Adaptive Multiservice Heterogeneous Network Selection Scheme in Mobile Edge Computing
TL;DR: Compared with commonly used simple additive weighting (SAW), random access selection (RAS), and price-based and QoS-based network selection scheme, this scheme has better performance in improving average user satisfaction and reducing access failures.
Abstract: With the coming of the fifth-generation (5G) mobile communications, in mobile edge computing (MEC), the growth of user services and the personalization of QoS requirements have posed great challenges for heterogeneous wireless networks (HWNs) access selection. Based on the multiattribute decision theory and the fuzzy logic theory, we propose a novel network selection scheme for multiservice QoS requirements in MEC. The main procedures of the scheme include dynamic adaptive process, fuzzy process, hierarchical analysis, and integrated attributes assessment. The scheme proposed contributes to efficiently reduce the ping-pong effect and effectively select accurate network in a dynamic environment. Simulation results show that our scheme can select network access according to the type of user services and whether to switch networks. In addition, compared with commonly used simple additive weighting (SAW), random access selection (RAS), and price-based and QoS-based network selection scheme, our scheme has better performance in improving average user satisfaction and reducing access failures.
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TL;DR: The concepts, backgrounds, and pros and cons of edge computing are introduced, how it operates and its structure hierarchically with artificial intelligence concepts are explained, examples of its applications in various fields are listed, and some improvements are suggested.
Abstract: The key to the explosion of the Internet of Things and the ability to collect, analyze, and provide big data in the cloud is edge computing, which is a new computing paradigm in which data is processed from edges. Edge Computing has been attracting attention as one of the top 10 strategic technology trends in the past two years and has innovative potential. It provides shorter response times, lower bandwidth costs, and more robust data safety and privacy protection than cloud computing. In particular, artificial intelligence technologies are rapidly incorporating edge computing. In this paper, we introduce the concepts, backgrounds, and pros and cons of edge computing, explain how it operates and its structure hierarchically with artificial intelligence concepts, list examples of its applications in various fields, and finally suggest some improvements and discuss the challenges of its application in three representative technological fields. We intend to clarify various analyses and opinions regarding edge computing and artificial intelligence.
79 citations
TL;DR: A two-tier MEC system is studied, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers.
Abstract: Mobile edge computing (MEC) can use wireless access network (RAN) to provide the services required by user's information technology (IT) and cloud computing functions nearby, which can create a high-performance and low latency service environment. Performing task offloading and data caching at access points (APs) in a cooperative manner can reduce the heavy backhaul load and the retransmission of content downloading. However, in edge networks (ENs), how to maximize storage utilization while reducing service latency and energy consumption is still a key issue, because the heterogeneity of ENs and the uneven distribution of users make it difficult to determine which MEC server and what data should be cached. In this paper, we study a two-tier MEC system, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers. The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. In order to solve the problem, we transform it into an equivalent task offloading convex optimization problem by fixing an optimization variable. Furthermore, we solve a cache placement problem by dynamic programming (DP) algorithm. Then we propose a distributed collaborative data caching and computing offloading (CDCCO) iterative algorithm. Simulation results demonstrate that our proposed CDCCO algorithm can significantly reduce the network cost and achieve better performance than other existing schemes.
38 citations
TL;DR: In this paper, a mobility-aware seamless handover method based on multipath transmission control protocol (MPTCP) is proposed to solve the problems of ping-pong effect and service interruption during vertical handover.
Abstract: In this article, the problem of vertical handover in software-defined network (SDN) based heterogeneous networks (HetNets) is studied. In the studied model, HetNets are required to offer diverse services for mobile users. Using an SDN controller, HetNets have the capability of managing users’ access and mobility issues but still have the problems of ping-pong effect and service interruption during vertical handover. To solve these problems, a mobility-aware seamless handover method based on multipath transmission control protocol (MPTCP) is proposed. The proposed handover method is executed in the controller of the software-defined HetNets (SDHetNets) and consists of three steps: location prediction, network selection, and handover execution. In particular, the method first predicts the user’s location in the next moment with an echo state network (ESN). Given the predicted location, the SDHetNet controller can determine the candidate network set for the handover to pre-allocate network wireless resources. Second, the target network is selected through fuzzy analytic hierarchical process (FAHP) algorithm, jointly considering user preferences, service requirements, network attributes, and user mobility patterns. Then, seamless handover is realized through the proposed MPTCP-based handover mechanism. Simulations using real-world user trajectory data from Korea Advanced Institute of Science & Technology show that the proposed method can reduce the handover times by 10.85% to 29.12% compared with traditional methods. The proposed method also maintains at least one MPTCP subflow connected during the handover process and achieves a seamless handover.
22 citations
01 Oct 2020
TL;DR: This paper formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users’ offloading selection and uplink transmission power, and provides the corresponding optimal solution of user selection and power allocation in MEC.
