Cloud Service Selection with Fuzzy C-Means Artificial Immune Network Memory Classifier

Ha, Weitao

doi:10.1109/cis2018.2018.00065

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…The authors also analyzed the sensitivity of their results. The author in [12] also employed a hybrid method using artificial immune network and fuzzy theory for selection of CSs.…”

Section: B Rank Cloud Services Using Vikor Methodsmentioning

confidence: 99%

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

Nadeem

2022

IEEE Access

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With the recent maturity of Cloud computing technology and the flexibility of the Cloud services, the offering of Cloud services has spread exponentially. Different Cloud service providers offer their services highlighting different features of their services. Because of the diversity of Cloud services and their highlighted features, the choice of the most suitable Cloud service is a complex problem for the Cloud users. Many Cloud users are unable to identify the Cloud services that best suit their needs and thus choose an unsuitable Cloud service, which results in financial loses as well as time delays. To this end, in this study, we propose an efficient three layered framework for evaluating and ranking IaaS, PaaS and SaaS Cloud services. We identified the functional and non-functional key performance indicators (KPIs) for Cloud services from 6 KPI classes. We classified these KPIs with respect to their types and criticality so that the Cloud user can easily choose according to his needs. The relative importance of the KPIs was determined using CRITIC method. We combined the KPIs values and their relative importance for an overall evaluation and ranking of the Cloud services using Vikor method. A case study is also presented for a step by step demonstration of the proposed method.

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“…The authors also analyzed the sensitivity of their results. The author in [12] also employed a hybrid method using artificial immune network and fuzzy theory for selection of CSs.…”

Section: B Rank Cloud Services Using Vikor Methodsmentioning

confidence: 99%

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

Nadeem

2022

IEEE Access

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“…Grandhi et al 25 propose a fuzzy decision making approach that considers predefined quality-of-service (QoS) attributes from industry experts to determine suitable Cloud instances for applications. Ha 26 proposes a fuzzy approach to recommend instances based on predefined QoS attributes and user constraints. Sohaib et al 27 propose a fuzzy approach to determine a Cloud service model (IaaS, SaaS, or PaaS) for e-commerce, with QoS attributes defined by an expert in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Samuel

Khan

Balos

et al. 2020

Concurrency and Computation

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This article proposes a random-forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud-ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single-precision (SP) floating-point operations (FLOPs), double-precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud-ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application-to-Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application-specific random forests (ARF) and application-class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application-class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real-world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

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“…Whilst numerous bio-inspired techniques for resilient cloud computing and other distributed systems have been investigated [14]. These can involve architectural models similar to this work using cellbased models [15], networking approaches based upon the distributed processing capabilities of ant colonies [16], intrusion detection methods [17] or service orchestration methods [18] based upon artificial immune systems, alternatives to public key infrastructure based upon family genetics [19] and many more.…”

Section: Related Work / Motivationmentioning

confidence: 99%

Bioinspired Multiagent Embryonic Architecture for Resilient Edge Networks

Welsh

Benkhelifa

2019

IEEE Trans. Ind. Inf.

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With the pervasive introduction of IoE technologies, use-cases are frequently being found operating within harsh environmental conditions. This decrees the need for solutions which permit service delivery to operate in a highly-resilient manner. This work presents an architecture for a novel cloud platform designed for resilient service delivery. It supports networks where poor communication links or high node failure will cause services to be delivered in an non-resilient manner. This could be the result of factors such as high-node mobility, poor environmental conditions, unreliable infrastructure from environment disaster or cyber-attack. This biologically inspired architecture uses a purely distributed multi-agent approach to provide self-healing and self-organising properties, modelled on the characteristics of embryonic development and biological cell communication. To permit high-levels of a node churn, this multi-agent approach uses local-only communication. Probabilistic Cellular Automata are used to simulate this architecture and evaluate the efficacy of this approach.

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Cloud Service Selection with Fuzzy C-Means Artificial Immune Network Memory Classifier

Cited by 5 publications

References 2 publications

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Bioinspired Multiagent Embryonic Architecture for Resilient Edge Networks

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