Edge Computing
4237186
Edge Computing: Vision and Challenges
The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
IEEE Internet of Things Journal
Weisong Shi Jie Cao Quan Zhang Youhuizi Li Lanyu Xu
25435563
Joint Task Offloading and Resource Allocation for Multi Server Mobile Edge Computing Networks
Mobile edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users in executing computation intensive tasks via task offloading. The problem of joint task offloading and resource allocation is studied in order to maximize the users' task offloading gains, which is measured by a weighted sum of reductions in task completion time and energy consumption. The considered problem is formulated as a mixed integer nonlinear program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the combinatorial nature of this problem, solving for optimal solution is difficult and impractical for a large scale network. To overcome this drawback, we propose to decompose the original problem into a resource allocation (RA) problem with fixed task offloading decision and a task offloading (TO) problem that optimizes the optimal value function corresponding to the RA problem. We address the RA problem using convex and quasi convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users' offloading utility over traditional approaches.
IEEE Transactions on Vehicular Technology
Tuyen X Tran Dario Pompili
52015330
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security critical and time sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.
J. Syst. Archit.
Ashkan Yousefpour Caleb Fung Tam Nguyen Krishna P Kadiyala Fatemeh Jalali Amirreza Niakanlahiji Jian Kong Jason P Jue
51921962
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Emerging technologies and applications including Internet of Things, social networking, and crowd sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient descent based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.
IEEE Journal on Selected Areas in Communications
Shiqiang Wang Tiffany Tuor Theodoros Salonidis Kin Kwong Leung Christian Makaya Ting He Kevin Sean Chan
165163986
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT) billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI) is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.
Proceedings of the IEEE
Zhi Zhou Xu Chen En Li Liekang Zeng Ke Luo Junshan Zhang
70172211
Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks
The drastically increasing volume and the growing trend on the types of data have brought in the possibility of realizing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i.e. the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources. However, road side units that primarily presume the role of vehicular edge computing servers cannot be fully trusted, which may lead to serious security and privacy challenges for such integrated platforms despite their promising potential and benefits. We exploit consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation based data sharing scheme to ensure high quality data sharing among vehicles. A three weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high level of security for data sharing in VECONs.
IEEE Internet of Things Journal
Jiawen Kang Rong Yu Xumin Huang Maoqiang Wu Sabita Maharjan Shengli Xie Yan Zhang
21728234
Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning
To improve the quality of computation experience for mobile devices, mobile edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN) which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep {Q} network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a {Q} function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.
IEEE Internet of Things Journal
Xianfu Chen Honggang Zhang Celimuge Wu Shiwen Mao Yusheng Ji Medhi Bennis
64986574
Edge server placement in mobile edge computing
Abstract With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e. big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom's base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing.
J. Parallel Distributed Comput.
Shangguang Wang Yali Zhao Jinlinag Xu Jie Yuan Ching-Hsien Hsu
86722298
Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges
Blockchain, as the underlying technology of crypto currencies, has attracted significant attention. It has been adopted in numerous applications, such as smart grid and Internet of Things. However, there is a significant scalability barrier for blockchain, which limits its ability to support services with frequent transactions. On the other side, edge computing is introduced to extend the cloud resources and services to be distributed at the edge of the network, but currently faces challenges in its decentralized management and security. The integration of blockchain and edge computing into one system can enable reliable access and control of the network, storage, and computation distributed at the edges, hence providing a large scale of network servers, data storage, and validity computation near the end in a secure manner. Despite the prospect of integrated blockchain and edge computing systems, its scalability enhancement, self organization, functions integration, resource management, and new security issues remain to be addressed before widespread deployment. In this survey, we investigate some of the work that has been done to enable the integrated blockchain and edge computing system and discuss the research challenges. We identify several vital aspects of the integration of blockchain and edge computing: motivations, frameworks, enabling functionalities, and challenges. Finally, some broader perspectives are explored.
IEEE Communications Surveys Tutorials
Ruizhe Yang Fei Richard Yu Pengbo Si Zhaoxin Yang Yanhua Zhang
70236084
Multimodel Framework for Indoor Localization Under Mobile Edge Computing Environment
Location estimation technology under the wireless environment has become a vital technology in the field of mobile edge computing. Especially, under the mobile edge of entire networks environment, indoor location estimation is gradually getting the interest research and application topic, due to technical constraints of global positioning system technology for indoor environment and the popularity of the mobile edge computing servers. In this paper, the widely used single model framework for indoor localization is presented as an introduction, which consists of three stages: 1) sample data collection; 2) model building; and 3) localization estimation. And then, through analyzing of the actual scene of indoor localization, a new framework for indoor localization under mobile edge computing environment, named Multimodel, is proposed from the theoretical perspective. It is mainly based on the observation that the environment of the sample data collection and that of localization data collection may change seriously. In order to make up for the shortcomings of this framework, two combinatorial optimization problems are proposed. Later, we discuss the NP hardness of them in several different cases. In addition, two heuristic algorithms are given, and the performance of which are illustrated by the corresponding experimental results.
IEEE Internet of Things Journal
Wenjun Li Zhenyu Chen Xingyu Gao Wenqiang Liu Jin Wang