An Approach for Selecting Software-as-a-Service (SaaS) Product

Godse, Manish; Mulik, Shrikant

doi:10.1109/cloud.2009.74

Cited by 220 publications

(135 citation statements)

References 7 publications

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“…Therefore, the selection of cloud SaaS products is a multi-criteria decision-making (MCDM) problem as vendors with the best technology are not always suitable for a given enterprise (Whaiduzzaman et al 2014). Being that MCDM problems cannot be solved with mere judgement or intuition, it is necessary therefore to have quantifiable values instead of subjective opinions to make an informed decision (Godse & Mulik, 2009). Besides, what becomes obvious in the preceding section(s) is that migrating to, or switching between SaaS vendors in the cloud requires making several decisions related to how the challenges of lock-in can be mitigated at pre-and post-deployment management stage(s).…”

Section: Decision Framework and Tools For Supporting Cloud Saas Migrmentioning

confidence: 99%

A Holistic Decision Framework to Avoid Vendor Lock-in for Cloud SaaS Migration

Opara-Martins¹,

Sahandi²,

Tian³

2017

CIS

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Cloud computing offers an innovative business model to enterprise for IT services consumption and delivery. Software as a Service (SaaS) is one of the cloud offerings that attract organisations as a potential solution in reducing their IT cost. However, the vast diversity among the available cloud SaaS services makes it difficult for customers to decide whose vendor services to use or even to determine a valid basis for their selections. Moreover, this variety of cloud SaaS services has led to proprietary architectures and technologies being used by cloud vendors, increasing the risk of vendor lock-in for customers. Therefore, when enterprises interact with SaaS providers within the purview of the current cloud marketplace, they often encounter significant lock-in challenges to migrating and interconnecting cloud. Hence, the complexity and variety of cloud SaaS service offerings makes it imperative for businesses to use a clear and well understood decision process to procure, migrate and/or discontinue cloud services. To date, the expertise and technological solutions to simplify such transition and facilitate good decision making to avoid lock-in risks in the cloud are limited. Besides, little investigation has been carried out to provide a comprehensive decision framework to support enterprises on how to avoid lock-in risks when selecting and implementing cloud-based SaaS solutions within existing environments. Such decision framework is important to reduce complexity and variations in implementation patterns on the cloud provider side, while at the same time minimising potential switching cost for enterprises by resolving integration issues with existing IT infrastructures. This paper proposes a holistic 6-step decision framework that enables an enterprise to assess its current IT landscape for potential SaaS replacement, and provides effective strategies to mitigate vendor lock-in risks in cloud (SaaS) migration. The framework follows research findings and addresses the core requirements for choosing vendor-neutral interoperable and portable cloud services without the fear of vendor lock-in, and architectural decisions for secure SaaS migration. Therefore, the results of this research can help IT managers have a safe and effective migration to cloud computing SaaS environment.

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Section: Decision Framework and Tools For Supporting Cloud Saas Migrmentioning

confidence: 99%

A Holistic Decision Framework to Avoid Vendor Lock-in for Cloud SaaS Migration

Opara-Martins¹,

Sahandi²,

Tian³

2017

CIS

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“…All the weights are shown in Table VII. Step 5: According to Table VI and Table VII, the fuzzy scores of C 1 based on the remaining eight decision-makers are computed by Equation (6). Finally, by Equation (7), the final score of C 1 is 74.…”

Section: A Case Studymentioning

confidence: 99%

“…In [6], Godse et al focus on the selection of Software-as-a-Service clouds based on AHP. Five factors (i.e., functionality, architecture, usability, vendor reputation and cost) are considered in their approach.…”

Section: Related Workmentioning

confidence: 99%

Cloud Service Selection Based on the Aggregation of User Feedback and Quantitative Performance Assessment

Wang

Orgun

2013

2013 IEEE International Conference on Services Computing

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Abstract-Cloud computing has been attracting huge attention in recent years. More and more individuals and organizations have been moving their work into cloud environments because of its flexibility and low-cost. Due to the emergence of a variety of cloud service providers, selecting the most suitable cloud service becomes increasingly important for potential cloud users. In prior studies, the selection and comparison of cloud services usually focus on objective performance analysis based on cloud monitoring and benchmark testing without considering the viewpoints of cloud users who are indeed consuming cloud services. This causes a problem that some vital aspects which concern cloud consumers (e.g., privacy and cloud providers' reputation) are not taken into account in cloud service selection.In this paper, we propose a novel model of cloud service selection by aggregating the information from both the feedback from cloud users and objective performance analysis from a trusted third party. Based on this model, we first propose a framework which supports our cloud service selection approach. Then after classifying subjective assessment and objective assessment, we present a novel cloud service selection approach to aggregate all subjective assessments and objective assessments through a fuzzy simple additive weighting system. In addition, to reduce the bias caused by unreasonable feedback from unprofessional or malicious cloud users, a method is proposed for filtering the feedback from such users. After processing, the aggregated result can quantitatively reflect the overall quality of a cloud service. Finally, a case study is presented to illustrate the advantages of our approach.

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“…The reciprocal evaluation matrix and the induced bias matrix are used to recognize inconsistent components and to make better consistency ratio. In [14], SaaS products are made available in a cloud computing environment that uses AHP to prioritize product characteristic decided by an expert scoring system. The problem review consists of a number, say K, of alternatives and a number, say N, of decision criteria.…”

Section: Fig 1 Decomposition Of Problem Into a Hierarchymentioning

confidence: 99%

Utilizing AHP: Resource Allocation Problem in Cloud

Singh¹,

Dutta²

2015

IJCA

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Cloud Computing is changing the scheme by providing different services to business and government sectors as well as to sole users irrespective of their location. Cloud Computing provides scalable and on demand services to users, but this technology has many challenges. In several applications the last decision is based on the estimate of a number of alternatives in terms of a number of criteria. This problem may become difficult one when criteria are expressed in different tasks or jobs, relevant data are difficult to be quantified. The Analytic Hierarchy Process (AHP) is an effective method dealing with this kind of decision problems. This paper deals with priorities, ranking with consistency method and their results are given through a numerical example. The results show that distributive mode has fast convergence and smaller computational complexity than ideal mode for close system when the AHP method is used in cloud computing applications.

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An Approach for Selecting Software-as-a-Service (SaaS) Product

Cited by 220 publications

References 7 publications

A Holistic Decision Framework to Avoid Vendor Lock-in for Cloud SaaS Migration

A Holistic Decision Framework to Avoid Vendor Lock-in for Cloud SaaS Migration

Cloud Service Selection Based on the Aggregation of User Feedback and Quantitative Performance Assessment

Utilizing AHP: Resource Allocation Problem in Cloud

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