Cloud Infrastructure Services Selection and Evaluation

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant increase of new cloud services almost every month by both large corporations (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g. Rackspace and FlexiScale), the selection scenarios become more and more complex. This is aggregated by confusing and ambiguous terminology and non-standardized interfaces. This is challenging for decision-makers such as application developers and chief information officers as they are overwhelmed by various choices available. In this thesis, I will address the above challenges by developing several techniques. Firstly, I define the Cloud Computing Ontology (CoCoOn). CoCoOn defines concepts, features, attributes and relations of Cloud infrastructure services. Secondly, I propose a service selection method that adopts an analytic hierarchy process (AHP)-based multi-criteria decision-making technique. It allows users to define multiple design-time constraints like renting costs, data centre locations, service features and real-time constraints, such as end-to-end message latency and throughput. These constraints are then matched against our model to compute the possible best-fit combinations of cloud Infrastructure, offered as a Service (IaaS). Pairwise comparisons are used to help users determine a relative preference among a pool of nonnumerical attributes. Criteria that are taken into consideration during comparison can be grouped into two categories: the benefit and the cost. Based on this, I define a cost-benefit-ratio-based evaluation function to calculate the ranking for Cloud service options. Thirdly, I suggest a theory-based queuing approach for estimating IaaS usage. Queuing theory is a widely studied method in QoS modelling and optimization. From the infrastructure system administrator perspective, I explore several ways to apply the queuing theory model to estimate the best-fit resource allocation for achieving the desired SLA. Finally, the thesis shows how an integrated system, CloudRecommender, can be built from our proposed approaches

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Cloud Services Brokerage: A Survey and Research Roadmap

A Cloud Services Brokerage (CSB) acts as an intermediary between cloud service providers (e.g., Amazon and Google) and cloud service end users, providing a number of value adding services. CSBs as a research topic are in there infancy. The goal of this paper is to provide a concise survey of existing CSB technologies in a variety of areas and highlight a roadmap, which details five future opportunities for research.Comment: Paper published in the 8th IEEE International Conference on Cloud Computing (CLOUD 2015

Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of B.W.M. and interval valued intuitionistic fuzzy T.O.D.I.M.

open access articleDeveloping and accepting industry 4.0 influences the industry structure and customer willingness. To a successful transition to industry 4.0, implementation strategies should be selected with a systematic and comprehensive view to responding to the changes flexibly. This research aims to identify and prioritise the strategies for implementing industry 4.0. For this purpose, at first, evaluation attributes of strategies and also strategies to put industry 4.0 in practice are recognised. Then, the attributes are weighted to the experts’ opinion by using the Best Worst Method (BWM). Subsequently, the strategies for implementing industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the Interval Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of ‘Technology’, ‘Quality’, and ‘Operation’ have respectively the highest importance. Furthermore, the strategies for “new business models development’, ‘Improving information systems’ and ‘Human resource management’ received a higher rank. Eventually, some research and executive recommendations are provided. Having strategies for implementing industry 4.0 is a very important solution. Accordingly, multi-criteria decision-making (MCDM) methods are a useful tool for adopting and selecting appropriate strategies. In this research, a novel and hybrid combination of BWM-TODIM is presented under IVIF information

Investigating Decision Support Techniques for Automating Cloud Service Selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac

Transferable knowledge for Low-cost Decision Making in Cloud Environments

Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenario