A Trustworhty Model for Reliable Cloud Service Discovery

O, Akinwunmi A.; Olajubu, Emmanuel Ajayi; Aderounmu, G. A.

doi:10.5120/15293-3962

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Finding resources in an exceedingly giant scale, multi-domain network could be a non-trivial task [4]. There is also several providers, each with totally different characteristics, therefore providing a spread of varied levels of user-requirement suitableness [9].…”

Section: Service Discovery Systemmentioning

confidence: 99%

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Multi-Agent System Approach for Trustworthy Cloud Service Discovery

Akinwunmi¹,

Olajubu²,

Aderounmu³

2019

Multi-Agent Systems - Control Spectrum

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Accessing the advantages of cloud computing requires that a prospective user has proper access to trustworthy cloud services. It is a strenuous and laborious task to find resources and services in a heterogeneous network such as cloud environment. The cloud computing paradigm being a form of distributed system with a complex collection of computing resources from different domains with different regulatory policies but having a lot of values could enhance the mode of computing. However, a monolithic approach to cloud service discovery cannot help the necessities of cloud environment efficiently. This study put forward a distributive approach for finding sincere cloud services with the use of Multi-Agents System for ensuring intelligent cloud service discovery from trusted providers. Experiments were carried out in the study using CloudAnalyst and the results indicated that extending the frontiers MAS approach into cloud service discovery by way of integrating trust into the process improves the quality of service in respect of response time and scalability. A further comparative analysis of the Multi-Agents System approach for cloud service discovery to monolithic approach showed that Multi-Agents System approach is highly efficient, and highly flexible for trustworthy cloud service discovery.

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Section: Service Discovery Systemmentioning

confidence: 99%

“…Such a collection of nodes is defined as a cloud [3]. Cloud is a distributed system with a fancy aggregation of computing resources from various domains with different management policies however having vast edges that would enhance the mode of computing [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent System Approach for Trustworthy Cloud Service Discovery

Akinwunmi¹,

Olajubu²,

Aderounmu³

2019

Multi-Agent Systems - Control Spectrum

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“…Different optimization techniques were adopted for cloud service selection in the literature. One of which were proposed in [16], which used a probabilistic and Bayesian network model. The authors modeled the discovery of cloud service as a directed acyclic graph DAG to represent the various entities in the system.…”

Section: Related Workmentioning

confidence: 99%

Towards an Efficient Federated Cloud Service Selection to Support Workflow Big Data Requirements

Serhani¹,

Kassabi²,

Taleb³

2018

Adv. sci. technol. eng. syst. j.

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Cloud Computing is considered nowadays an attractive solution to serve the Big Data storage, processing, and analytics needs. Given the high complexity of Big Data workflows and their contingent requirements, a single cloud provider might not be able alone to satisfy these needs. A multitude of cloud providers that offer myriad of cloud services and resources can be selected. However, such selection is not straightforward since it has to deal with the scaling of Big Data requirements, and the dynamic cloud resources fluctuation. This work proposes a novel cloud service selection approach which evaluates Big Data requirements, matches them in real time to most suitable cloud services, after which suggests the best matching services satisfying various Big Data processing requests. Our proposed selection scheme is performed throughout three phases: 1) capture Big Data workflow requirements using a Big Data task profile and map these to a set of QoS attributes, and prioritize cloud service providers (CSPs) that best fulfil these requirements, 2) rely on the pool of selected providers by phase 1 to then choose the suitable cloud services from a single provider to satisfy the Big Data task requirements, and 3) implement multiple providers selection to better satisfy requirements of Big Data workflow composed of multiples tasks. To cope with the multi-criteria selection problem, we extended the Analytic Hierarchy Process (AHP) to better provide more accurate rankings. We develop a set of experimental scenarios to evaluate our 3-phase selection schemes while verifying key properties such as scalability and selection accuracy. We also compared our selection approach to well-known selection schemes in the literature. The obtained results demonstrate that our approach perform very well compared to the other approaches and efficiently select the most suitable cloud services that guarantee Big Data tasks and workflow QoS requirements.

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