Jamilson Dantas

An availability model for eucalyptus platform: An analysis of warm-standy replication mechanism

High availability in cloud computing services is essential for maintaining customer confidence and avoiding revenue losses due to SLA violation penalties. Since the software and hardware components of cloud infrastructures may have limited reliability, fault tolerance mechanisms are a means of achieving the necessary dependability requirements. This paper investigates the benefits of a warm-standy replication mechanism in a Eucalyptus cloud computing environment. A hierarchical heterogeneous modeling approach is used to represent a redundant architecture and compare its availability to that of a non-redundant architecture. Both hardware and software failures are considered in the proposed analytical models. The results show an enhanced dependability for the proposed redundant system, as well as a decrease in the annual downtime. The results also demonstrate that the simple replacement of hardware by more reliable machines would not produce improvements in system availability to the same extent as would the fault tolerant approach.

Redundant Eucalyptus Private Clouds: Availability Modeling and Sensitivity Analysis

Cloud computing infrastructures are designed to be accessible anywhere and anytime. This requires various fault tolerance mechanisms for coping with software and hardware failures. Hierarchical modeling approaches are often used to evaluate the availability of such systems, leveraging the representation of complex failure and repair events in distinct parts of the system. This paper presents an availability evaluation for redundant private clouds, represented by RBDs and Markov chains, hierarchically assembled. These private clouds follow the basic architecture of Eucalyptus-based environments, but employing warm-standby redundant hosts for some of its main components. Closed-form equations for the steady-state availability are presented, allowing direct analytical solution for large systems. The availability equations are symbolically differentiated, allowing parametric sensitivity analysis. The results from sensitivity analysis enables system planning for improving the steady- state availability. The sensitivity indices show that failure of the Eucalyptus Cloud Manager subsystem and the respective repair activities deserve priority for maximizing the system availability.

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.

Capacity-Oriented Availability Model for Resources Estimation on Private Cloud Infrastructure

Predicting the amount of resources available to systems users has become a task of interest to services providers even with the advent of elastic cloud computing, because the number of resources is finite despite being virtually infinite on the customer view. This paper proposes a model to evaluate nodes capacity in a cloud computing environment based on the amount of available hardware resources. By combining models to availability evaluation, such as reliability block diagrams, representing the operational infrastructure mode, and stochastic Petri net for capacity-oriented availability evaluation, we can determine the real amount of resources available at a predetermined time interval. Sensitivity analysis is also used to determine the component with highest impact in the metric of interest. The models, methods, and results of this research shall aid companies to plan the deployment and configuration of their services and cloud computing infrastructure.

Hierarchical Model and Sensitivity Analysis for a Cloud-Based VoD Streaming Service

Cloud computing environments provide storage capacity, processing power, and other computational resources in a flexible way, enabling fast adaptation to highly dynamic workloads. Multimedia services, such as video streaming, are examples of applications that can use cloud computing to leverage their provisioning capacity. This way, it is possible to offer a large variety of multimedia content in many formats, so the users will be able to watch videos as they wish, with a proper resolution and quality, according to his preferences and connection speed. Private infrastructures for Video on Demand (VoD) and live video streaming are especially useful for e-learning on large corporations, universities, and governments. Analytical models are effective tools to evaluate the availability of software, hardware, and other computational resources. In this paper, we study a VoD service hosted in a private cloud computing environment. We present availability models considering the VoD streaming server components that are necessary for viewers access. Hierarchical modeling techniques are used to deal with the complexity of representing such system. Sensitivity analysis is used to determine the parameters that cause greatest impact on the availability, identifying which components require attention when attempting to achieve increased availability in a system. The proposed models are useful for planning private cloud infrastructures for VoD services.

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.

Mobile cloud face recognition based on smart cloud ranking

A new trend has been explored to tackle the limited resources of mobile devices: mobile cloud computing. However, there is a difficulty in deciding—at runtime—the more appropriate target resources where to run offloaded mobile device tasks. This study presents SmartRank, a scheduling approach to perform load partitioning and offloading for mobile applications using cloud computing to increase performance in terms of response time. We have applied the approach to a face recognition process based on cloudlet federation and resource ranking through balanced metrics. Besides, the tool was evaluated by two ways. First, by using system modeling (continuous-time Markov chain). Second, by using a full factorial experimental design to calibrate the SmartRank with the most suitable partitioning decision. Nevertheless, SmartRank uses an equation that is extensible to include new parameters and make it applicable to other scenarios.

Availability models for synchronization server infrastructure

Users of computer systems wish to keep their personal data safe, updated, fair and accessible by other terminals, like personal computers, smart phones, portable consoles and PDAs. To perform these activities, one technology has become popular in our daily lives: data synchronization. Companies that provide this kind of service must do it with the greatest availability possible since their clients need their data to be available whenever they want to access it, and their customers in the legal field must avoid financial losses through SLA contract breaches. This paper presents hierarchical models for evaluating the availability of a data synchronization server infrastructure. The results show an availability of 98.82% for the proposed architecture, which means an annual downtime of 103 hours, this is more than 4 days of unavailability, where users cannot perform data synchronization.

An Algorithm to Optimize Electrical Flows of Private Cloud Infrastructures

The reduction of cloud computing energy consumption can be related to the application layer management with virtualization services, or a software layer. All clouds demand a power infrastructure with high availability. A data center power system is generally classified according to four redundancy levels, named tier I to IV. This paper proposes a power load distribution algorithm in depth search (PLDA-D) to optimize the power distribution of private cloud electrical infrastructures. The PLDA-D adopts the Energy Flow Model (EFM) as the basis to design power infrastructures. The EFM is a model that computes sustainability impacts and cost issues while it respects the restrictions of each component to provide energy. In addition, a case study illustrates the applicability of the proposed PLDA-D through the analysis of a private cloud running over a data center made up of tier II electrical architecture. Considerable results were achieved though the evaluation of the proposed algorithm. For instance, a reduction of 2.95% in energy consumption as well as an improvement of over 17% on the environment impact.