Interconnecting Federated Clouds by Using Publish-Subscribe Service

Cloud Federation is an emerging computing model where multiple resources from independent Cloud providers are leveraged to create large-scale distributed virtual computing clusters, operating as into a single Cloud organization. This model enables the implementation of environmental diversity for Cloud applications, and overcomes the provisioning and scalability limits of a single Cloud, by introducing minimal additional cost for the Cloud consumer. In such a scenario, it is necessary to leverage on specific networking technologies that enable the effective support of inter-Cloud communication services between Cloud providers. This paper proposes an interconnection solution for Cloud federations based on publish/subscribe services. Moreover, we discuss some fundamental concerns needed to satisfy the inter-Cloud communication requirements in terms of reliability and availability. Finally, we present some experimental results that highlight some key reliability and denial of service vulnerability concerns in this domain.     

CryptoGA: a cryptosystem based on genetic algorithm for cloud data security

Cluster Computing - Cloud Computing is referred to as a set of hardware and software that are being combined to deliver various services of computing. The cloud keeps the services for delivery of...     

Software architecture definition for on-demand cloud provisioning

Cloud computing is a promising paradigm for the provisioning of IT services. Cloud computing infrastructures, such as those offered by the RESERVOIR project, aim to facilitate the deployment, management and execution of services across multiple physical locations in a seamless manner. In order for service providers to meet their quality of service objectives, it is important to examine how software architectures can be described to take full advantage of the capabilities introduced by such platforms. When dealing with software systems involving numerous loosely coupled components, architectural constraints need to be made explicit to ensure continuous operation when allocating and migrating services from one host in the Cloud to another. In addition, the need for optimising resources and minimising over-provisioning requires service providers to control the dynamic adjustment of capacity throughout the entire service lifecycle. We discuss the implications for software architecture definitions of distributed applications that are to be deployed on Clouds. In particular, we identify novel primitives to support service elasticity, co-location and other requirements, propose language abstractions for these primitives and define their behavioural semantics precisely by establishing constraints on the relationship between architecture definitions and Cloud management infrastructures using a model denotational approach in order to derive appropriate service management cycles. Using these primitives and semantic definition as a basis, we define a service management framework implementation that supports on demand cloud provisioning and present a novel monitoring framework that meets the demands of Cloud based applications.     

OPSA: an optimized prediction based scheduling approach for scientific applications in cloud environment

Cluster Computing - Cloud computing has attracted scientists to deploy scientific applications by offering services such as Infrastructure-as-a-service (IaaS), Software-as-a-service (SaaS), and...     

An in-depth and systematic literature review on the blockchain-based approaches for cloud computing

Cluster Computing - Cloud computing enables businesses to decrease the total costs by outsourcing their required services. Therefore, it provides a new challenge of data protection regarding...     

Dynamic large branching hash tree based secure and efficient dynamic auditing protocol for cloud environment

Cluster Computing - Cloud computing model offers various platforms services and provides a scalable, on-demand service at any time-anywhere manner. However, in the outsourcing strategy, users no...     

Fault tolerance and QoS scheduling using CAN in mobile social cloud computing

The performance of mobile devices including smart phones and laptops is steadily rising as prices plummet sharply. So, mobile devices are changing from being a mere interface for requesting services to becoming computing resources for providing and sharing services due to immeasurably improved performance. With the increasing number of mobile device users, the utilization rate of SNS (Social Networking Service) is also soaring. Applying SNS to the existing computing environment enables members of social network to share computing services without further authentication. To use mobile device as a computing resource, temporary network disconnection caused by user mobility and various HW/SW faults causing service disruption should be considered. Also these issues must be resolved to support mobile users and to provide user requirements for services. Accordingly, we propose fault tolerance and QoS (Quality of Services) scheduling using CAN (Content Addressable Network) in Mobile Social Cloud Computing (MSCC). MSCC is a computing environment that integrates social network-based cloud computing and mobile devices. In the computing environment, a mobile user can, through mobile devices, become a member of a social network through real world relationships. Essentially, members of a social network share cloud service or data with other members without further authentication by using their mobile device. We use CAN as the underlying MSCC to logically manage the locations of mobile devices. Fault tolerance and QoS scheduling consists of four sub-scheduling algorithms: malicious-user filtering, cloud service delivery, QoS provisioning, and replication and load-balancing. Under the proposed scheduling, a mobile device is used as a resource for providing cloud services, faults caused from user mobility or other reasons are tolerated and user requirements for QoS are considered. We simulate scheduling both with and without CAN. The simulation results show that our proposed scheduling algorithm enhances cloud service execution time, finish time and reliability and reduces the cloud service error rate.     

