Abstract: The performance of mobile computing would be significantly improved by leveraging cloud computing and migrating mobile workloads for remote execution at the cloud. In this paper, to efficiently handle the peak load and satisfy the requirements of remote program execution, we propose to deploy cloud servers at the network edge and design the edge cloud as a tree hierarchy of geo-distributed servers, so as to efficiently utilize the cloud resources to serve the peak loads from mobile users. The hierarchical architecture of edge cloud enables aggregation of the peak loads across different tiers of cloud servers to maximize the amount of mobile workloads being served. To ensure efficient utilization of cloud resources, we further propose a workload placement algorithm that decides which edge cloud servers mobile programs are placed on and how much computational capacity is provisioned to execute each program. The performance of our proposed hierarchical edge cloud architecture on serving mobile workloads is evaluated by formal analysis, small-scale system experimentation, and large-scale trace-based simulations.

Abstract: Cloud computing is a new computational paradigm that offers an innovative business model for organizations to adopt IT without upfront investment. Despite the potential gains achieved from the cloud computing, the model security is still questionable which impacts the cloud model adoption. The security problem becomes more complicated under the cloud model as new dimensions have entered into the problem scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. In this paper we introduce a detailed analysis of the cloud security problem. We investigated the problem from the cloud architecture perspective, the cloud offered characteristics perspective, the cloud stakeholders' perspective, and the cloud service delivery models perspective. Based on this analysis we derive a detailed specification of the cloud security problem and key features that should be covered by any proposed security solution.

Abstract: Vendor lock-in is a major barrier to the adoption of cloud computing, due to the lack of standardization. Current solutions and efforts tackling the vendor lock-in problem are predominantly technology-oriented. Limited studies exist to analyse and highlight the complexity of vendor lock-in problem in the cloud environment. Consequently, most customers are unaware of proprietary standards which inhibit interoperability and portability of applications when taking services from vendors. This paper provides a critical analysis of the vendor lock-in problem, from a business perspective. A survey based on qualitative and quantitative approaches conducted in this study has identified the main risk factors that give rise to lock-in situations. The analysis of our survey of 114 participants shows that, as computing resources migrate from on-premise to the cloud, the vendor lock-in problem is exacerbated. Furthermore, the findings exemplify the importance of interoperability, portability and standards in cloud computing. A number of strategies are proposed on how to avoid and mitigate lock-in risks when migrating to cloud computing. The strategies relate to contracts, selection of vendors that support standardised formats and protocols regarding standard data structures and APIs, developing awareness of commonalities and dependencies among cloud-based solutions. We strongly believe that the implementation of these strategies has a great potential to reduce the risks of vendor lock-in.

Abstract: Mobile cloud computing is applied in multiple industries to obtain cloud-based services by leveraging mobile technologies. With the development of the wireless networks, defending threats from wireless communications have been playing a remarkable role in the Web security domain. Intrusion detection system IDS is an efficient approach for protecting wireless communications in the Fifth Generation 5G context. In this paper, we identify and summarize the main techniques being implemented in IDSs and mobile cloud computing with an analysis of the challenges for each technique. Addressing the security issue, we propose a higher level framework of implementing secure mobile cloud computing by adopting IDS techniques for applying mobile cloud-based solutions in 5G networks. On the basis of the reviews and synthesis, we conclude that the implementation of mobile cloud computing can be secured by the proposed framework because it will provide well-protected Web services and adaptable IDSs in the complicated heterogeneous 5G environment. Copyright © 2015John Wiley & Sons, Ltd.

Abstract: The rapid development of the latest distributed computing paradigm, i.e., cloud computing, generates a highly fragmented cloud market composed of numerous cloud providers and offers tremendous parallel computing ability to handle big data problems. One of the biggest challenges in multiclouds is efficient workflow scheduling. Although the workflow scheduling problem has been studied extensively, there are still very few primal works tailored for multicloud environments. Moreover, the existing research works either fail to satisfy the quality of service (QoS) requirements, or do not consider some fundamental features of cloud computing such as heterogeneity and elasticity of computing resources. In this paper, a scheduling algorithm, which is called multiclouds partial critical paths with pretreatment (MCPCPP), for big data workflows in multiclouds is presented. This algorithm incorporates the concept of partial critical paths, and aims to minimize the execution cost of workflow while satisfying the defined deadline constraint. Our approach takes into consideration the essential characteristics of multiclouds such as the charge per time interval, various instance types from different cloud providers, as well as homogeneous intrabandwidth vs. heterogeneous interbandwidth. Various types of workflows are used for evaluation purpose and our experimental results show that the MCPCPP is promising.

