Abstract: The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. We present a novel multilayered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services, an intelligent parking cloud service and a vehicular data mining cloud service, for vehicle warranty analysis in the IoT environment are also presented. Two modified data mining models for the vehicular data mining cloud service, a Naive Bayes model and a Logistic Regression model, are presented in detail. Challenges and directions for future work are also provided.

Abstract: With the trend going on in ubiquitous computing, everything is going to be connected to the Internet and its data will be used for various progressive purposes, creating not only information from it, but also, knowledge and even wisdom. Internet of Things (IoT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IoT's could generate and their requirement to have the privilege of virtual resources utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IoT's and develop smart applications for the users. This IoT and cloud computing integration is referred to as Cloud of Things in this paper. IoT's and cloud computing integration is not that simple and bears some key issues. Those key issues along with their respective potential solutions have been highlighted in this paper.

Abstract: The success of the IoT world requires service provision attributed with ubiquity, reliability, high-performance, efficiency, and scalability. In order to accomplish this attribution, future business and research vision is to merge the Cloud Computing and IoT concepts, i.e., enable an “Everything as a Service” model: specifically, a Cloud ecosystem, encompassing novel functionality and cognitive-IoT capabilities, will be provided. Hence the paper will describe an innovative IoT centric Cloud smart infrastructure addressing individual IoT and Cloud Computing challenges.

Abstract: We live and operate in the world of computing and computers. The Internet has drastically changed the computing world from the concept of parallel computing to distributed computing to grid computing and now to cloud computing. Cloud computing is a new wave in the field of information technology. Some see it as an emerging field in computer science. It consists of a set of resources and services offered through the Internet. Hence, "cloud computing" is also called "Internet computing." The word "cloud" is a metaphor for describing the Web as a space where computing has been preinstalled and exists as a service. Operating systems, applications, storage, data, and processing capacity all exist on the Web, ready to be shared among users. Figure 1 shows a conceptual diagram of cloud computing.

Abstract: ing them into more general ones, such as progress bars, traffic lights, or change tendencies (increase/decrease) as depicted in Fig. 4.16. If the application scenario for such abstractions and approximations, thus, if the user of the applications does not require consistent data values, a data abstractor should be implemented to increase the beneficial effects of eventually consistent storage offerings.

Abstract: Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

Abstract: The focus on this paper is to build an Android platform based mobile application for the healthcare domain, which uses the idea of Internet of Things (IoT) and cloud computing. We have built an application called 'ECG Android App' which provides the end user with visualization of their Electro Cardiogram (ECG) waves and data logging functionality in the background. The logged data can be uploaded to the user's private centralized cloud or a specific medical cloud, which keeps a record of all the monitored data and can be retrieved for analysis by the medical personnel. Though the idea of building a medical application using IoT and cloud techniques is not totally new, there is a lack of empirical studies in building such a system. This paper reviews the fundamental concepts of IoT. Further, the paper presents an infrastructure for the healthcare domain, which consists of various technologies: IOIO microcontroller, signal processing, communication protocols, secure and efficient mechanisms for large file transfer, data base management system, and the centralized cloud. The paper emphasizes on the system and software architecture and design which is essential to overall IoT and cloud based medical applications. The infrastructure presented in the paper can also be applied to other healthcare domains. It concludes with recommendations and extensibilities found for the solution in the healthcare domain.

Abstract: The ability to deliver guaranteed QoS (Quality of Service) is crucial for the commercial success of cloud platforms This paper presents a model based on queuing theory to study computer service QoS in cloud computing Cloud platforms are modeled with an open Jackson network that can be used to determine and measure the QoS guarantees the cloud can offer regarding the response time The analysis can be performed according to different parameters, such as the arrival rate of customer services and the number and service rate of processing servers, among others Detailed results for the model are presented When scaling the system and depending on the types of bottleneck in the system, we show how our model can provide us with the best option to guarantee QoS The results obtained confirm the usefulness of the model presented for designing real cloud computing systems

