The Experts below are selected from a list of 80931 Experts worldwide ranked by ideXlab platform
Yuan Liu - One of the best experts on this subject based on the ideXlab platform.
-
price based distributed offloading for mobile Edge computing with computation capacity constraints
2018Co-Authors: Mengyu Liu, Yuan LiuAbstract:Mobile-Edge computing is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to Network Edge. In this letter, we propose a price-based distributed method to manage the offloaded computation tasks from users. A Stackelberg game is formulated to model the interaction between the Edge cloud and users, where the Edge cloud sets prices to maximize its revenue subject to its finite computation capacity, and for given prices, each user locally makes offloading decision to minimize its own cost which is defined as latency plus payment. Depending on the Edge cloud’s knowlEdge of the Network information, we develop the uniform and differentiated pricing algorithms, which can both be implemented in distributed manners. Simulation results validate the effectiveness of the proposed schemes.
-
Price-Based Distributed Offloading for Mobile-Edge Computing with Computation Capacity Constraints
2018Co-Authors: Mengyu Liu, Yuan LiuAbstract:Mobile-Edge computing (MEC) is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to Network Edge.
Tarik Taleb - One of the best experts on this subject based on the ideXlab platform.
-
providing ultra short latency to user centric 5g applications at the mobile Network Edge
2018Co-Authors: Ivan Farris, Tarik Taleb, Hannu Flinck, Antonio IeraAbstract:Mobile Edge Computing (MEC) will play a key role in next-generation mobile Networks to extend the range of supported delay-sensitive applications. Furthermore, an increasing attention is paid to provide user-centric services, to better address the strict requirements of novel immersive applications. In this scenario, MEC solutions need to efficiently cope with user mobility, which requires fast relocation of service instances to guarantee the desired Quality of Experience. However, service migration is still an open issue, especially for resource-constrained Edge nodes interconnected by high-latency and low-bandwidth links. In this paper, by leveraging the potential of lightweight container-based virtualization techniques, we investigate a novel approach to support service provisioning in dynamic MEC environments. In particular, we present a framework where proactive service replication for stateless applications is exploited to drastically reduce the time of service migration between different cloudlets and to meet the latency requirements. The performance evaluation shows promising results of our approach with respect to classic reactive service migration.
-
Optimizing service replication for mobile delay-sensitive applications in 5G Edge Network
2017Co-Authors: Ivan Farris, Miloud Bagaa, Tarik Taleb, Hannu FlickAbstract:Extending cloud infrastructure to the Network Edge represents a breakthrough to support delay-sensitive applications in next 5G cellular systems. In this context, to enable ultrashort response times, fast relocation of service instances between Edge nodes is required to cope with user mobility. To face this issue, proactive service replication is considered a promising strategy to reduce the overall migration time and to guarantee the desired Quality of Experience (QoE). On the other hand, the provisioning of replicas over multiple Edge nodes increases the resource consumption of constrained Edge nodes and the relevant deployment cost. Given the two conflicting objectives, in this paper we investigate different optimization models for proactive service migration at the Network Edge, which can exploit prediction of user mobility patterns. In particular, we define two Integer Linear Problem optimization schemes, which aim at respectively minimizing the QoE degradation due to service migration, and the cost of replicas' deployment. Performance evaluation shows the effectiveness of our proposed solutions.
-
on multi access Edge computing a survey of the emerging 5g Network Edge cloud architecture and orchestration
2017Co-Authors: Tarik Taleb, Sunny Dutta, Badr Mada, Konstantinos Samdanis, Hannu Flinck, Dario SabellaAbstract:Multi-access Edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the Edge of the radio access Network. MEC offers storage and computational resources at the Edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core Networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a Network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.
-
Evaluating Performance of Containerized IoT Services for Clustered Devices at the Network Edge
2017Co-Authors: Roberto Morabito, Ivan Farris, Antonio Iera, Tarik TalebAbstract:The constant and fast increase in the number of heterogeneous Internet of Things (IoT) devices that populate everyday life environments brings new challenges to the full exploitation of the computation, memory, sensing, and actuation resources associated to them. In this context, device virtualization solutions and platforms may definitely play a key role in enabling the desired tradeoff between flexibility and performance. This paper focuses on lightweight virtualization technologies for IoT devices, suitably thought to effectively deploy new integrated applications and to create a novel distributed and virtualized ecosystem. Two different frameworks for container-based IoT service provisioning are compared, the one based on a direct interaction between two cooperating devices and the other based on the presence of a manager supervising the operations between cooperating devices forming a cluster. In the latter case, accounting for the growing impetus to move intelligence toward the Edge of the Network, management features are implemented at the Network access point to provide short latency responses. We also introduce the outcomes of a thorough performance evaluation campaign conducted via a real IoT testbed. The measurements, performed by accounting for the constraints of typical IoT nodes, shed light on the actual feasibility of container-based IoT frameworks.
Xu Chen - One of the best experts on this subject based on the ideXlab platform.
