An Experimental Study on Microservices based Edge Computing Platforms

Qu, Qian; Xu, Ronghua; Nikouei, Seyed Yahya; Chen, Yu

doi:10.1109/infocomwkshps50562.2020.9163068

Cited by 27 publications

(13 citation statements)

References 29 publications

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“…Some opensource microservices have been built for TensorFlow, MongoDB, Grafana and Eclipse Mosquito. Combining multiple microservices in a single container is sometimes inevitable when fulfilling complex and computationally demanding tasks [13,48]. However, this increases the container file size.…”

Section: B Container-based Virtualizationmentioning

confidence: 99%

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Oleghe

2021

IEEE Access

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Containers are a form of software virtualization, rapidly becoming the de facto way of providing edge computing services. Research on container-based edge computing is plentiful, and this has been buoyed by the increasing demand for single digit, milliseconds latency computations. A container scheduler is part of the architecture that is used to manage and orchestrate multiple container-based applications on heterogenous computing nodes. The scheduler decides how incoming computing requests are allocated to containers, which edge nodes the containers are placed on, and where already deployed containers are migrated to. This paper aims to clarify the concept of container placement and migration in edge servers and the scheduling models that have been developed for this purpose. The study illuminates the frameworks and algorithms upon which the scheduling models are built. To convert the problem to one that can be solved using an algorithm, the container placement problem in mostly abstracted using multi-objective optimization models or graph network models. The scheduling algorithms are predominantly heuristic-based algorithms, which are able to arrive at sub-optimal solutions very quickly. There is paucity of container scheduling models that consider distributed edge computing tasks. Research in decentralized scheduling systems is gaining momentum and the future outlook is in scheduling containers for mobile edge nodes.

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Section: B Container-based Virtualizationmentioning

confidence: 99%

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Oleghe

2021

IEEE Access

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“…Their work identified several research challenges that should be considered and addressed to enhance the performance of applications in edge computing. Qu et al [ 32 ] investigated various microservice deployment strategies on the edge computing platform. They implemented microservices with Docker containers and conducted comprehensive experiments on four cases to explore the performance of microservice deployment strategies.…”

Section: Background and Related Workmentioning

confidence: 99%

Fuzzy-Based Microservice Resource Management Platform for Edge Computing in the Internet of Things

Huang

Tseng

et al. 2021

Sensors

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Edge computing exhibits the advantages of real-time operation, low latency, and low network cost. It has become a key technology for realizing smart Internet of Things applications. Microservices are being used by an increasing number of edge computing networks because of their sufficiently small code, reduced program complexity, and flexible deployment. However, edge computing has more limited resources than cloud computing, and thus edge computing networks have higher requirements for the overall resource scheduling of running microservices. Accordingly, the resource management of microservice applications in edge computing networks is a crucial issue. In this study, we developed and implemented a microservice resource management platform for edge computing networks. We designed a fuzzy-based microservice computing resource scaling (FMCRS) algorithm that can dynamically control the resource expansion scale of microservices. We proposed and implemented two microservice resource expansion methods based on the resource usage of edge network computing nodes. We conducted the experimental analysis in six scenarios and the experimental results proved that the designed microservice resource management platform can reduce the response time for microservice resource adjustments and dynamically expand microservices horizontally and vertically. Compared with other state-of-the-art microservice resource management methods, FMCRS can reduce sudden surges in overall network resource allocation, and thus, it is more suitable for the edge computing microservice management environment.

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“…Than in another loop each of n i − Q i containers decides with probability p i if it wants to migrate to another node. If so, then it starts a search for a suitable node that matches the conditions Equations ( 20) and (21). If the search fails nothing happens.…”

Section: Computer Simulation Of a Large Cloudmentioning

confidence: 99%

“…Microservices running inside virtualization containers represent a valid solution for code execution in such systems [21], and their controlled migration is expected to play an important role in future IoT deployments [22,23]. Possible migration scenarios involve moving IoT devices, such as cars and robots, in which case computation and storage service may need to be moved from one Edge node to another one in order remain in close proximity of the IoT device.…”

Section: Introductionmentioning

confidence: 99%

Swarm-Like Distributed Algorithm for Scheduling a Microservice-Based Application to the Cloud Servers

Rusek

Dwornicki

2021

Electronics

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Introduction of virtualization containers and container orchestrators fundamentally changed the landscape of cloud application development. Containers provide an ideal way for practical implementation of microservice-based architecture, which allows for repeatable, generic patterns that make the development of reliable, distributed applications more approachable and efficient. Orchestrators allow for shifting the accidental complexity from inside of an application into the automated cloud infrastructure. Existing container orchestrators are centralized systems that schedule containers to the cloud servers only at their startup. In this paper, we propose a swarm-like distributed cloud management system that uses live migration of containers to dynamically reassign application components to the different servers. It is based on the idea of “pheromone” robots. An additional mobile agent process is placed inside each application container to control the migration process. The number of parallel container migrations needed to reach an optimal state of the cloud is obtained using models, experiments, and simulations. We show that in the most common scenarios the proposed swarm-like algorithm performs better than existing systems, and due to its architecture it is also more scalable and resilient to container death. It also adapts to the influx of containers and addition of new servers to the cloud automatically.

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An Experimental Study on Microservices based Edge Computing Platforms

Cited by 27 publications

References 29 publications

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Fuzzy-Based Microservice Resource Management Platform for Edge Computing in the Internet of Things

Swarm-Like Distributed Algorithm for Scheduling a Microservice-Based Application to the Cloud Servers

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