Abstract: Cloud computing is an ascending technology that has introduced a new paradigm by rendering a rational computational model possible. It has changed the dynamics of IT consumption by means of a model that provides on-demand services over the Internet. Unlike the traditional hosting service, cloud computing services are paid for per usage and may expand or shrink based on demand. Such services are, in general, fully managed by cloud providers that require users nothing but a personal computer and an Internet access. In recent years, this model has attracted the attention of researchers, investors and practitioners, many of whom have proposed a number of applications, structures and fundamentals of cloud computing, resulting in various definitions, requirements and models. Despite the interest and advances in the field, issues such as security and privacy, service layer agreement, resource sharing, and billing have opened up new questions about the real gains of the model. Although cloud computing is based on a 50-year-old business model, evidence from this study indicates that cloud computing still needs to expand and overcome present limitations that prevent the full use of its potential. In this study, we critically review the state of the art in cloud computing with the aim of identifying advances, gaps and new challenges.

Abstract: Cloud computing is considered one of the most dominant paradigms in the Information Technology (IT) industry these days. It offers new cost effective services on-demand such as Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). However, with all of these services promising facilities and benefits, there are still a number of challenges associated with utilizing cloud computing such as data security, abuse of cloud services, malicious insider and cyber-attacks. Among all security requirements of cloud computing, access control is one of the fundamental requirements in order to avoid unauthorized access to systems and protect organizations assets. Although, various access control models and policies have been developed such as Mandatory Access Control (MAC) and Role Based Access Control (RBAC) for different environments, these models may not fulfil cloud's access control requirements. This is because cloud computing has a diverse set of users with different sets of security requirements. It also has unique security challenges such as multi-tenant hosting and heterogeneity of security policies, rules and domains. This paper presents a detailed access control requirement analysis for cloud computing and identifies important gaps, which are not fulfilled by conventional access control models. This paper also proposes an access control model to meet the identified cloud access control requirements. We believe that the proposed model can not only ensure the secure sharing of resources among potential untrusted tenants, but also has the capacity to support different access permission to the same cloud user and gives him/her the ability to use multiple services securely.

Abstract: Understanding the characteristics and patterns of workloads within a Cloud computing environment is critical in order to improve resource management and operational conditions while Quality of Service (QoS) guarantees are maintained. Simulation models based on realistic parameters are also urgently needed for investigating the impact of these workload characteristics on new system designs and operation policies. Unfortunately there is a lack of analyses to support the development of workload models that capture the inherent diversity of users and tasks, largely due to the limited availability of Cloud tracelogs as well as the complexity in analyzing such systems. In this paper we present a comprehensive analysis of the workload characteristics derived from a production Cloud data center that features over 900 users submitting approximately 25 million tasks over a time period of a month. Our analysis focuses on exposing and quantifying the diversity of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload created by such components. Our derived model is implemented by extending the capabilities of the CloudSim framework and is further validated through empirical comparison and statistical hypothesis tests. We illustrate several examples of this work's practical applicability in the domain of resource management and energy-efficiency.

Abstract: Load balancing is the major concern in the cloud computing environment. Cloud comprises of many hardware and software resources and managing these will play an important role in executing a client's request. Now a day's clients from different parts of the world are demanding for the various services in a rapid rate. In this present situation the load balancing algorithms built should be very efficient in allocating the request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur in the cloud environment. In the present work, a novel VM-assign load balance algorithm is proposed which allocates the incoming requests to the all available virtual machines in an efficient manner. Further, the performance is analyzed using Cloudsim simulator and compared with existing Active-VM load balance algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all available virtual machines without under/over utilization.

Abstract: Cloud computing has formed the conceptual and infrastructural basis for tomorrow’s computing. The global computing infrastructure is rapidly moving towards cloud based architecture. While it is important to take advantages of could based computing by means of deploying it in diversified sectors, the security aspects in a cloud based computing environment remains at the core of interest. Cloud based services and service providers are being evolved which has resulted in a new business trend based on cloud technology. With the introduction of numerous cloud based services and geographically dispersed cloud service providers, sensitive information of different entities are normally stored in remote servers and locations with the possibilities of being exposed to unwanted parties in situations where the cloud servers storing those information are compromised. If security is not robust and consistent, the flexibility and advantages that cloud computing has to offer will have little credibility. This paper presents a review on the cloud computing concepts as well as security issues inherent within the context of cloud computing and cloud infrastructure.

