Abstract: Cloud computing has become a significant technology trend Experts believe cloud computing is currently reshaping information technology and the IT marketplace The advantages of using cloud computing include cost savings, speed to market, access to greater computing resources, high availability, and scalability Handbook of Cloud Computing includes contributions from world experts in the field of cloud computing from academia, research laboratories and private industry This book presents the systems, tools, and services of the leading providers of cloud computing; including Google, Yahoo, Amazon, IBM, and Microsoft The basic concepts of cloud computing and cloud computing applications are also introduced Current and future technologies applied in cloud computing are also discussed Case studies, examples, and exercises are provided throughout Handbook of Cloud Computing is intended for advanced-level students and researchers in computer science and electrical engineering as a reference book This handbook is also beneficial to computer and system infrastructure designers, developers, business managers, entrepreneurs and investors within the cloud computing related industry

Abstract: Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.

Abstract: Cloud computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters have been extensively studied, however, these solutions are not viable for a cloud environment. It is because, a cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public cloud. The proposed technique aims to exploit the advantages offered by cloud computing while taking into account the virtual machine (VM) performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.

Abstract: During the recent years, Information and Communication Technologies (ICT) play a significant role in the field of education and e-learning has become a very popular trend of the education technology. However, with the huge growth of the number of users, data and educational resources generated, e-learning systems have become more and more expansive in terms of hardware and software resources, and many educational institutions cannot afford such ICT investments. Due to its tremendous advantages, cloud computing technology rises swiftly as a natural platform to provide support to e-learning systems. This paper focuses on the research on the application of cloud computing in e-learning. The aim of this paper is to give an overview of the current state and the impact of the use of cloud computing for e-learning. Thus, at first the paper introduces concepts of e-learning and cloud computing infrastructure with their key characteristics. The paper analyzes also challenges facing e-learning systems deployment. In follow the paper considers cloud-based e-learning solutions by focusing on the raisons of the convenience of cloud computing for e-learning. Therefore cloud computing benefits are introduced as a solution for these challenges. Finally, the paper presents some solutions of cloud computing in e-learning and describes the most common architecture adopted. Issues in implementing cloud-based e-learning systems and some potential ways to overcome them are also discussed.

Abstract: Effective resource distribution to regulate load uniformly in heterogeneous cloud environments is crucial. Resource allotment which is taken after capable task scheduling is a critical worry in cloud environment. The incoming job requests are assigned to resources equally by load balancer in such a way that resources are utilized effectively. Number of cloud clients is very great in number, degree of approaching job requests is uninformed and information is tremendous in cloud application. Resources in cloud environment are constrained. Hence, it is not easy to deploy different applications with unpredictable limits and functionalities in heterogeneous cloud environment. The proposed method has two phases such as allocation of resources and scheduling of tasks. Effective resource allocation is proposed using social group optimization algorithm and scheduling of tasks using shortest-job-first scheduling algorithm for minimizing the makespan time and maximizing throughput. Experimentations are performed for accurate simulations on artificial data for heterogeneous cloud environment. Experimental results are compared with first-in, first-out and genetic algorithm-based shortest-job-first scheduling. Validity of the proposed method noticeably gives improved performance of the system in provisions of makespan time and throughput.

Abstract: In hybrid cloud computing, cloud users have the ability to procure resources from multiple cloud vendors, and furthermore also the option of selecting different combinations of resources. The problem of procuring a single resource from one of many cloud vendors can be modeled as a standard winner determination problem, and there are mechanisms for single item resource procurement given different QoS and pricing parameters. There however is no compatible approach that would allow cloud users to procure arbitrary bundles of resources from cloud vendors. We design the cloud - $\mathcal {CABOB}$ algorithm to solve the multiple resource procurement problem in hybrid clouds. Cloud users submit their requirements, and in turn vendors submit bids containing price, QoS and their offered sets of resources. The approach is scalable, which is necessary given that there are a large number of cloud vendors, with more continually appearing. We perform experiments for procurement cost and scalability efficacy on the cloud - $\mathcal {CABOB}$ algorithm using various standard distribution benchmarks like random, uniform, decay and CATS. Simulations using our approach with prices procured from several cloud vendors’ datasets show its effectiveness at multiple resource procurement.

