, a backhaul bottleneck may occur. To solve this backhaul bottleneck problem, one can use wireless caching or device-to-device (D2D) caching technology that installs a cache on a mobile end device, use the cache to store video content, and then supply the video content when requested by other end devices. Prior to peak traffic hours, video content can be stored on mobile end devices according to the content preferences, but it is difficult to solve cell overload problems that occur during peak traffic hours. This is because peak traffic hours can persist for a long time, and thus device positions, content preferences, and cell loads are liable to change during peak traffic hours. Providing broadband radio and high-capacity backhaul to all cells can create cost issues, but if there are some cells with broadband wireless resources and high-capacity backhaul, these cells may often support sufficient data even during peak traffic hours. If a mobile end device can intermittently enter a cell with sufficient data supply, it can update its cache using some short-term information. In this paper, we compare wireless caching schemes that update the caches of mobile end devices during peak traffic hours with the goal of reducing overload data. If high-capacity base stations are installed within a certain distance interval from each other to reduce the content update period, then good performance can be obtained by frequently updating the caches while considering the short-term content preferences of nearby overload cells. In addition, if a mobile device can predict its movement path, performance can be further improved by considering only the overload cells in the predicted path.