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Hongzhi Guo - One of the best experts on this subject based on the ideXlab platform.
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Computation Offloading for multi access mobile edge computing in ultra dense networks
IEEE Communications Magazine, 2018Co-Authors: Hongzhi Guo, Jiajia Liu, Jing ZhangAbstract:The ultra-dense network (UDN) is envisioned to be an enabling and highly promising technology to enhance spatial multiplexing and network capacity in future 5G networks. Moreover, to address the conflict between Computation-intensive applications and resource-constrained IoT mobile devices (MDs), multi-access mobile edge computing (MA-MEC), which provides the IoT MDs with cloud capabilities at the edge of radio access networks, has been proposed. UDN and MA-MEC are regarded as two distinct but complementary enabling technologies for 5G IoT applications. Over the past several years, lots of research on mobile edge Computation Offloading (MECO) -- the key technique in MA-MEC -- has emerged. However, it is noticed that all these works focused on the single-tier base station scenario and Computation Offloading between the MD and the MEC server connected to the macro base station, and few works can be found on the problem of Computation Offloading for MA-MEC in UDN (i.e., a multi-user ultra-dense MEC server scenario). Toward this end, we study in this article the MECO problem in UDN and propose a heuristic greedy Offloading scheme as our solution. Extensive numerical results and comparisons demonstrate the necessity for and superior performance of conducting Computation Offloading over multiple MEC servers.
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mobile edge Computation Offloading for ultradense iot networks
IEEE Internet of Things Journal, 2018Co-Authors: Hongzhi Guo, Jiajia Liu, Jie Zhang, Wen Sun, Nei KatoAbstract:The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By Offloading the IoT MDs’ Computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge Computation Offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and Computation Offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy Offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting Computation Offloading among multiple edge servers in ultradense IoT networks.
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collaborative mobile edge Computation Offloading for iot over fiber wireless networks
IEEE Network, 2018Co-Authors: Hongzhi Guo, Jiajia Liu, Huiling QinAbstract:Mobile edge computing is envisioned to be a promising paradigm to address the conflict between Computationally intensive IoT applications and resource-constrained lightweight mobile devices. However, most existing research on mobile edge Computation Offloading has only taken the resource allocation between the mobile devices and the MEC servers into consideration, ignoring the huge Computation resources in the centralized cloud computing center. To make full use of the centralized cloud and distributed MEC resources, designing a collaborative Computation Offloading mechanism becomes particularly important. Note that current MEC hosted networks, which mostly adopt the networking technology integrating cellular and core networks, face new challenges of single networking mode, long latency, poor reliability, high congestion, and high energy consumption. Hybrid fiber-wireless networks integrating both low-latency fiber optic and flexible wireless technologies should be a promising solution. Toward this end, we provide in this article a generic fiber-wireless architecture with coexistence of centralized cloud and distributed MEC for IoT connectivity. The problem of cloud-MEC collaborative Computation Offloading is defined, and a game-theoretic collaborative Computation Offloading scheme is proposed as our solution. Numerical results corroborate that our proposed scheme can achieve high energy efficiency and scales well as the number of mobile devices increases.
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collaborative Computation Offloading for multiaccess edge computing over fiber wireless networks
IEEE Transactions on Vehicular Technology, 2018Co-Authors: Hongzhi Guo, Jiajia LiuAbstract:By Offloading the Computation tasks of the mobile devices (MDs) to the edge server, mobile-edge computing (MEC) provides a new paradigm to meet the increasing Computation demands from mobile applications. However, existing mobile-edge Computation Offloading (MECO) research only took the resource allocation between the MDs and the MEC servers into consideration, and ignored the huge Computation resources in the centralized cloud computing center. Moreover, current MEC hosted networks mostly adopt the networking technology integrating cellular and backbone networks, which have the shortcomings of single access mode, high congestion, high latency, and high energy consumption. Toward this end, we introduce hybrid fiber–wireless (FiWi) networks to provide supports for the coexistence of centralized cloud and multiaccess edge computing, and present an architecture by adopting the FiWi access networks. The problem of cloud-MEC collaborative Computation Offloading is studied, and two schemes are proposed as our solutions, i.e., an approximation collaborative Computation Offloading scheme, and a game-theoretic collaborative Computation Offloading scheme. Numerical results corroborate that our solutions not only achieve better Offloading performance than the available MECO schemes but also scale well with the increasing number of Computation tasks.