Abstract: With the emergence of big data computing and analysis, cloud computing services become more and more popular, which has recently drawn researchers’ great attentions to develop various new applications and mechanisms. In this paper, we consider the on-demand mechanism design in the infrastructure as a service (IaaS), including resource allocation and pricing issues under dynamic scenarios. Most of existing works on mechanism design assumed static and independent individual utility, while the cloud computing services are provided in a dynamic environment. To solve such problems, we start with analyzing the Google cluster-usage dataset to draw the statistical and stochastic characteristics of the IaaS consumers and providers. Based on the characteristics mined from real data, we propose a stochastic matching algorithm with Markov Decision Process (MDP), which aims at optimizing the long-term system efficiency, with its online version using Q-learning method to address the imperfect model estimation problem. We further design an efficient (EF), incentive compatible (IC), individual rational (IR) auction mechanism, which is an extension of traditional Vickrey-Clarke-Groves (VCG) mechanism. The proposed mechanism is studied under two application scenario: quality sensitive services, where unilateral MDP-VCG auction is implemented; and quality insensitive services, where MDP-VCG double auction is implemented. To verify the performance of our proposed mechanism, we conduct experiment using the Google dataset and show that the proposed MDP-based VCG auction mechanism can achieve EF, IC and IR properties simultaneously.

Abstract: Automatic resource provisioning is a challenging and complex task. It requires for applications, services and underlying platforms to be continuously monitored at multiple levels and time intervals. The complex nature of this task lays in the ability of the monitoring system to automatically detect runtime configurations in a cloud service due to elasticity action enforcement. Moreover, with the adoption of open cloud standards and library stacks, cloud consumers are now able to migrate their applications or even distribute them across multiple cloud domains. However, current cloud monitoring tools are either bounded to specific cloud platforms or limit their portability to provide elasticity support. In this article, we describe the challenges when monitoring elastically adaptive multi-cloud services. We then introduce a novel automated, modular, multi-layer and portable cloud monitoring framework. Experiments on multiple clouds and real-life applications show that our framework is capable of automatically adapting when elasticity actions are enforced to either the cloud service or to the monitoring topology. Furthermore, it is recoverable from faults introduced in the monitoring configuration with proven scalability and low runtime footprint. Most importantly, our framework is able to reduce network traffic by 41 percent, and consequently the monitoring cost, which is both billable and noticeable in large-scale multi-cloud services.

Abstract: Due to the increasing sizes of cloud data centers, the number of virtual machines (VMs) and applications rises quickly. The rapid growth of large scale Internet services results in unbalanced load of network resource. The bandwidth utilization rate of some physical hosts is too high, and this causes network congestion. This paper presents a layered VM migration algorithm (LVMM). At first, the algorithm will divide the cloud data center into several regions according to the bandwidth utilization rate of the hosts. Then we balance the load of network resource of each region by VM migrations, and ultimately achieve the load balance of network resource in the cloud data center. Through simulation experiments in different environments, it is proved that the LVMMalgorithm can effectively balance the load of network resource in cloud computing.

Abstract: This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it’s a meta-heuristic. Cloud computing is a one of the emerging technology. It’s enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

Abstract: Cloud Computing refers to the use of computer resources as a service on-demand via internet. It is mainly based on data and applications outsourcing, traditionally stored on users' computers, to remote servers (datacenters) owned, administered and managed by third parts. This paper is an overview of data security issues in the cloud computing. Its objective is to highlight the principal issues related to data security that raised by cloud environment. To do this, these issues was classified into three categories: 1-data security issues raised by single cloud characteristics compared to traditional infrastructure, 2-data security issues raised by data life cycle in cloud computing (stored, used and transferred data), 3-data security issues associated to data security attributes (confidentiality, integrity and availability). For each category, the common solutions used to secure data in the cloud were emphasized.

