The Experts below are selected from a list of 3936 Experts worldwide ranked by ideXlab platform

Lichao Yang - One of the best experts on this subject based on the ideXlab platform.

  • A Distributed Computation Offloading Strategy in Small-Cell Networks Integrated With Mobile Edge Computing
    IEEE ACM Transactions on Networking, 2018
    Co-Authors: Lichao Yang, Heli Zhang, Xi Li, Hong Ji, Victor C. M. Leung
    Abstract:

    Mobile edge Computing is conceived as an appealing technology to enhance Cloud Computing Capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks' high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.

  • GLOBECOM Workshops - Decentralized Computation Offloading in Mobile Edge Computing Empowered Small-Cell Networks
    2017 IEEE Globecom Workshops (GC Wkshps), 2017
    Co-Authors: Jun Guo, Heli Zhang, Lichao Yang
    Abstract:

    Mobile edge Computing (MEC) is a key technology to provide Cloud-Computing Capability in close proximity to mobile devices at the edge of networks. In this paper, we propose a distributed computation offloading strategy for small- cell networks integrated with MEC. Firstly, we formulate a distributed computation offloading problem, where we minimize the overhead of each mobile device. Secondly, in order to satisfy the interests requirements of different small cells and analyze the interactions among multiple small cells, we reformulate the proposed problem as a potential game and prove it can achieve the Nash equilibrium(NE). Then, we design a potential game based offloading algorithm to lower the overhead of the system, which jointly optimizes energy consumption and latency of each mobile device. In addition, the lower bound of the iteration times is also derived. Finally, simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each mobile device compared with different other existing algorithms.

Sudarshan Mukherjee - One of the best experts on this subject based on the ideXlab platform.

  • Edge Computing-Enabled Cell-Free Massive MIMO Systems
    IEEE Transactions on Wireless Communications, 2020
    Co-Authors: Sudarshan Mukherjee
    Abstract:

    Mobile edge Computing (MEC) has been introduced to provide additional Computing capabilities at network edges in order to improve performance of latency critical applications. In this paper, we consider the cell-free (CF) massive MIMO framework with implementing MEC functionalities. We consider multiple types of users with different average time requirements for Computing/processing the tasks, and consider access points (APs) with MEC servers and a central server (CS) with the Cloud Computing Capability. After deriving successful communication and Computing probabilities using stochastic geometry and queueing theory, we present the successful edge Computing probability (SECP) for a target computation latency. Through numerical results, we also analyze the impact of the AP coverage and the offloading probability to the CS on the SECP. It is observed that the optimal probability of offloading to the CS in terms of the SECP decreases with the AP coverage. Finally, we numerically characterize the minimum required energy consumption for guaranteeing a desired level of SECP. It is observed that for any desired level of SECP, it is more energy efficient to have larger number of APs as compared to having more number of antennas at each AP with smaller AP density.

Jun Guo - One of the best experts on this subject based on the ideXlab platform.

  • GLOBECOM Workshops - Decentralized Computation Offloading in Mobile Edge Computing Empowered Small-Cell Networks
    2017 IEEE Globecom Workshops (GC Wkshps), 2017
    Co-Authors: Jun Guo, Heli Zhang, Lichao Yang
    Abstract:

    Mobile edge Computing (MEC) is a key technology to provide Cloud-Computing Capability in close proximity to mobile devices at the edge of networks. In this paper, we propose a distributed computation offloading strategy for small- cell networks integrated with MEC. Firstly, we formulate a distributed computation offloading problem, where we minimize the overhead of each mobile device. Secondly, in order to satisfy the interests requirements of different small cells and analyze the interactions among multiple small cells, we reformulate the proposed problem as a potential game and prove it can achieve the Nash equilibrium(NE). Then, we design a potential game based offloading algorithm to lower the overhead of the system, which jointly optimizes energy consumption and latency of each mobile device. In addition, the lower bound of the iteration times is also derived. Finally, simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each mobile device compared with different other existing algorithms.

Heli Zhang - One of the best experts on this subject based on the ideXlab platform.

  • A Distributed Computation Offloading Strategy in Small-Cell Networks Integrated With Mobile Edge Computing
    IEEE ACM Transactions on Networking, 2018
    Co-Authors: Lichao Yang, Heli Zhang, Xi Li, Hong Ji, Victor C. M. Leung
    Abstract:

    Mobile edge Computing is conceived as an appealing technology to enhance Cloud Computing Capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks' high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.

  • GLOBECOM Workshops - Decentralized Computation Offloading in Mobile Edge Computing Empowered Small-Cell Networks
    2017 IEEE Globecom Workshops (GC Wkshps), 2017
    Co-Authors: Jun Guo, Heli Zhang, Lichao Yang
    Abstract:

    Mobile edge Computing (MEC) is a key technology to provide Cloud-Computing Capability in close proximity to mobile devices at the edge of networks. In this paper, we propose a distributed computation offloading strategy for small- cell networks integrated with MEC. Firstly, we formulate a distributed computation offloading problem, where we minimize the overhead of each mobile device. Secondly, in order to satisfy the interests requirements of different small cells and analyze the interactions among multiple small cells, we reformulate the proposed problem as a potential game and prove it can achieve the Nash equilibrium(NE). Then, we design a potential game based offloading algorithm to lower the overhead of the system, which jointly optimizes energy consumption and latency of each mobile device. In addition, the lower bound of the iteration times is also derived. Finally, simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each mobile device compared with different other existing algorithms.

Branka Vucetic - One of the best experts on this subject based on the ideXlab platform.

  • Online Learning Enabled Task Offloading for Vehicular Edge Computing
    IEEE Wireless Communications Letters, 2020
    Co-Authors: Rui Zhang, Peng Cheng, Zhuo Chen, Sige Liu, Branka Vucetic
    Abstract:

    Vehicular edge Computing pushes the Cloud Computing Capability to the distributed network edge nodes, enabling computation-intensive and latency-sensitive Computing services for smart vehicles through task offloading. However, the inherent mobility introduces fast variation of network structure, which are usually unknown a priori . In this letter, we formulate the vehicular task offloading as a mortal multi-armed bandit problem, and develop a new online algorithm to enable distributed decision making on the node selection. The key is to exploit the contextual information of edge nodes and transform the infinite exploration space to a finite one. Theoretically, we prove that the proposed algorithm has a sublinear learning regret. Simulation results verify its effectiveness.