A Reference Architecture for High-Availability Automatic Failover between PaaS Cloud Providers

Addo, Ivor D.; Ahamed, Sheikh Iqbal; Chu, William Cheng‐Chung

doi:10.1109/tsa.2014.12

Cited by 21 publications

(4 citation statements)

References 9 publications

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“…For example, if the data load in one of the frameworks in the data engineering pipeline explodes, additional public resources in the cloud can be helpful until the data load levels drop back below a certain threshold. Hybrid options can also be beneficial for high availability and disaster recovery scenarios [209]. Day-to-day production systems can be maintained on-promise while a backup or recovery environment can be moved to the cloud to provide agility in a disaster recovery scenario.…”

Section: A Gap Analysismentioning

confidence: 99%

Recent Advances in Data Engineering for Networking

Zeydan

Mangues‐Bafalluy

2022

IEEE Access

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Section: A Gap Analysismentioning

confidence: 99%

Recent Advances in Data Engineering for Networking

Zeydan

Mangues‐Bafalluy

2022

IEEE Access

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“…-Failover to boost overall application availability [30] when on-premises application is unhealthy [1]. • Data replication is also being supported between Cloud and on-premises.…”

Section: Hybridmentioning

confidence: 99%

“…This usually depends on the purpose of the application as described in Sections 6 and 7. The typical choices are (a) replication across clouds for failover [1], (b) ability to run on any cloud to maintain the optionally to exit or move to any other cloud, (c) connecting clouds due to integration of acquisitions [120], and (d) using best of breed products on each cloud [97]. Depending on the choice, the deployment models will be different.…”

Section: Best Practicesmentioning

confidence: 99%

Deployment Archetypes for Cloud Applications

Berenberg

Calder

2022

ACM Comput. Surv.

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This is a survey article that explores six Cloud-based deployment archetypes for Cloud applications and the tradeoffs between them to achieve high availability, low end-user latency, and acceptable costs. These are (1) Zonal, (2) Regional, (3) Multi-regional, (4) Global, (5) Hybrid, and (6) Multi-cloud deployment archetypes. The goal is to classify cloud applications into a set of deployment archetypes and deployment models that tradeoff their needs around availability, latency, and geographical constraints with a focus on serving applications. This enables application owners to better examine the tradeoffs of each deployment model and what is needed for achieving the availability and latency goals for their application.

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“…One particular architecturally significant requirement is considered by Addo et al [1], who describe an architecture for automatically routing traffic to other cloud providers if one fails. This points back to the CAP theorem, and raises the question of how to replicate data and ensure consistency across multiple cloud providers.…”

Section: Measuring Continuous Deliverymentioning

confidence: 99%

A framework for evaluating continuous microservice delivery strategies

Lehmann

Sandnes

2017

Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing

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The emergence of service-oriented computing, and in particular microservice architecture, has introduced a new layer of complexity to the already challenging task of continuously delivering changes to the end users. Cloud computing has turned scalable hardware into a commodity, but also imposes some requirements on the software development process. Yet, the literature mainly focuses on quantifiable metrics such as number of manual steps and lines of code required to make a change. The industry, on the other hand, appears to focus more on qualitative metrics such as increasing the productivity of their developers. These are common goals, but must be measured using different approaches. Therefore, based on interviews of industry stakeholders a framework for evaluating and comparing approaches to continuous microservice delivery is proposed. We show that it is possible to efficiently evaluate and compare strategies for continuously delivering microservices.

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A Reference Architecture for High-Availability Automatic Failover between PaaS Cloud Providers

Cited by 21 publications

References 9 publications

Recent Advances in Data Engineering for Networking

Recent Advances in Data Engineering for Networking

Deployment Archetypes for Cloud Applications

A framework for evaluating continuous microservice delivery strategies

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