Xu Chen - One of the best experts on this subject based on the ideXlab platform.
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an efficient social aware Computation Offloading algorithm in cloudlet system
Global Communications Conference, 2016Co-Authors: Ling Tang, Xu ChenAbstract:Cloudlet is a new paradigm in mobile cloud computing to provide resources to nearby mobile users via one-hop wireless connections. In this study, we leverage the social tie structure among mobile users to achieve mutual-beneficial Computation Offloading decision making and hence enhance the system-wide performance.Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to offload or not as a social-aware Computation Offloading game (COG). We analyze the structural property of the SGUM-based COG (SCOG) and show that there exists a social-aware Nash equilibrium (SNE). We then design a distributed Computation Offloading algorithm that can achieve the SNE of SCOG and quantify its performance gap with respect to the social optimal solution. Numerical results show that the Computation Offloading performance can be significantly enhanced by leveraging the social ties among the users.
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when social network meets mobile cloud a social group utility approach for optimizing Computation Offloading in cloudlet
IEEE Access, 2016Co-Authors: Ling Tang, Xu ChenAbstract:Cloudlet is a new paradigm in mobile cloud computing to provide resources to nearby mobile users via one-hop wireless connections. In this paper, we leverage the social tie structure among mobile users to achieve mutually beneficial Computation Offloading decision making, and hence, enhance the system-wide performance. Drawing on a social group utility maximization (SGUM) framework, we cast users’ decision making of whether to offload or not as a Socially aware Computation Offloading game (COG). We study the SGUM-based COG for both strong and weak information cases. For the strong information case, where each user has the knowledge of other users’ actions and the perfect observation of its achieved social group utility, we show that there exists a socially aware Nash equilibrium (SNE). We then design a distributed algorithm to achieve the SNE and quantify its performance gap with respect to the social optimal solution. For the weak information case, where each user does not have the knowledge of other users’ actions and observes a noise-corrupted social group utility, we develop a distributed reinforcement learning algorithm, which is shown to converge almost surely to an $\epsilon $ -SNE. The numerical results show that the Computation Offloading performance can be significantly enhanced by leveraging the social ties among the users.
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Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing
IEEE ACM Transactions on Networking, 2016Co-Authors: Xu Chen, Lei Jiao, Wenzhong Li, Xiaoming FuAbstract: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.
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decentralized Computation Offloading game for mobile cloud computing
arXiv: Networking and Internet Architecture, 2014Co-Authors: Xu ChenAbstract:Mobile cloud computing is envisioned as a promising approach to augment Computation capabilities of mobile devices for emerging resource-hungry mobile applications. In this paper, we propose a game theoretic approach for achieving efficient Computation Offloading for mobile cloud computing. We formulate the decentralized Computation Offloading decision making problem among mobile device users as a decentralized Computation Offloading game. We analyze the structural property of the game and show that the game always admits a Nash equilibrium. We then design a decentralized Computation Offloading mechanism that can achieve a Nash equilibrium of the game and quantify its efficiency ratio over the centralized optimal solution. Numerical results demonstrate that the proposed mechanism can achieve efficient Computation Offloading performance and scale well as the system size increases.
Jiajia Liu - One of the best experts on this subject based on the ideXlab platform.
