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A privacy-preserving data analytics system enables a cloud user to perform the distributed job in a secure manner such that the data privacy can be guaranteed during the cloud-outsourced computation. However, many SGX-based solutions are vulnerable to some side-channel attacks, including the access pattern leakage from both network and memory. Several data-oblivious algorithms with full obliviousness have been proposed in the literature, but they are impractical to be used in the cloud due to the expensive computational overhead. In this paper, we propose a DPSpark system with the security defined in a notion of (,)-differentially private obliviousness ((,)-DPO), which relaxes full obliviousness to enable an efficiency improvement. Based on this definition, we present a perturbation-shuffle-analysis (PSA) computing architecture and design several typical differentially oblivious operators. In further, we optimize the system efficiency by reducing the number of oblivious shuffles and choosing an appropriate privacy budget. Finally, we benchmark the system in different parameters. The experimental results show that DPSpark significantly outperforms two state-of-the-art solutions, only with 10.1-85.4% additional overhead performing an SGX-based data analysis application.

The volatile nature of Cloud user’s needs is considered a worthy challenge in formulating the Service Level Agreement (SLA) negotiation. The sterility of the standard SLA templates in handling the ...

Cloud deployments now increasingly provision FPGA accelerators as part of virtual instances. While commercial clouds still essentially expose single-tenant FPGAs to the users, the growing demand for hardware acceleration raises the need for architectures supporting FPGA multi-tenancy. In this work, we explore the trade-off between hardware consolidation and performance. Experiments show that FPGA multi-tenancy increases hardware utilization and decreases IO performance in the order of microseconds compared to the single-tenant model. The experiments also demonstrate that implementing on-chip communication between the hardware workloads of a cloud user significantly reduces the overall communication overhead.

Cloud computing is a rising technology that has gained significant attention over past decades. It offers various features such as-on-demand access, broad-network access, unlimited resource pool, etc. Despite so many merits, cloud computing has been full several challenges. Security indeed has remained one of the biggest challenges. Researchers, all over world, have been working on designing different types of security mechanism which can work on different levels on cloud environment. However, the literature on cloud security is quite wide and touches several issues pertaining to different stakeholders. The current study addresses the above issue by presenting a detailed review of challenges of security that are faced by different stake holders, such as cloud service provider (CSP), cloud user (CU), etc. This study will help the various researchers to find all security concerns at one place with critical reviews.

Cloud computing has recently emerged as a compelling method for deploying and delivering services over the Internet. In this paper, we aim to shed new light on the learning of cloud user interest. Our study for the first time shows the existence of cloud users in such real-world content distribution systems as BitTorrent. Based on this observation, we further explore the similarity of content preferences between cloud and non-cloud users. Surprisingly, our statistical model analysis indicates that the users in the cloud AS have significantly different interests from all the observed non-cloud ASes. More dedicated researches are therefore required to better manage this elevating yet unique cloud traffic in the future.

Searchable symmetric encryption has been a promising primitive as it enables a cloud user to search over outsourced encrypted data efficiently by only leaking small amount of controllable leakage. However, recent leakage-abuse attacks demonstrate that those stand leakage profiles can be exploited to perform severe attacks – the attacker can recover query or document with high probability. Ideal defending methods by leveraging heavy cryptographic primitives, e.g. Oblivious RAM, Multiparty Computation, are still too inefficient for practice nowadays.

The Cloud computing paradigm provides numerous attractive services to customers such as the provision of the on-demand self-service, usage-based pricing, ubiquitous network access, transference of risk, and location independent resource sharing. However, the security of cloud computing, especially its data privacy, is a highly challengeable task. To address the data privacy issues, several mechanisms have been proposed that use the third party auditor (TPA) to ensure the integrity of outsourced data for the satisfaction of cloud users (CUs). However, the role of the TPA could be the potential security threat itself and can create new security vulnerabilities for the customer’s data. Moreover, the cloud service providers (CSPs) and the CUs could also be the adversaries while deteriorating the stored private data. As a result, the objective of this research is twofold. Our first research goal is to analyze the data privacy-preserving issues by identifying unique privacy requirements and presenting a supportable solution that eliminates the possible threats towards data privacy. Our second research goal is to develop the privacy-preserving model (PPM) to audit all the stakeholders in order to provide a relatively secure cloud computing environment. Specifically, the proposed model ensures the quality of service (QoS) of cloud services and detects potential malicious insiders in CSPs and TPAs. Furthermore, our proposed model provides a methodology to audit a TPA for minimizing any potential insider threats. In addition, CUs can use the proposed model to periodically audit the CSPs using the TPA to ensure the integrity of the outsourced data. For demonstrating and validating the performance, the proposed PPM is programmed in C++ and tested on GreenCloud with NS2 by applying merging processes. The experimental results help to identify the effectiveness, operational efficiency, and reliability of the CSPs. In addition, the results demonstrate the successful rate of handling the negative role of the TPA and determining the TPA’s malicious insider detection capabilities.

