A Framework for SOFTWARE-AS-A-SERVICE Selection and Provisioning

Badidi, Elarbi

doi:10.5121/ijcnc.2013.5314

Cited by 24 publications

(13 citation statements)

References 20 publications

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“…In Badidi, 22 the author proposes a conceptual framework for service selection and provisioning. The framework is based on a cloud service broker as a mediator between consumers and providers.…”

Section: Cloud Negotiationmentioning

confidence: 99%

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Towards a generic multilayer negotiation framework for efficient application provisioning in the cloud

Omezzine

Saoud

Tazi

et al. 2017

Concurrency and Computation

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The cloud market is nowadays a complex environment where cloud application providers need to maximize their monetary profit and end users look for the most efficient services with the lowest prices. For efficient cloud provisioning, both providers and users should be satisfied in spite of their conflicting needs. Negotiation is the most flexible solution to solve conflicts and enable reaching win-win agreements. Cloud application provisioning has 2 main properties that highly impact the negotiation decision-making models: (1) The interdependence between the business layer and the resource layer, and (2) the dynamic provisioning context (eg, negotiators' preferences and application scheduler). Despite the importance of these 2 properties, the current negotiation models do not take these properties into account. This paper addresses both issues. We propose a multilayer negotiation framework, which also encompasses the potential for dynamic provisioning context. For that, we propose, first, a generic negotiation model dedicated to cloud provisioning. And second, we present its instantiation among cloud layers for efficient SaaS provisioning, to maximize provider profit and increase user satisfaction. The experiments show the benefits of adding negotiation to the provisioning process by improving it for provider profit, number of accepted requests, and client satisfaction. KEYWORDS automated negotiation, cloud provisioning, decision making, quality of service (QoS), SaaS application, service level agreement (SLA) 1 ing the quality of service (QoS) as part of the service level agreement (SLA) contract. 1Cloud provisioning is generally governed by the SLA contract established between the parties concerned. An SLA is a formal representation of QoS, with penalties and obligations agreed on by the contractors. 2 For an efficient cloud application provisioning, business providers aim to optimize profit (costs of rented VMs, budget, and penalty costs) and to satisfy end users (budget, response time, deadline, etc). For this reason, providers require SLA-and profit-aware provisioning strategies. In fact, applying classical provisioning strategies 3 (such as 1VMperAll, 1VMperJob, and BinPacking heuristics) may lead to a less than the optimal profit. These strategies focus only on resource utilization, which may lead either to an SLA violation, when resources are overloaded, or profit loss due to an underutilization of rented resources.Most SLA-based provisioning algorithms use a take-it-or-leave-it strategy. 4 When it is not possible to provide the requested service (while satisfying the obligations of the SLA), the provider rejects the request to avoid an SLA violation. By considering the constraints of resource provisioning (cost, availability, etc), the business provider may reject several requests, which may lead to a loss of potential profit.With the evolution of the cloud market and the competitiveness among cloud providers, a take-it-or-leave-it strategy may be a bad wileyonlinelibrary.com/journal/cpe

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Section: Cloud Negotiationmentioning

confidence: 99%

“…These agents are communicating using a semantic engine including a unique Cloud ontology facilitating semantic service discovery. In Badidi 22 and Venticinque et al, 23 the authors propose a general overview about cloud service negotiation without giving details about negotiation approach.…”

Section: Cloud Negotiationmentioning

confidence: 99%

Towards a generic multilayer negotiation framework for efficient application provisioning in the cloud

Omezzine

Saoud

Tazi

et al. 2017

Concurrency and Computation

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“…The algorithm proposed in paper [13] gives ranking of SaaS providers according to their offerings matching the consumer requirements. A CS Recommender in [17] consists of five main components as crawler, cloud service identifier, indexer, search engine and recommender system.…”

Section: Related Workmentioning

confidence: 99%

“…An example of SaaS would be Google docs. A Cloud Service Broker helps finding suitable SaaS services [13].…”

Section: Introductionmentioning

confidence: 99%

A User-Centered Approach for Cloud Service Selection and Recommendation

Ashraf¹,

Rabbani²,

Martinez-Enriquez³

et al. 2016

RCS

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Cloud computing is one of the most emerging industries and offers scalable computing resources and on-demand packages. A large number of cloud services are being offered by many service providers, a vendor supplies many services or same service is provided by many vendors, which creates the selection issue of appropriate cloud service from a big pool. Quality of service plays important role for better service choice. For this reason, cloud service recommendation systems are proposed in the literature that makes it easy for a user to decide which service to choose. However, user needs and requirements like economy, feedback, etc. are least considered, In our approach, an efficient cloud service recommendation system has been developed enabling users to hire a cloud service according to their own requirements. The aim is to test services according to user needs and some stated parameters. Users select the criteria from given parameter repository, the quality attributes like security, privacy, data storage, etc. and thus services are evaluated. Finally, results are shown providing user with the flexibility and opportunity to select best cloud service.

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“…As the value of (β i ) increases the sensitivity considered by the end user for the specific parameter also increases. The individual utility function (U i ) is calculated using the formula given below which is used in [14 ] and [15 ].…”

Section: The Aggregated Utility Functionmentioning

confidence: 99%

A Service Broker Model for Cloud based Render Farm Selection

Annette¹,

Banu²

2014

IJCA

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Cloud computing is gaining popularity in the 3D Animation industry for rendering the 3D images. Rendering is an inevitable task in creating the 3d animated scenes. It is a process where the scene files to be animated is read and converted into 3D photorealistic images automatically. Since it is a computationally intensive task, this process consumes the majority of the time taken for 3D images production. As the scene files could be processed in parallel, clusters of computers called render farms can be used to speed up the rendering process. The advantage of using Cloud based render farms is that it is scalable and can be availed on demand. One of the important challenges faced by the 3D studios is the comparison and selection of the cloud based render farm service provider who could satisfy their functional and the non functional Quality of Service (QoS) requirements. In this paper we propose, a frame work for Cloud Service Broker (CSB) responsible for the selection and provision of the cloud based render farm. The Cloud Service Broker matches the functional and the non functional Quality of Service requirements (QoS) of the user with the service offerings of the render farm service providers and helps the user in selecting the right service provider using an aggregate utility function. The CSB also facilitates the process of Service Level Agreement (SLA) negotiation and monitoring by the third party monitoring services.

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A Framework for SOFTWARE-AS-A-SERVICE Selection and Provisioning

Abstract: ABSTRACT

Cited by 24 publications

References 20 publications

Towards a generic multilayer negotiation framework for efficient application provisioning in the cloud

Towards a generic multilayer negotiation framework for efficient application provisioning in the cloud

A User-Centered Approach for Cloud Service Selection and Recommendation

A Service Broker Model for Cloud based Render Farm Selection

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