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Computation Offloading for multi access mobile edge computing in ultra dense networks
IEEE Communications Magazine, 2018Co-Authors: Hongzhi Guo, Jiajia Liu, Jing ZhangAbstract:The ultra-dense network (UDN) is envisioned to be an enabling and highly promising technology to enhance spatial multiplexing and network capacity in future 5G networks. Moreover, to address the conflict between Computation-intensive applications and resource-constrained IoT mobile devices (MDs), multi-access mobile edge computing (MA-MEC), which provides the IoT MDs with cloud capabilities at the edge of radio access networks, has been proposed. UDN and MA-MEC are regarded as two distinct but complementary enabling technologies for 5G IoT applications. Over the past several years, lots of research on mobile edge Computation Offloading (MECO) -- the key technique in MA-MEC -- has emerged. However, it is noticed that all these works focused on the single-tier base station scenario and Computation Offloading between the MD and the MEC server connected to the macro base station, and few works can be found on the problem of Computation Offloading for MA-MEC in UDN (i.e., a multi-user ultra-dense MEC server scenario). Toward this end, we study in this article the MECO problem in UDN and propose a heuristic greedy Offloading scheme as our solution. Extensive numerical results and comparisons demonstrate the necessity for and superior performance of conducting Computation Offloading over multiple MEC servers.
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mobile edge Computation Offloading for ultradense iot networks
IEEE Internet of Things Journal, 2018Co-Authors: Hongzhi Guo, Jiajia Liu, Jie Zhang, Wen Sun, Nei KatoAbstract:The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By Offloading the IoT MDs’ Computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge Computation Offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and Computation Offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy Offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting Computation Offloading among multiple edge servers in ultradense IoT networks.
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collaborative mobile edge Computation Offloading for iot over fiber wireless networks
IEEE Network, 2018Co-Authors: Hongzhi Guo, Jiajia Liu, Huiling QinAbstract:Mobile edge computing is envisioned to be a promising paradigm to address the conflict between Computationally intensive IoT applications and resource-constrained lightweight mobile devices. However, most existing research on mobile edge Computation Offloading has only taken the resource allocation between the mobile devices and the MEC servers into consideration, ignoring the huge Computation resources in the centralized cloud computing center. To make full use of the centralized cloud and distributed MEC resources, designing a collaborative Computation Offloading mechanism becomes particularly important. Note that current MEC hosted networks, which mostly adopt the networking technology integrating cellular and core networks, face new challenges of single networking mode, long latency, poor reliability, high congestion, and high energy consumption. Hybrid fiber-wireless networks integrating both low-latency fiber optic and flexible wireless technologies should be a promising solution. Toward this end, we provide in this article a generic fiber-wireless architecture with coexistence of centralized cloud and distributed MEC for IoT connectivity. The problem of cloud-MEC collaborative Computation Offloading is defined, and a game-theoretic collaborative Computation Offloading scheme is proposed as our solution. Numerical results corroborate that our proposed scheme can achieve high energy efficiency and scales well as the number of mobile devices increases.
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collaborative Computation Offloading for multiaccess edge computing over fiber wireless networks
IEEE Transactions on Vehicular Technology, 2018Co-Authors: Hongzhi Guo, Jiajia LiuAbstract:By Offloading the Computation tasks of the mobile devices (MDs) to the edge server, mobile-edge computing (MEC) provides a new paradigm to meet the increasing Computation demands from mobile applications. However, existing mobile-edge Computation Offloading (MECO) research only took the resource allocation between the MDs and the MEC servers into consideration, and ignored the huge Computation resources in the centralized cloud computing center. Moreover, current MEC hosted networks mostly adopt the networking technology integrating cellular and backbone networks, which have the shortcomings of single access mode, high congestion, high latency, and high energy consumption. Toward this end, we introduce hybrid fiber–wireless (FiWi) networks to provide supports for the coexistence of centralized cloud and multiaccess edge computing, and present an architecture by adopting the FiWi access networks. The problem of cloud-MEC collaborative Computation Offloading is studied, and two schemes are proposed as our solutions, i.e., an approximation collaborative Computation Offloading scheme, and a game-theoretic collaborative Computation Offloading scheme. Numerical results corroborate that our solutions not only achieve better Offloading performance than the available MECO schemes but also scale well with the increasing number of Computation tasks.