Over the last decade, cloud computing has changed the IT world, mainly in the ways of storing, accessing and securing the users' data. However, there is no single cloud management platform in the market that can be used to manage different clouds (ex: AWS and AZURE), together with container communication between the clouds, to cater different cloud user requirements. We have proposed a platform for cloud service providers that automates the multi-cloud management related services with following modules: (1) communication, (2) resource allocation and (3) log view. Our results show that, using our proposed modules, the multi-cloud management related services can be automated efficiently and reliably.

Most of the organizations are stuck in a situation to cloudify due to concerns regarding security of sensitive data. Cloud computing provides many aids to the users and organizations in accordance with expenditure and savings. Other than these benefits, cloud computing has some hurdles that result in restriction of it’s usage. Cloud security is the bigger hurdle which is regularly taken into consideration. This paper will give a broad view of major threats challenges in security which are encountered in cloud computing. Cloud computing entities include cloud user, cloud provider, and data owner. The study is carried out based on selection of open-source cloud offerings. This paper can be an exploration tool for any IT person to gain knowledge into security-related risks and challenges which are concerned with cloud computing.

In cloud computing environment there are Cloud Service Providers (CSP)/Vendor, and Cloud User/Client. CSP provides application, infrastructure, and/or software. Cloud user demand for a service to the CSP via the internet which is accounted on a pay-per-usage basis. Resource allocation related parameters are optimization, cost efficiency, security, quality of service (QoS), reliability, compatibility, efficiency, and delay. In this survey, we have reviewed resource allocation algorithms and mechanisms used by researchers in the recent past and classified these techniques according to the parameters considered in the approach. According to the survey, we noticed that few parameters are well addressed by many of the researches while some are yet not much investigated. The survey will guide the researchers to achieve more vision in the field of resource allocation for IaaS in Cloud Computing.

Many scientific research communities and institutions are adopting the cloud as a primary platform to support their computing needs. Rapid adoption of the cloud for scientific computing is a result of the simplicity with which an individual researcher can obtain large amounts of customized compute and storage resources. At the same time, most public cloud providers have rolled out many advanced networking services. Many of these services, like their compute services, are simple to access by any cloud user (e.g. routing between regions and private networking spaces within a cloud). However, it is not possible for most researchers to access expensive low-level, externally facing cloud network services without complicated support by campus IT staff, as well as national and regional network providers. This paper describes how to use Chameleon, ExoGENI, and Internet2’s Cloud Connect service to deploy research experiments that use AWS Direct Connect without requiring a privately owned Direct Connect endpoint or support from local campus IT staff.

Cloud computing provides services from the available pool of resources. Even with the available condition, cloud computing can reach the peak of success amongst cloud user. The issue they face is the barrier of trust between the end users for using the given services. Conventional security and protection controls keeps on being executed on cloud; however, because of its liquid and dynamic nature, a testable trust estimate of the cloud is required. This research paper exhibits an analysis of the present trust administration strategies for cloud operations. In this research paper, we proposed a model for trust administration using Fuzzy Logic, which can be beneficial for cloud service providers to select trusted datacenter for consumers.

The IaaS cloud computing model is the foundation over which all other cloud computing services can be built. Thus, the migration from the traditional datacenter model to the cloud-computing model is highly dependent on the guarantees that the IaaS Cloud Provider can offer Cloud User to sustain service quality. These guarantees are expressed using Service Level Agreement (SLA) whose need to be aligned and tightly coupled with the cloud service lifecycle so as to reflect the financial and technical dynamicity of the SLA. However, we argue that the SLAs used at present are deficient in expressing solid guarantees on the service quality and lack the auto-negotiation feature required by cloud services. In that respect, SLAs used in practice do not fully abide by the "Pay as you Go" model for cloud computing. In this paper, we propose the "Get as you Pay" model that extends the "Pay as You Go" model by augmenting it with a descriptive SLA that supports the auto-scaling of resources. For validating the "Get as you Pay" model, we walk through a scenario to show how applying the model provides an expressive and negotiable SLA.

