The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Pan Zhou - One of the best experts on this subject based on the ideXlab platform.
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collaborative service placement for Edge computing in dense small cell networks
IEEE Transactions on Mobile Computing, 2021Co-Authors: Lixing Chen, Cong Shen, Pan ZhouAbstract:Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service latency and saving backhaul network bandwidth. Although computation offloading for MEC has been extensively studied in the literature, service placement is an equally important design topic yet receives much less attention. Service placement refers to configuring service platforms and storing the related libraries/databases at the Edge Server. Due to the limited computing resource, the Edge Server can host only a small number of services and hence which services to host must be judiciously decided to maximize the system performance. We investigate collaborative service placement in MEC-enabled small-cell networks. A decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where MEC-enabled base stations (BS) optimize service placement decision collaboratively to handle service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small-cell network. CSP significantly improves time efficiency and guarantees convergence and optimality. CSP is extended to work with selfish BSs, where BSs can choose "to cooperate" or "not to cooperate". We employ coalitional game to design a coalition formation scheme that produces stable BS coalitions.
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Collaborative Service Placement for Edge Computing in Dense Small Cell Networks
IEEE Transactions on Mobile Computing, 2021Co-Authors: Lixing Chen, Cong Shen, Pan ZhouAbstract:Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading for MEC systems has been extensively studied in the literature, service placement is an equally, if not more, important design topic of MEC, yet receives much less attention. Service placement refers to configuring the service platform and storing the related libraries/databases at the Edge Server, e.g., MEC-enabled Base Station (BS), which enables corresponding computation tasks to be executed. Due to the limited computing resource, the Edge Server can host only a small number of services and hence which services to host has to be judiciously decided to maximize the system performance. In this paper, we investigate collaborative service placement in MEC-enabled dense small cell networks. An efficient decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where a network of small cell BSs optimize service placement decisions collaboratively to address a number of challenges in MEC systems, including service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small cell network. The algorithm significantly improves the time efficiency compared to conventional Gibbs sampling, yet guarantees provable convergence and optimality. CSP is further extended to work with selfish BSs, where BSs are allowed to choose “to cooperate” or “not to cooperate.” We employ coalitional game to investigate the strategic behaviors of selfish BSs and design a coalition formation scheme to form stable BS coalitions using merge-and-split rules. Simulations results show that CSP can effectively reduce Edge system operational cost for both cooperative and selfish BSs.
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Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks
Proceedings - IEEE INFOCOM, 2018Co-Authors: Jie Xu, Lixing Chen, Pan ZhouAbstract:Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the network Edge, thereby meeting the latency requirements of many emerging mobile applications and saving backhaul network bandwidth. Although many existing works have studied computation offloading policies, service caching is an equally, if not more important, design topic of MEC, yet receives much less attention. Service caching refers to caching application services and their related databases/libraries in the Edge Server (e.g. MEC-enabled BS), thereby enabling corresponding computation tasks to be executed. Because only a small number of application services can be cached in resource-limited Edge Server at the same time, which services to cache has to be judiciously decided to maximize the Edge computing performance. In this paper, we investigate the extremely compelling but much less studied problem of dynamic service caching in MEC-enabled dense cellular networks. We propose an efficient online algorithm, called OREO, which jointly optimizes dynamic service caching and task offloading to address a number of key challenges in MEC systems, including service heterogeneity, unknown system dynamics, spatial demand coupling and decentralized coordination. Our algorithm is developed based on Lyapunov optimization and Gibbs sampling, works online without requiring future information, and achieves provable close-to-optimal performance. Simulation results show that our algorithm can effectively reduce computation latency for end users while keeping energy consumption low.
Lixing Chen - One of the best experts on this subject based on the ideXlab platform.
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collaborative service placement for Edge computing in dense small cell networks
IEEE Transactions on Mobile Computing, 2021Co-Authors: Lixing Chen, Cong Shen, Pan ZhouAbstract:Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service latency and saving backhaul network bandwidth. Although computation offloading for MEC has been extensively studied in the literature, service placement is an equally important design topic yet receives much less attention. Service placement refers to configuring service platforms and storing the related libraries/databases at the Edge Server. Due to the limited computing resource, the Edge Server can host only a small number of services and hence which services to host must be judiciously decided to maximize the system performance. We investigate collaborative service placement in MEC-enabled small-cell networks. A decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where MEC-enabled base stations (BS) optimize service placement decision collaboratively to handle service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small-cell network. CSP significantly improves time efficiency and guarantees convergence and optimality. CSP is extended to work with selfish BSs, where BSs can choose "to cooperate" or "not to cooperate". We employ coalitional game to design a coalition formation scheme that produces stable BS coalitions.