Energy aware scheduling of deadline-constrained tasks in cloud computing

Energy efficiency is the predominant issue which troubles the modern ICT industry. The ever-increasing ICT innovations and services have exponentially added to the energy demands and this proliferated the urgency of fostering the awareness for development of energy efficiency mechanisms. But for a successful and effective accomplishment of such mechanisms, the support of underlying ICT platform is significant. Eventually, Cloud computing has gained attention and has emerged as a panacea to beat the energy consumption issues. This paper scrutinizes the importance of multicore processors, virtualization and consolidation techniques for achieving energy efficiency in Cloud computing. It proposes Green Cloud Scheduling Model (GCSM) that exploits the heterogeneity of tasks and resources with the help of a scheduler unit which allocates and schedules deadline-constrained tasks delimited to only energy conscious nodes. GCSM makes energy-aware task allocation decisions dynamically and aims to prevent performance degradation and achieves desired QoS. The evaluation and comparative analysis of the proposed model with two other techniques is done by setting up a Cloud environment. The results indicate that GCSM achieves 71 % of energy savings and high performance in terms of deadline fulfillment.     

Ontology as a Service (OaaS): extracting and replacing sub-ontologies on the cloud

Cloud computing is founded by the concept of service computing, where everything is a service—computing services are now utilities. There are various known services in cloud computing. At the moment, there are Software as a Service (SaaS), Platform as a Service (PaaS), Hardware/Infrastructure as a Service (HaaS/IaaS), and Database as a Service (DaaS). In this paper, we propose Ontology as a Service (OaaS), which is an ontology tailoring process service in the cloud. In particular, we focus on sub-ontology extraction and replacement on the cloud. We use the Maximum Extraction method to facilitate this. UMLS meta-thesaurus ontology is used as a walk-through case study to illustrate our proposed method.     

Developing a Model Driven Approach for engineering applications based on mOSAIC

In many scientific and engineering areas there are emerging software services available over the Web. The reason for deploying such services in the Cloud is either to reduce the operational costs or to support the peaks in their usage profiles. The algorithms employed in such services are usually result of a long term research and technology development work, so it is beneficial to reuse those critical application parts when developing new Cloud applications. This paper investigates the possibilities to introduce a Model Driven Architecture (MDA) for the Cloud computing domain, which would support composition, customization, flexibility, maintenance and reusability of Cloud application components in the particular case of scientific and engineering applications. The underlying middleware technology of choice is the mOSAIC Platform as a Service (PaaS) solution. This choice is motivated by the fact that in mOSAIC a Cloud application consists of loosely coupled components, which are either generic and provide for key resource types needed by an application (computation, storage, communication) or custom made, e.g. based on existing legacy software. The MDA approach is illustrated through the design and operation of an application for analysis of structures under static loading. It is shown that a relatively simple design can be used to address two application bottlenecks: the varying number of users and the computational complexity of the given problem. The design reduces the necessary application development efforts and the key components can be reused for similar applications.     

Classified power capping by network distribution trees for green computing

Power management is becoming very important in data centers. To apply power management in cloud computing, Green Computing has been proposed and considered. Cloud computing is one of the new promising techniques, that are appealing to many big companies. In fact, due to its dynamic structure and property in online services, cloud computing differs from current data centers in terms of power management. To better manage the power consumption of web services in cloud computing with dynamic user locations and behaviors, we propose a power budgeting design based on the logical level, using distribution trees. By setting multiple trees or forest, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs, e.g. response time) in terms of power characteristics. Based on these, we introduce classified power capping for different services as the control reference to maximize power saving when there are mixed workloads.     

Secure outsourcing of modular exponentiations in cloud and cluster computing

Cloud computing and cluster computing are user-centric computing services. The shared software and hardware resources and information can be provided to the computers and other equipments according to the demands of users. A majority of services are deployed through outsourcing. Outsourcing computation allows resource-constrained clients to outsource their complex computation workloads to a powerful server which is rich of computation resources. Modular exponentiation is one of the most complex computations in public key based cryptographic schemes. It is useful to reduce the computation cost of the clients by using outsourcing computation. In this paper, we propose a novel outsourcing algorithm for modular exponentiation based on the new mathematical division under the setting of two non-colluding cloud servers. The base and the power of the outsourced data can be kept private and the efficiency is improved compared with former works.     

