Deep Q‐network‐based auto scaling for service in a multi‐access edge computing environment

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.

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

10.36227/techrxiv.12457985.v1 ◽

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.

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

10.36227/techrxiv.12457985.v2 ◽

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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Joint Optimization Strategy of Computation Offloading and Resource Allocation in Multi-Access Edge Computing Environment

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2020.3003898 ◽

2020 ◽

Vol 69 (9) ◽

pp. 10214-10226 ◽

Cited By ~ 3

Author(s):

Huilin Li ◽

Haitao Xu ◽

Chengcheng Zhou ◽

Xing Lu ◽

Zhu Han

Keyword(s):

Resource Allocation ◽

Edge Computing ◽

Computation Offloading ◽

Joint Optimization ◽

Optimization Strategy ◽

Computing Environment ◽

Multi Access

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

10.36227/techrxiv.12457985 ◽

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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NeiMEC: Automatically building neighbor relationship between mobile edge platforms in multi-access edge computing environment

2018 Third International Conference on Fog and Mobile Edge Computing (FMEC) ◽

10.1109/fmec.2018.8364040 ◽

2018 ◽

Author(s):

Yen-Chiu Chen ◽

Chun-Chieh Wang ◽

Jian-Cheng Chen

Keyword(s):

Edge Computing ◽

Computing Environment ◽

Neighbor Relationship ◽

Multi Access

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Horizontal Auto-Scaling for Multi-Access Edge Computing Using Safe Reinforcement Learning

ACM Transactions on Embedded Computing Systems ◽

10.1145/3475991 ◽

2021 ◽

Vol 20 (6) ◽

pp. 1-33

Author(s):

Kaustabha Ray ◽

Ansuman Banerjee

Keyword(s):

Reinforcement Learning ◽

Large Scale ◽

Edge Computing ◽

Cloud Services ◽

Test Bed ◽

New Paradigm ◽

Markov Decision ◽

Multi Access ◽

Reward Mechanism ◽

Auto Scaling

Multi-Access Edge Computing (MEC) has emerged as a promising new paradigm allowing low latency access to services deployed on edge servers to avert network latencies often encountered in accessing cloud services. A key component of the MEC environment is an auto-scaling policy which is used to decide the overall management and scaling of container instances corresponding to individual services deployed on MEC servers to cater to traffic fluctuations. In this work, we propose a Safe Reinforcement Learning (RL)-based auto-scaling policy agent that can efficiently adapt to traffic variations to ensure adherence to service specific latency requirements. We model the MEC environment using a Markov Decision Process (MDP). We demonstrate how latency requirements can be formally expressed in Linear Temporal Logic (LTL). The LTL specification acts as a guide to the policy agent to automatically learn auto-scaling decisions that maximize the probability of satisfying the LTL formula. We introduce a quantitative reward mechanism based on the LTL formula to tailor service specific latency requirements. We prove that our reward mechanism ensures convergence of standard Safe-RL approaches. We present experimental results in practical scenarios on a test-bed setup with real-world benchmark applications to show the effectiveness of our approach in comparison to other state-of-the-art methods in literature. Furthermore, we perform extensive simulated experiments to demonstrate the effectiveness of our approach in large scale scenarios.

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