edge computing
37250382
Edge Computing
Edge computing is a new computing paradigm in which substantial compute and storage resources are placed at the edge of the Internet, in close proximity to mobile devices, sensors, end users, and Internet of Things devices. This physical proximity improves latency, bandwidth, trust, and survivability.
Computer
Mahadev Satyanarayanan
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
6909107
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
Technological evolution of mobile user equipment (UEs) such as smartphones or laptops, goes hand in hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC) has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user oriented use case in the MEC, i.e. computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.
IEEE Communications Surveys Tutorials
Pavel Mach Zdenek Becvar
206578365
A Survey on Mobile Edge Computing: The Communication Perspective
Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC) The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g. base stations and access points) so as to enable computation intensive and latency critical applications at the resource limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state of the art MEC research with a focus on joint radio and computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache enabled MEC, mobility management for MEC, green MEC, as well as privacy aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.
IEEE Communications Surveys Tutorials
Yuyi Mao Changsheng You Jingmin Zhang Kaibin Huang Khaled Ben Letaief
1663452
Efficient Multi User Computation Offloading for Mobile Edge Cloud Computing
Mobile edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi user computation offloading problem for mobile edge cloud computing in a multi channel wireless interference environment. We show that it is NP hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi user computation offloading in the multi channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.
IEEE/ACM Transactions on Networking
Xu Chen Lei Jiao Wenzhong Li Xiaoming Fu
6171299
Deterministic edge preserving regularization in computed imaging
Many image processing problems are ill posed and must be regularized. Usually, a roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of edges, which are very important attributes of the image. In this paper, we first give conditions for the design of such an edge preserving regularization. Under these conditions, we show that it is possible to introduce an auxiliary variable whose role is twofold. First, it marks the discontinuities and ensures their preservation from smoothing. Second, it makes the criterion half quadratic. The optimization is then easier. We propose a deterministic strategy, based on alternate minimizations on the image and the auxiliary variable. This leads to the definition of an original reconstruction algorithm, called ARTUR. Some theoretical properties of ARTUR are discussed. Experimental results illustrate the behavior of the algorithm. These results are shown in the field of 2D single photon emission tomography, but this method can be applied in a large number of applications in image processing.
IEEE Trans. Image Process.
Pierre Charbonnier Laure Blanc-Feraud Gilles Aubert Michel Barlaud
206448923
The Promise of Edge Computing
The success of the Internet of Things and rich cloud services have helped create the need for edge computing, in which data processing occurs in part at the network edge, rather than completely in the cloud. Edge computing could address concerns such as latency, mobile devices' limited battery life, bandwidth costs, security, and privacy.
Computer
Weisong Shi Schahram Dustdar
31429854
Mobile Edge Computing: A Survey
Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.
IEEE Internet of Things Journal
Nasir Abbas Yan Zhang Amirhosein Taherkordi Tor Skeie
12563598
The Emergence of Edge Computing
Industry investment and research interest in edge computing, in which computing and storage nodes are placed at the Internet's edge in close proximity to mobile devices or sensors, have grown dramatically in recent years. This emerging technology promises to deliver highly responsive cloud services for mobile computing, scalability and privacy policy enforcement for the Internet of Things, and the ability to mask transient cloud outages. The web extra at www.youtube.com/playlist?list=PLmrZVvFtthdP3fwHPy_4d61oDvQY_RBgS includes a five video playlist demonstrating proof of concept implementations for three tasks: assembling 2D Lego models, freehand sketching, and playing Ping Pong.
Computer
Mahadev Satyanarayanan
16133152
Authenticating query results in edge computing
Edge computing pushes application logic and the underlying data to the edge of the network, with the aim of improving availability and scalability. As the edge servers are not necessarily secure, there must be provisions for validating their outputs. This paper proposes a mechanism that creates a verification object (VO) for checking the integrity of each query result produced by an edge server that values in the result tuples are not tampered with, and that no spurious tuples are introduced. The primary advantages of our proposed mechanism are that the VO is independent of the database size, and that relational operations can still be fulfilled by the edge servers. These advantages reduce transmission load and processing at the clients. We also show how insert and delete transactions can be supported.
Proceedings. 20th International Conference on Data Engineering
HweeHwa Pang Kian-Lee Tan