An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment

Cluster Computing - Cloud computing is an emerging distributed computing model that offers computational capability over internet. Cloud provides a huge level collection of powerful and scalable...     

Software Service Signature (S3) for authentication in cloud computing

Cloud computing provides many kinds of application services for cloud users, but security problems have caused great impact on Software as a Service (SaaS). As a commercial model, SaaS is related among different participants who could be malicious or dishonest. This paper presents a Software Service Signature (S³) to deal with several security issues in SaaS and keep the interests and rights of all participants in safety. Our design is based on ID-based proxy signatures from pairings. The analysis shows that the proposed scheme can effectively strengthen the security through authentication in cloud computing.     

RALBA: a computation-aware load balancing scheduler for cloud computing

Cloud computing serves as a platform for remote users to utilize the heterogeneous resources in data-centers to compute High-Performance Computing jobs. The physical resources are virtualized in Cloud to entertain user services employing Virtual Machines (VMs). Job scheduling is deemed as a quintessential part of Cloud and efficient utilization of VMs by Cloud Service Providers demands an optimal job scheduling heuristic. An ideal scheduling heuristic should be efficient, fair, and starvation-free to produce a reduced makespan with improved resource utilization. However, static heuristics often lead to inefficient and poor resource utilization in the Cloud. An idle and underutilized host machine in Cloud still consumes up to 70% of the energy required by an active machine (Ray, in Indian J Comput Sci Eng 1(4):333–339, 2012). Consequently, it demands a load-balanced distribution of workload to achieve optimal resource utilization in Cloud. Existing Cloud scheduling heuristics such as Min–Min, Max–Min, and Sufferage distribute workloads among VMs based on minimum job completion time that ultimately causes a load imbalance. In this paper, a novel Resource-Aware Load Balancing Algorithm (RALBA) is presented to ensure a balanced distribution of workload based on computation capabilities of VMs. The RABLA framework comprises of two phases: (1) scheduling based on computing capabilities of VMs, and (2) the VM with earliest finish time is selected for jobs mapping. The outcomes of the RALBA have revealed that it provides substantial improvement against traditional heuristics regarding makespan, resource utilization, and throughput.     

Improving the performance of data center with real-time service image placement in mobile cloud environment

Mobile Cloud Computing (MCC) is broadening the ubiquitous market for mobile devices. Because of the hardware limitation of mobile devices, the heavy computing tasks should be processed by service images (SIs) on the cloud. Due to the scalability and mobility of users and services, dynamic resource demands and time-varying network condition, SIs must be re-located to adapt the new circumstances. In this paper, we formulate the SI placement as an optimization problem which minimizes the communication cost subject to resource demand constraints. We then propose a real-time SI placement scheme which includes two sequent stages of clustering/filtering and condensed placement to solve the formulated problem. The former omits the infeasible slots prior to placement in order to improve computational complexity. The latter focuses on the SI placement through a novel condensed solution. The numerical results show that our solution converges to the global optimum with a negligible gap while performing much faster execution time compared with the exhaustive search method. This improvement leverages the real-time services especially in MCC environment.     

A strengthening plan for enterprise information security based on cloud computing

Cloud computing environment came about in order to effectively manage and use enormous amount of data that have become available with the development of the Internet. Cloud computing service is widely used not only to manage the users’ IT resources, but also to use enterprise IT resources in an effective manner. Various security threats have occurred while using cloud computing and plans for reaction are much needed, since they will eventually elevate to security threats to enterprise information. Plans to strengthen the security of enterprise information by using cloud security will be proposed in this research. These cloud computing security measures must be supported by the governmental policies. Publications on guidelines to information protection will raise awareness among the users and service providers. System of reaction must be created in order to constantly monitor and to promptly respond to any security accident. Therefore, both technical countermeasures and governmental policy must be supported at the same time. Cloud computing service is expanding more than ever, thus active research on cloud computing security is expected.     

Blockchain-enabled real-time SLA monitoring for cloud-hosted services

Cluster Computing - Cloud computing is an important technology for businesses and individual users to obtain computing resources over the Internet on-demand and flexibly. Although cloud computing...     

A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services

Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).