An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach

Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling

Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection

Application Performance Optimization in Multicloud Environment

Through the development and accessibility of the Internet, nowadays the cloud computing has become a very popular. Through the development and accessibility of the Internet, nowadays the cloud computing has become a very popular. This concept has the potential to change the use of information technologies. Cloud computing is the technology that provides infrastructure, platform or software as a service via the network to a huge number of remote users. The main benefit of cloud computing is the utilization of elastic resources and virtualization. Two main properties are required from clouds by users: interoperability and privacy. This article focuses on interoperability. Nowadays it is difficult to migrate an application between clouds offered by different providers. The article deals with that problem in multicloud environment. Specifically, it focuses on the application performance optimization in a multicloud environment. A new method is suggested based on the state of the art. The method is divided into three parts: multicloud architecture, method of a horizontal scalability, and taxonomy for multicriteria optimization. The principles of the method were applied in a design of multicriteria optimization architecture, which we verified experimentally. The aim of our experiment is carried on a portal offering a platform according to the users' requirements

Profit Driven Decision Assist System to Select Efficient IaaS Providers

IaaS providers provide infrastructure to the end users with various pricing schemes and models. They provide different types of virtual machines (small, medium, large, etc.). Since each IaaS provider uses their own pricing schemes and models, price varies from one provider to the other for the same requirements. To select a best IaaS provider, the end users need to consider various parameters such as SLA, pricing models/schemes, VM heterogeneity, etc. Since many parameters are involved, selecting an efficient IaaS provider is a challenging job for an end user. To address this issue, in this work we have designed, implemented and tested a decision-assist system which assists the end users to select efficient IaaS provider(s). Our decision-assist system consists of an analytical model to calculate the cost and decision strategies to assist the end user in selecting the efficient IaaS provider(s). The decision assist system considers various relevant parameters such as VM configuration, price, availability, etc. to decide the efficient IaaS provider(s). Rigorous experiments have been conducted by emulating various IaaS providers, and we have observed that our DAS successfully suggests the efficient IaaS provider/ providers by considering the input parameters given by the user

Planning and Optimization During the Life-Cycle of Service Level Agreements for Cloud Computing

Ein Service Level Agreement (SLA) ist ein elektronischer Vertrag zwischen dem Kunden und dem Anbieter eines Services. Die beteiligten Partner kl aren ihre Erwartungen und Verp ichtungen in Bezug auf den Dienst und dessen Qualit at. SLAs werden bereits f ur die Beschreibung von Cloud-Computing-Diensten eingesetzt. Der Diensteanbieter stellt sicher, dass die Dienstqualit at erf ullt wird und mit den Anforderungen des Kunden bis zum Ende der vereinbarten Laufzeit ubereinstimmt. Die Durchf uhrung der SLAs erfordert einen erheblichen Aufwand, um Autonomie, Wirtschaftlichkeit und E zienz zu erreichen. Der gegenw artige Stand der Technik im SLA-Management begegnet Herausforderungen wie SLA-Darstellung f ur Cloud- Dienste, gesch aftsbezogene SLA-Optimierungen, Dienste-Outsourcing und Ressourcenmanagement. Diese Gebiete scha en zentrale und aktuelle Forschungsthemen. Das Management von SLAs in unterschiedlichen Phasen w ahrend ihrer Laufzeit erfordert eine daf ur entwickelte Methodik. Dadurch wird die Realisierung von Cloud SLAManagement vereinfacht. Ich pr asentiere ein breit gef achertes Modell im SLA-Laufzeitmanagement, das die genannten Herausforderungen adressiert. Diese Herangehensweise erm oglicht eine automatische Dienstemodellierung, sowie Aushandlung, Bereitstellung und Monitoring von SLAs. W ahrend der Erstellungsphase skizziere ich, wie die Modellierungsstrukturen verbessert und vereinfacht werden k onnen. Ein weiteres Ziel von meinem Ansatz ist die Minimierung von Implementierungs- und Outsourcingkosten zugunsten von Wettbewerbsf ahigkeit. In der SLA-Monitoringphase entwickle ich Strategien f ur die Auswahl und Zuweisung von virtuellen Cloud Ressourcen in Migrationsphasen. Anschlie end pr ufe ich mittels Monitoring eine gr o ere Zusammenstellung von SLAs, ob die vereinbarten Fehlertoleranzen eingehalten werden. Die vorliegende Arbeit leistet einen Beitrag zu einem Entwurf der GWDG und deren wissenschaftlichen Communities. Die Forschung, die zu dieser Doktorarbeit gef uhrt hat, wurde als Teil von dem SLA@SOI EU/FP7 integriertem Projekt durchgef uhrt (contract No. 216556)

Selecting cloud computing service provider with fuzzy ahp

With the growing demand for outsourcing the ICT section of enterprises, Cloud Computing service providers increased their popularity. Selecting the most appropriate provider for a demanding enterprise depends on many criteria that are based on the strategies, requirements, and resources of the enterprise. Since this problem is a kind of decision problem and depends on criteria of decision-maker, it can be modeled as Multi-criteria Decision Making (MCDM) problem. In this research, a pilot case study is conducted in which the Cloud Computing service provider selection problem is modeled as a MCDM problem. For selecting the most appropriate provider, Fuzzy Extend Analysis is implemented in the case study

Selecting Cloud Deployment Model Using a Delphi Analytic Hierarchy Process (DAHP)

Cloud computing is a significant paradigm shift in information technology (IT) service offerings that has been receiving enormous attention in academic and IT industry. Recent years has seen exponential growth in cloud use adoption, where many organizations are moving their IT resources into cloud due to flexibility and low-cost. However, on account of rapid innovation and growth in cloud technologies and service providers, selecting the right cloud services, provider and strategy is becoming increasing a common challenge to organization during cloud adoption. In an attempt to address this challenge, we propose application of Delphi Analytic Hierarchy Process (DAHP) method in selecting cloud deployment model. There are several cloud deployment models and organizations must identify the right model that best suits their business needs. The proposed approach facilitates a collaborative decision making process, consisting a number of decision makers whom, with consensus facilitated by the DAHP process, identifies feasible approaches, decision making factors and ultimate selection of a cloud deployment model alternative that is based on organizational business needs and capabilities. The DAHP process is illustrated by a means of a case study. The DAHP result analysis, as was illustrated in the case study, helps in explaining and justifying the choice selected as the best cloud deployment model