RETRACTED: Modeling and verifying a resource allocation algorithm for secure service migration for commercial cloud systems

With the advent of gaining popularity in cloud computing, there is an increase in demand of resources among the heterogeneous workload types on cloud. Resource management is a key challenging problem that is faced by the cloud service providers, by achieving business goals and agreed level of service with the subscribers. This paper focuses on dynamic resource allocation with risk analysis by meeting service level agreements. Further proposed framework handles heterogeneous workload types by dynamic capacity planning with risk assessment to maximise the profit and resource utilisation on clouds. In addition to advanced resource reservation, SLA-based scheduling/rescheduling with risks involved in resource allocation is considered in the proposed model. The experimental results demonstrate that proposed framework maximises the resource utilisation and profit gain of the cloud service provider when evaluated against widely used static configuration strategy.

Cloud computing is currently the most hyped and popular paradigm in the domain of distributed computing. Multiple vendors are providing infrastructure, platform, and software-related cloud products or solutions, and the number of such vendors is growing rapidly. But the reality is that there is very little in the form of standards in general and interoperability standards in particular. This leads to a challenging situation for cloud service consumers as they need to use solutions provided by multiple cloud vendors in order to fulfill their business requirements, and the lack of standardization leads to lack of interoperability among the solutions provided by varied vendors. Service brokers act as intermediaries between the cloud service providers and consumers. The role of service brokers becomes critical in situations that need integration among multiple systems as they bridge the interoperability gaps among multiple cloud solutions and thus compensate for the lack of standards. They do so by acting as agents adding value on behalf of the service consumers. The usage of such cloud brokers also helps to future-proof the solution as service brokers add a layer of indirection, thus enabling loose coupling among the cloud service consumers and providers. The aim of this chapter is to review the role of cloud service brokers in cloud computing. The review includes consolidation of the definitions and characteristics of cloud service brokers, review of the current brokerage solutions, and interfacing requirements. Also presented are a high-level cloud service broker functional architecture, criteria for selection of a cloud service broker, future trends and review of benefits, and challenges of using cloud service brokers.

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

Cloud service, which has changed the business computing pattern, redefines the calculation scope of distributed systems by cloud computing approaches. The existing cloud service model provides different users an advanced on-demand computing model, and then achieves resource interaction between cloud service providers and users by cloud computing. However, this model, working as a centralized service strategy of resource allocation, also generates much scattered space debris resulting in a low efficiency of resource scheduling and utilization. To address this issue, we devise an improved model of crowd sourcing service and propose a technique of storage resource allocation and scheduling, where cloud service providers, as a resource agent, only provide leasing storage space for individual applicants instead of providing storage resources directly. This technique achieves allocation and scheduling of individual storage resources dynamically, according to user's expectations and individual storage providers constraints. The experimental results based on a large data set show that the improved model reduces the storage space of cloud service providers by 88.2% and improves storage space utilization rate of individuals by 65.2%, The improved model reduces the user fees by 64.8% and the cloud services development cost by 78.6% than that of existing models, In addition, the improved model and methods provide higher profits and efficiency for cloud service providers.

Cloud storage provides great benefits and advantages like reduction in cost, increased flexibility, elasticity and the efficient use of resources. With these great advantages, there are concerns and problem for cloud users. Some obstacles in using cloud computing services are because of its distributed and untransparent nature. That's why users have no control over their data and information and are not sure of whether or not to trust cloud providers. Therefore, the objective of this research is to examine the influence of customer and provider characteristics on cloud service outcome, impact of Trust on cloud service outcome, and to examine if Trust mediate the relationship of customer characteristics and provider’s characteristics with cloud service outcome. In order to achieve the objectives of this research, the analyses of measurement model, factor analysis, assessment of structure model and regression analysis has been conducted. The Findings of this study shows that the existing threats in cloud service is one of the reason why users are not trusting cloud service providers. These threats which are privacy and security cause unwillingness to use cloud services. This study also found that Trust plays an enormous role for cloud service users and providers because it increases usage, willingness to use among customers, and reputation of cloud service providers. It also found that Outcome is really low if there is no trust in these services. This study will help cloud service providers/developers to know the reasons why some people and organizations are not willing to use their services.

