Efficient and Cost-effective Service Broker Policy Based on User Priority in VIKOR for Cloud Computing

Cloud computing provides distributed computing resources such as servers, storage and applications to the end users through data centers. The data centers are geographically located at different locations. The client applications or requests being serviced by cloud service providers on “pay per use”. So, different pricing models are adapted to compute the cost and revenue of the data centers. The cost of VM is computed depending on its placement in the data centers through broker policies. So, the broker policies have a significant role in evaluating the cost of the VM which directly impacts on revenue of the service provider. In the computing competition, the VM cost should be minimized by which service demand will be maximized. Moreover, the response time and processing time of the data centers need to be minimized to posses commendatory Quality of Service. In this work, we propose a new service broker policy to minimize the total cost. The total cost considers the VM cost and the data transfer cost. The proposed mechanism also reduces the response time and data processing time of the data centers. The proposed policy is simulated in Cloud Analyst. We examine the performance of the proposed mechanism with ten different scenarios. Finally, we compare performance results with respect to VM cost, data transfer cost, total cost, processing time and response time of data centers with the existing policies and observe better than these.

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.

Internet is a great marvel of science. Internet has changed the face of world. It is most essential and revolutionary creation in this world. Cloud computing is associated with internet computing. Cloud computing has undoubtedly benefited both service provider and clients in great extent. There is rapid increase in cloud's customers constantly. Although, the cloud data centers comprised of tremendous power but due to expeditious requests of users there is sudden need of balancing load. However, load balancing emerged as the conspicuous issue in the cloud heterogeneous environment. This study highlights the performance analysis of load balancing policies which are taken in a combination with service broker policy. In Fact, this study addresses that there can be reduction in response time and data center request processing time by using efficient load balancing policies. These all evaluations and results are carried out using cloud analyst simulation tool.

Demand of data storage and processing of datasets in cloud computing consumes a huge volume of power and energy. Exhaustive usage of power and energy by the cloud service providers globally increases carbon footprint which is an environmental concern. Latest research results on carbon emission indicate a warning sign for environmental sustainability. Data centers, their resources and the digital waste created in the cloud environment have created a major impact on the environment. Taking this as an alarm sign on a global perspective, we rigorously worked on to develop “green software” in order to minimize the carbon waste from the environment. The design of this software focuses on to measure energy consumption and the carbon footprint level. This green software shall motivate the cloud services to uphold the concern on carbon emission and to take immediate attention to predict the waste of CPU power. This green software design framework provides a separate track session on energy consumption by the data center and cloud service provider along with the acquired energy efficiency level by the specific center. The software architecture insights are the working elements to analyze the level of emission, time period of the effect and the impact assessment of the carbon footprint. We propose an energy-efficient and cost-effective solution to minimize carbon emission and its impact on the environment.

Fog computing idea is presented to reduce the burden on cloud and deliver similar facilities as cloud. However, fog encompasses small area relatively to cloud by saving the data for shorter amount of time and sending it to cloud for permanent storage. In this paper, a joint cloud and fog centered environment for efficient energy supervision of buildings is proposed. It caters for the data of groups of buildings at buyers’ end. 12 fogs are utilized for 6 different regions in the world which are based on 6 continents. Additionally, each fog is linked to a group of buildings and two fogs are linked to two groups. Each group comprises of multiple smart buildings and these buildings has at least 100 apartments. To manage the energy requirement of consumers, micro grids (MGs) are available near the buildings and accessible by the fogs. Energy is managed for the apartments and fog helps the consumers to fulfill their load demands through nearby MGs and cloud servers’ communication. So, the load on cloud and fog should be balanced and load balancing algorithms are used to manage the load using VMs. These algorithms are round robin (RR) and throttled and Priority Based load balancing and these algorithms are compared for a single service broker policy. Service broker policy considered in this paper is; dynamically reconfigure with load. Priority based load balancing is proposed for balancing the load on fog and results of proposed balancing algorithm are compared with other algorithms. While considering the proposed algorithm, results are compared with two load balancing algorithms and from this, the proposed algorithm gives better results than RR algorithm rather than throttled.

Fog computing concept is introduced to reduce the load on cloud and provide similar services as cloud. However, fog covers small area rather than cloud by storing the data temporarily and sends data to cloud for permanent storage. In this paper, an integrated fog and cloud based environment for effective energy management of buildings is proposed. So, the load on cloud and fog should be balanced. Various load balancing algorithms are used to manage the load among virtual machines (VMs). In this scenario, algorithm used for load balancing among VMs is round robin (RR). Service broker policies considered in this paper are; dynamically reconfigure with load (DR) and the proposed policy. New dynamic service proximity (DSP) service broker policy is proposed for fog selection and results of DSP policy are compared with DR policy. Therefore, a tradeoff is observed between cost and response time.

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.

