关键词： Mobile edge computing   partial offloading   computation energy efficiency  

摘要： This paper proposes a new algorithm to evaluate the performance of the mobile edge computing system. Specifically, a new metric called computation efficiency is defined as the number of calculated data bits divided by the corresponding energy consumption. In order to compute the required data timely, we combine two schemes, local computing and data offloading, into a joint computation algorithm. An optimization problem is formulated with the objective to maximize the sum of computation efficiency among users with weighting factors. With iterative and gradient descent methods, the problem can be solved efficiently. Simulation results show that the proposed scheme outperforms traditional approaches. In addition, the tradeoff study between local computing and data offloading reveals that when data size is small, local computing plays a more important role, but when the size grows, data offloading becomes preferable.

关键词： Cooperative task offloading   mobile edge computing   cloud computing   ADMM   wireless network  

摘要： The deployment of cloud and edge computing forms a three-tier mobile computing network, where each task can be processed locally, by the edge nodes, or by the remote cloud server. In this paper, we consider a cooperative three-tier computing network by leveraging the vertical cooperation among devices, edge nodes and cloud servers, as well as the horizontal cooperation between edge nodes. In this network, we jointly optimize the offloading decision and the computation resource allocation to minimize the average task duration subject to the limited battery capacity of devices. However, the formulated problem is a large-scale mixed integer non-linear optimization problem with the growing number of base stations and devices, which is NP-hard in general. To develop an efficient offloading scheme with low complexity, we conduct a series of reformulation based on reformulation linearization technology and further propose a parallel optimization framework by utilizing alternating direction method of multipliers (ADMM) method and difference of convex functions (D.C.) programming. The proposed scheme decomposes the large-scale problem into some smaller subproblems, which are done across the multiple computation units in a parallel fashion to speed up the computation process. Simulation results demonstrate that the proposed scheme can obtain a near-optimal performance with low complexity, and can reduce up to 24% of the task duration compared with other schemes. Simulation also shows how much the vertical and horizontal computation cooperations affect the system performance under different network parameters.

关键词： Mobile edge computing   Energy efficiency   Computation offloading   Resource allocation   Distributed algorithm  

摘要： Mobile edge computing is an emerging computing paradigm to augment computational capabilities of mobile devices by offloading computation-intensive tasks from resource-constrained smart mobile device onto edge clouds nearby with potential computation capability. However, in general, edge clouds have limited computation resource and energy. Thus it is critical to achieve high energy efficiency while ensuring satisfactory user experience. In this paper, we first formulate the computation offloading problem into the system energy-efficiency cost minimization problem by taking into account the completion time and energy. Then, by solving the optimization problem, we propose a distributed algorithm consisting of clock frequency configuration, transmission power allocation, channel rate scheduling and offloading strategy selection. In this algorithm, the clock frequency for local execution and the transmission power in edge cloud execution are optimized jointly. Furthermore, to optimize the queue delay, we formulate an M/M/n queue model with individual capacity between different windows and propose an algorithm for optimal task offloading rate. Finally, the experimental results show that our algorithm can achieve energy-efficient offloading performance compared to other existing algorithms. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Autonomous driving   Mobile edge computing   Task scheduling   Earliest deadline first  

摘要： Autonomous driving has received widespread attention in recent years, while the limited battery life and computing capability of autonomous vehicles cannot support some necessary computation-intensive and urgent tasks with strict response time requirements. The results of the tasks would be useless and may cause serious safety hazards if the given time constraints are exceeded. On the other side, mobile edge computing (MEC) offers the possibility of autonomous vehicles to complete these time-constraint tasks due to its proximity and strong computing capabilities, with the faster 5G wireless networks to enable vehicles and MEC servers to exchange data in milliseconds. Then, it is a key issue to make the MEC servers to execute and complete these time-constraint autonomous-driving tasks as many as possible. So, we propose a task scheduling algorithm that can consider characteristics of autonomous-driving tasks and select more suitable MEC servers with task migration, based on an improved earliest deadline first algorithm through the replacement and recombination of tasks. From the experimental results, it can be concluded that the algorithm can schedule more tasks and benefit the urgent tasks effectively with the increase of the task amounts.

关键词： Internet of Things   Mobile code offloading   Mobile edge computing   Software defined networks  

摘要： Mobile code offloading is a popular technique for enhancing the apparent capabilities of resource constrained devices. It is of particular interest for developers trying to push ever more complex algorithms into embedded devices that belong to the Internet of Things (loT). Although there is much research into this area in the realm of smartphones and tablets, the same cannot be said for loT devices, for which very few code offloading frameworks exist. This paper presents MobiCOP-loT, an extension of the MobiCOP mobile code offloading solution for the smartphone and tablet ecosystem, that supports embedded devices and Google's newest loT initiative, Android Things. MobiCOP-loT allows developers to deploy surrogates on both distant clouds and proximate nodes located on the edge. As such, MobiCOP-loT enabled applications can take advantage of the benefits of mobile edge computing to further enhance their capabilities. We tested MobiCOP-loT in a variety of scenarios and we found improvements of up to 9 times in terms of performance and energy for centralized cloud deployments, and up to 16 times in the case of edge scenarios. Moreover, we show the platform is capable of automatic horizontal scaling, which allows it to adapt to varying network traffic conditions;and multitenancy, wherein a single server instance is capable of attending multiple clients' requests with minimal loss in performance. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing  