In the process of using the cloud platform, how to ensure the safety of users is a matter we must concern. The user authentication can provide a certain degree of security, but when the user information was leaked, this method will not be effective. Therefore, this article proposes a trust evaluation model based on user behavior data. In this model, the user’s historical behavior will be used to construct a set of trusted behavior of the cloud users. On this basis, the direct trust of the user’s behavior can be obtained. Then, the recommendation trust can be calculated by the interaction between the users and other cloud users. Given the current historical trust, the comprehensive trust can be obtained using the weighted average method. Among them, the initial value of historical trust is set to a constant and then updated by the comprehensive trust. In order to control the user’s abnormal behavior more effectively, the suspicious threshold value and the abnormal threshold value were defined, which are used to punish the historical trust. Through the simulation of the virtual digital library cloud platform, the method can effectively evaluate the cloud users.

Cloud computing services can be accessed via browsers or client applications on networked devices such as desktop computers, laptops, tablets and smartphones, which are generally referred to as endpoint devices. Data relevant to forensic investigations may be stored on endpoint devices and/or at cloud service providers. When cloud services are accessed from an endpoint device, several files and folders are created on the device; the data can be accessed by a digital forensic investigator using various tools. An investigator may also use an application programming interface made available by a cloud service provider to obtain forensic information from the cloud related to objects, events and file metadata associated with a cloud user. This chapter presents a taxonomy of the forensic tools used to extract data from endpoint devices and from cloud service providers. The tool taxonomy provides investigators with an easily searchable catalog of tools that can meet their technical requirements during cloud forensic investigations.

In this chapter, we start by introducing Cloud Computing management terms, with a special reference to Amazon Web Services (AWSs). Next, we review the performance characteristics of public, multi-tenanted data centers, including the concept of noisy neighbors. If a particular VM does excessive I/O or memory accesses, then other VMs running on that same server will experience a slowdown in their access of the same physical resource. This results in a performance variation experienced by a VM user over time. Such factors impact run-times of users’ applications and result in different cost to finish a task. A cloud user may want to maximize their performance or minimize their costs. We will look at various monitoring tools, to manage the user costs.

Cloud computing (CC) has emerged as the next generation of computing in IT Enterprise. Earlier, IT services are under proper physical and personnel control, CC migrates the application softwares and databases to the huge data centers, where the management of data along with services is done by the cloud provider. However, this characteristic poses many security challenges which have not been well understood. This paper focuses on security of data at cloud storage, which has always been a most important issue in CC security. Many approaches have been proposed to protect data in cloud which are not sufficient to meet the requirements of a cloud user. So in this paper, a model for data security at cloud storage site has been proposed.

Considering the problem how to protect the cloud services from being destroyed by cloud users, the risk- prediction model based on improved AdaBoost method is proposed. The risk prediction is regarded as two-class classifi- cation problem, and the risk of new cloud users could be predicted by the attributes of historical cloud users. In order to improve the result of predicted, AdaBoost method is adopted in this paper. The error rate of the last training is used to ad- just the sample distribution of the next training, which can make the next training have stronger ability of identification for the error-classified samples. At the same time the weight of each weak classifier is set. After all, the strong classifier is generated by combined the weak classifiers through voting, which can improve the overall result of classification. Con- sidering the wrongly-predicted cost, AdaBoost method is improved. The method of cost-sensitive is added into the model in order to minimal the misclassified-cost. Experiments show that the cost-sensitive AdaBoost method has better classifi- cation result than the traditional ones and it can predict the risk of the new cloud user effectively and protect the security of the cloud services.

Deploying applications which require computational power or data intensive capabilities into a cloud environment might became overwhelming, due to the diversity of cloud services and resources. CloudLightning allows users to design their application topology and deploy it without the need for selecting the most suitable resources. The task of finding the resources for an application is taken care of by the CloudLightning self-organizing self-management component. In this paper we describe the functionality of the Gateway Service, the component that enables users to access the CloudLightning functionality.

