Analyzing the IT subsystem failure impact on availability of cloud services

Santos, Guto Leoni; Endo, Patrícia Takako; Gonçalves, Glauco Estácio; Rosendo, Daniel; Gomes, Demis; Kelner, Judith; Sadok, Djamel; Mahloo, Mozhgan

doi:10.1109/iscc.2017.8024612

Cited by 28 publications

(19 citation statements)

References 11 publications

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“…As each subsystem is composed of several devices with complex relationships between them, it is important to evaluate how the failures of a given individual subsystem can impact the overall data center availability . With regard to this point, understanding which data center subsystem is less reliable is crucial to proposing improvements and making new investments …”

Section: Resultsmentioning

confidence: 99%

“…The immediate transition IT_IT_1 fires when any network component fails, turning the server unavailable (producing a token at place IT_SERV_1_UN); and the immediate transition IT_IT_2 fires after the recovery process, when all network components are working properly, making the server available again (a token is produced at place IT_SERV_1_UP). For a detailed description of all data center subsystems modeling, please consult our previous works …”

Section: Modeling a Data Centermentioning

confidence: 99%

“…Using the values of MTTF and MTTR of a data center model previously proposed, we verified the impact of failures of data center subsystems in the SAMU service, over a period of one year. Tier III architecture reduces in 90% the number of affected calls in comparison to tier I and over 60% in comparison to tier II.…”

Section: Final Considerationsmentioning

confidence: 99%

“…For a detailed description of all data center subsystems modeling, please consult our previous works. [2][3][4]8 The behavior of immediate transitions is assured by guard functions, which represent conditions to enable the transitions. These conditions are added in all immediate transitions in order to check the state of the components.…”

Section: Modeling a Data Centermentioning

confidence: 99%

“…In this paper, we use some previously proposed stochastic models to represent a cloud data center (see other works) and analyze how failures of such infrastructure impact an emergency call service. We evaluate different redundancy architectures following TIA‐942, a standard that defines the recommended redundancy strategies and best practices to reach a specified availability level.…”

Section: Introductionmentioning

confidence: 99%

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Measuring the impact of data center failures on a cloud‐based emergency medical call system

Gomes

Santos

Rosendo

et al. 2019

Concurrency and Computation

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Summary Emergency call services are expected to be highly available in order to minimize the loss of urgent calls and, as a consequence, minimize loss of life due to lack of timely medical response. This service availability depends heavily on the cloud data center on which it is hosted. However, availability information alone cannot provide sufficient understanding of how failures impact the service and users' perception. In this paper, we evaluate the impact of failures on an emergency call system, considering service‐level metrics such as the number of affected calls per failure and the time an emergency service takes until it recovers from a failure. We analyze a real data set from an emergency call center for a large Brazilian city. From stochastic models that represent a cloud data center, we evaluate different data center architectures to observe the impact of failures on the emergency call service. Results show that changing data center's architecture in order to improve availability from two to three nines cannot decrease the average number of affected calls per failure. On the other hand, it can decrease the probability to affect a considerable number of calls at the same time.

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Section: Resultsmentioning

confidence: 99%

Section: Modeling a Data Centermentioning

confidence: 99%

Section: Final Considerationsmentioning

confidence: 99%

Section: Modeling a Data Centermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Measuring the impact of data center failures on a cloud‐based emergency medical call system

Gomes

Santos

Rosendo

et al. 2019

Concurrency and Computation

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Generic input template for cloud simulators: A case study of CloudSim

et al. 2018

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Cloud computing and its service models, such as Platform as a Service (PaaS), have changed the way that computing resources are allocated to Information and Communications Technology enterprises and users. Although multiple cloud providers support dynamic service provisioning, it is necessary to facilitate the management of the cloud infrastructure and applications in order to allow the continuous refinement of cloud models. Therefore, issues are raised regarding the cloud orchestration, including the flexible portability and interoperability of cloud applications among multiple cloud providers. Having said that, there is a need for a standardized design and management of the cloud use cases (during the creation of scenarios, application's deployment, and patching) to ensure efficient applications' migration between different providers. This paper proposes an artifact, GITS, a generic input template for CloudSim and other cloud simulators. GITS can be provided by PaaS offering to manage the creation, monitoring, administration, and patching of infrastructure and applications in the cloud.GITS defines the cloud schema that can be used with conforming cloud models and independent cloud providers; thus, portability and interoperability can be enabled in PaaS cloud models. GITS focuses on the architecture-based modeling for cloud infrastructure and application not only in terms of computational resources but also in terms of high availability properties associated with infrastructure and applications. The main objective of the GITS template is to provide the cloud user with a modular, simple, readable, and reusable model that still supports the essential components and provide them with the ability to control the applications' execution, deployment, and other management needs in addition to the allocation environment. This paper describes GITS usage, specifically as an input template for CloudSim.

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DCAV: A software system to evaluate next‐generation cloud data center availability through a friendly graphical interface

et al. 2019

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To assess the availability of different data center configurations, understand the main root causes of data center failures and represent its low-level details, such as subsystem's behavior and their interconnections, we have proposed, in previous works, a set of stochastic models to represent different data center architectures (considering three subsystems: power, cooling, and IT) based on the TIA-942 standard. In this paper, we propose the Data Center Availability (DCAV), a web-based software system to allow data center operators to evaluate the availability of their data center infrastructure through a friendly interface, without need of understanding the technical details of the stochastic models.DCAV offers an easy step-by-step interface to create and configure a data center model. The main goal of the DCAV system is to abstract low-level details and modeling complexities, becoming the data center availability analysis a simple and less time-consuming task.Next-generation cloud data center infrastructures are being built on a hyperscale, also known as composable data centers, focused on promoting flexibility, automation, optimization, and scalability. For that, the next-generation data center refactors traditional ones into pools of disaggregated resource units (processors, memory, storage, and networking, plus power and cooling sources). According to Li et al 5 this design approach "offers the potential advantage of enabling continuous peak workload performance while minimizing resource fragmentation for fast evolving heterogeneous workloads."Several industry initiatives have emerged to turn this paradigm a reality. The Intel Rack Scale Design (Intel RSD) is an architecture for disaggregated composable data center infrastructure based on Redfish a Distributed Management Task Force (DMTF) industry standard. Many partners have developed solutions based on Intel RSD and Redfish; for instance, Ericsson has proposed the Hyperscale Datacenter System 6 that allows data center operators to configure and control a composable data center by using virtual performance-optimized resources (named virtual Performance-Optimized Data Center (vPOD)). To compose vPOD systems, only the required hardware resources are allocated and may be reallocated easily on demand reducing management complexities, idle resources, energy consumption, and maintenance time, which reflects in higher availability levels.Despite this, data center managers or planners must know in detail the data center architecture low-level details; how the power, cooling and IT subsystems work; and how they are interconnected to plan the data center with minimum downtime. Furthermore, the management of challenges, such as guaranteeing service level agreements with established availability levels, requires data center's planners to assess the availability of a data center, resulting in a complex task as it requires gathering information of a large-scale infrastructure.

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Analyzing the IT subsystem failure impact on availability of cloud services

Cited by 28 publications

References 11 publications

Measuring the impact of data center failures on a cloud‐based emergency medical call system

Measuring the impact of data center failures on a cloud‐based emergency medical call system

Generic input template for cloud simulators: A case study of CloudSim

DCAV: A software system to evaluate next‐generation cloud data center availability through a friendly graphical interface

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