A Survey of Resource Management Challenges in Multi-cloud Environment: Taxonomy and Empirical Analysis

Cloud computing has seen a great deal of interest by researchers and industrial firms since its first coined. Different perspectives and research problems, such as energy efficiency, security and threats, to name but a few, have been dealt with and addressed from cloud computing perspective. However, cloud computing environment still encounters a major challenge of how to allocate and manage computational resources efficiently. Furthermore, due to the different architectures and cloud computing networks and models used (i.e., federated clouds, VM migrations, cloud brokerage), the complexity of resource management in the cloud has been increased dramatically. Cloud providers and service consumers have the cloud brokers working as the intermediaries between them, and the confusion among the cloud computing parties (consumers, brokers, data centres and service providers) on who is responsible for managing the request of cloud resources is a key issue. In a traditional scenario, upon renting the various cloud resources from the providers, the cloud brokers engage in subletting and managing these resources to the service consumers. However, providers’ usually deal with many brokers, and vice versa, and any dispute of any kind between the providers and the brokers will lead to service unavailability, in which the consumer is the only victim. Therefore, managing cloud resources and services still needs a lot of attention and effort. This paper expresses the survey on the systems of the cloud brokerage resource management issues in multi-cloud environments

A Decision Making Model for the Adoption of Cloud Computing in Jamaican Organizations

Cloud computing is the current technological silver bullet that has been proposed for solving a variety of Information Systems (IS) problems facing organizations in developing countries including bridging the digital divide. However, the large number of cloud options available can make determining the most applicable solution to an organization non-trivial. This paper looks at these options and the barriers to adoption facing Small/Medium Enterprises (SMEs) in Jamaica. A Simple Additive Weighting (SAW) model which can be used in the cloud adoption decision process is then developed and tested using an example

A novel cloud services recommendation system based on automatic learning techniques

The Cloud Computing technology is evolving constantly but essence remains the same that is to offer distinct cost saving opportunities by consolidating and restructuring information technology as a service. With the continuously increasing cloud provisions, cloud consumers start to have difficulties to find the best relevant services that suit their requirements. Therefore, selecting best services by cloud users is becoming a greater challenge. In this paper, we present a framework of services' recommendation system in a Cloud environment, using automatic learning techniques. The system aims at finding the services that suit the interests and preferences of cloud consumers by combining content based and behaviour based recommendations. In this paper, we present, USTHBCLOUD, a cloud services recommendation prototype evaluated with an experimental study. © 2017 IEEE

Trusted Energy-Efficient Cloud-based Services Brokerage Platform

The use of cloud computing can increase service efficiency and service level agreements for cloud users, by linking them to an appropriate cloud service provider, using the cloud services brokerage paradigm. Cloud service brokerage represents a promising new layer which is to be added to the cloud computing network, which manages the use, performance and delivery of cloud services, and negotiates relationships between cloud service providers and cloud service consumers. The work presented in this paper studies the research related to cloud service brokerage systems along with the weaknesses and vulnerabilities associated with each of these systems, with a particular focus on the multicloud-based services environment. In addition, the paper will conclude with a proposed multi-cloud framework that overcomes the weaknesses of other listed cloud brokers. The new framework aims to find the appropriate data centre in terms of energy efficiency, QoS and SLA. Moreover, it presents a security model aims to protect the proposed multicloud framework and highlights the key features that must be available in multi-cloud-based brokerage systems

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

A service concept recommendation system for enhancing the dependability of semantic service matchmakers in the service ecosystem environment

A Service Ecosystem is a biological view of the business and software environment, which is comprised of a Service Use Ecosystem and a Service Supply Ecosystem. Service matchmakers play an important role in ensuring the connectivity between the two ecosystems. Current matchmakers attempt to employ ontologies to disambiguate service consumers’ service queries by semantically classifying service entities and providing a series of human computer interactions to service consumers. However, the lack of relevant service domain knowledge and the wrong service queries could prevent the semantic service matchmakers from seeking the service concepts that can be used to correctly represent service requests. To resolve this issue, in this paper, we propose the framework of a service concept recommendation system, which is built upon a semantic similarity model.This system can be employed to seek the concepts used to correctly represent service consumers’ requests, when a semantic service matchmaker finds that the service concepts that are eventually retrieved cannot match the service requests. Whilst many similar semantic similarity models have been developed to date, most of them focus on distance-based measures for the semantic network environment and ignore content-based measures for the ontology environment. For the ontology environment in which concepts are defined with sufficient datatype properties, object properties, and restrictions etc., the content of concepts should be regarded as an important factor in concept similarity measures. Hence, we present a novel semantic similarity model for the service ontology environment. The technical details and evaluation details of the framework are discussed in this paper

Modelo Comparativo de Plataformas Cloud y Evaluación de Microsoft Azure, Google App Engine y AmazonEC2

[ES] Existe una gran cantidad de proveedores de servicios en la nube siendo los más importantes Microsoft, Google y Amazon. Otros proveedores también son Rackspace, IBM, Oracle, Salesforce, etc. Un aspecto relevante para los desarrolladores y clientes es conocer las características de estos proveedores para tener información objetiva de cómo elegir entre una plataforma u otra dependiendo de sus objetivos y necesidades. En este proyecto se ha realizado un estudio para determinar las características de calidad relevantes de las plataformas cloud y se ha propuesto un modelo de calidad basado en la ISO/IEC 25010 para guiar a los usuarios en la comparación y selección de dichas plataformas. El modelo está soportado por un sistema de recomendación que permite a los usuarios especificar sus objetivos y comparar plataformas cloud mediante un conjunto de atributos y métricas de calidad. Este modelo se ha aplicado a un estudio para comparar las plataformas Microsoft Azure, Google App Engine y Amazon Elastic Compute Cloud (EC2) permitiendo la evaluación de sus características de calidad más relevantes.[EN] There is a large number of cloud service providers being the most important Microsoft, Google and Amazon. Other providers are also Rackspace, IBM, Oracle, Salesforce, etc. A relevant aspect for developers and customers is to determine the characteristics of these providers to have objective information on how to choose between one platform or another depending on their objectives and needs. In this project, we have carried out a study to determine the relevant quality characteristics of cloud platforms and a quality model based on ISO/IEC 25010 has been proposed to guide users in the comparison and selection of these platforms. The model is supported by a recommendation system that allows users to specify their objectives and compare cloud platforms through a set of quality attributes and metrics. This model has been applied to a case study which compares the Microsoft Azure, Google App Engine and Amazon Elastic Compute Cloud (EC2) platforms allowing the evaluation of its most relevant quality characteristics.Álvarez Vañó, JM. (2018). Modelo Comparativo de Plataformas Cloud y Evaluación de Microsoft Azure, Google App Engine y AmazonEC2. http://hdl.handle.net/10251/101221TFG