In order to arm yourself and assess risk correctly in the cloud, carrying out investigations are necessary. Though Cloud Computing is the buzzing technology now a days, with the advent with which cloud computing is developing and providing solutions to the upcoming technologies where on the other side of it there also lies a down fall with respect to theft of data in the cloud, loss of data in the cloud which are related to forensic, these things create an distrustful relation between cloud user and cloud providers, during the past half decade cloud forensic has emerged as a challenging point that has to be solved in cloud computing, the prevailing existing solutions which were suggested at the initial stage of Cloud Forensics were satisfied at that time but those solutions cannot rule now also, due to which they are not up to the mark to give much satisfaction which there by strengthen the faith of users in cloud, and makes Cloud Service Providers to provide the services and make a path to be laid for the coming technologies to utilize the flavors of cloud computing. This paper inculcates the contents related to cloud forensic which are traced out which are being known, to be known. In this paper we have traced out the major causes of Cloud Forensics, and thrown a light on the causes and origins which are helpful for laying a success ladder for Cloud Forensic concepts, and provided relevant content which would be helpful to trace out the solutions which are causing obstacles in the field of Cloud Forensics, utilizing which one can get an overview of the challenges and if considered can definitely lead to a good solution methods which if implemented in a proper way can lead to recovery of lost data as well as to know the real cause of the effect and also in order to reduce their effect.

The previous part of the book addressed how to design a good cloud service benchmark. In this part, we shift our focus from the design to its implementation as part of a benchmarking tool and (later on) its runtime execution. In this chapter, we start by introducing implementation objectives for cloud service benchmarks. Even based on a careful benchmark design considering all design objectives, the actual benchmark implementation can still run afoul of the goals initially set. In this chapter, next to outlining implementation objectives, we provide concrete examples on how they can be achieved in practice.

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters wh...

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters which are the fundamental part of SLAs. The current dynamic SLA monitoring approaches have considered the cloud provider's perspective only. To take into consideration the user's perspective; this research proposes a dynamic SLA monitoring approach by generating new SLA terms based on analysing the SLA penalties. A new proposed trade-off mechanism is applied between monthly cost and QoS guarantee values. Thus; we propose a dynamic model for updating QoS parameters of SLA in cloud environment. Java language is used with a QoS measurement dataset to experiment the proposed approach. The current Static SLA (SSLA) approaches are compared with the proposed Dynamic SLA (DSLA) approach. The results of this experimental study show that the number of SLA violations in DSLA are less than the number of SLA violations in SSLA.

With the development of cloud computing technology, many cloudbased data centres are beginning to connect with each other aiming at building large-scale federated cloud platforms. In such a federated cloud platform, the efficiency and effectiveness of traditional schedulers will be significantly degraded due to the unpredictable resource availability and high networking latency. In this paper, we propose a multi-policy adaptive scheduling framework, which is capable of evaluating the scheduling scheme made by existing schedulers. In this way, the proposed scheduling framework can make full use of the advantages of existing scheduling policies and avoid their shortcomings, and therefore makes the final scheduling decisions more efficient and effectiveness. In addition, the proposed scheduling framework is designed as extensible component that can be easily extended by incorporating other scheduling policies. The prototype of this scheduling framework is tested in a real-world federated cloud platform. The experimental results show that it can significantly improve the quality-of-service for the underlying cloud infrastructures as well as cloud user's satisfactory.

Vehicular cloud (VC) extends cloud computing to vehicles participating in vehicular ad hoc networks, aiming to provide computing and storage services at low cost to vehicles, improve traffic efficiency and safety, ensure real-time services, etc. Due to the highly dynamic nature of VC, it is challenging to efficiently form a dynamic VC securely and anonymously or to securely deliver messages to the dynamic VC without potentially violating the privacy of cloud users. In this paper, we present a concrete secure and privacy-preserving communication scheme for VC establishment and data dissemination. Our scheme allows a group of vehicles that are geographically close to each other to form a VC securely, anonymously and dynamically. This allows vehicle resources to be integrated and shared securely. Once a VC is formed, any cloud user may deliver messages to be securely and anonymously processed in the VC.