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Collaborative Service Placement for Edge Computing in Dense Small Cell Networks
IEEE Transactions on Mobile Computing, 2021Co-Authors: Lixing Chen, Cong Shen, Pan ZhouAbstract:Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading for MEC systems has been extensively studied in the literature, service placement is an equally, if not more, important design topic of MEC, yet receives much less attention. Service placement refers to configuring the service platform and storing the related libraries/databases at the Edge Server, e.g., MEC-enabled Base Station (BS), which enables corresponding computation tasks to be executed. Due to the limited computing resource, the Edge Server can host only a small number of services and hence which services to host has to be judiciously decided to maximize the system performance. In this paper, we investigate collaborative service placement in MEC-enabled dense small cell networks. An efficient decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where a network of small cell BSs optimize service placement decisions collaboratively to address a number of challenges in MEC systems, including service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small cell network. The algorithm significantly improves the time efficiency compared to conventional Gibbs sampling, yet guarantees provable convergence and optimality. CSP is further extended to work with selfish BSs, where BSs are allowed to choose “to cooperate” or “not to cooperate.” We employ coalitional game to investigate the strategic behaviors of selfish BSs and design a coalition formation scheme to form stable BS coalitions using merge-and-split rules. Simulations results show that CSP can effectively reduce Edge system operational cost for both cooperative and selfish BSs.
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Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks
Proceedings - IEEE INFOCOM, 2018Co-Authors: Jie Xu, Lixing Chen, Pan ZhouAbstract:Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the network Edge, thereby meeting the latency requirements of many emerging mobile applications and saving backhaul network bandwidth. Although many existing works have studied computation offloading policies, service caching is an equally, if not more important, design topic of MEC, yet receives much less attention. Service caching refers to caching application services and their related databases/libraries in the Edge Server (e.g. MEC-enabled BS), thereby enabling corresponding computation tasks to be executed. Because only a small number of application services can be cached in resource-limited Edge Server at the same time, which services to cache has to be judiciously decided to maximize the Edge computing performance. In this paper, we investigate the extremely compelling but much less studied problem of dynamic service caching in MEC-enabled dense cellular networks. We propose an efficient online algorithm, called OREO, which jointly optimizes dynamic service caching and task offloading to address a number of key challenges in MEC systems, including service heterogeneity, unknown system dynamics, spatial demand coupling and decentralized coordination. Our algorithm is developed based on Lyapunov optimization and Gibbs sampling, works online without requiring future information, and achieves provable close-to-optimal performance. Simulation results show that our algorithm can effectively reduce computation latency for end users while keeping energy consumption low.
Hai Jin - One of the best experts on this subject based on the ideXlab platform.
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online collaborative data caching in Edge computing
IEEE Transactions on Parallel and Distributed Systems, 2021Co-Authors: Xiaoyu Xia, Feifei Chen, John Grundy, Mohamed Abdelrazek, Hai JinAbstract:In the Edge computing (EC) environment, Edge Servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on Edge Servers can ensure low latency in app users’ retrieval of app data. However, an Edge Server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative Edge data caching problem (CEDC) as a constrained optimization problem and prove that it is $\mathcal {NP}$ NP -complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.
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trading off between user coverage and network robustness for Edge Server placement
IEEE Transactions on Cloud Computing, 2020Co-Authors: Guangming Cui, Feifei Chen, Hai Jin, Yun YangAbstract:Edge Cloud Computing (ECC) provides a new paradigm for app vendors to serve their users with low latency by deploying their services on Edge Servers in close proximity to mobile users. From the Edge infrastructure provider's perspective, a cost-effective k-Edge Server placement aims to place k-Edge Servers within a particular geographic area to maximize the number of covered mobile users (i.e., user coverage). However, in the distributed and volatile ECC environment, Edge Servers are subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. Users connected to a failed Edge Server have to access services in the remote cloud if they are not covered by any other Edge Servers. This significantly impacts users quality of experience. Thus, the robustness of the Edge Server network (i.e., network robustness) must be considered. In this paper, we formally model this joint user coverage and network robustness oriented k-Edge Server placement (kESP-CR) problem, and prove that finding the optimal solution to this problem is NP-hard. To tackle this kESP-CR problem, we propose an integer programming-based optimal approach for the small-scale kESP-CR problems and approximation approach for the large-scale kESP-CR problem. Finally, extensive experiments are conducted to evaluate their performance.