-
on demand privacy preservation for cost efficient Edge intelligence model training
2019Co-Authors: Zhi Zhou, Xu ChenAbstract:With the advancement of Internet-of-Things (IoT), enormous IoT data are generated at the Network Edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to Network Edge so as to fully unleash the potential of the IoT big data. To match this trend, Edge intelligence—an emerging paradigm that hosts AI applications at the Network Edge—is being recognized as a promising solution. While pilot efforts on Edge intelligence have mostly focused on facilitating efficient model inference at the Network Edge, the training of Edge intelligence model has been greatly overlooked. To bridge this gap, in this paper, we investigate how to coordinate the Edge and the cloud to train Edge intelligence model, with the goal of simultaneously optimizing the resource cost and preserving data privacy in an on-demand manner. Leveraging Lyapunov optimization theory, we design and analyze a cost-efficient optimization framework to make online decisions on training data scheduling to balance the tradeoff between cost efficiency and privacy preservation. With rigorous theoretical analysis, we verify the efficacy of the presented framework.
-
Edge intelligence paving the last mile of artificial intelligence with Edge computing
2019Co-Authors: Zhi Zhou, Xu Chen, Liekang Zeng, Ke Luo, Junshan ZhangAbstract:With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the Network Edge. Driving by this trend, there is an urgent need to push the AI frontiers to the Network Edge so as to fully unleash the potential of the Edge big data. To meet this demand, Edge computing, an emerging paradigm that pushes computing tasks and services from the Network core to the Network Edge, has been widely recognized as a promising solution. The resulted new interdiscipline, Edge AI or Edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the Network Edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the Network Edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.
-
Edge intelligence paving the last mile of artificial intelligence with Edge computing
2019Co-Authors: Zhi Zhou, Xu Chen, Liekang Zeng, Ke Luo, Junshan ZhangAbstract:With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the Network Edge. Driving by this trend, there is an urgent need to push the AI frontiers to the Network Edge so as to fully unleash the potential of the Edge big data. To meet this demand, Edge computing, an emerging paradigm that pushes computing tasks and services from the Network core to the Network Edge, has been widely recognized as a promising solution. The resulted new inter-discipline, Edge AI or Edge intelligence, is beginning to receive a tremendous amount of interest. However, research on Edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of Edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on Edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the Network Edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the Network Edge. Finally, we discuss future research opportunities on Edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on Edge intelligence.
-
exploiting massive d2d collaboration for energy efficient mobile Edge computing
2017Co-Authors: Xu Chen, Lin GaoAbstract:In this article we propose a novel D2D Crowd framework for 5G mobile Edge computing, where a massive crowd of devices at the Network Edge leverage Network-assisted D2D collaboration for computation and communication resource sharing. A key objective of this framework is to achieve energy-efficient collaborative task executions at the Network Edge for mobile users. Specifically, we first introduce the D2D Crowd system model in detail, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph-matching-based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows superior performance of more than 50 percent energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into account a variety of application factors.
-
Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
2017Co-Authors: Xu Chen, Lingjun Pu, Weigang Wu, Lin Gao, Di WuAbstract:In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile Edge computing, where a massive crowd of devices at the Network Edge leverage the Network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve energy-efficient collaborative task executions at Network-Edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.
Mengyu Liu - One of the best experts on this subject based on the ideXlab platform.
-
price based distributed offloading for mobile Edge computing with computation capacity constraints
2018Co-Authors: Mengyu Liu, Yuan LiuAbstract:Mobile-Edge computing is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to Network Edge. In this letter, we propose a price-based distributed method to manage the offloaded computation tasks from users. A Stackelberg game is formulated to model the interaction between the Edge cloud and users, where the Edge cloud sets prices to maximize its revenue subject to its finite computation capacity, and for given prices, each user locally makes offloading decision to minimize its own cost which is defined as latency plus payment. Depending on the Edge cloud’s knowlEdge of the Network information, we develop the uniform and differentiated pricing algorithms, which can both be implemented in distributed manners. Simulation results validate the effectiveness of the proposed schemes.
-
Price-Based Distributed Offloading for Mobile-Edge Computing with Computation Capacity Constraints
2018Co-Authors: Mengyu Liu, Yuan LiuAbstract:Mobile-Edge computing (MEC) is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to Network Edge.
Lin Gao - One of the best experts on this subject based on the ideXlab platform.
-
exploiting massive d2d collaboration for energy efficient mobile Edge computing
2017Co-Authors: Xu Chen, Lin GaoAbstract:In this article we propose a novel D2D Crowd framework for 5G mobile Edge computing, where a massive crowd of devices at the Network Edge leverage Network-assisted D2D collaboration for computation and communication resource sharing. A key objective of this framework is to achieve energy-efficient collaborative task executions at the Network Edge for mobile users. Specifically, we first introduce the D2D Crowd system model in detail, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph-matching-based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows superior performance of more than 50 percent energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into account a variety of application factors.
-
Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
2017Co-Authors: Xu Chen, Lingjun Pu, Weigang Wu, Lin Gao, Di WuAbstract:In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile Edge computing, where a massive crowd of devices at the Network Edge leverage the Network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve energy-efficient collaborative task executions at Network-Edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.
