Real-Time Fault-Tolerance in Federated Cloud Environments

Garraghan, Peter; Townend, Paul; Xu, Jie

doi:10.1109/isorcw.2012.30

Cited by 6 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Duration depends on the architectural characteristics of the server where the task is allocated [16]; describing the task in terms of length in MI allows us to perform an architectureagnostic workload analysis. x To calculate the task length we consider the processing capacity of the Primergy RX200 S7 architecture, as it is described in SpecPower2008 benchmark results [17].…”

Section: Task Length Is Calculated Based On the Duration And The Avmentioning

confidence: 99%

An Approach for Characterizing Workloads in Google Cloud to Derive Realistic Resource Utilization Models

Moreno

Garraghan

Townend

et al. 2013

2013 IEEE Seventh International Symposium on Service-Oriented System Engineering

View full text Add to dashboard Cite

Abstract-Analyzing behavioral patterns of workloads is critical to understanding Cloud computing environments. However, until now only a limited number of real-world Cloud datacenter tracelogs have been available for analysis. This has led to a lack of methodologies to capture the diversity of patterns that exist in such datasets. This paper presents the first large-scale analysis of real-world Cloud data, using a recently released dataset that features traces from over 12,000 servers over the period of a month. Based on this analysis, we develop a novel approach for characterizing workloads that for the first time considers Cloud workload in the context of both user and task in order to derive a model to capture resource estimation and utilization patterns. The derived model assists in understanding the relationship between users and tasks within workload, and enables further work such as resource optimization, energy-efficiency improvements, and failure correlation. Additionally, it provides a mechanism to create patterns that randomly fluctuate based on realistic parameters. This is critical to emulating dynamic environments instead of statically replaying records in the tracelog. Our approach is evaluated by contrasting the logged data against simulation experiments, and our results show that the derived model parameters correctly describe the operational environment within a 5% of error margin, confirming the great variability of patterns that exist in Cloud computing.

show abstract

Section: Task Length Is Calculated Based On the Duration And The Avmentioning

confidence: 99%

An Approach for Characterizing Workloads in Google Cloud to Derive Realistic Resource Utilization Models

Moreno

Garraghan

Townend

et al. 2013

2013 IEEE Seventh International Symposium on Service-Oriented System Engineering

View full text Add to dashboard Cite

show abstract

Cloud Service Recommendation Based on a Correlated QoS Ranking Prediction

Jayapriya¹,

Mary

Rajesh³

2015

J Netw Syst Manage

View full text Add to dashboard Cite

The ability to utilize the computing resources based on the need has taken the Cloud computing to a greater height and it has increased the potential to extend the flexibility and efficiency of any resource. Considering the advantages, there are various Cloud Services Providers (CSP) that can offer services based on the user request and finding optimal services among those Cloud Services can be a great dispute. The proposed work relies on a QoS Ranking prediction that chooses the appropriate services offered by the various different CSPs. Based on those predicted analysis, the best CSP will be marked with a Ranking framework, according to which the Services will be directed to the users.

show abstract