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robustness oriented k Edge Server placement
Cluster Computing and the Grid, 2020Co-Authors: Guangming Cui, Feifei Chen, Hai Jin, Xiaoyu Xia, Yun YangAbstract:Mobile Edge Computing (MEC) is an emerging and prospective computing paradigm that supports low-latency content delivery. In a MEC environment, Edge Servers are attached to base stations or access points in closer proximity to end-users to reduce the end-to-end latency in their access to online content. From an Edge infrastructure provider’s perspective, a cost-effective k Edge Server placement (kESP) places k Edge Servers within a particular geographic area to maximize their coverage. However, in the distributed MEC environment, Edge Servers are often subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. End-users connected to a failed Edge Server have to access online content from the remote cloud if they are not covered by any other Edge Servers. This significantly jeopardizes end-users’ quality of experience. Thus, the robustness of an Edge Server network must be considered in Edge Server placement. In this paper, we formally model this Robustness-oriented k Edge Server Placement (RkESP) problem, and prove that finding the optimal solution to this problem is $\mathcal{N}\mathcal{P}$-hard. Thus, we firstly propose an integer programming based optimal approach, namely Opt, to find optimal solutions to small-scale RkESP problems. Then, we propose an approximate approach, namely Approx, for solving large-scale RkESP problems efficiently with an O(k)-approximation ratio. Finally, the performance of the two approaches is experimentally evaluated against five state-of-the-art approaches on a real-world dataset and a large-scale synthesized dataset.
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Constrained App Data Caching over Edge Server Graphs in Edge Computing Environment
IEEE Transactions on Services Computing, 1Co-Authors: Xiaoyu Xia, Feifei Chen, John Grundy, Mohamed Abdelrazek, Hai JinAbstract:In recent years, Edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the Edge computing environment, Edge Servers are deployed at base stations to offer highly-accessible computing capacities to nearby end-users, e.g., CPU, RAM, storage, etc. From a service provider's perspective, caching app data on Edge Servers can ensure low latency in its users' data retrieval. Given constrained cache spaces on Edge Servers due to their physical sizes, the optimal data caching strategy must minimize overall user latency. In this paper, we formulate this Constrained Edge Data Caching (CEDC) problem as a constrained optimization problem from the service provider's perspective and prove its NP-hardness. We propose an optimal approach named CEDC-IP to solve this CEDC problem exactly with the Integer Programming technique. We also provide an approximation algorithm named CEDC-A for finding approximate solutions to large-scale CEDC problems efficiently and prove its approximation ratio. CEDC-IP and CEDC-A are evaluated on a real-world data set and a synthesized data set. The results demonstrate that they significantly outperform four representative approaches.
Feifei Chen - One of the best experts on this subject based on the ideXlab platform.
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online collaborative data caching in Edge computing
IEEE Transactions on Parallel and Distributed Systems, 2021Co-Authors: Xiaoyu Xia, Feifei Chen, John Grundy, Mohamed Abdelrazek, Hai JinAbstract:In the Edge computing (EC) environment, Edge Servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on Edge Servers can ensure low latency in app users’ retrieval of app data. However, an Edge Server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative Edge data caching problem (CEDC) as a constrained optimization problem and prove that it is $\mathcal {NP}$ NP -complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.
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trading off between user coverage and network robustness for Edge Server placement
IEEE Transactions on Cloud Computing, 2020Co-Authors: Guangming Cui, Feifei Chen, Hai Jin, Yun YangAbstract:Edge Cloud Computing (ECC) provides a new paradigm for app vendors to serve their users with low latency by deploying their services on Edge Servers in close proximity to mobile users. From the Edge infrastructure provider's perspective, a cost-effective k-Edge Server placement aims to place k-Edge Servers within a particular geographic area to maximize the number of covered mobile users (i.e., user coverage). However, in the distributed and volatile ECC environment, Edge Servers are subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. Users connected to a failed Edge Server have to access services in the remote cloud if they are not covered by any other Edge Servers. This significantly impacts users quality of experience. Thus, the robustness of the Edge Server network (i.e., network robustness) must be considered. In this paper, we formally model this joint user coverage and network robustness oriented k-Edge Server placement (kESP-CR) problem, and prove that finding the optimal solution to this problem is NP-hard. To tackle this kESP-CR problem, we propose an integer programming-based optimal approach for the small-scale kESP-CR problems and approximation approach for the large-scale kESP-CR problem. Finally, extensive experiments are conducted to evaluate their performance.
