Risk-Aware Data Offloading in Multi-Server Multi-Access Edge Computing Environment

2020 ◽  
Vol 28 (3) ◽  
pp. 1405-1418 ◽  
Author(s):  
Pavlos Athanasios Apostolopoulos ◽  
Eirini Eleni Tsiropoulou ◽  
Symeon Papavassiliou
Keyword(s):  
Edge Computing ◽  
Computing Environment ◽  
Data Offloading ◽  
Multi Access ◽  
Multi Server

scholarly journals Arithmetic Framework to Optimize Packet Forwarding among End Devices in Generic Edge Computing Environments

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 421
Author(s):  
Pedro Juan Roig ◽  
Salvador Alcaraz ◽  
Katja Gilly ◽  
Cristina Bernad ◽  
Carlos Juiz
Keyword(s):  
Edge Computing ◽  
Modular Arithmetic ◽  
Packet Forwarding ◽  
Computing Environment ◽  
Arithmetic Means ◽  
Simple Arithmetic ◽  
Efficient Performance ◽  
Integer Division ◽  
Computing Environments ◽  
Multi Access

Multi-access edge computing implementations are ever increasing in both the number of deployments and the areas of application. In this context, the easiness in the operations of packet forwarding between two end devices being part of a particular edge computing infrastructure may allow for a more efficient performance. In this paper, an arithmetic framework based in a layered approach has been proposed in order to optimize the packet forwarding actions, such as routing and switching, in generic edge computing environments by taking advantage of the properties of integer division and modular arithmetic, thus simplifying the search of the proper next hop to reach the desired destination into simple arithmetic operations, as opposed to having to look into the routing or switching tables. In this sense, the different type of communications within a generic edge computing environment are first studied, and afterwards, three diverse case scenarios have been described according to the arithmetic framework proposed, where all of them have been further verified by using arithmetic means with the help of applying theorems, as well as algebraic means, with the help of searching for behavioral equivalences.

scholarly journals Collaborative Learning of Communication Routes in Edge-enabled Multi-access Vehicular Environment

2020 ◽  
Author(s):  
Celimuge Wu ◽  
Zhi Liu ◽  
Tsutomu Yoshinaga ◽  
Fuqiang Liu ◽  
Yusheng Ji ◽  
...  
Keyword(s):  
Collaborative Learning ◽  
Learning Algorithm ◽  
Research Problem ◽  
Edge Computing ◽  
Communication Overhead ◽  
Computing Environment ◽  
Short Delay ◽  
Efficient Communication ◽  
Strict Requirement ◽  
Multi Access

Some vehicular Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the ``proactive'' and ``preemptive'' approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.

scholarly journals Collaborative Learning of Communication Routes in Edge-enabled Multi-access Vehicular Environment

2020 ◽  
Author(s):  
Celimuge Wu ◽  
Zhi Liu ◽  
Fuqiang Liu ◽  
Tsutomu Yoshinaga ◽  
Yusheng Ji ◽  
...  
Keyword(s):  
Collaborative Learning ◽  
Learning Algorithm ◽  
Research Problem ◽  
Edge Computing ◽  
Communication Overhead ◽  
Computing Environment ◽  
Short Delay ◽  
Efficient Communication ◽  
Strict Requirement ◽  
Multi Access

Some vehicular Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the ``proactive'' and ``preemptive'' approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.

Data Offloading in UAV-assisted Multi-access Edge Computing Systems under Resource Uncertainty

2021 ◽  
pp. 1-1
Author(s):  
Pavlos Athanasios Apostolopoulos ◽  
Georgios Fragkos ◽  
Eirini Eleni Tsiropoulou ◽  
Symeon Papavassiliou
Keyword(s):  
Edge Computing ◽  
Computing Systems ◽  
Data Offloading ◽  
Multi Access ◽  
Resource Uncertainty

scholarly journals Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2434 ◽  
Author(s):  
Giorgos Mitsis ◽  
Eirini Eleni Tsiropoulou ◽  
Symeon Papavassiliou
Keyword(s):  
Utility Function ◽  
Cooperative Game ◽  
Learning Rule ◽  
Edge Computing ◽  
Maximization Problem ◽  
Response Dynamics ◽  
Pricing Policy ◽  
Data Offloading ◽  
Pricing Mechanism ◽  
Multi Access

Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits.

scholarly journals Collaborative Learning of Communication Routes in Edge-enabled Multi-access Vehicular Environment

2020 ◽  
Author(s):  
Celimuge Wu ◽  
Zhi Liu ◽  
Fuqiang Liu ◽  
Tsutomu Yoshinaga ◽  
Yusheng Ji ◽  
...  
Keyword(s):  
Collaborative Learning ◽  
Learning Algorithm ◽  
Research Problem ◽  
Edge Computing ◽  
Communication Overhead ◽  
Computing Environment ◽  
Short Delay ◽  
Efficient Communication ◽  
Strict Requirement ◽  
Multi Access

Some vehicular Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the ``proactive'' and ``preemptive'' approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.

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