Multi Objective Optimization of Cloud Computing Services for Consumers

Weintraub, Eli; Cohen, Yuval

doi:10.14569/ijacsa.2017.080219

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Also, scaling down of unused resources can be used for the warm standby stack in failover archetypes described in Sections 2.2 and 3.2. • Overall Efficient Application Deployment-Applications can get efficiency gains by optimizing the choice of the computing platform (VMs, containers, serverless, functions), the choice of storage and database, and the choice of RPC or work-queue-based service communication [106,128]. Reference [24] describes the modeling of applications based on the choice of suitable VM type to meet the deployment constraints for each service in the application.…”

Section: Deployment Efficiencymentioning

confidence: 99%

“…The goal is to choose the VMs that consume the minimum number of resources that provide the performance needed and minimize execution costs. To achieve overall efficient application deployment [106,128], developers design their application for the tasks to be accomplished, from serving real time traffic to backend pipeline processing, from mission critical 24/7 applications to ones that can tolerate some downtime, from daemons or work-item-based application that run for a short time, and more. • Sustainable Application Model- [43] suggests that serving applications use thread and tasks to parallelize execution for efficiency.…”

Section: Deployment Efficiencymentioning

confidence: 99%

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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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Section: Deployment Efficiencymentioning

confidence: 99%