Abstract: Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue in cloud computing. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better quality of service. Hence it is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a metaheuristic load balancing algorithm using Particle Swarm Optimization (MPSO) has been proposed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization. Performance comparisons are made with Genetic Algorithm (GA) and other popular algorithms on different measures like makespan calculation and resource utilization. Different cloud configurations are considered with varying Virtual Machines (VMs) and Cloudlets to analyze the efficiency of proposed algorithm. The proposed approach performs better than existing schemes.

Abstract: According to NIST definition of cloud computing, it has five characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service, while mobile computing focuses on device mobility and context awareness considering networking and mobile resource/data access. Mobile cloud computing is usually regarded as building on cloud computing and mobile computing; however, it has some unique features such as service offloading, migration, composition, etc. Mobile cloud computing enriches mobile computing technologies and leverages unified elastic resources of varied clouds and network technologies. This chapter provides an overview of various important concepts that are highly related to mobile cloud computing and illustrate their relations through real-world examples.

Abstract: Efficient virtualization methodologies constitute the core of cloud computing data center implementation. Clients are attracted to the cloud model by its ability to scale the resources dynamically and the flexibility in payment options that it offers. However, performance hiccups may push them to go back to the buy-and-maintain model. Virtualization plays a key role in the synchronous management of the thousands of servers along with clients' data living on them. To achieve seamless virtualization, cloud providers require a system that performs the function of virtual network provisioning. This includes receiving the cloud client requests and allocating their computational and network resources in a way that guarantees the quality-of-service conditions for clients while maximizing the data center resource utilization and providers' revenue. We introduce a comprehensive system to solve the problem of virtual network mapping for a set of connection requests sent by cloud clients. Connections are collected in time intervals called windows. Consequently, node and link provisioning is performed. Different window size selection schemes are introduced and evaluated. Three schemes to prioritize connections are used, and their effect is assessed. Moreover, a technique dealing with connections spanning over more than a window is introduced. The proposed algorithm is compared with previous work well known in the literature. Simulation results show that the dynamic window size algorithm achieves cloud service providers' objectives in terms of generated revenue, served-connection ratio, resource utilization, and computational overhead. In addition, experimental results show that handling spanning connections independently improves the performance of the system.

Abstract: The proliferation of cloud computing allows users to flexibly store, re-compute or transfer large generated datasets with multiple cloud service providers. However, due to the pay-as-you-go model, the total cost of using cloud services depends on the consumption of storage, computation and bandwidth resources which are three key factors for the cost of IaaS-based cloud resources. In order to reduce the total cost for data, given cloud service providers with different pricing models on their resources, users can flexibly choose a cloud service to store a generated dataset, or delete it and choose a cloud service to regenerate it whenever reused. However, finding the minimum cost is a complicated yet unsolved problem. In this paper, we propose a novel algorithm that can calculate the minimum cost for storing and regenerating datasets in clouds, i.e., whether datasets should be stored or deleted, and furthermore where to store or to regenerate whenever they are reused. This minimum cost also achieves the best trade-off among computation, storage and bandwidth costs in multiple clouds. Comprehensive analysis and rigid theorems guarantee the theoretical soundness of the paper, and general (random) simulations conducted with popular cloud service providers’ pricing models demonstrate the excellent performance of our approach.