Abstract: Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption However, how to select the best edge cloud in a dense cell to execute tasks remains challenging To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user’s energy efficiency, defined as the ratio of user throughput to its energy consumption We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users’ offloading selection and uplink transmission power To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency
19 citations
TL;DR: A multiagent $Q -learning network selection (MAQNS) algorithm based on Nash $Q$ -learning, which can learn a joint optimal selection strategy to improve system throughput and reduce user blocking on the premise of ensuring the requirements of IoT services is proposed.
Abstract: The 5G heterogeneous network architecture integrates different radio access technologies (RATs), which will support the large-scale communication connection of massive Internet-of-Things (IoT) devices. However, as the rapid growth of IoT connections, personalized requirements of services requested and heterogeneity deepening of the network system, how to design an intelligent network selection scheme for user devices (UDs) is becoming a crucial challenge in the 5G heterogeneous network system. Most of the existing network selection methods only optimize the selection strategies from the user side or network side, which results in heavy network congestion, poor user experience, and system performance degradation. Accordingly, we propose a multiagent $Q$ -learning network selection (MAQNS) algorithm based on Nash $Q$ -learning, which can learn a joint optimal selection strategy to improve system throughput and reduce user blocking on the premise of ensuring the requirements of IoT services. In particular, we apply the discrete-time Markov chains to model the network selection, and the analytic hierarchy process (AHP) and gray relation analysis (GRA) are jointly utilized to obtain user preferences for each network. Finally, performance evaluation demonstrates that comparing to the existing schemes, MAQNS proposed cannot only improve system throughput and reduce user blocking but also promote user experience on average energy efficiency and delay.
19 citations
References
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TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
Abstract: 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.
2,992 citations
Posted Content•
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.
Abstract: 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 towards 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 present a research outlook consisting of a set of promising 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.
2,289 citations
TL;DR: The definition of MEC, its advantages, architectures, and application areas are provided; where the security and privacy issues and related existing solutions are also discussed.
Abstract: 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.
1,815 citations
"Adaptive Multiservice Heterogeneous..." refers background in this paper
...A Key Technology toward 5G,” the GTI’s “5G MEC White Paper,” the IMT-2020 promotion group’s “5G Network White Paper” [1], [37]–[41], MEC is one of the core capabilities of 5G and is the key to achieving 5G performance improvement....
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...In the MEC environment, core functionalities such as communication and processing capabilities are pushed to radio access network to guarantee 5G communication [1]....
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...W ITH the development of communication technologies and the popularization of smart mobile edge terminals, the user’s requirements for communication quality of different service types are increasing [1]....
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...In addition, according to ETSI’s white paper “Mobile Edge Computing—A Key Technology toward 5G,” the GTI’s “5G MEC White Paper,” the IMT-2020 promotion group’s “5G Network White Paper” [1], [37]–[41], MEC is one of the core capabilities of 5G and is the key to achieving 5G performance improvement....
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TL;DR: A real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at theedge is envisions.
Abstract: MEC is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile RAN. MEC servers are deployed on a generic computing platform within the RAN, and allow for delay-sensitive and context-aware applications to be executed in close proximity to end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisions a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use cases ranging from mobile edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open research issues that need to be addressed in order to efficiently integrate MEC into the 5G ecosystem.
700 citations
"Adaptive Multiservice Heterogeneous..." refers background in this paper
...become the basic technology of 5G network [37]–[41]....
[...]
...In addition, according to ETSI’s white paper “Mobile Edge Computing—A Key Technology toward 5G,” the GTI’s “5G MEC White Paper,” the IMT-2020 promotion group’s “5G Network White Paper” [1], [37]–[41], MEC is one of the core capabilities of 5G and is the key to achieving 5G performance improvement....
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TL;DR: This survey paper investigates the key rationale, the state-of-the-art efforts, the key enabling technologies and research topics, and typical IoT applications benefiting from edge cloud.
Abstract: The Internet is evolving rapidly toward the future Internet of Things (IoT) which will potentially connect billions or even trillions of edge devices which could generate huge amount of data at a very high speed and some of the applications may require very low latency. The traditional cloud infrastructure will run into a series of difficulties due to centralized computation, storage, and networking in a small number of datacenters, and due to the relative long distance between the edge devices and the remote datacenters. To tackle this challenge, edge cloud and edge computing seem to be a promising possibility which provides resources closer to the resource-poor edge IoT devices and potentially can nurture a new IoT innovation ecosystem. Such prospect is enabled by a series of emerging technologies, including network function virtualization and software defined networking. In this survey paper, we investigate the key rationale, the state-of-the-art efforts, the key enabling technologies and research topics, and typical IoT applications benefiting from edge cloud. We aim to draw an overall picture of both ongoing research efforts and future possible research directions through comprehensive discussions.
563 citations
"Adaptive Multiservice Heterogeneous..." refers background in this paper
..., guarantee edge terminals/users to access the most suitable network so as to build up the reliable connection between edge users and edge cloud according to their individual requirements [4]....
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