A Lightweight Container Middleware for Edge Cloud Architectures

Leon, David von; Miori, Lorenzo; Sanin, Julian; Ioini, Nabil El; Helmer, Sven; Pahl, Claus

doi:10.1002/9781119525080.ch7

Cited by 25 publications

(5 citation statements)

References 21 publications

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“…However, we aim to explore this in more concrete application settings. We consider here traffic management and coordinated cars as a concrete IoT and edge setting, where traffic and car movement is captured and processed, maybe combined by infotainment information with image and video data [6,16,20,25]. This mobile setting would need to be supported by local clusters that act autonomously, but require some higher-level coordination [7,14,27].…”

Section: Discussionmentioning

confidence: 99%

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Azimi

Pahl

Shirvani

2021

Communications in Computer and Information Science

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Recently distributed computing capacities are brought to the edge of the Internet, permitting Internet-of-Things applications to process calculation all the more locally and subsequently more productively and this has brought a totally different scope of apparatuses and usefulness. This instrument can The most significant characterizing highlights of edge processing are low latency, location awareness, wide geographic distribution, versatility, support for countless nodes, etc. We want to likely limit the latency and delay in edge-based structures. We center around a progressed compositional setting that considers communication and processing delays and the management effort notwithstanding a real request execution time in an operational efficiency situation. Our design is based on multi-cluster edge layer with nearby autonomous edge node clusters. We will contend that particle swarm optimization as a bio-motivated optimization approach is a perfect candidate for distributed IoT load handling in self-managed edge clusters. By designing a controller and utilizing a particle swarm optimization algorithm, we show that delay and end-to-end latency can be reduced.

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Section: Discussionmentioning

confidence: 99%

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Azimi

Pahl

Shirvani

2021

Communications in Computer and Information Science

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“…This system was designed to reduce the service delay. Leon et al [12] designed a lightweight container model that is suitable for the cloud architecture. This scheme utilized the integration of cloud and container devices to enable reliable connectivity along with reduced computational power.…”

Section: Methodsmentioning

confidence: 99%

Development And Analysis of Ant Colony Optimization-Based Light Weight Container (ACO-LWC) Algorithm For Efficient Load Balancing

Aruna

Pradeep

2021

Preprint

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Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest TPS of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58ms, 60ms and 48ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.

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“…A Raspberry Pi is a widely used single-board computer based on an ARM-processor. Its original purpose was to introduce school children to programming [8,73,74]. Due to its low price and various use cases it has found its way into more industrial IT projects.…”

Section: Raspberry Pimentioning

confidence: 99%

Fuzzy Container Orchestration for Self-adaptive Edge Architectures

Gand

Fronza

Ioini

et al. 2021

Communications in Computer and Information Science

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The edge or edge computing refers to the computational infrastructure between sensors and Internet-of-Things world on the one side and centralised cloud data centres on the other. Edge clusters could consist of small single-board devices, which are widely used in different applications. This includes microcontrollers regulating an industrial process or controllers monitoring and managing traffic roadside. Although generally hardware capabilities of devices are growing, resources at the edge are often still limited, requiring an intelligent resource management. This can done through self-adaptive scaling mechanisms in these clusters allowing to scale components of the application in the cluster. We introduce here an auto-scalable cluster architecture for lightweight container-based edge devices. A serverless architecture forms the core of this architecture. The auto-scaling component is based on fuzzy logic in order to better manage uncertainty problems arising in such contexts. Our evaluations show that our platform architecture and the auto-scaling functionality satisfy the need of lightweightness in edge architecture.

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A Lightweight Container Middleware for Edge Cloud Architectures

Cited by 25 publications

References 21 publications

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Development And Analysis of Ant Colony Optimization-Based Light Weight Container (ACO-LWC) Algorithm For Efficient Load Balancing

Fuzzy Container Orchestration for Self-adaptive Edge Architectures

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