Yan Zhang - One of the best experts on this subject based on the ideXlab platform.
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deep reinforcement learning for stochastic Computation Offloading in digital twin networks
arXiv: Learning, 2020Co-Authors: Yueyue Dai, Ke Zhang, Sabita Maharjan, Yan ZhangAbstract:The rapid development of Industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through Computation Offloading to reduce energy consumption while enhancing data processing efficiency. In this paper, we first propose a new paradigm Digital Twin Networks (DTN) to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic Computation Offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present Asynchronous Actor-Critic (AAC) algorithm to find the optimal stochastic Computation Offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.
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cooperative and distributed Computation Offloading for blockchain empowered industrial internet of things
IEEE Internet of Things Journal, 2019Co-Authors: Wuhui Chen, Sabita Maharjan, Zhen Zhang, Zicong Hong, Chuan Chen, Zibin Zheng, Yan ZhangAbstract:Offloading Computation-intensive blockchain mining tasks to the edge servers (ESs) is a promising solution for blockchain-empowered Industrial Internet of Things (IIoT) because the computing capabilities in IIoT are usually limited, whereas the blockchain mining tasks are Computationally intensive. However, the Computation Offloading solutions for data processing tasks and for blockchain mining tasks have been studied separately. Moreover, most of the existing solutions for Offloading assume that all IIoT devices can directly connect to the ESs or cloud data centers. To address these issues, in this paper, we propose a multihop cooperative and distributed Computation Offloading algorithm that considers the data processing tasks and the mining tasks together for blockchain-empowered IIoT. First, we study the multihop Computation Offloading problem for both the data processing tasks and the mining tasks to minimize the economic cost of IIoT devices. Second, we formulate the Offloading problem as a potential game in which the IIoT devices can make their decisions autonomously and prove the existence of Nash equilibrium (NE) for the game. Third, we design an efficient distributed algorithm based on exchanging messages between IIoT devices to achieve the NE with low Computational complexity. Lastly, our experimental results demonstrate that our distributed algorithm scales well as the number of IIoT devices increases and has the minimum system cost compared with other approaches.
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online learning and optimization for Computation Offloading in d2d edge computing and networks
Mobile Networks and Applications, 2019Co-Authors: Guanhua Qiao, Supeng Leng, Yan ZhangAbstract:This paper introduces a framework of device-to-device edge computing and networks (D2D-ECN), a new paradigm for Computation Offloading and data processing with a group of resource-rich devices towards collaborative optimization between communication and Computation. However, the Computation process of task intensive applications would be interrupted when capacity-limited battery energy run out. In order to tackle this issue, the D2D-ECN with energy harvesting technology is applied to provide a green Computation network and guarantee service continuity. Specifically, we design a reinforcement learning framework in a point-to-point Offloading system to overcome challenges of the dynamic nature and uncertainty of renewable energy, channel state and task generation rates. Furthermore, to cope with high-dimensionality and continuous-valued action of the Offloading system with multiple cooperating devices, we propose an online approach based on Lyapunov optimization for Computation Offloading and resource management without priori energy and network information. Numerical results demonstrate that our proposed scheme can reduce system operation cost with low task execution time in D2D-ECN.