Cloud computing is developing rapidly and the open environment in cloud computing is much more complex and unpredictable than other applications, so just providing the identity certification cannot ensure the security of cloud user and cloud service provider, in order to find a effective way to ensure cloud security, we have to solve the problem of trustiness of cloud user's behavior first, in this paper, we propose a dynamic trust evaluate method to deal with cloud user's bahavior, through using Entropy Method to reflect the essential regular patterm of user's behavior evidence, making the evaluate way become a dynamic model, weaken the subjectivity of simply using AHP, moreover, still need AHP to make the result fit people's subjective experience, so we also put forward a integrate algorithm that combine Entropy Method and AHP, in this way, the final evaluate value will keep the balance between objective and subjective and provide quantitative analysis foundation for security control, The simulation shows that the dynamic trust evaluate method can effectively distinguish user's abnormal behavior.

Chapter 2 provides a literature review, together with theoretical topics pertaining to Information System Theory, Information and Communication Technologies (ICT), Service Delivery Platforms and System Design for User needs, and relates current literature to all of the these topics within this investigation.

The trustiness of cloud platform is the key issues relating to its success or failure, and the authentication of the trustiness of user behavior is an important part of ensuring the credibility of cloud platform. In order to solve the problem of trustiness of cloud users' behaviors, a cloud user behavior authentication model based on multi-partite graphs is proposed, including the layer of user behavior evidence, the layer of building behavior multi-partite graphs and the layer of behavior authentication; Identity re-certification and Risk Game are introduced to enhance security and accuracy of the model. Simulation experiments show that, the model is accurate and effective in measuring the normal behavior of cloud users and in distinguishing malicious user with the risk user, and has a higher detection ratio and a lower false positive ratio.

Job scheduling is a very challenging issue in cloud computing. Traditional backfill algorithms such as Easy and conservative are extensively used as job scheduling algorithms. Backfill algorithms require the shorter job to come forward if sufficient resources for the execution of this job are available and run in parallel with the currently running jobs provided it does not delay the next queued jobs. This technique is highly dependent on runtime estimations of job execution. Moreover in real life scenario it has seen that submitted job’s may or may not be independent to each other. In this paper we have proposed a technique that uses dynamic grouping method to consider job dependencies and doubling runtime estimation method in cloud metaschedular to improve performance of backfill algorithm. Results have shown that doubling runtime estimations can significantly improve performance of backfill scheduling algorithms provided that the runtime estimations are correct.

International audience; The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world Cloud client application traces. We also present an overall evaluation of this approach , its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.

In cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the tasks execution order. Furthermore, a resource allocation mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose an adaptive resource allocation algorithm for the cloud system with preemptable tasks. Our algorithms adjust the resource allocation adaptively based on the updated of the actual task executions. And the experimental results show that our algorithms works significantly in the situation where resource contention is fierce.

The paper proposes agent collaboration-based service provisioning strategy for large enterprise business in hybrid cloud environment. The proposed service provisioning model is constructed by the agents, which include hybrid cloud user agent, hybrid cloud service agent and hybrid cloud provider agent. Hybrid cloud user agents adjust needed service according to the prices of hybrid cloud services. Hybrid cloud service agents compete actively for the requests from hybrid cloud user agents and execute them for gaining profits. Hybrid cloud service agents dynamically change cloud services' prices and send updated prices to all hybrid cloud user agents. Hybrid cloud services' prices are adjusted to accommodate the service demands from hybrid cloud user agents. Hybrid cloud provider agent tries to maximise its profit based on its resource capability and energy consumption. The paper also presents an agent collaboration-based service provisioning algorithm. The simulations are designed to compare the performance of the proposed algorithm with the other related works. Copyright © 2015 John Wiley & Sons, Ltd.

Due to the open and dynamic nature of cloud environment, the service quality of a cloud service is not always trusted as advertised by cloud provider. Feedback rating-based reputation system is an effective approach to determine the trust of a cloud service. Unfortunately, a comprehensive reputation system is absent from present major cloud providers and not supported by present major cloud providers, e.g., Amazon, Google and Microsoft, which hampers a cloud user from selecting a trusted cloud service. In view of this challenge, in this paper, we first study why reputation system is absent from cloud, from perspectives of cloud provider and cloud user respectively. Afterwards, taking e-Commerce experience for reference, we put forward a novel reputation system tailored to cloud service delivery, i.e., R3 (Rating-Review-based Reputation system, R3). The proposed R3 is scalable towards more personalized cloud delivery applications, and can be easily integrated into the present cloud architecture. KeywordsTrust; Feedback rating; Reputation system; Service quality; cloud service