Abstract: Cloud computing is becoming an increasingly admired paradigm that delivers high-performance computing resources over the Internet to solve the large-scale scientific problems, but still it has various challenges that need to be addressed to execute scientific workflows. The existing research mainly focused on minimizing finishing time (makespan) or minimization of cost while meeting the quality of service requirements. However, most of them do not consider essential characteristic of cloud and major issues, such as virtual machines (VMs) performance variation and acquisition delay. In this paper, we propose a meta-heuristic cost effective genetic algorithm that minimizes the execution cost of the workflow while meeting the deadline in cloud computing environment. We develop novel schemes for encoding, population initialization, crossover, and mutations operators of genetic algorithm. Our proposal considers all the essential characteristics of the cloud as well as VM performance variation and acquisition delay. Performance evaluation on some well-known scientific workflows, such as Montage, LIGO, CyberShake, and Epigenomics of different size exhibits that our proposed algorithm performs better than the current state-of-the-art algorithms.

Abstract: The advent of both Cloud computing and Internet of Things (IoT) is changing the way of conceiving information and communication systems. Generally, we talk about IoT Cloud to indicate a new type of distributed system consisting of a set of smart objects, e.g., single board computers running Linux- based operating systems, interconnected with a remote Cloud infrastructure, platform, or software through the Internet and able to provide IoT as a Service (IoTaaS). In this context, container-based virtualization is a lightweight alternative to the hypervisor-based approach that can be adopted on smart objects, for enhancing the IoT Cloud service provisioning. In particular, considering different IoT application scenarios, container-based virtualization allows IoT Cloud providers to deploy and customize in a flexible fashion pieces of software on smart objects. In this paper, we explore the container-based virtualization on smart objects in the perspective of a IoT Cloud scenarios analyzing its advantages and performances.

Abstract: Infrastructure-as-a-service (IaaS) cloud providers offer tenants elastic computing resources in the form of virtual machine (VM) instances to run their jobs. Recently, providing predictable performance (i.e., performance guarantee) for tenant applications is becoming increasingly compelling in IaaS clouds. However, the hardware heterogeneity and performance interference across the same type of cloud VM instances can bring substantial performance variation to tenant applications, which inevitably stops the tenants from moving their performance-sensitive applications to the IaaS cloud. To tackle this issue, this paper proposes Heifer, a He terogeneity and i nter fer ence-aware VM provisioning framework for tenant applications, by focusing on MapReduce as a representative cloud application. It predicts the performance of MapReduce applications by designing a lightweight performance model using the online-measured resource utilization and capturing VM interference. Based on such a performance model, Heifer provisions the VM instances of the good-performing hardware type (i.e., the hardware that achieves the best application performance) to achieve predictable performance for tenant applications, by explicitly exploring the hardware heterogeneity and capturing VM interference. With extensive prototype experiments in our local private cloud and a real-world public cloud (i.e., Microsoft Azure) as well as complementary large-scale simulations, we demonstrate that Heifer can guarantee the job performance while saving the job budget for tenants. Moreover, our evaluation results show that Heifer can improve the job throughput of cloud datacenters, such that the revenue of cloud providers can be increased, thereby achieving a win-win situation between providers and tenants.

Abstract: Cloud computing technology is a relatively new concept of providing scalable and virtualized resources, software and hardware on demand to consumers. It presents a new technology to deliver computing resources as a service. It offers a variety of benefits like services on demand and provisioning and suffers from several weaknesses. In fact, security presents a major obstacle in cloud computing adoption. In this paper, the authors will deal with security problems in cloud computing systems and show how to solve these problems using a quantitative security risk assessment model named Multi-dimensional Mean Failure Cost (M2FC). In fact, they summarize first security issues related to cloud computing environments and then propose a generic framework that analysis and evaluate cloud security problems and then propose appropriate countermeasures to solve these problems.

