A framework and tool to manage Cloud Computing service quality

Cloud Computing has generated considerable interest in both companies specialized in Information and Communication Technology and business context in general. The Sourcing Capability Maturity Model for service (e-SCM) is a capability model for offshore outsourcing services between clients and providers that offers appropriate strategies to enhance Cloud Computing implementation. It intends to achieve the required quality of service and develop an effective working relationship between clients and providers. Moreover, quality evaluation framework is a framework to control the quality of any product and/or process. It offers a tool support that can generate software artifacts to manage any type of product and service efficiently and effectively. Thus, the aim of this paper was to make this framework and tool support available to manage Cloud Computing service quality between clients and providers by means of e-SCM.Ministerio de Ciencia e Innovación TIN2013-46928-C3-3-RJunta de Andalucía TIC-578

Enhanced Cloud Computing Model Using Systematic Approach Towards The Quality Of Service In A Cloud Computing

Cloud computing is modrendevelopingtechnoloy which provides on-claim resources in cloud computing envoirnment.  Cloud computing is modern technology which guarantees to provide elastic Infrastructure, resources accessible via the Internet with low cost. Cloud refers to a huge bundle of computing and data resources which can be access to different protocols and interfaces. Cloud service model containsSoftware-as-a-service (SaaS),Infrastructure-as-a service (IaaS), and Platform-as-a-service (PaaS. Cloud users can enjoy these services without knowing the underlying technology behind the cloud. Quality of service playsa vital role in any network while providing efficient resourcesto users. To competitive gain, it is compulsory to cloud computing network operator  to gain  trust of users by providing the best quality of services. Resource virtualization, share pool of resources, on-demand network access, large datacentres, and highly-interactive web applications needs quality of services. In this paper we put an effort to enhance the cloud computing model to show the “Quality as-a-service(QaaS)”layer. This service layer will help the cloud provider how to enhance the quality of service to cloud users to gain competitive advantage over other cloud service providers.  Parameters which are to useto measure the quality of services includeService Response Time, Reliability, Interoperability, Accuracy,Execution time etc

Integrated Green Cloud Computing Architecture

Arbitrary usage of cloud computing, either private or public, can lead to uneconomical energy consumption in data processing, storage and communication. Hence, green cloud computing solutions aim not only to save energy but also reduce operational costs and carbon footprints on the environment. In this paper, an Integrated Green Cloud Architecture (IGCA) is proposed that comprises of a client-oriented Green Cloud Middleware to assist managers in better overseeing and configuring their overall access to cloud services in the greenest or most energy-efficient way. Decision making, whether to use local machine processing, private or public clouds, is smartly handled by the middleware using predefined system specifications such as service level agreement (SLA), Quality of service (QoS), equipment specifications and job description provided by IT department. Analytical model is used to show the feasibility to achieve efficient energy consumption while choosing between local, private and public Cloud service provider (CSP).Comment: 6 pages, International Conference on Advanced Computer Science Applications and Technologies, ACSAT 201

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Security and Privacy Issues in Cloud Computing

Cloud computing transforming the way of information technology (IT) for consuming and managing, promising improving cost efficiencies, accelerate innovations, faster time-to-market and the ability to scale applications on demand (Leighton, 2009). According to Gartner, while the hype grew ex-ponentially during 2008 and continued since, it is clear that there is a major shift towards the cloud computing model and that the benefits may be substantial (Gartner Hype-Cycle, 2012). However, as the shape of the cloud computing is emerging and developing rapidly both conceptually and in reality, the legal/contractual, economic, service quality, interoperability, security and privacy issues still pose significant challenges. In this chapter, we describe various service and deployment models of cloud computing and identify major challenges. In particular, we discuss three critical challenges: regulatory, security and privacy issues in cloud computing. Some solutions to mitigate these challenges are also proposed along with a brief presentation on the future trends in cloud computing deployment

Multi-Criteria Service Selection Agent for Federated Cloud

Federated cloud interconnects small and medium-sized cloud service providers for service enhancement to meet demand spikes. The service bartering technique in the federated cloud enables service providers to exchange their services. Selecting an optimal service provider to share services is challenging in the cloud federation. Agent-based and Reciprocal Resource Fairness (RRF) based models are used in the federated cloud for service selection. The agent-based model selects the best service provider using Quality of Service (quality of service). RRF model chooses fair service providers based on service providers\u27 previous service contribution to the federation. However, the models mentioned above fail to address free rider and poor performer problems during the service provider selection process. To solve the above issue, we propose a Multi-criteria Service Selection (MCSS) algorithm for effectively selecting a service provider using quality of service, Performance-Cost Ratio (PCR), and RRF. Comprehensive case studies are conducted to prove the effectiveness of the proposed algorithm. Extensive simulation experiments are conducted to compare the proposed algorithm performance with the existing algorithm. The evaluation results demonstrated that MCSS provides 10% more services selection efficiency than Cloud Resource Bartering System (CRBS) and provides 16% more service selection efficiency than RPF

Preferences based Customized Trust Model for Assessment of Cloud Services

In cloud environment, many functionally similar cloud services are available. But, the services differ in Quality of Service (QoS) levels, offered by them. There is a diversity in user requirements about the expected qualities of cloud services. Trust is a measure to understand whether a cloud service can adequately meet the user requirements. Consequently, trust assessment plays a significant role in selecting the suitable cloud service. This paper proposes preferences based customized trust model (PBCTM) for trust assessment of cloud services. PBCTM takes into account user requirements about the expected quality of services in the form of preferences. Accordingly, it performs customized trust assessment based on the evidences of various attributes of cloud service. PBCTM enables elastic trust computation, which is responsive to dynamically changing user preferences with time. The model facilitates dynamic trust based periodic selection of cloud services according to varying user preferences. Experimental results demonstrate that the proposed preferences based customized trust model outperforms the other model in respect of accuracy and degree of satisfaction