Allocating and managing resources while considering the quality of service is considered a fundamental and complex research problem in a cloud environment. An optimal resource allocation optimizes several parameters such as optimizing cost and resource utilization or maximizing any quality parameters. However, to ensure better customer service, Cloud Service Providers (CSPs) should consider most of the quality attributes while allocating resources to the cloud infrastructures. Existing research does not evaluate trust as a quantitative attribute, thus a trade-off between trust and performance in resource allocation is also absent in the research area. We propose a model to consider both trust and delay in this paper. The trust of a CSP is quantitatively estimated through some attributes and metrics. Availability, reliability, data integrity, and efficiency are considered to estimate the trust. The objective is to maximize the trust of the allocation while minimizing the communication delay. The proposed joint optimization model combines the previous credentials of the CSPs and the present resource constraints. To solve the problem heuristically, a genetic algorithm is applied. The model uses a number of parameters that provide the flexibility to adapt several service requirements. The effectiveness and applicability of the proposed approach are demonstrated through experiments. The results ensure that the effectiveness in the estimation of trust evaluation for different CSPs with the proposed attributes. Moreover, integrating trust in the resource allocation model allocates appropriate resources while enhancing the trust and reducing the communication delay in the overall allocation.

Cloud Service Providers supply services to clients in terms of their demands. They need to be constantly under monitoring for their services with respect to consensus agreements between clients and service providers. A Third Party Auditor or TPA as a trusted organization appears to be necessary to monitor executing agreements of cloud services. Using a third party as an extra component creates cost overheads for clients in a cloud environment. Thus, introducing a cost efficient framework for a cloud environment which includes a third party is an eminent achievement to make a TPA feasible and practical in cloud environments. In this paper, we propose a TPA framework for monitoring service level agreements between cloud service providers and cloud clients using several cloud resources. This framework employs different types of service deployments from various cloud service providers excluding the cloud service provider which is being monitored. Then, we demonstrate that the framework can mitigate costs of a third party auditor in a cloud environment. Simulations of trends for costs exhibits cost efficiency of at least forty percent over ten years when a TPA follows our proposed framework in comparison to other frameworks. Finally, we provide an analysis to compare characteristics of our framework with other frameworks and discuss the advantages of our proposed framework. Our results indicate that TPA as a component of the framework not only reduces overall costs of its presentation in a cloud environment but additionally improves management efficiency and security.

Global health engagement (GHE) is an integral part of the cooperation efforts of the U.S. Department of Defense (DoD) and the geographic combatant commands (GCCs) with partner nations and provides support in training and preparing their military and civilian health systems. These activities encompass a wide spectrum of engagements-military-to-military, military-to-civilian, and multilateral-and support joint missions of humanitarian aid and disaster response, deterrence, access and presence, counterterrorism, and homeland defense. Global health engagements and activities require extensive planning, funding, and resource allocation within the GCCs and component commands. For a continuously growing breadth of GHE and the need to support joint exercises with partner military and civilian medical professionals for partner capacity-building, GHE also requires a robust information technology infrastructure. In this study, the authors assess the technology and process requirements to support the life cycle of GHE activities and assessments-from planning to evaluation-and the information- and knowledge-sharing needs of the GHE community. To do so, they conducted a literature review related to GHE activities, funding sources, and stakeholders; the evolution of technology solutions to support GHE; systems in use by GHE practitioners; and technology solutions in the market, focusing particularly on cloud infrastructure and services and cloud service providers. They held discussions with GHE subject-matter experts to document and analyze GHE technology platform requirements. And they assessed the available and planned platforms according to their features, enhancements, support and maintenance, data integration, interoperability, and future road maps.