Abstract: This paper discusses the security of data in cloud computing. It is a study of data in the cloud and aspects related to it concerning security. The paper will go in to details of data protection methods and approaches used throughout the world to ensure maximum data protection by reducing risks and threats. Availability of data in the cloud is beneficial for many applications but it poses risks by exposing data to applications which might already have security loopholes in them. Similarly, use of virtualization for cloud computing might risk data when a guest OS is run over a hypervisor without knowing the reliability of the guest OS which might have a security loophole in it. The paper will also provide an insight on data security aspects for Data-in-Transit and Data-at-Rest. The study is based on all the levels of SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service).

Abstract: Cloud computing has proposed a new perspective for provisioning the large-scale computing resources by using virtualization technology and a payper-use cost model. Load balancing is taken into account as a vital part for parallel and distributed systems. It helps cloud computing systems by improving the general performance, better computing resources utilization, energy consumption management, enhancing the cloud services’ QoS, avoiding SLA violation and maintaining system stability through distribution, controlling and managing the system workloads. In this paper we study the necessary requirements and considerations for designing and implementing a suitable load balancer for cloud environments. In addition we represent a complete survey of current proposed cloud load balancing solutions which according to our classification, they can be classified into three categories: General Algorithm-based, Architectural-based and Artificial Intelligence-based load balancing mechanisms. Finally, we propose our evaluation of these solutions based on suitable metrics and discuss their pros and cons.

Abstract: Cloud computing has innovated the IT industry in recent years, as it can delivery subscription-based services to users in the pay-as-you-go model. Meanwhile, multimedia cloud computing is emerging based on cloud computing to provide a variety of media services on the Internet. However, with the growing popularity of multimedia cloud computing, its large energy consumption cannot only contribute to greenhouse gas emissions, but also result in the rising of cloud users' costs. Therefore, the multimedia cloud providers should try to minimize its energy consumption as much as possible while satisfying the consumers' resource requirements and guaranteeing quality of service (QoS). In this paper, we have proposed a remaining utilization-aware (RUA) algorithm for virtual machine (VM) placement, and a power-aware algorithm (PA) is proposed to find proper hosts to shut down for energy saving. These two algorithms have been combined and applied to cloud data centers for completing the process of VM consolidation. Simulation results have shown that there exists a trade-off between the cloud data center's energy consumption and service-level agreement (SLA) violations. Besides, the RUA algorithm is able to deal with variable workload to prevent hosts from overloading after VM placement and to reduce the SLA violations dramatically.

Abstract: Purpose – The purpose of this paper is to review and explore the evolution, advances and future trends of cloud manufacturing, placing the focus on the quality of services. Moreover, moving toward the new trend of cyber-physical systems (CPS), a cloud-based cyber-physical system (CBCPS) is proposed combining the key enabling techniques of this decade, namely Internet of Things (IoT), cloud computing, Big Data analytics and CPS. Design/methodology/approach – First, an extensive review is made on cloud computing and its applications in manufacturing sectors, namely product development, manufacturing processes and manufacturing systems management. Second, a conceptual CBCPS which combines key enabling techniques including cloud computing, CPS and IoT is proposed. Finally, a review on the quality of the services (QoS) presented in the second step, along with the main security issues of cloud manufacturing, is conducted. Findings – The findings of this review indicate that the combination of the key enabling techniques presented in the CBCPS will lead to a new manufacturing paradigm capable of facing the new challenges and trends. The opportunities, as well as the challenges and barriers of the proposed framework are presented, concluding that the transition into this whole new era of networked computing and manufacturing has a valuable impact, but also generates several security and quality issues. Originality/value – The paper is the first to specifically study the QoS as a factor in the proposed manufacturing paradigm.

Abstract: This paper addresses performance issues of resource allocation in cloud computing We review requirements of different cloud applications and identify the need of considering communication processes explicitly and equally to the computing tasks Following this observation, we propose a new communication-aware model of cloud computing applications, called CA-DAG This model is based on Directed Acyclic Graphs that in addition to computing vertices include separate vertices to represent communications Such a representation allows making separate resource allocation decisions: assigning processors to handle computing jobs, and network resources for information transmissions The proposed CA-DAG model creates space for optimization of a number of existing solutions to resource allocation and for developing novel scheduling schemes of improved efficiency

Abstract: Data centers are experiencing an exponential increase in the amount of network traffic that they have to sustain due to cloud computing and several emerging web applications. To face this network load, large data centers are required with thousands of servers interconnected with high bandwidth switches. Current data center, based on general purpose processor, consume excessive power while their utilization is quite low. Hardware accelerators can provide high energy efficiency for many cloud applications but they lack the programming efficiency of processors. In the last few years, there several efforts for the efficient deployment of hardware accelerators in the data centers. This paper presents a thorough survey of the frameworks for the efficient utilization of the FPGAs in the data centers. Furthermore it presents the hardware accelerators that have been implemented for the most widely used cloud computing applications. Furthermore, the paper provides a qualitative categorization and comparison of the proposed schemes based on their main features such as speedup and energy efficiency.

