Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute- and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions.

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

We consider Device-to-Device (D2D)-enabled mobile edge computing offloading scenario, where a device can partially offload its computation task to the edge server or exploit the computation resources of proximal devices. Keeping in view the millisecond-scale latency requirement in 5G service scenarios and the spectrum scarcity, we focus on minimizing the sum of task execution latency of all the devices in a shared spectrum with interference. In particular, we provide an integrated framework for partial offloading and interference management using orthogonal frequency-division multiple access (OFDMA) scheme. Accordingly, we formulate total latency minimization as a mixed integer nonlinear programming (MINLP) problem by considering desired energy consumption, partial offloading, and resource allocation constraints. We use decomposition approach to solve our problem and propose a novel scheme named Joint Partial Offloading and Resource Allocation (JPORA). With aim to reduce the task execution latency, JPORA iteratively adjusts data segmentation and solves the underlying problem of quality of service (QoS)-aware communication resource allocation to the cellular links, and interference-aware communication resource allocation to D2D links. Extensive evaluation results demonstrate that JPORA achieves the lowest latency as compared to the other baseline schemes, meanwhile limiting the local energy consumption of user devices.

Latency Minimization for D2D-Enabled Partial Computation Offloading in Mobile Edge Computing

With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and functions located in the cloud to the proximity of users, edge computing can provide powerful communication, storage, networking, and communication capacity. The resource scheduling in edge computing, which is the key to the success of edge computing systems, has attracted increasing research interests. In this paper, we survey the state-of-the-art research findings to know the research progress in this field. Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed. Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning. Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared. Also, we summarize the main performance indicators based on the surveyed literature. To shed light on the significance of resource scheduling in real-world scenarios, we discuss several typical application scenarios involved in the research of resource scheduling in edge computing. Finally, we highlight some open research challenges yet to be addressed and outline several open issues as the future research direction.

Resource Scheduling in Edge Computing: A Survey

With the rapid growth of computation demands from mobile applications, mobile-edge computing (MEC) provides a new method to meet requirement of high data rate and high computation capability. By offloading the latency-critical or computation-intensive tasks to the edge server, mobile devices (MDs) could save energy consumption and extend battery life. However, unlike cloud servers, resource bottlenecks in MEC servers limit the scalability of offloading. Hence, computation offloading and resource allocation need to be optimized. Toward this end, we consider a multi-access MEC servers system in which Orthogonal Frequency-Division Multiplexing Access (OFDMA) is used as the transmission mechanism for uplink. In order to minimize energy consumption of MDs, we propose a joint optimization strategy for computation offloading, subcarrier allocation, and computing resource allocation, which is a mixed integer non-linear programming (MINLP) problem. First, we design a bound improving branch-and-bound (BnB) algorithm to find the global optimal solution. Then, we present a combinational algorithm to obtain the suboptimal solution for practical application. Simulation results reveal that the combinational algorithm performs very closely to the BnB algorithm in energy saving, but it has a better performance in average algorithm time. Furthermore, our proposed solutions outperform other benchmark schemes.

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Energy Efficiency Based Joint Computation Offloading and Resource Allocation in Multi-Access MEC Systems

In recent years, novel mobile applications such as augmented reality, virtual reality, and three‐dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource‐intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)‐based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT‐based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state‐of‐the‐art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid‐base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.

A review on the computation offloading approaches in mobile edge computing: A game-theoretic perspective

Sorting software modules in order of defect count can help testers to focus on software modules with more defects. One of the most popular methods for sorting modules is generalized linear regression. However, our previous study showed the poor performance of these regression models, which might be caused by severe multicollinearity. Ridge regression (RR) can improve the prediction performance for multicollinearity problems. Lasso regression (LAR) is a worthy competitor to RR. Therefore, we investigate both RR and LAR models for cross-version defect prediction. Cross-version defect prediction is an approximate to real applications. It constructs prediction models from a previous version of projects and predicts defects in the next version. Experimental results based on 11 projects from the PROMISE repository consisting of 41 different versions show that: 1) there exist severe multicollinearity problems in the experimental datasets; 2) both RR and LAR models perform better than linear regression and negative binomial regression for cross-version defect prediction; and 3) compared with two best methods in our previous study for sorting software modules according to the predicted number of defects, RR has comparable performance and less model construction time.