In the current technological era, the use of computers becomes an essential part of our life. The Internet has been become as mandatory in the human-being life. The cloud computing has the potential to transform the current static Internet into a fully integrated future Internet. It is a style of computing in which resources are provided “as a service” to users who need not have knowledge or expertise and or control over the infrastructure. The cloud computing technology has become a popular and necessary in the present decade. The cloud computing is the way of sharing resources for the use of various resources via the internet. Hence there are many cloud service providers are competing with each other in order to provide cloud services to the cloud users. In fact, the growth of cloud users has been increased tremendously from last decade to the present decade. In directives to provide right cloud service to users is aimed at almost all cloud service providers. The cloud service providers are also increasing many features of their cloud service products to attract the cloud service users. But cloud service users are facing difficulties to select the right service from a pool of cloud services. Hence it has become necessary for cloud service users to evaluate cloud services and select the best one, for this purpose a new evaluation model has been proposed in this paper based on data mining Fuzzy clustering algorithm.

Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.

The recent increase in the use of cloud service has led to the need for cloud service broker(CSB) for supporting the use of various cloud computing platforms. CSB plays the role of not only providing overall services needed for individuals and companies to use cloud computing but also managing and inspecting them. As cloud computing became commonplace, it is expected that the demand for more useful cloud-based service at more affordable prices will increase exponentially. Accordingly, the current situation calls for CSB that connects cloud service provider & customer and provides the service of managing and inspecting cloud service. This paper presents a framework of the components and role of cloud service broker system that supports the allocation, control and management of virtual system based on brokering function between cloud service provider(CSP) and cloud service customer (CSC) by integrating and managing cloud resources in multiple heterogeneous cloud environment. Based on this, it is expected that this paper will contribute to the revitalization of cloud service industry.

Cloud Security is still considered one of the main factors inhibiting the diffusion of the Cloud Computing paradigm. Potential Cloud Service Customers (CSCs) do not trust delegating every kind of resources and data to external Cloud Service Providers (CSPs). The problem grows in complexity due to the increasing adoption of complex supply chains: CSPs that offer Sofware-as-a-Service (SaaS) cloud services often do not have their own data centers, but just acquire resources and services from other CSPs. This makes ithard, if not impossible, to ascribe the responsibility of a securityincident. A possible solution is the adoption of Security ServiceLevel Agreements (SLAs): CSPs should deliver services withan SLA that details each guarantee offered in terms of security, and CSCs should be able to compare offerings from differentCSPs and verify that SLAs are respected during service lifecycle. This paper shows how it is possible to build up a per-serviceSecurity SLA in a chain of cloud services, proposing asolution based on a security evaluation technique to comparedifferent cloud service supply chains based on their SecuritySLAs.

Cloud computing has become an alternative IT infrastructure where users, infrastructure providers, and service providers all share and deploy resources for their business processes and applications. In order to deliver cloud services cost effectively, users' data is stored in a cloud where applications are able to perform requests from clients efficiently. As data is transferred to the cloud, data owners are concerned about the loss of control of their data and cloud service providers (CSPs) are concerned about their ability to protect data when it is moved about both within and out of its own environment. Many security and protection mechanisms have been proposed to protect cloud data by employing various policies, encryption techniques, and monitoring and auditing approaches. However, data is still exposed to potential disclosures and attacks if it is moved and located at another cloud where there is no equivalent security measure at visited sites. In a realistic cloud scenario with hierarchical service chain, the handling of data in a cloud can be delegated by a CSP to a subprovider or another. However, CSPs do not often deploy the same protection schemes. Movement of user's data is an important issue in cloud, and it has to be addressed to ensure the data is protected in an integrated manner regardless of its location in the environment. The user is concerned whether its data is located in locations covered by the service level agreement, and data operations are protected from unauthorized users. When user's data is moved to data centers located at locations different from its home, it is necessary to keep track of its locations and data operations. This chapter discusses data protection and mobility management issues in cloud environment and in particular the implementation of a trust-oriented data protection framework.

The emergence of cloud computing has completely changed the entire IT world. There is no doubt that making a profit is a cloud service provider (CSP)’s ultimate goal. To make a profit, a CSP needs a reasonable pricing strategy. At the same time, in order to increase the attractiveness of cloud service, CSPs tend to write default clauses in the service level agreement (SLA) and the default cost will affect the profit of CSPs. To determine an optimal pricing strategy, this paper builds some bi-level programming models of CSPs and users. The basic model does not consider default compensation and the default cost is included in the extended model. Finally, the comparison results of the two models prove that it is significant for the CSP to set default compensation clauses.

Data Portability: Key to Cloud Portability and Interoperability