Abstract: Cloud service providers offer access to their resources through formal service level agreements (SLA), and need well-balanced infrastructures so that they can maximise the quality of service (QoS) they offer and minimise the number of SLA violations. This paper focuses on a specific aspect of risk assessment as applied in cloud computing: methods within a framework that can be used by cloud service providers and service consumers to assess risk during service deployment and operation. It describes the various stages in the service lifecycle whereas risk assessment takes place, and the corresponding risk models that have been designed and implemented. The impact of risk on architectural components, with special emphasis on holistic management support at service operation, is also described. The risk assessor is shown to be effective through the experimental evaluation of the implementation, and is already integrated in a cloud computing toolkit.

Abstract: Edge cloud is a promising architecture in order to address the latency problem in mobile cloud computing. However, as compared with remote clouds, edge clouds have limited computational resources, and higher operating costs. In this paper, we design policies which carry out the assignment of tasks that are generated at the mobile subscribers with edge clouds in an online fashion. The proposed policies achieve an optimal power-delay trade-off in the system. Here, the delay experienced by a mobile computing task includes the time spent waiting for transmission to the edge cloud, and the execution time at the edge cloud servers. We perform a theoretical analysis after modeling the system as a continuous-time queueing system. The contribution of this paper is two-fold: Firstly, the algorithm to determine the optimal policy is obtained by proposing an equivalent discrete-time Markov decision process. Secondly, an easily implementable index policy is proposed by analyzing the dual of the original problem. Extensive simulations illustrate the effectiveness of the proposed policies.

Abstract: Never before have data sharing been more convenient with the rapid development and wide adoption of cloud computing. However, how to ensure the cloud user’s data security is becoming the main obstacles that hinder cloud computing from extensive adoption. Proxy re-encryption serves as a promising solution to secure the data sharing in the cloud computing. It enables a data owner to encrypt shared data in cloud under its own public key, which is further transformed by a semitrusted cloud server into an encryption intended for the legitimate recipient for access control. This paper gives a solid and inspiring survey of proxy re-encryption from different perspectives to offer a better understanding of this primitive. In particular, we reviewed the state-of-the-art of the proxy re-encryption by investigating the design philosophy, examining the security models and comparing the efficiency and security proofs of existing schemes. Furthermore, the potential applications and extensions of proxy re-encryption have also been discussed. Finally, this paper is concluded with a summary of the possible future work.

Abstract: This paper gives a comprehensive discussion on applying the cloud computing technology as the new information infrastructure for the next-generation power system. First, this paper analyzes the main requirements of the future power grid on the information infrastructure and the limitations of the current information infrastructure. Based on this, a layered cloud-based information infrastructure model for next-generation power grid is proposed. Thus, this paper discussed how different categories of the power applications can benefit from the cloud-based information infrastructure. For the demonstration purpose, this paper develops three specific cloud-enabled power applications. The first two applications demonstrate how to develop practical compute-intensive and data-intensive power applications by utilizing different layered services provided by the state-of-the-art public cloud computing platforms. In the third application, we propose a cloud-based collaborative direct load control framework in a smart grid and show the merits of the cloud-based information infrastructure on it. Some cybersecurity considerations and the challenges and limitations of the cloud-based information infrastructure are also discussed.

Abstract: The demand for cloud computing is increasing dramatically due to the high computational requirements of business, social, web and scientific applications. Nowadays, applications and services are hosted on the cloud in order to reduce the costs of hardware, software and maintenance. To satisfy this high demand, the number of large-scale data centers has increased, which consumes a high volume of electrical power, has a negative impact on the environment, and comes with high operational costs. In this paper, we discuss many ongoing or implemented energy aware resource allocation techniques for cloud environments. We also present a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud. Finally, we come up with further research issues and challenges for future cloud environments.