Integrated and autonomic cloud resource scaling

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.

Service level agreements (SLAs) gain more and more importance in the area of cloud computing. An SLA is a contract between a customer and a cloud service provider (CSP) in which the CSP guarantees functional and non-functional quality of service parameters for cloud services. Since CSPs have to pay for the hardware used as well as penalties for violating SLAs, they are eager to fulfill these agreements while at the same time optimizing the utilization of their resources.In this paper we examine SLA-aware VM scheduling strategies for cloud data centers. The service level objectives considered are resource usage and availability. The sample resources are CPU and RAM. They can be overprovisioned by the CSPs which is the main leverage to increase their revenue. The availability of a VM is affected by migrating it within and between data centers. To get realistic results, we simulate the effect of the strategies using the FederatedCloudSim framework and real-world workload traces of business-critical VMs.Our evaluation shows that there are considerable differences between the scheduling strategies in terms of SLA violations and the number of migrations. From all strategies considered, the combination of the Minimization of Migrations strategy for VM selection and the Worst Fit strategy for host selection achieves the best results.

This paper proposes a privacy-aware collaborative energy management framework for cloud data centers (CDCs) operating within an electricity market environment. Each CDC's energy agent (CDCEA) minimizes the total CDC's energy cost by optimizing its energy profile in response to dynamic locational marginal prices and the nodal carbon intensity of the power system issued by the independent system operator (ISO), who clears the energy market. CDCEAs integrate the on-site energy resources of CDCs, such as renewable energy and energy storage systems, and leverage the spatiotemporal flexibility of computing workloads through coordination with cloud service providers (CSPs). CSPs manage privacy-preserving workload scheduling and resource allocation by collaborating with associated CDCEAs and with other CSPs, while ensuring quality of service and adherence to service-level agreements. A two-stage optimization model is developed to coordinate decision-making between the ISO and CDCEAs in energy market in the first stage, and to address the coordination of CSPs with associated CDCEAs and other CSPs in the second stage. The proposed two-stage model is solved using a three-nested distributed iterative algorithm that combines privacy-aware hierarchical and peer-to-peer ADMM methods with an analytical target cascading (ATC) approach. The method is validated using a real-world case study in Northern Virginia, USA, demonstrating its effectiveness in reducing costs and enhancing sustainability.

An efficient and optimized Markov chain-based prediction for server consolidation in cloud environment

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.

The article is devoted to the analysis of modern approaches that ensure the security of cloud services. Since cloud computing is one of the fastest growing areas among information technology, it is extremely important to ensure the safety and reliability of processes occurring in the clouds and to secure the interaction between the client and the provider of cloud services. Given that fears about data loss and their compromise are one of the main reasons that some companies do not transfer their calculations to the clouds. The object of research and analysis of this work are cloud services, which are provided by various cloud service providers. The aim of the study of this work is to compare existing approaches that provide information security for cloud services, as well as offer a new approach based on the principle of diversity. There are many approaches that ensure their safety, using both traditional and cloud-specific. The multi-cloud approach is one of the most promising strategies for improving reliability by reserving cloud resources on the servers of various cloud service providers. It is shown that it is necessary to use diversity to ensure the reliability and safety of critical system components. The principle of diversity is to use a unique version of each resource thanks to a special combination of a cloud computing provider, the geographical location of data centers, cloud service presentation models, and cloud infrastructure deployment models. The differences between cloud providers and which combination of services are preferable to others in terms of productivity are discussed in detail. In addition, best practices for securing cloud resources are reviewed. As a result, this paper concludes that there is a problem of insufficient security and reliability of cloud computing and how to reduce threats in order to avoid a common cause failure and, as a result, loss of confidential data or system downtime using diversity of cloud services.

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.

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.

For a cloud service provider, it necessitates an emerging cloud ecosystem to consolidate the existing users and attract more potential users, further gaining its market share. Therefore, in this article, we design a price-incentive resource auction mechanism in cloud environment. In response to the cloud resource price, each user synthesizes her bidding budget and QoS requirement, and purchases cloud resources according to her resource demand in a strategic manner. The cloud service provider, meanwhile, can regulate the resource demands of users through conducting a market-based pricing strategy, against too low prices to cover the operational costs (i.e., energy costs) or too high prices resulting in user churn. In virtue of an elaborate market-based pricing strategy, the interests of users and the cloud service provider are balanced. Our price-incentive resource auction mechanism targets to stimulate maximum users willing to purchase resources and perform their applications at the cloud, on the premise of a minimum profit rate guaranteed for the cloud service provider. It is also able to provide budge balance and truthfulness guarantee, and satisfy the envy-freeness. In order to carry out the above objectives, we carefully design the user utility function reflecting the complicated user interest, and formulate our resource pricing and auction problem as a bin packing problem, which has non-polynomial computational complexity. Regarding the NP-hardness of optimization problem and the concavity of user utility, we present a computational-efficient ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1+\epsilon $ </tex-math></inline-formula> )-approximate algorithm namely PIRA. Finally, we conduct simulations based on the real-world dataset to validate the effectiveness of our proposed approach.

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

Energy consumption in data center is currently the main focus of many large-scale enterprises and cloud service providers. Dynamic virtual machine (VM) consolidation technologies are widely used to improve resource utilization of data centers and reduce energy. They try to identify the poorly utilized physical hosts and make the most of the resources. Then idle hosts can be switched to sleep or active mode, meanwhile considering the real-time fluctuation of service workload. In this paper, we propose a Reinforcement Learning (RL) based Virtual Machine Consolidation (RL-VMC) framework with an application to the cloud data center operation. The RL-VMC method uses an agent corresponding to a VM planner to interact with the environment, which encapsulates the data center running state, and learns the optimal policy to determine the migration mapping from VMs to physical hosts. Specifically, we adopt the on-policy method State-Action-Reward-State-Action (SARSA) in RL-VMC, which learns from experience to get the optimal VMs migration strategy and manage the host power mode as well. Additionally, we use the PBRS technology to speed up the convergence of the RL-VMC. Experimental results show that the RL-VMC method can adapt to the dynamic workload maintaining an improved balance between energy consumption and Service Level Agreements (SLA).

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.

Cloud data centers traditionally use grid energy which emits carbon dioxide and dissipates heat to the environment. To decrease the amount of carbon emissions, many Cloud Service Providers (CSPs) are exploring the possibility of using renewable energy to operate their data centers showing a clear trend to migrate towards green cloud datacenters. Green cloud data centers typically use solar and wind as renewable energy sources. To meet the highly dynamic user demand and to ensure effective power management, an efficient methodology is proposed that performs various load scheduling and power management plans with the integration of renewables for geographically distributed green cloud datacenters. The proposed approach applies nature-inspired Modified Bacterial Foraging Optimization Algorithm (MBFOA) to balance load and reduce eco-aware energy cost. Simulations were conducted to determine the efficacy of the proposed MBFOA algorithm. The algorithm efficiently reduces eco-aware power cost of the system than the baseline methods.

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 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.