Application deployment using containers with auto-scaling for microservices in cloud environment

Srirama, Satish Narayana; Adhikari, Mainak; Paul, Souvik

doi:10.1016/j.jnca.2020.102629

Cited by 61 publications

(37 citation statements)

References 14 publications

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“…Elastic container deployment in cloud computing. The problem of elastic container deployment in Cloud computing has been studied intensively at different resource levels: container deployment, 16‐19 cluster deployment 20‐22 or both 23,24 . These approaches use horizontal methods, 17,21‐23 vertical methods 18,24 or hybrid approaches 16,19,20 depending on the elasticity dimensions.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of elastic container deployment in Cloud computing has been studied intensively at different resource levels: container deployment, 16‐19 cluster deployment 20‐22 or both 23,24 . These approaches use horizontal methods, 17,21‐23 vertical methods 18,24 or hybrid approaches 16,19,20 depending on the elasticity dimensions. However these solutions lack the ability to include spatial aspects in their adaptation processes, which is essential to reduce network latency—a key performance factor for global web applications.…”

Section: Related Workmentioning

confidence: 99%

“…Such manual adaptation decisions are naive and inefficient as they can lead to costly, over‐provisioning issues (excessive deployment of clusters), performance issues (improper cluster placement) and/or adaptation issues, for example, performance degradation during adaptation and/or unnecessary cluster relocation. Much of the prior work on elastic container deployment problems in the Cloud does not consider spatial aspects of workload variations as they focus on local techniques within a single data center to handle fluctuations in workload volumes through localized auto‐scaling of containers, 16‐19 clusters, 20‐22 or both 23,24 . Other work proposes techniques to handle geo‐workload variations, 11,25 however, they do not use containers and hence do not benefit from the benefits of container‐based solutions, and they do not maintain application performance during adaptation.…”

Section: Introductionmentioning

confidence: 99%

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Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Aldwyan

Sinnott

Jayaputera

2021

Concurrency and Computation

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Containers such as Docker provide a lightweight virtualization technology. They have gained popularity in developing, deploying and managing applications in and across Cloud platforms. Container management and orchestration platforms such as Kubernetes run application containers in virtual clusters that abstract the overheads in managing the underlying infrastructures to simplify the deployment of container solutions. These platforms are well suited for modern web applications that can give rise to geographic fluctuations in use based on the location of users. Such fluctuations often require dynamic global deployment solutions. A key issue is to decide how to adapt the number and placement of clusters to maintain performance, whilst incurring minimum operating and adaptation costs. Manual decisions are naive and can give rise to: over‐provisioning and hence cost issues; improper placement and performance issues, and/or unnecessary relocations resulting in adaptation issues. Elastic deployment solutions are essential to support automated and intelligent adaptation of container clusters in geographically distributed Clouds. In this article, we propose an approach that continuously makes elastic deployment plans aimed at optimizing cost and performance, even during adaptation processes, to meet service level objectives (SLOs) at lower costs. Meta‐heuristics are used for cluster placement and adjustment. We conduct experiments on the Australia‐wide National eResearch Collaboration Tools and Resources Research Cloud using Docker and Kubernetes. Results show that with only a 0.5 ms sacrifice in SLO for the 95th percentile of response times we are able to achieve up to 44.44% improvement (reduction) in cost compared to a naive over‐provisioning deployment approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Aldwyan

Sinnott

Jayaputera

2021

Concurrency and Computation

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“…As a complement to this microservicesbased architecture, we use the "service instance per container" and "database per service" implementation patterns [64]. • Code repository: A Git repository is used to maintain version control of the code from the development stream of each service [29], [65]. [49].…”

Section: Figure 2 Framework For the Automation And Continuous Deploymentioning

confidence: 99%

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

et al. 2020

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This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.

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“…To solve the first problem, several edge computing service management platforms have adopted an auto-scaling algorithm, which is the primary resource management method for cloud computing application services [ 20 , 21 , 22 ]. Auto-scaling includes three basic strategies: automatic, predictive, and event-reactive scaling [ 23 ]. During the automatic scaling of a scheduling program, the corresponding rules for system time and the resource deployment scale should be written in advance; thus, this technique can only be used in a network environment in which the number of service requirements and time conditions are stable.…”

Section: Introductionmentioning

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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Application deployment using containers with auto-scaling for microservices in cloud environment

Cited by 61 publications

References 14 publications

Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

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

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