Redundant Eucalyptus Private Clouds: Availability Modeling and Sensitivity Analysis

Matos, Rubens; Dantas, Jamilson; Araújo, Jean; Trivedi, Kishor S.; Maciel, Paulo

doi:10.1007/s10723-016-9381-z

Cited by 34 publications

(25 citation statements)

References 30 publications

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“…In Reference , the authors propose a set of hierarchical models to evaluate availability of private cloud infrastructures. The considered cloud infrastructures are based on the Eucalyptus cloud framework, using redundant components for the most important ones.…”

Section: Related Workmentioning

confidence: 99%

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Availability analysis of design configurations to compose virtual performance‐optimized data center systems in next‐generation cloud data centers

et al. 2020

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Next-generation cloud data centers are based on software-defined data center infrastructures that promote flexibility, automation, optimization, and scalability. The Redfish standard and the Intel Rack Scale Design technology enable software-defined infrastructure and disaggregate bare-metal compute, storage, and networking resources into virtual pools to dynamically compose resources and create virtual performance-optimized data centers (vPODs) tailored to workload-specific demands. This article proposes four chassis design configurations based on Distributed Management Task Force's Redfish industry standard applied to compose vPOD systems, namely, a fully shared design, partially shared homogeneous design, partially shared heterogeneous design, and not shared design; their main difference is based on the used hardware disaggregation level. Furthermore, we propose models that combine reliability block diagram and stochastic Petri net modeling approaches to represent the complexity of the relationship between the pool of disaggregated hardware resources and their power and cooling sources in a vPOD. These four proposed design configurations were analyzed and compared in terms of availability and component's sensitivity indexes by scaling their configurations considering different data center infrastructure. From the obtained results, we can state that, in general, when one increases the hardware disaggregation, availability is improved. However, after a given point, the availability level of the fully shared, partially shared homogeneous, and partially shared heterogeneous configurations remain almost equal, while the not shared configuration is still able to improve its availability. K E Y W O R D Savailability, composable systems, next-generation cloud data center, Redfish standard, stochastic models Softw: Pract Exper. 2020;50:805-826.wileyonlinelibrary.com/journal/spe

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Section: Related Workmentioning

confidence: 99%

“…Some works from the literature have proposed the assessment of data center availability, such as References . However, our work differs from these as our focus is on the availability evaluation of next‐generation cloud data centers.…”

Section: Introductionmentioning

confidence: 99%

Availability analysis of design configurations to compose virtual performance‐optimized data center systems in next‐generation cloud data centers

et al. 2020

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“…They also described and discussed some existing commercial and non‐commercial solutions focused on middleware. Matos et al presented an availability evaluation for redundant private clouds, represented by reliability black diagrams and Markov chains, hierarchically assembled. They performed differential sensitivity analysis on models that are similar to those found in the work of Dantas et al Ever presented analytical models for performability evaluation of cloud centers.…”

Section: Related Workmentioning

confidence: 99%

“…() Many studies focused on cloud computing. () However, the causes of system unavailability in enterprise environments is not well understood and is a challenge. Organizations are not convinced about the impacts of unavailability on their businesses and ways of working.…”

Section: Related Workmentioning

confidence: 99%

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The influencing causes of software unavailability: A case study from industry

Ebad

2018

Softw Pract Exp

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Summary Software unavailability can lead to disastrous consequences ranging from delays and cancellation to a loss of millions of dollars of technology. However, research on the causes that can make systems unavailable is still little. The aim of this paper is to investigate such causes in an industrial context using 2 qualitative approaches ((a) interview and (b) retrospective analysis) using archival data and records from a student information system. As a result, connectivity, human, hardware, storage, and software were found to be the important categories of causes for the unavailability. Besides, the critical lessons to be learned that relate to activities of software management and software business are also discussed. This includes vendor support, systems documentation, health check process, licensing, and software updating or upgrading. To strengthen the claim that our findings are promising, we compare our main findings with those of a previous study. This study can generally assist software engineering people including engineers, developers, project managers, vendors. Additionally, this paper dis cusses the threats to the study's validity and suggests open problems for future research.

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Availability modeling in redundant OpenStack private clouds

et al. 2021

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In cloud computing services, high availability is one of the quality of service requirements which is necessary to maintain customer confidence. High availability systems can be built by applying redundant nodes and multiple clusters in order to cope with software and hardware failures. Due to cloud computing complexity, dependability analysis of the cloud may require combining state-based and nonstate-based modeling techniques. This article proposes a hierarchical model combining reliability block diagrams and continuous time Markov chains to evaluate the availability of OpenStack private clouds, by considering different scenarios. The steady-state availability, downtime, and cost are used as measures to compare different scenarios studied in the article. The heterogeneous workloads are considered in the proposed models by varying the number of CPUs requested by each customer. Both hardware and software failure rates of OpenStack components used in the model are collected via setting up a real OpenStack environment applying redundancy techniques. Results obtained from the proposed models emphasize the positive impact of redundancy on availability and downtime. Considering the tradeoff between availability and cost, system providers can choose an appropriate scenario for a specific purpose. K E Y W O R D S availability modeling, cloud computing, continuous time Markov chains, hierarchical models, reliability block diagrams INTRODUCTIONCloud computing includes different resources of one or more data centers, such as storage, network and compute, shared through virtualization technology. 1 Infrastructure as a service (IaaS) is an important service model of the cloud computing paradigm in which providers allocate virtual computing resources as virtual machines (VMs) on physical machines (PMs) to customers over the internet. [2][3][4] According to Gartner, 5 the market for public cloud will reach over $306.9 billion at the end of 2021 and $364 billion at the end of 2022. Gartner expects the hyperscale providers, such as Amazon AWS, Google Cloud Platform, Alibaba, and Microsoft Azure, to account for 70% of the market by the end of 2021. 6 Large organization and government agencies usually like to develop their own cloud data centers for satisfying their needs. By contrast, small organizations usually rent cloud resources from cloud providers to solve their own problems. 7

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Redundant Eucalyptus Private Clouds: Availability Modeling and Sensitivity Analysis

Cited by 34 publications

References 30 publications

Availability analysis of design configurations to compose virtual performance‐optimized data center systems in next‐generation cloud data centers

Availability analysis of design configurations to compose virtual performance‐optimized data center systems in next‐generation cloud data centers

The influencing causes of software unavailability: A case study from industry

Availability modeling in redundant OpenStack private clouds

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