A Cloud is a very dynamic environment where resources offered by a Cloud Service Provider (CSP), out of one or more Cloud Data Centers (DCs) are acquired or released (by an enterprise (tenant) on-demand and at any scale. Typically a tenant will use Cloud service interfaces to acquire or release resources directly. This process can be automated by a CSP by providing auto-scaling capability where a tenant sets policies indicating under what condition resources should be auto-scaled. This is specially needed in a Cloud environment because of the huge scale at which a Cloud operates. Typical solutions are naive causing spurious auto-scaling decisions. For example, they are based on only thresholding triggers and the thresholding mechanisms themselves are not Cloud-ready. In a Cloud, resources from three separate domains, compute, storage and network, are acquired or released on-demand. But in typical solutions resources from these three domains are not auto-scaled in an integrated fashion. Integrated auto-scaling prevents further spurious scaling and reduces the number of auto-scaling systems to be supported in a Cloud management system. In addition, network resources typically are not auto-scaled. In this paper we describe a Cloud resource auto-scaling system that addresses and overcomes above limitations.

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

Due to the increasing demand of cloud resources, the ever-increasing number and scale of cloud data centers make their massive power consumption a prominent issue today. Evidence reveals that the behaviors of cloud servers make the major impact on data centers’ power consumption. Although extensive research can be found in this context, a systematic review of the models and modeling methods for the entire hierarchy (from underlying hardware components to the upper-layer applications) of the cloud server is still missing, which is supposed to cover the relevant studies on physical and virtual cloud server instances, server components, and cloud applications. In this article, we summarize a broad range of relevant studies from three perspectives: power data acquisition, power models, and power modeling methods for cloud servers (including bare-metal, virtual machine (VM), and container instances). We present a comprehensive taxonomy on the collection methods of server-level power data, the existing mainstream power models at multiple levels from hardware to software and application, and commonly used methods for modeling power consumption including classical regression analysis and emerging methods like reinforcement learning. Throughout the work, we introduce a variety of models and methods, illustrating their implementation, usability, and applicability while discussing the limitations of existing approaches and possible ways of improvement. Apart from reviewing existing studies on server power models and modeling methods, we further figure out several open challenges and possible research directions, such as the study on modeling the power consumption of lightweight virtual units like unikernel and the necessity of further explorations toward empowering server power estimation/prediction with machine learning. As power monitoring is drawing increasing attention from cloud service providers (CSPs), this survey provides useful guidelines on server power modeling and can be inspiring for further research on energy-efficient data centers.

A Variable Service Broker Routing Policy for data center selection in cloud analyst

Cloud services are offered to consumers based on Service Level Agreements (SLAs) signed between Cloud Service Provider (CSP) and consumer. Due to on-demand provisioning of resources there is exponential growth of cloud consumers. Job scheduling is one of the areas that has attracted researchers to improve performance of cloud management system. Along with the on premise infrastructure, Small and Medium Enterprises (SMEs) also depend on public cloud infrastructure (leading to hybrid cloud) for seamless continuity of their businesses. In this context, ensuring SLAs and effective management of hybrid cloud resources are major challenging issues to be considered. Hence, there is a need for an effective scheduling algorithm which considers multiple objective functions like SLA (deadline), cost and energy while making scheduling decisions. Most of the state of the art schedulers in hybrid cloud environment considered single objective function. However, in real world, it is inadequate for scheduling effectiveness. To overcome this problem, we proposed an integrated framework which ensures SLAs (deadline), cost effectiveness and energy efficiency with an underlying scheduling algorithm known as SCE-TS. This algorithm is evaluated with different workloads and SLAs using a cloud platform. The empirical study revealed that the proposed framework improves scheduling efficiency in terms of meeting SLAs, cost and energy efficiency. It is evaluated and compared with the state of the art and found to be effective in making scheduling decisions in cloud environment.

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.

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.

Cloud computing is the delivery of on‐demand computing resources. It shares the resources or provides vir‐utilization that enables single user to access various Cloud services such as CPU, memory, storage devices, network, and so on. However, more commercial cloud services offered by several cloud service providers (CSPs) are available in the market place. Most CSPs must, therefore, deal with the dynamic resource allocation where the mobile services are migrating from one cloud to another cloud environment to provide heterogeneous resources based on user needs. There is still a lack of heuristics that are able to check requested resources and available resources to allocate and deallocate before it begins the secure service migration. We proposed a resource allocation security protocol that allows resources to be allocated and migrated efficiently in a secure service migration between cloud infrastructures. Furthermore, formal methods can be used for protocols to verify the desired properties, detecting attacks and producing accurate outcomes. This article presents formal modeling and verification of this abstract protocol using ProVerif cryptographic tool to validate the security properties such as secrecy of resources, authentication from both parties and key exchange in order to securely migrate resources in commercial cloud environments.