IRENE: Interference and High Availability Aware Microservice-based Applications Placement for Edge Computing

A Deployment Management of High-Availability Microservices for Edge Computing

2020 International Symposium on Computer, Consumer and Control (IS3C) ◽

10.1109/is3c50286.2020.00036 ◽

2020 ◽

Author(s):

Hung-Ming Chen ◽

Shih-Ying Chen ◽

Zhong-Xiang Zheng ◽

Ti-Wei Huang ◽

Cheng-Yu Huang

Keyword(s):

High Availability ◽

Edge Computing

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A Decentralized Edge Computing Latency-Aware Task Management Method with High Availability for IoT Applications

IEEE Access ◽

10.1109/access.2021.3116717 ◽

2021 ◽

pp. 1-1

Author(s):

Muhammad Bukhsh ◽

Saima Abdullah ◽

Imran Sarwar Bajwa

Keyword(s):

High Availability ◽

Edge Computing ◽

Task Management ◽

Iot Applications ◽

Management Method

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Adaptive Management of Volatile Edge Systems at Runtime With Satisfiability

ACM Transactions on Internet Technology ◽

10.1145/3470658 ◽

2022 ◽

Vol 22 (1) ◽

pp. 1-21

Author(s):

Cosmin Avasalcai ◽

Christos Tsigkanos ◽

Schahram Dustdar

Keyword(s):

Adaptive Management ◽

High Availability ◽

Edge Computing ◽

Failure Recovery ◽

Low Latency ◽

Distributed Resources ◽

High Volatility ◽

Application Deployment ◽

Adaptive Framework ◽

End To End

Edge computing offers the possibility of deploying applications at the edge of the network. To take advantage of available devices’ distributed resources, applications often are structured as microservices, often having stringent requirements of low latency and high availability. However, a decentralized edge system that the application may be intended for is characterized by high volatility, due to devices making up the system being unreliable or leaving the network unexpectedly. This makes application deployment and assurance that it will continue to operate under volatility challenging. We propose an adaptive framework capable of deploying and efficiently maintaining a microservice-based application at runtime, by tackling two intertwined problems: (i) finding a microservice placement across device hosts and (ii) deriving invocation paths that serve it. Our objective is to maintain correct functionality by satisfying given requirements in terms of end-to-end latency and availability, in a volatile edge environment. We evaluate our solution quantitatively by considering performance and failure recovery.

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Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain

Journal of Sensor and Actuator Networks ◽

10.3390/jsan8010015 ◽

2019 ◽

Vol 8 (1) ◽

pp. 15 ◽

Cited By ~ 32

Author(s):

Ammar Muthanna ◽

Abdelhamied A. Ateya ◽

Abdukodir Khakimov ◽

Irina Gudkova ◽

Abdelrahman Abuarqoub ◽

...

Keyword(s):

High Reliability ◽

Fog Computing ◽

Network Virtualization ◽

Software Defined Networking ◽

High Availability ◽

Edge Computing ◽

Iot Applications ◽

Distributed Controllers ◽

Multi Access ◽

Reliability And Availability

Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization.

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