Abstract: The decision of migrating information technology to cloud computing, by an organisation, encompasses various decisions that must be undertaken in order to minimise risks and to perform a smooth and accurate transition to the cloud. Accordingly, to migrate information technology services to the cloud in a straightforward way, with more control and in a more accurate way, the organisation must use the right tools. Having in mind the cloud computing focus on information technology services shared with the Information Technology Infrastructure Library and processes that have been tested by distinct organisations, it makes sense to research whether the Information Technology Infrastructure Library processes can be used in the migration to cloud computing. Accordingly, in this paper, we investigate, on the one hand, how Information Technology Infrastructure Library could be useful to the migration of services, applications and data to cloud computing, and on the other hand, we discuss how these processes help people to improve their skills in the knowledge accessibility. The research was validated with the implementation of a case study and with interviews with stakeholders of the whole process. With this research, we were able to verify that the ITIL could be used to support the migration to cloud computing.

Abstract: We present ElasTest, an open-source generic and extensible platform supporting end-to-end testing of large complex cloud systems, including web, mobile, network and WebRTC applications. ElasTest is developed following a fully transparent and open agile process around which a community of developers, contributors and users is collected. We demonstrate ElasTest in action by testing the FullTeachingest application: the video is available from http://elastest.io/videos/icse2018-demo.

Abstract: Cloud computing is the new bending curve in the line of Information Technology and computing paradigm. Cloud computing has simultaneously revolutionized business and government sectors by stretching its arms in every possible field, from not only to Information communication technology to medical advancements to a more diverse agriculture field which was untouched by technology for a decade. Cloud computing enlightens path for another dimension of computing which is expected to rise even more than mobile computing, i.e., Internet of Things (IOT) or internet of everything. As new dimensions are being added up to the cloud domain every day this gives a window to hackers and intruders to breach the doors. The security problem is amplified under cloud computing as it introduces new problem domains. One of the major problem domain identified is data security, where security of user’s data is the utmost priority. Providing privacy to the user and the data stored by the user is to be ensured by the cloud service provider. This paper aims at the security model for cloud computing which ensures the data security and integrity of user’s data stored in the cloud using cryptography.

Abstract: Performance evaluation of cloud center is a necessary prerequisite to fulfilling contractual quality of service, particularly in big data applications. However, effectively evaluating performance of cloud services is challenging due to the complexity of cloud services and the diversity of big data applications. In this paper, we propose a performance evaluation model for parallel computing models deployed in cloud centers to support big data applications. In this evaluation model, a big data application is divided into lots of parallel tasks and the task arrivals follow a general distribution. In our approach, we also consider factors associated with resource heterogeneity, resource contention among cloud nodes, and data storage strategy, which have an impact on the performance of parallel computing models. Our model also allows us to calculate key performance indicators of cloud center such as mean number of tasks in the system, probability that a task obtains immediate service, and task waiting time. The model can also be used to predict the time of performing applications. We then demonstrate the utility of the model based on simulations and benchmarking using WordCount and TeraSort applications.

Abstract: Cloud computing systems are popular in computing industry for their ease of use and wide range of applications. These systems offer services that can be used over the Internet. Due to their wide popularity and usage, cloud computing systems and their services often face issues resource management related challenges. In this paper, we present v-Mapper, a resource consolidation scheme which implements network resource management concepts through software-defined networking (SDN) control features. The paper makes three major contributions: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a cloud service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. We tested our scheme with other techniques in terms of average task processing time, service delay and bandwidth usage. Our results demonstrate that v-Mapper outperforms other techniques and delivers significant improvement in system’s performance.

Abstract: This paper proposed the Cloud Storage Service Selection Strategy under the cross-datacenter environment. Due to the dynamic network environment and the independence between the data centers, this paper presented Cloud Storage Service Selection Strategy across the data center based on AHP–backward cloud generator algorithm. The strategy combines the theory of analytic hierarchy process (AHP) analysis and uncertainty reasoning of cloud method by means of collecting cloud storage providers’ quantitative performance data and inferring qualitative classification of service capability, to select Cloud Storage Service Selection Strategy across the data center. Simulation results show that the strategy has a great advantage in system load balance, replica access rate, and data reliability.