Maria Clara Bezerra

Availability modeling and analysis of a VoD service for eucalyptus platform

Cloud computing environments are an emerging technology that aims at proving several cloud services over the Internet, making it possible to provide a huge variety of applications with different purposes to users. Multimedia services are examples of these new services that use cloud computing, like video streaming, where the user can access their videos from cloud environments. For cloud service systems, assurance of high-availability of cloud services is a challenging and critical issue. In this paper, we evaluated the video streaming services availability for video on demand cloud system. Hierarchical modeling strategy, availability models combining reliability blocks diagrams (RBD) and continuous time Markov chain (CTMC) were used, indicating a system availability of 1.92 nines. The critical component of the system was indicated through sensibility analysis. We realized a model validation technique to demonstrate that the models represent the behavior of the real system.

Redundant VoD Streaming Service in a Private Cloud: Availability Modeling and Sensitivity Analysis

For several years cloud computing has been generating considerable debate and interest within IT corporations. Since cloud computing environments provide storage and processing systems that are adaptable, efficient, and straightforward, thereby enabling rapid infrastructure modifications to be made according to constantly varying workloads, organizations of every size and type are migrating to web-based cloud supported solutions. Due to the advantages of the pay-per-use model and scalability factors, current video on demand (VoD) streaming services rely heavily on cloud infrastructures to offer a large variety of multimedia content. Recent well documented failure events in commercial VoD services have demonstrated the fundamental importance of maintaining high availability in cloud computing infrastructures, and hierarchical modeling has proved to be a useful tool for evaluating the availability of complex systems and services. This paper presents an availability model for a video streaming service deployed in a private cloud environment which includes redundancy mechanisms in the infrastructure. Differential sensitivity analysis was applied to identify and rank the critical components of the system with respect to service availability. The results demonstrate that such a modeling strategy combined with differential sensitivity analysis can be an attractive methodology for identifying which components should be supported with redundancy in order to consciously increase system dependability.

Video on Demand Hosted in Private Cloud: Availability Modeling and Sensitivity Analysis

Cloud computing environments have recently emerged as a new computing paradigm for organizing a shared pool of servers in data centres into a cloud infrastructure that can provide on demand service utilities. Due to the business potential of the pay-per-use model, as well as the advantages of easy scalability, up-to-date Multimedia Services can rely on cloud infrastructures to offer a wide variety of services, like video streaming, where the user can access their videos from cloud environments. Hierarchical analytical models are effective tools to evaluate the availability of complex systems and services such as these. This paper proposes the application of availability models to a cloud environment designed for a video streaming service. The hierarchical models thus created comprise Reliability Block Diagrams (RBDs) and Markov chains. Sensitivity analysiss used to determine the parameters that cause the greatest impact on the availability. The results obtained from case studies clearly demonstrate that sensitivity analysis is a valuable tool for identifying which components require attention when attempting to achieve increased availability in a system.

Availability Evaluation of a VoD Streaming Cloud Service

The increasing development and utilization of services based on cloud facilitates the development of sectors like entertainment through media streaming. Platforms such as Eucalyptus provide Infrastructure as a Service (IaaS), allowing to create and manage a wide variety of services on cloud. Redundant mechanisms are commonly adopted to achieve improvements of availability, since this issue is critical for providing a cloud service. This paper analyses the impact of adopting a redundant component for a VoD streaming service based on Eucalyptus platform, comparing these results to a non-redundant proposal. The proposed solution revealed an reduction of 51.01% in annual downtime compared to the non-redundant proposal. Sensitivity analysis was used to identify availability bottlenecks to the proposed redundant architecture.

Sensitivity analysis techniques applied in cloud computing environments

Nowdays, several streaming servers are available to provide a variety of multimedia applications such as Video on Demand in cloud computing environments. These environments have the business potential because of the pay-per-use model, as well as the advantages of easy scalability and, up-to-date of the packages and programs. This paper uses hierarchical modeling and different sensitivity analysis techniques to determine the parameters that cause the greatest impact on the availability of a Video on Demand. The results show that distinct approaches provide similar results regarding the sensitivity ranking, with specific exceptions. A combined evaluation indicates that system availability may be improved effectively by focusing on a reduced set of factors that produce large variation on the measure of interest.

Availability and sensitivity analysis in video streaming services hosted on private clouds

Cloud computing have one technology development that favor various computing services and storage over the Internet. The multimedia services are examples of these new services will now use this new technology. About the multimedia services, we highlight the streaming video service, where the user can access your videos from these cloud environments with the same quality practiced in traditional environments. In this paper, we used the models to find the RBD availability of streaming video services in a test environment assembled in the laboratory to evaluate the system. Sensitivity analysis was used to identify the critical points of the system with respect to availability so that you can act on them and thus improve the system availability.

Sensitivity analysis of availability of video streaming service in cloud computing

Cloud computing environments provide powerful storage and processing capabilities, as well as other computational resources. Due to the business potential of the pay-peruse model, as well as the advantages of easy scalability, up-to-date Video on Demand (VoD) streaming services rely on cloud infrastructures to offer a wide variety of multimedia content. This paper proposes the application of availability models to a cloud environment designed for a video streaming service. Sensitivity analysis is proposed to identify the availability bottlenecks.

VoD in eucalyptus platform: Availability modeling and sensibility analysis

Due to advantages such as the pay-per-use model and a highly scalable environment, cloud infrastructures are commonly employed by Video on Demand (VoD) streaming services to supply an extensive range of multimedia content to users. However, recent events have demonstrated to commercial VoD service providers the fundamental importance of ensuring highly available cloud computing infrastructures. This paper examines the effectiveness of applying availability modeling to a VoD cloud environment which has no redundancy mechanisms included in its architecture. The analysis employs a combinatorial modeling strategy consisting of RDB models and Markov chains. Sensitivity analysis is also applied to identify the critical points in the system with respect to availability. The outcome of the research clearly shows that the modeling strategy, together with the sensitivity analysis, has a key role to play in the identification of those components on which resources should be focused in order to achieve increased system availability.