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joint Computation Offloading and user association in multi task mobile edge computing
IEEE Transactions on Vehicular Technology, 2018Co-Authors: Yueyue Dai, Sabita Maharjan, Yan ZhangAbstract:Computation intensive and delay-sensitive applications impose severe requirements on mobile devices of providing required Computation capacity and ensuring latency. Mobile edge computing (MEC) is a promising technology that can alleviate Computation limitation of mobile users and prolong their lifetime through Computation Offloading. However, Computation Offloading in an MEC environment faces severe issues due to dense deployment of MEC servers. Moreover, a mobile user has multiple mutually dependent tasks, which make Offloading policy design even more challenging. To address the above-mentioned problems in this paper, we first propose a novel two-tier Computation Offloading framework in heterogeneous networks. Then, we formulate joint Computation Offloading and user association problem for multi-task mobile edge computing system to minimize overall energy consumption. To solve the optimization problem, we develop an efficient Computation Offloading algorithm by jointly optimizing user association and Computation Offloading where Computation resource allocation and transmission power allocation are also considered. Numerical results illustrate fast convergence of the proposed algorithm, and demonstrate the superior performance of our proposed algorithm compared to state of the art solutions.
Ling Tang - One of the best experts on this subject based on the ideXlab platform.
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Multi-User Computation Offloading in Mobile Edge Computing: A Behavioral Perspective
IEEE Network, 2018Co-Authors: Ling Tang, Shibo HeAbstract:By providing cloud computing capabilities at the network edge in proximity of mobile device users, mobile edge computing offers an effective solution to help mobile devices with Computation- intensive and delay-sensitive tasks. In this article, we investigate the multi-user Computation Offloading problem in an uncertain wireless environment. Most of the existing works assume that mobile device users are rational and make Offloading decisions to maximize their expected objective utilities. However, in practice, users tend to have subjective perceptions under uncertainty, such that their behavior deviates considerably from the conventional rationality assumption. Drawing on the framework of prospect theory (PT), we formulate users' decision making of whether to offload or not as a PT-based non-cooperative game. We propose a distributed Computation Offloading algorithm to achieve the Nash equilibrium of the game. Numerical results assess the impact of mobile device users' behavioral biases on Offloading decision making.
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an efficient social aware Computation Offloading algorithm in cloudlet system
Global Communications Conference, 2016Co-Authors: Ling Tang, Xu ChenAbstract:Cloudlet is a new paradigm in mobile cloud computing to provide resources to nearby mobile users via one-hop wireless connections. In this study, we leverage the social tie structure among mobile users to achieve mutual-beneficial Computation Offloading decision making and hence enhance the system-wide performance.Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to offload or not as a social-aware Computation Offloading game (COG). We analyze the structural property of the SGUM-based COG (SCOG) and show that there exists a social-aware Nash equilibrium (SNE). We then design a distributed Computation Offloading algorithm that can achieve the SNE of SCOG and quantify its performance gap with respect to the social optimal solution. Numerical results show that the Computation Offloading performance can be significantly enhanced by leveraging the social ties among the users.
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when social network meets mobile cloud a social group utility approach for optimizing Computation Offloading in cloudlet
IEEE Access, 2016Co-Authors: Ling Tang, Xu ChenAbstract:Cloudlet is a new paradigm in mobile cloud computing to provide resources to nearby mobile users via one-hop wireless connections. In this paper, we leverage the social tie structure among mobile users to achieve mutually beneficial Computation Offloading decision making, and hence, enhance the system-wide performance. Drawing on a social group utility maximization (SGUM) framework, we cast users’ decision making of whether to offload or not as a Socially aware Computation Offloading game (COG). We study the SGUM-based COG for both strong and weak information cases. For the strong information case, where each user has the knowledge of other users’ actions and the perfect observation of its achieved social group utility, we show that there exists a socially aware Nash equilibrium (SNE). We then design a distributed algorithm to achieve the SNE and quantify its performance gap with respect to the social optimal solution. For the weak information case, where each user does not have the knowledge of other users’ actions and observes a noise-corrupted social group utility, we develop a distributed reinforcement learning algorithm, which is shown to converge almost surely to an $\epsilon $ -SNE. The numerical results show that the Computation Offloading performance can be significantly enhanced by leveraging the social ties among the users.