Ridge and Lasso Regression Models for Cross-Version Defect Prediction

Mobile edge computing (MEC) is a promising paradigm to integrate computing and communication resources in mobile networks. MEC can improve mobile service quality and enhance Quality of Experience (QoE) by offloading computation tasks to MEC servers. However, a MEC server only can provide limited computational resources for users. In this paper, we consider a mobile edge computing system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in a local region, (iii)offloading tasks to servers in a nearby region. In the policy (iii), mobile user equipment can utilize computational resources of MEC servers in nearby regions to solve the problem of insufficient computational resources in local region servers. We formulate the computation offloading problem as a potential game and propose a Distributed Offloading strategy based on Jacobi algorithm (DOJ) for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system costs and guarantee the QoE of users.

Energy-efficient Offloading Policy for Resource Allocation in Distributed Mobile Edge Computing

Dynamic requests of viewers from sparse and dispersed locations for crowdsourced-live-streaming (CSLS) service make current cloud service providers (CSPs) inadequate to provide sufficient quality of experience (QoE). To solve this issue, we propose a multi-CDN-assisted-CSLS (MCACLS) architecture, a novel cloud architecture complemented by multiple content delivery networks (Multi-CDNs). MCACLS architecture can enhance a CSP’s capacity of video distribution service and improve the quality of CSLS service for end-users while reducing the overall operational cost. MCACLS adaptively adjusts resources between a CSP and its leased CDN service in a fine granularity to deal with the volatility of user requests. However, scheduling resources cost-effectively in response to user requests from different regions is a critical issue that must be addressed. We formulate the above problem into a constrained stochastic optimization problem and propose an algorithm based on the Nash bargaining solution. Our proposed algorithm makes tradeoff between QoE of users and the overall operational cost for CSPs. Illustrative studies validate the advantages of MCACLS and show that it is more cost-effective, reducing the overall operational cost by up to 15% compared with other alternatives while achieving sufficient QoE for viewers.

A Novel Distribution Service Policy for Crowdsourced Live Streaming in Cloud Platform

Empowered by today’s rich media generating devices and convenient Internet access, crowdsourced live streaming (CSLS) service has developed rapidly and become one of the most popular Internet services. Large crowdsourced live streaming providers (CSLSPs) are migrating their services to geo-distributed cloud platforms (GDCPs) for lower costs and higher availability. A CSLSP may rent compute and network resources from cloud providers for video transcoding, video delivering, user-requests handling, and other related tasks. However, due to dynamic requests by viewers and widely spread locations of broadcasters and viewers, it is still challenging for a CSLSP to serve demands of users with reasonable resources from the cloud-based geo-distributed data centers. To overcome this challenge cost-effectively, we propose an online algorithm to save operational costs for CSLSPs by jointly and dynamically choosing right data centers for broadcasters and viewers. Mathematical analysis is presented and proves that our proposed online algorithm can ensure operational costs to be within an upper bound above the optimal solution, while guaranteeing the QoE for viewers. We conduct extensive trace-driven illustrative studies and show that the proposed method can achieve suboptimal results and outperforms other alternative methods.

Joint Optimization of Data-Center Selection and Video-Streaming Distribution for Crowdsourced Live Streaming in a Geo-Distributed Cloud Platform

The offloading policy has attracted much attention in the study of Device-to-Device (D2D) Communication underlaying cellular network integrated with Mobile Edge Computing (MEC) service. D2D communication can improve energy efficiency and prolong the battery life of user equipment (UEs) by offloading computation tasks from a UE to a nearby UE via a D2D link. How to coordinate UEs to offload tasks in D2D underlay communication integrated with MEC Service for reducing interference and improving network energy efficiency is a very important subject. In this paper, we study a D2D underlay communication integrated with MEC service system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in local region, (iii) offloading tasks to a nearby UE. Furthermore, we formulate the computation offloading problem as a potential game and propose a D2D communication integrated with MEC service offloading computation algorithm for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system energy consumption under time constraints.

Xiaoxing Yang

Papers

Ridge and Lasso Regression Models for Cross-Version Defect Prediction

Energy-efficient Offloading Policy for Resource Allocation in Distributed Mobile Edge Computing

A Novel Distribution Service Policy for Crowdsourced Live Streaming in Cloud Platform

Joint Optimization of Data-Center Selection and Video-Streaming Distribution for Crowdsourced Live Streaming in a Geo-Distributed Cloud Platform

Energy-efficient offloading policy in D2D underlay communication integrated with MEC service