Enhanced Cloud Service Provisioning for Social Networks

Cloud brokering service is gaining momentum as a promising area of research and business opportunity. As cloud computing enables the user to obtain the infrastructure, platform and software required as a service, there is no need to invest huge amount as the capital to run the business and hence more and more companies are moving to the cloud. But collecting the information from the multiple cloud service providers and comparing the various services in order to zero down the right service provider is a time consuming task given the plethora and the variety of cloud service offerings. As many cloud service providers are entering the market, the users look for the cloud brokers who could help them in the dynamic selection and aggregation of services based on their Quality of Service (QoS) requirements and the cost. The other important aspects considered by the users while selecting the service provider is their reputation. This paper introduces a Cloud Broker Service (CBS) framework that facilitates the dynamic selection of the IaaS service providers based on the QoS requirements, cost and the reputation of the service providers. The proposed CBS framework provides a ranking mechanism that evaluates the services offered by the various IaaS service providers like Amazon EC2, Rackspace etc., for various Quality of Service requirements based on the Service Measurement Index (SMI) and the reputation of the company. The evaluation of the services using the Service Measurement Index proposed by CSMIC (Cloud Service Measurement Index Consortium) to identify the various measurement indexes that are important for evaluating a cloud service enables to zero down the right service provider who could satisfy the user's requirements. The applicability of the ranking algorithm and the cloud broker frame work is shown using a case study.

2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), 2018

Cloud service selection is complicated by the prospect that there exist a large number of services and each service is characterized by multiple QoS attributes. Various commercial tools have been developed in order to help cloud consumers with selecting best cloud services. This paper provides an in-depth analysis of the three commercial cloud service selection tools as well as the way they represent QoS attributes. Accordingly, it proposes a new model that succinctly represents QoS attributes which cloud consumers can easily use (and understand) when selecting cloud services. The proposed model also classifies the QoS attributes into four main categories of technical, strategic & organizational, economic and political & legislative. These QoS attributes can also be seamlessly fed into the multi-criteria decision (e.g., MCDM)-which compares and ranks different QoS attributes of multiple alternatives in order to decide which services are most suitable for cloud consumers.

cloudbus.org

With the growth of Cloud Computing, more and more companies are offering different cloud services. From the customer's point of view, it is always difficult to decide whose services they should use, based on users' requirements. Currently there is no software framework which can automatically index cloud providers based on their needs. In this work, we propose a framework and a mechanism, which measure the quality and prioritize Cloud services. Such framework can make significant impact and will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their Quality of Services (QoS).

Building high quality cloud applications becomes an urgently required research problem. Nonfunctional performance of cloud services is usually described by quality-of-service (QoS). In cloud applications, cloud services are invoked remotely by internet connections. The QoS Ranking of cloud services for a user cannot be transferred directly to another user, since the locations of the cloud applications are quite different. Personalized QoS Ranking is required to evaluate all candidate services at the user-side but it is impractical in reality. To get QoS values, the service candidates are usually required and it's very expensive. To avoid time consuming and expensive realworld service invocations, this paper proposes a CloudRank framework which predicts the QoS ranking directly without predicting the corresponding QoS values. This framework provides an accurate ranking but the QoS values are same in both algorithms so, an optimal VM allocation policy is used to improve the QoS performance of cloud services and it also provides better ranking accuracy than CloudRank2 algorithm.

2009

In recent years, Cloud computing is gaining much popularity as it can efficiently utilize the computing resources and hence can contribute to the issue of Green IT to save energy. So to make the Cloud services commercialized, Cloud markets are necessary and are being developed. As the increasing numbers of various Cloud services are rapidly evolving in the Cloud market, how to select the best and optimal services will be a great challenge. In this paper we present a Cloud service selection framework in the Cloud market that uses a recommender system (RS) which helps a user to select the best services from different Cloud providers (CP) that matches user requirements. The RS recommends a service based on the network QoS and Virtual Machine (VM) platform factors of difference CPs. The experimental results show that our Cloud service recommender system (CSRS) can effectively recommend a good combination of Cloud services to consumers.

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.

Computational Intelligence and Neuroscience

Cloud computing has grown as a computing paradigm in the last few years. Due to the explosive increase in the number of cloud services, QoS (quality of service) becomes an important factor in service filtering. Moreover, it becomes a nontrivial problem when comparing the functionality of cloud services with different performance metrics. Therefore, optimal cloud service selection is quite challenging and extremely important for users. In the existing approaches of cloud service selection, the user’s preferences are offered by the user in a quantitative form. With fuzziness and subjectivity, it is a hurdle task for users to express clear preferences. Moreover, many QoS attributes are not independent but interrelated; therefore, the existing weighted summation method cannot accommodate correlations among QoS attributes and produces inaccurate results. To resolve this problem, we propose a cloud service framework that takes the user’s preferences and chooses the optimal cloud service b...

International Journal of Cloud Applications and Computing, 2014

Cloud Computing is a promising computing paradigm that provides flexible, Internet-accessible resources allocation on demand on a pay-as-you-go basis. With the growth and expansion of Cloud services and participation of various services providers, the description of quality parameters and measurement units started to diverse and sometimes contradict. Such ambiguity does not only result in the raise of various QoS interoperability problems, but also in the distraction of the services consumers who find themselves unable to match their quality requirements with the providers' offerings. Influenced by such diversity, the available QoS models are limited to either cost-benefit analysis or performance evaluation, without being able to cover a comprehensive set of well-defined quality aspects. In this paper, we provide a complete framework for such problem. We firstly propose a novel QoS ontology that combine and define all of the existing quality aspects in a unified way to efficient...

Lecture Notes in Computer Science, 2020

Nowadays, the cloud computing industry is enjoying an exponential growth, where several cloud service providers compete to be one of the market leaders. Usually, providers offering similar services use different non-functional attributes to describe them. Thus, given the heterogeneity and diversity of services descriptions, the selection process of the appropriate cloud service becomes challenging. Architects no longer know what criteria to use to make the suitable cloud services selection. In this paper, we highlight the challenge of identifying key elements of comparisons and their relationship for selecting cloud services. Further, we propose a methodology to solve this issue based on real data available from service providers and benchmark work. Our methodology is validated based on two case studies of cloud relational databases and cloud queuing services.

Cloud computing plays an important role in the access of infrastructure and application services on a subscription basis. As a result, several enterprises have started to offer different cloud services to their customers. Due to the vast diversity in the available cloud services, from the users point of view, it is difficult for them to choose the service that fits best into their requirements though there are ample number of system that help the users assist in choosing the appropriate service provider, they are still not completely satisfactory. This paper discusses an ontology based system that help the users evaluate the cloud service providers based on their requirements and quality of service by the providers. Here the system is designed as three phases, they are (i) processing of functional requirements (ii) processing of non-functional requirements and (iii) ranking of service providers. The using ontology with explicit meaning, making it easier to automatically process and integrate information available for comparison, selection and ranking of cloud service providers using Analytic Hierarchy Process (AHP) to prioritize the cloud services. This system also enables the service providers to improve their Quality of Services (QoS).