An Empirical Failure-Analysis of a Large-Scale Cloud Computing Environment

Garraghan, Peter; Townend, Paul; Xu, Jie

doi:10.1109/hase.2014.24

Cited by 60 publications

(44 citation statements)

References 30 publications

(39 reference statements)

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“…The presented analysis and model captures the characteristics and behavioral patterns of user and task variability across the entire system as well as different observational periods. We further quantify the interferenceenergy model in which we comprehensively analyze the energy-efficiency of massive system impacted by performance interference [41] and failure-energy model which depicts the energy-efficiency reduction and wastes due to constant failures in Cloud data center [43].…”

Section: Data-driven Methodologymentioning

confidence: 99%

“…In fact, the machines are constantly supplemented into the computing cluster over time, using whatever configuration was most cost-effective [13]. Despite these benefits, these commodity servers are very vulnerable to kinds of hardware and software failures [43]. Therefore, the Cloud datacenter providers have to be constantly under great pressure to face increasing failures in such systems in order to provision uninterrupted reliable services to their consumers.…”

Section: A Varying Request and Resource Heterogeneitymentioning

confidence: 99%

“…Furthermore, Cloud service providers are constantly under great pressure to provision uninterrupted reliable services and platforms to consumers while reducing their operational costs due to significant software and hardware failures in such scale systems [43]. A widely used means to achieve such a goal is to use redundant system components to implement usertransparent failover, but its effectiveness must be balanced carefully without incurring heavy overhead when deployedan important practical consideration for systems at sufficient In this paper, we present our understanding of the current challenges in this particular area based on the emerging characteristics within computing at scale.…”

Section: Introductionmentioning

confidence: 99%

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Computing at Massive Scale: Scalability and Dependability Challenges

Yang

2016

2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)

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-Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, the heterogeneity of workloads and resources, together with the dynamicity of massive user requests, the uncertainties and complexity of resource scheduling and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and negative user-perceived performance impacts. In this paper, we present our understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example, incremental resource scheduling and incremental messaging communication, decentralized scheduling, rapid system failover, and request handling parallelism etc. We integrate these techniques together with the data analytics methodology to drive a novel big data and service engineering to facilitate the system optimization, tuning and verification. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications.

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Section: Data-driven Methodologymentioning

confidence: 99%

Section: A Varying Request and Resource Heterogeneitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computing at Massive Scale: Scalability and Dependability Challenges

Yang

2016

2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)

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“…In this section we cover the most relevant and closely related to our study. Specific Reliability Studies: There have been a number of studies focusing on data center systems, specific components or service providers infrastructure dependability [2]- [4], [6], [11]- [15]. While studies are important to understand the characteristics of the different components and help in designing new approaches to overcome their failures at a different level (e.g., through redundancy and fault-tolerant approaches), they are also limited because it is not clear how these individual component failures would affect a service's overall dependability.…”

Section: Related Workmentioning

confidence: 99%

Failure process characteristics of cloud-enabled services

Tola

Jiang

Helvik

2017

2017 9th International Workshop on Resilient Networks Design and Modeling (RNDM)

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Abstract-The design of cloud computing technologies need to guarantee high levels of availability and for this reason there is a large interest in new fault tolerant techniques that are able to keep the resilience of the systems at the desired level. The modeling of these techniques require input information about the operational state of the systems that have a stochastic nature. The aim of this paper is to provide insights into the stochastic behavior of cloud services. By exploiting the willingness of service providers to publicly expose failure incident information on the web, we collected and analyzed dependability features of a large number of incident reports counting more than 10,600 incidents related to 106 services. Through the analysis of failure data information we provide some useful insights about the Poisson nature of cloud service's failure processes by fitting well known models and assessing their suitability.

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“…Due to its huge commercial value, it has been paid great attention by peoples over the whole world. However, emerging fault events of cloud computing not only make peoples be worried about the dependability of cloud computing, but also block the migration process of core business services from traditional IT infrastructure to cloud computing platform [9]. Currently, the research in cloud computing field focuses on functionality, performance, application, and energy consumption.…”

Section: Related Workmentioning

confidence: 99%