Abstract: In this paper, we discuss security issues for cloud computing, Big data, Map Reduce and Hadoop environment. The main focus is on security issues in cloud computing that are associated with big data. Big data applications are a great benefit to organizations, business, companies and many large scale and small scale industries.We also discuss various possible solutions for the issues in cloud computing security and Hadoop. Cloud computing security is developing at a rapid pace which includes computer security, network security, information security, and data privacy. Cloud computing plays a very vital role in protecting data, applications and the related infrastructure with the help of policies, technologies, controls, and big data tools . Moreover, cloud computing, big data and its applications, advantages are likely to represent the most promising new frontiers in science.

Abstract: Cloud computing has recently emerged as a new computing paradigm in many application areas comprising office and enterprise systems. It offers various solutions to provide a dynamic and flexible infrastructure to host computing resources and deliver them as a service on-demand. Since industrial automation systems of the future have to be adaptable and agile, cloud computing can be considered as a promising solution for this area. However, the requirements of industrial automation systems differ significantly from the office and enterprise world. In this paper we describe a case study that implements a concept of PLC as a service within a cloud based infrastructure and provides a performance evaluation with respect to legacy PLCs.

Abstract: Cloud computing is an important component of the backbone of the Internet of Things (IoT). Clouds will be required to support large numbers of interactions with varying quality requirements. Service quality will therefore be an important differentiator among cloud providers. In order to distinguish themselves from their competitors, cloud providers should offer superior services that meet customers' expectations. A quality model can be used to represent, measure, and compare the quality of the providers, such that a mutual understanding can be established among cloud stakeholders. In this paper, we take a service perspective and initiate a quality model named CLOUDQUAL for cloud services. It is a model with quality dimensions and metrics that targets general cloud services. CLOUDQUAL contains six quality dimensions, i.e., usability, availability, reliability, responsiveness, security, and elasticity, of which usability is subjective, whereas the others are objective. To demonstrate the effectiveness of CLOUDQUAL, we conduct empirical case studies on three storage clouds. Results show that CLOUDQUAL can evaluate their quality. To demonstrate its soundness, we validate CLOUDQUAL with standard criteria and show that it can differentiate service quality.

Abstract: Cloud computing is the newest computing paradigm that makes computing resources available over the Internet on a utility costing basis. Cloud computing offers many advantages to users in terms of reduced cost, elimination of system administrative functions, increased flexibility, better reliability and location independence. Though these are definite advantages, cloud computing also suffers from certain limitations. These limitations arise from the very same reason that is considered an advantage too. Hosting of cloud data centres in the Internet creates large and unpredictable network latencies and undefined security issues as sensitive data is now entrusted to a third party. Also location independence of processing in cloud computing may also not desirable for certain types of networks such as sensor networks and Internet of Things. These services are known as location aware services and require location dependent fast processing. In order to overcome these limitations, researchers have proposed a new cloud computing model called fog computing where the cloud system is located either at the edge of the private network or very close to it. In this paper, the authors take an in depth look at both these technologies to investigate fog computing can reliably overcome the limitations of cloud computing and effectively replace it and become the de facto cloud computing model of the future.

Abstract: The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. Cloud providers must ensure that their service delivery is flexible to meet various consumer requirements. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In this paper, for cloud environments, a novel QoS-aware VMs consolidation approach is proposed that adopts a method based on resource utilization history of virtual machines. Proposed algorithms have been implemented and evaluated using CloudSim simulator. Simulation results show improvement in QoS metrics and energy consumption as well as demonstrate that there is a trade-off between energy consumption and quality of service in the cloud environment.

Abstract: As Cloud Computing becomes the trend of information technology computational model, the Cloud security is becoming a major issue in adopting the Cloud where security is considered one of the most critical concerns for the large customers of Cloud (i.e. governments and enterprises). Such valid concern is mainly driven by the Multi-Tenancy situation which refers to resource sharing in Cloud Computing and its associated risks where confidentiality and/or integrity could be violated. As a result, security concerns may harness the advancement of Cloud Computing in the market. So, in order to propose effective security solutions and strategies a good knowledge of the current Cloud implementations and practices, especially the public Clouds, must be understood by professionals. Such understanding is needed in order to recognize attack vectors and attack surfaces. In this paper we will propose an attack model based on a threat model designed to take advantage of Multi-Tenancy situation only. Before that, a clear understanding of Multi-Tenancy, its origin and its benefits will be demonstrated. Also, a novel way on how to approach Multi-Tenancy will be illustrated. Finally, we will try to sense any suspicious behavior that may indicate to a possible attack where we will try to recognize the proposed attack model empirically from Google trace logs. Google trace logs are a 29-day worth of data released by Google. The data set was utilized in reliability and power consumption studies, but not been utilized in any security study to the extent of our knowledge.