摘要： With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-To-device (D2D)-enabled multi-helperMEC system, in which a local user solicits its nearbyWDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user's task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer nonlinear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Next, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile Social Networks (MSNs) have been evolving and enabling various fields in recent years. Recent advances in mobile edge computing, caching, and device-to-device communications, can have significant impacts on 5G systems. In those settings, identifying central users is crucial. It can provide important insights into designing and deploying diverse services and applications. However, it is challenging to evaluate the centrality of nodes in MSNs with dynamic environments. In this paper, we propose a Social-Relation based Centrality (SoReC) measure, in which social network information is used to quantify the influence of each user in MSNs. We first introduce a new metric to estimate direct social relations among users via direct contacts, and then extend the metric to explore indirect social relations among users bridging to their neighbors. Based on direct and indirect social relations, we detect the influence spheres of users and quantify their influence in the networks. Simulations on real-world networks show that the proposed measure can perform well in identifying future influential users in MSNs. Copyright © 2019, The Authors. All rights reserved.

关键词： collaborative task execution   mobile edge computing   offloading decision   platooning  

摘要： In this paper, we study the offloading decision of collaborative task execution between platoon and Mobile Edge Computing (MEC) server. The mobile application is represented by a series of fine-grained tasks that form a linear topology, each of which is either executed on a local vehicle, offloaded to other members of the platoon, or offloaded to a MEC server. The objective of the design is to minimize the cost of tasks offloading and meets the deadline of tasks execution. The cost minimized task decision problem is transformed into the shortest path problem, which is limited by the deadline of the tasks on a directed acyclic graph. The classical Lagrangian Relaxation-based Aggregated Cost (LARAC) algorithm is adopted to solve the problem approximately. Numerical analysis shows that the scheduling method of the tasks decision can be well applied to the platoon scenario and execute the tasks in cooperation with the MEC server. In addition, compared with task local execution, platoon execution and MEC server execution, the optimal offloading decision for collaborative task execution can significantly reduce the cost of task execution and meet deadlines.

关键词： Cloudlet computing   Crowdsensing   Crowdsourcing   Edge computing   Fog computing   Internet of things   Mobile cloud   Mobile cloud computing   Mobile edge computing   Opportunistic sensing   Participatory sensing   Semantic web of things   Web of things   Wisdom web of things  

摘要： Currently, we are experiencing a technological shift, which is expected to change the way we program and interact with the world. Cloud computing and mobile computing are two prominent research areas that have already had such an impact. The Internet of Things (IoT), which is concerned with building a network of Internet-enabled devices to promote a smart environment, is another promising area of research. Numerous emerging computing paradigms related to those areas of research and/or their intersections have come into play. These include Mobile Cloud Computing (MCC), cloudlet computing, mobile clouds, mobile IoT computing, IoT cloud computing, fog computing, Mobile Edge Computing (MEC), edge computing, the Web of Things (WoT), the Semantic WoT (SWoT), the Wisdom WoT (W2T), opportunistic sensing, participatory sensing, mobile crowdsensing, and mobile crowdsourcing. Unfortunately, those paradigms suffer from the lack of standard definitions, and so we frequently encounter a single term referring to various paradigms or several terms referring to a single paradigm. Accordingly, this paper attempts to disambiguate those paradigms and explain how and where they fit in the above three areas of research and/or their intersections before it becomes a serious problem. They are tracked back to their inception as much as possible. This is in addition to discussing research directions in each area. The paper also introduces technologies related to the IoT such as ubiquitous and pervasive computing, the Internet of Nano Things (IoNT), and the Internet of Underwater Things (IoUT).

关键词： Internet of Things   Network functions virtualization   Virtual Object   Mobile Edge Computing  

摘要： The Internet of Things paradigm will certainly be one of the main drivers of the tomorrow's 5th generation (5G) wireless networks. In order to support the algorithms required for real time exchange and analysis of great amounts of data among the involved smart devices, Virtual Objects (VO) have become a key component to improve the objects energy management efficiency, as well as to address heterogeneity and scalability issues. Following the research trend of exploiting on-demand cloud computing resources to augment the processing and storage capabilities of IoT devices, this paper addresses the design of a novel infrastructure and paradigm to support the deployment of new personal IoT services inside the infrastructure provider premises. The goal is to bring cloud-computing services much closer to the end users and to be able to replace physical IoT devices with their "Virtual Images". Among other benefits, this approach will ensure a longer lifetime to IoT constrained devices and enable the inclusion of new protocols making the development of logic and the configuration of IoT smart application environment technology-agnostic. To achieve this goal, we have developed an open-source software platform that exploits OpenStack APIs and leverages a web interface providing all the functionality typically available in a home gateway through the OpenWRT Linux distribution. Results show this approach brings a manifest reduction in the amount of data transmitted, with benefits in terms of reduced workload and power consumption as well as extended device lifetime. (C) 2018 Elsevier B.V. All rights reserved.