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robustness oriented k Edge Server placement
Cluster Computing and the Grid, 2020Co-Authors: Guangming Cui, Feifei Chen, Hai Jin, Xiaoyu Xia, Yun YangAbstract:Mobile Edge Computing (MEC) is an emerging and prospective computing paradigm that supports low-latency content delivery. In a MEC environment, Edge Servers are attached to base stations or access points in closer proximity to end-users to reduce the end-to-end latency in their access to online content. From an Edge infrastructure provider’s perspective, a cost-effective k Edge Server placement (kESP) places k Edge Servers within a particular geographic area to maximize their coverage. However, in the distributed MEC environment, Edge Servers are often subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. End-users connected to a failed Edge Server have to access online content from the remote cloud if they are not covered by any other Edge Servers. This significantly jeopardizes end-users’ quality of experience. Thus, the robustness of an Edge Server network must be considered in Edge Server placement. In this paper, we formally model this Robustness-oriented k Edge Server Placement (RkESP) problem, and prove that finding the optimal solution to this problem is $\mathcal{N}\mathcal{P}$-hard. Thus, we firstly propose an integer programming based optimal approach, namely Opt, to find optimal solutions to small-scale RkESP problems. Then, we propose an approximate approach, namely Approx, for solving large-scale RkESP problems efficiently with an O(k)-approximation ratio. Finally, the performance of the two approaches is experimentally evaluated against five state-of-the-art approaches on a real-world dataset and a large-scale synthesized dataset.
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Constrained App Data Caching over Edge Server Graphs in Edge Computing Environment
IEEE Transactions on Services Computing, 1Co-Authors: Xiaoyu Xia, Feifei Chen, John Grundy, Mohamed Abdelrazek, Hai JinAbstract:In recent years, Edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the Edge computing environment, Edge Servers are deployed at base stations to offer highly-accessible computing capacities to nearby end-users, e.g., CPU, RAM, storage, etc. From a service provider's perspective, caching app data on Edge Servers can ensure low latency in its users' data retrieval. Given constrained cache spaces on Edge Servers due to their physical sizes, the optimal data caching strategy must minimize overall user latency. In this paper, we formulate this Constrained Edge Data Caching (CEDC) problem as a constrained optimization problem from the service provider's perspective and prove its NP-hardness. We propose an optimal approach named CEDC-IP to solve this CEDC problem exactly with the Integer Programming technique. We also provide an approximation algorithm named CEDC-A for finding approximate solutions to large-scale CEDC problems efficiently and prove its approximation ratio. CEDC-IP and CEDC-A are evaluated on a real-world data set and a synthesized data set. The results demonstrate that they significantly outperform four representative approaches.
Cong Shen - One of the best experts on this subject based on the ideXlab platform.
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collaborative service placement for Edge computing in dense small cell networks
IEEE Transactions on Mobile Computing, 2021Co-Authors: Lixing Chen, Cong Shen, Pan ZhouAbstract:Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service latency and saving backhaul network bandwidth. Although computation offloading for MEC has been extensively studied in the literature, service placement is an equally important design topic yet receives much less attention. Service placement refers to configuring service platforms and storing the related libraries/databases at the Edge Server. Due to the limited computing resource, the Edge Server can host only a small number of services and hence which services to host must be judiciously decided to maximize the system performance. We investigate collaborative service placement in MEC-enabled small-cell networks. A decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where MEC-enabled base stations (BS) optimize service placement decision collaboratively to handle service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small-cell network. CSP significantly improves time efficiency and guarantees convergence and optimality. CSP is extended to work with selfish BSs, where BSs can choose "to cooperate" or "not to cooperate". We employ coalitional game to design a coalition formation scheme that produces stable BS coalitions.
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Collaborative Service Placement for Edge Computing in Dense Small Cell Networks
IEEE Transactions on Mobile Computing, 2021Co-Authors: Lixing Chen, Cong Shen, Pan ZhouAbstract:Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading for MEC systems has been extensively studied in the literature, service placement is an equally, if not more, important design topic of MEC, yet receives much less attention. Service placement refers to configuring the service platform and storing the related libraries/databases at the Edge Server, e.g., MEC-enabled Base Station (BS), which enables corresponding computation tasks to be executed. Due to the limited computing resource, the Edge Server can host only a small number of services and hence which services to host has to be judiciously decided to maximize the system performance. In this paper, we investigate collaborative service placement in MEC-enabled dense small cell networks. An efficient decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where a network of small cell BSs optimize service placement decisions collaboratively to address a number of challenges in MEC systems, including service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small cell network. The algorithm significantly improves the time efficiency compared to conventional Gibbs sampling, yet guarantees provable convergence and optimality. CSP is further extended to work with selfish BSs, where BSs are allowed to choose “to cooperate” or “not to cooperate.” We employ coalitional game to investigate the strategic behaviors of selfish BSs and design a coalition formation scheme to form stable BS coalitions using merge-and-split rules. Simulations results show that CSP can effectively reduce Edge system operational cost for both cooperative and selfish BSs.
