Architectural Design of Cloud Applications: A Performance-Aware Cost Minimization Approach

Ciavotta, Michele; Gibilisco, Giovanni Paolo; Ardagna, Danilo; Nitto, Elisabetta Di; Lattuada, Marco; Silva, M. A. Almeida da

doi:10.1109/tcc.2020.3015703

Cited by 10 publications

(6 citation statements)

References 64 publications

(96 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a dynamic structure both internal and external can be the origins or by triggering the connection between ecosystem members. In the dynamic structure of the core ecosystem, the multi-sided platforms are connecting different members and adding the value of the platform players [12].…”

Section: Proposed Methodologymentioning

confidence: 99%

Hyper-Convergence Storage Framework for EcoCloud Correlates

Tabassum¹,

Alyas²,

Hamid³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Cloud computing is an emerging domain that is capturing global users from all walks of life-the corporate sector, government sector, and social arena as well. Various cloud providers have offered multiple services and facilities to this audience and the number of providers is increasing very swiftly. This enormous pace is generating the requirement of a comprehensive ecosystem that shall provide a seamless and customized user environment not only to enhance the user experience but also to improve security, availability, accessibility, and latency. Emerging technology is providing robust solutions to many of our problems, the cloud platform is one of them. It is worth mentioning that these solutions are also amplifying the complexity and need of sustenance of these rapid solutions. As with cloud computing, new entrants as cloud service providers, resellers, tech-support, hardware manufacturers, and software developers appear on a daily basis. These actors playing their role in the growth and sustenance of the cloud ecosystem. Our objective is to use convergence for cloud services, software-defined networks, network function virtualization for infrastructure, cognition for pattern development, and knowledge repository. In order to gear up these processes, machine learning to induce intelligence to maintain ecosystem growth, to monitor performance, and to become able to make decisions for the sustenance of the ecosystem. Workloads may be programmed to "superficially" imitate most business applications and create large numbers using lightweight workload generators that merely stress the storage. In today's current IT environment, when many enterprises use the cloud to service some of their application demands, a different performance testing technique that assesses more than the storage is necessary. Compute and storage are merged into a single building block with HCI(Hyper-converged infrastructure), resulting in a huge pool of compute and storage resources when clustered with other building blocks. The novelty of this work to design and test cloud storage using the measurement of This work is licensed under a Creative Commons Attribution 4.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Hyper-Convergence Storage Framework for EcoCloud Correlates

Tabassum¹,

Alyas²,

Hamid³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…• 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. The goal is to choose the VMs that consume the minimum number of resources that provide the performance needed and minimize execution costs.…”

Section: Deployment Efficiencymentioning

confidence: 99%

Deployment Archetypes for Cloud Applications

Berenberg

Calder

2022

ACM Comput. Surv.

View full text Add to dashboard Cite

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.

show abstract

“…The VMs were created and migrated using the Xen hypervisor. The hardware is also monitored and managed using different hardware control systems in the data center [6].…”

Section: Introductionmentioning

confidence: 99%

Performance Framework for Virtual Machine Migration in Cloud Computing

Alyas¹,

Ghazal²,

Alfurhood³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In the cloud environment, the transfer of data from one cloud server to another cloud server is called migration. Data can be delivered in various ways, from one data centre to another. This research aims to increase the migration performance of the virtual machine (VM) in the cloud environment. VMs allow cloud customers to store essential data and resources. However, server usage has grown dramatically due to the virtualization of computer systems, resulting in higher data centre power consumption, storage needs, and operating expenses. Multiple VMs on one data centre manage share resources like central processing unit (CPU) cache, network bandwidth, memory, and application bandwidth. In multi-cloud, VM migration addresses the performance degradation due to cloud server configuration, unbalanced traffic load, resource load management, and fault situations during data transfer. VM migration speed is influenced by the size of the VM, the dirty rate of the running application, and the latency of migration iterations. As a result, evaluating VM migration performance while considering all of these factors becomes a difficult task. The main effort of this research is to assess migration problems on performance. The simulation results in Matlab show that if the VM size grows, the migration time of VMs and the downtime can be impacted by three orders of magnitude. The dirty page rate decreases, the migration time and the downtime grow, and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VM transfer is completed. All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.

show abstract

Architectural Design of Cloud Applications: A Performance-Aware Cost Minimization Approach

Cited by 10 publications

References 64 publications

Hyper-Convergence Storage Framework for EcoCloud Correlates

Hyper-Convergence Storage Framework for EcoCloud Correlates

Deployment Archetypes for Cloud Applications

Performance Framework for Virtual Machine Migration in Cloud Computing

Contact Info

Product

Resources

About