Risk-Driven Framework for Decision Support in Cloud Service Selection

Abstract

The growth in the number of cloud computing users has led to the availability of a variety of cloud based services provided by different vendors. This has made the task of selecting a suitable set of services quite difficult. There has been a lot of research towards the development of suitable decision support system (DSS) to assist users in making an optimal selection of cloud services. However, existing decision support systems cannot address two crucial issues: firstly, the involvement of both business and technical perspectives in decision making simultaneously and, secondly, the multiple-clouds services based selection using a single DSS. In this paper, we tackle these issues in the light of solving the problem of cloud service discovery. In particular, we present the following novel contributions: Firstly, we present a critical analysis of the state-of-the-art in decision support systems. Based on our analysis, we identify critical shortcomings in the existent tools and develop the set of requirements which should be met by a potential DSS. Secondly, we present a new holistic framework for the development of DSS which allows a pragmatic description of user requirements. Additionally, the data gathering and analysis is studied as an integral part of the proposed DSS and therefore, we present concrete algorithms to assess the data for an optimal service discovery. Thirdly, we assess our framework for applicability to cloud service selection using an industrial case study. We also demonstrate the implementation and performance of our proposed framework using a prototype which serves as a proof of concept. Overall, this paper provides a novel and holistic framework for development of a multiple cloud service discovery based decision support system. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-1-4799-8006-2/15 $31.00

Daleel: Simplifying cloud instance selection using machine learning

“…This feature is still largely lacking from cloud brokerage solutions [26], although some efforts have started to surface, e.g. STRATOS [23], MODACloud [10], Cloud4SOA [15], mOSAIC [24], ARTIST [20], Broker@Cloud [22], and [6]. We conjecture that there is still a long way to go in terms of providing dynamic decision making that can effectively optimise deployment to the specific functional and non-functional requirements on a per-application basis.…”

On Supporting Service Selection for Collaborative Multi-cloud Ecosystems in Community Networks

“…In particular, the novelty of this DSS over the one proposed in [7] lies in accounting for multi-cloud environments unique to community clouds.…”

“…For instance, DSS adopting three dimensional approach of satisfying risk, cost and quality based aspects in such multi-cloud environments are particularly effective. In terms of cloud service selection, a novel DSS proposed in [7] is based on such approach. This DSS implements a risk-driven methodology to translate the user requirements into technical characteristics of the cloud services, and recommends the most accurate service selection that minimises the risk and maximises the quality at an appropriate cost.…”

“…However, they do not explicitly analyse these aspects in a multi-cloud context. The DSS proposed in [7] is inspired by the principles of SMI. It takes into account the multi-cloud ecosystems in three dimensional approach.…”

BPM in the Cloud: The BOC Case

“…In particular, BOC contributed a number of functional requirements, an initial set of decision criteria, and general success criteria for a Decision Support System to be developed [4]. Based on the requirements gathered, a risk-driven framework for decision support [3], a methodology for eliciting risk, cost and quality aspects in multi-Cloud environments [5,6], and a prototype of a Decision Support System (DSS) have Data privacy, data integrity, maintainability, end user performance, service availability, cost stability been developed. BOC used this methodology and the prototypical DSS to perform a light weight risk analysis by first defining a set of relevant business-oriented and technology oriented assets, determining risks related to these assets, and treatments mitigating these risks [4]).…”