The Need for End-to-End Evaluation of Cloud Availability

Hu, Zi; Liang, Zhu; Ardi, Calvin; Katz-Bassett, Ethan; Madhyastha, Harsha V.; Heidemann, John; Yu, Minlan

doi:10.1007/978-3-319-04918-2_12

Cited by 23 publications

(15 citation statements)

References 19 publications

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“…In particular, customers of CS critically depend on service availability [27] [28] and should consider related risks before any cloud-sourcing decision is made. Corresponding assessments should be part of any process for ranking and selecting CSPs [29,30,31,32,33] and formal certication processes [13]. However, even though many articles and security guidelines for cloud computing (CC) discuss service and data availability in the cloud [34] [35], they usually focus on hardware and data redundancy at the CSP site while only briey discussing general Internet backbone outages.…”

Section: Related Work On Cloud Availabilitymentioning

confidence: 99%

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Topological analysis of cloud service connectivity

Fabian

Baumann

Lackner

2015

Computers & Industrial Engineering

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Section: Related Work On Cloud Availabilitymentioning

confidence: 99%

“…This tool is used for comparing four major CSPs. An assessment of methods for measuring end-to-end availability of CSPs is presented in [32], including Internet Control Message Protocol (ICMP) traceroutes and Hypertext Transfer Protocol (HTTP) requests. These methods are evaluated by measurements for three CSPs.…”

Section: Related Work On Cloud Availabilitymentioning

confidence: 99%

Topological analysis of cloud service connectivity

Fabian

Baumann

Lackner

2015

Computers & Industrial Engineering

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“…In the following we focus on the literature on cloud availability analysis. In [17], cloud service availability was evaluated from a user-centric point of view, unlike our work that considers a cloud service provider's point of view.…”

Section: Model-based For Cdc Availabilitymentioning

confidence: 99%

Model-based sensitivity analysis of IaaS cloud availability

Liu

Chang

Han

et al. 2018

Future Generation Computer Systems

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The increasing shift of various critical services towards Infrastructure-as-a-Service (IaaS) cloud data centers (CDCs) creates a need for analyzing CDCs' availability, which is affected by various factors including repair policy and system parameters. This paper aims to apply analytical modeling and sensitivity analysis techniques to investigate the impact of these factors on the availability of a large-scale IaaS CDC, which (1) consists of active and two kinds of standby physical machines (PMs), (2) allows PM moving among active and two kinds of standby PM pools, and (3) allows active and two kinds of standby PMs to have different mean repair times. Two repair policies are considered: (P1) all pools share a repair station and (P2) each pool uses its own repair station. We develop monolithic availability models for each repair policy by using Stochastic Reward Nets and also develop the corresponding scalable two-level models in order to overcome the monolithic model's limitations, caused by the large-scale feature of a CDC and the complicated interactions among CDC components. We also explore how to apply differential sensitivity analysis technique to conduct parametric sensitivity analysis in the case of interacting submodels. Numerical results of monolithic models and simulation results are used to verify the approximate accuracy of interacting sub-models, which are further applied to examine the sensitivity of the large-scale CDC availability with respect to repair policy and system parameters.

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“…Statistics of working periods and outages have been modelled in [19] with data coming from a small private cloud. Active measurement systems based on ICMP probing packets have been investigated in [20]- [22]. A major issue with all measurements campaigns conducted so far is that they do measure the quality of service experienced by the user, but in doing so they include the loss contribution provided by the network located between the cloud user and the cloud server.…”

Section: Introductionmentioning

confidence: 99%

Whose Fault is It? Correctly Attributing Outages in Cloud Services

Adriani

Naldi

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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Cloud availability is a major performance parameter in cloud Service Level Agreements (SLA). Its correct evaluation is essential to SLA enforcement and possible litigation issues. Current methods fail to correctly identify the fault location, since they include the network contribution. We propose a procedure to identify the failures actually due to the cloud itself and provide a correct cloud availability measure. The procedure employs tools that are freely available, i.e. traceroute and whois, and arrives at the availability measure by first identifying the boundaries of the cloud. We evaluate our procedure by testing it on three major cloud providers: Google Cloud, Amazon AWS, and Rackspace. The results show that the procedure arrives at a correct identification in 95% of cases. The cloud availability obtained in the test after correct identification lies between 3 and 4 nines for the three platforms under test.

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The Need for End-to-End Evaluation of Cloud Availability

Cited by 23 publications

References 19 publications

Topological analysis of cloud service connectivity

Topological analysis of cloud service connectivity

Model-based sensitivity analysis of IaaS cloud availability

Whose Fault is It? Correctly Attributing Outages in Cloud Services

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