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

A Survey on Resource Allocation Techniques in Cloud Computing

Cloud is an important and emerging technology utilized by various fields for storing, processing and retrieving of data anywhere and anytime without any interruption. Cloud is now acting as a platform for many companies for storing and other computational purposes to reduce infrastructure and maintenance cost similarly they can utilize their application widely based on pay per use. To make available of data to all cloud users Resource Allocation (RA) is mandatory process. In cloud hardware, software and platform are the resources utilized to satisfy user needs hence sharing these resources according to users need is a difficult task. Cloud service provider and cloud service consumer plays the major role in RA. The parameters under resource allocation, its issues and challenges are needed to be analyzed deeply before implementing any optimizing approach in RA. Hence in this work various resource allocation methods have been studied and issues in it is analyzed and presented as a survey. This work is useful for both cloud users and researchers in overcoming the challenges faced in RA

Formulating and managing viable SLAs in cloud computing from a small to medium service provider's viewpoint: A state-of-the-art review

© 2017 Elsevier Ltd In today's competitive world, service providers need to be customer-focused and proactive in their marketing strategies to create consumer awareness of their services. Cloud computing provides an open and ubiquitous computing feature in which a large random number of consumers can interact with providers and request services. In such an environment, there is a need for intelligent and efficient methods that increase confidence in the successful achievement of business requirements. One such method is the Service Level Agreement (SLA), which is comprised of service objectives, business terms, service relations, obligations and the possible action to be taken in the case of SLA violation. Most of the emphasis in the literature has, until now, been on the formation of meaningful SLAs by service consumers, through which their requirements will be met. However, in an increasingly competitive market based on the cloud environment, service providers too need a framework that will form a viable SLA, predict possible SLA violations before they occur, and generate early warning alarms that flag a potential lack of resources. This is because when a provider and a consumer commit to an SLA, the service provider is bound to reserve the agreed amount of resources for the entire period of that agreement – whether the consumer uses them or not. It is therefore very important for cloud providers to accurately predict the likely resource usage for a particular consumer and to formulate an appropriate SLA before finalizing an agreement. This problem is more important for a small to medium cloud service provider which has limited resources that must be utilized in the best possible way to generate maximum revenue. A viable SLA in cloud computing is one that intelligently helps the service provider to determine the amount of resources to offer to a requesting consumer, and there are number of studies on SLA management in the literature. The aim of this paper is two-fold. First, it presents a comprehensive overview of existing state-of-the-art SLA management approaches in cloud computing, and their features and shortcomings in creating viable SLAs from the service provider's viewpoint. From a thorough analysis, we observe that the lack of a viable SLA management framework renders a service provider unable to make wise decisions in forming an SLA, which could lead to service violations and violation penalties. To fill this gap, our second contribution is the proposal of the Optimized Personalized Viable SLA (OPV-SLA) framework which assists a service provider to form a viable SLA and start managing SLA violation before an SLA is formed and executed. The framework also assists a service provider to make an optimal decision in service formation and allocate the appropriate amount of marginal resources. We demonstrate the applicability of our framework in forming viable SLAs through experiments. From the evaluative results, we observe that our framework helps a service provider to form viable SLAs and later to manage them to effectively minimize possible service violation and penalties

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling

SLA-based trust model for secure cloud computing

Cloud computing has changed the strategy used for providing distributed services to many business and government agents. Cloud computing delivers scalable and on-demand services to most users in different domains. However, this new technology has also created many challenges for service providers and customers, especially for those users who already own complicated legacy systems. This thesis discusses the challenges of, and proposes solutions to, the issues of dynamic pricing, management of service level agreements (SLA), performance measurement methods and trust management for cloud computing.In cloud computing, a dynamic pricing scheme is very important to allow cloud providers to estimate the price of cloud services. Moreover, the dynamic pricing scheme can be used by cloud providers to optimize the total cost of cloud data centres and correlate the price of the service with the revenue model of service. In the context of cloud computing, dynamic pricing methods from the perspective of cloud providers and cloud customers are missing from the existing literature. A dynamic pricing scheme for cloud computing must take into account all the requirements of building and operating cloud data centres. Furthermore, a cloud pricing scheme must consider issues of service level agreements with cloud customers.I propose a dynamic pricing methodology which provides adequate estimating methods for decision makers who want to calculate the benefits and assess the risks of using cloud technology. I analyse the results and evaluate the solutions produced by the proposed scheme. I conclude that my proposed scheme of dynamic pricing can be used to increase the total revenue of cloud service providers and help cloud customers to select cloud service providers with a good quality level of service.Regarding the concept of SLA, I provide an SLA definition in the context of cloud computing to achieve the aim of presenting a clearly structured SLA for cloud users and improving the means of establishing a trustworthy relationship between service provider and customer. In order to provide a reliable methodology for measuring the performance of cloud platforms, I develop performance metrics to measure and compare the scalability of the virtualization resources of cloud data centres. First, I discuss the need for a reliable method of comparing the performance of various cloud services currently being offered. Then, I develop a different type of metrics and propose a suitable methodology to measure the scalability using these metrics. I focus on virtualization resources such as CPU, storage disk, and network infrastructure.To solve the problem of evaluating the trustworthiness of cloud services, this thesis develops a model for each of the dimensions for Infrastructure as a Service (IaaS) using fuzzy-set theory. I use the Takagi-Sugeno fuzzy-inference approach to develop an overall measure of trust value for the cloud providers. It is not easy to evaluate the cloud metrics for all types of cloud services. So, in this thesis, I use Infrastructure as a Service (IaaS) as a main example when I collect the data and apply the fuzzy model to evaluate trust in terms of cloud computing. Tests and results are presented to evaluate the effectiveness and robustness of the proposed model

The Contemporary Review of Notable Cloud Resource Scheduling Strategies

Cloud computing has become a revolutionary development that has changed the dynamics of business for the organizations and in IT infrastructure management. While in one dimension, it has improved the scope of access, reliability, performance and operational efficiency, in the other dimension, it has created a paradigm shift in the way IT systems are managed in an organizational environment. However, with the increasing demand for cloud based solutions, there is significant need for improving the operational efficiency of the systems and cloud based services that are offered to the customers. As cloud based solutions offer finite pool of virtualized on-demand resources, there is imperative need for the service providers to focus on effective and optimal resource scheduling systems that could support them in offering reliable and timely service, workload balancing, optimal power efficiency and performance excellence. There are numerous models of resource scheduling algorithms that has been proposed in the earlier studies, and in this study the focus is upon reviewing varied range of resource scheduling algorithms that could support in improving the process efficiency. In this manuscript, the focus is upon evaluating various methods that could be adapted in terms of improving the resource scheduling solutions