Abstract: Security concerns are widely seen as an obstacle to the adoption of cloud computing solutions. Information Flow Control (IFC) is a well understood Mandatory Access Control methodology. The earliest IFC models targeted security in a centralised environment, but decentralised forms of IFC have been designed and implemented, often within academic research projects. As a result, there is potential for decentralised IFC to achieve better cloud security than is available today. In this paper we describe the properties of cloud computing—Platform-as-a-Service clouds in particular—and review a range of IFC models and implementations to identify opportunities for using IFC within a cloud computing context. Since IFC security is linked to the data that it protects, both tenants and providers of cloud services can agree on security policy, in a manner that does not require them to understand and rely on the particulars of the cloud software stack in order to effect enforcement.

Abstract: With the rapid development of Cloud computing, more and more users deposit their data and application on the cloud. But the development of Cloud computing is hindered by many Cloud security problem. Cloud computing has many characteristics, e.g. multi-user, virtualization, scalability and so on. Because of these new characteristics, traditional security technologies can't make Cloud computing fully safe. Therefore, Cloud computing security becomes the current research focus and is also this paper's research direction[1]. In order to solve the problem of data security in cloud computing system, by introducing fully homomorphism encryption algorithm in the cloud computing data security, a new kind of data security solution to the insecurity of the cloud computing is proposed and the scenarios of this application is hereafter constructed. This new security solution is fully fit for the processing and retrieval of the encrypted data, and effectively leading to the broad applicable prospect, the security of data transmission and the storage of the cloud computing[2].

Abstract: The present disclosure provides a novel approach for shifting or distributing various information (e.g., protocols, analysis methods, sample preparation data, sequencing data, etc.) to a cloud-based network. For example, the techniques relate to a cloud computing environment (12) configured to receive this information from one or more individual sample preparation devices (38), sequencing devices (18), and/or computing systems. In turn, the cloud computing environment (12) may generate information for use in the cloud computing environment (12) and/or to provide the generated information to the devices to guide a genomic analysis workflow. Further, the cloud computing environment (12) may be used to facilitate the sharing of sample preparation protocols for use with generic sample preparation cartridges and/or monitoring the popularity of the sample preparation protocols.

Abstract: In a large-scale cloud computing environment the cloud data centers and end users are geographically distributed across the globe. The biggest challenge for cloud data centers is how to handle and service the millions of requests that are arriving very frequently from end users efficiently and correctly. In cloud computing, load balancing is required to distribute the dynamic workload evenly across all the nodes. Load balancing helps to achieve a high user satisfaction and resource utilization ratio by ensuring an efficient and fair allocation of every computing resource. Proper load balancing aids in minimizing resource consumption, implementing fail-over, enabling scalability, avoiding bottlenecks and over-provisioning etc. In this paper, we propose “Central Load Balancer” a load balancing algorithm to balance the load among virtual machines in cloud data center. Results show that our algorithm can achieve better load balancing in a large-scale cloud computing environment as compared to previous load balancing algorithms.

Abstract: Cloud computing is a unique technique for outsourcing and aggregating computational hardware needs. By abstracting the underlying machines cloud computing is able to share resources among multiple mutually distrusting clients. While there are numerous practical benefits to this system, this kind of resource sharing enables new forms of information leakage such as hardware side-channels. In this paper, we investigate the usage of CPU-cache based side-channels in the cloud and how they compare to traditional side-channel attacks. We go on to demonstrate that new techniques are necessary to mitigate these sorts of attacks in a cloud environment, and specify the requirements for such solutions. Finally, we design and implement two new cache-based side-channel mitigation techniques, implementing them in a state-of-the-art cloud system, and testing them against traditional cloud technology.