PRESS: PRedictive Elastic ReSource Scaling for cloud systems

By Zhenhuan Gong, Xiaohui Gu, John Wilkes. 2010 International Conference on Network and Service Management, 2010

628 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/9e98d529d158e2230d722f497fbc36373eaa8583

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Citations in the corpus

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, methodology): “The last technique considered in this section is the histogram, that has been used by some authors to predict the resource usage of applications, considering the mean of the distribution [49], or the mean of the bin with highest frequency [57].”
Quasar: resource-efficient and QoS-aware cluster management (2014, methodology): “At the moment, Quasar does not employ load prediction for user-facing services [29, 46].”
Resource Management in Clouds: Survey and Research Challenges (2015, methodology): “[83] also apply time series analysis: they use a combination of a Fast Fourier Transform (FFT) to detect repeating resource usage patterns for one subset of applications and a discrete-time Markov chain to build short-term demand predictions for the subset of applications that do not exhibit repeati”
Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS (2015, methodology): “[12] propose a method for predicting resource demand of VMs based on predicted application workload.”
A Survey on Cloud Computing Elasticity (2012, methodology): “CHALLENGES AND OPEN ISSUES Although many elasticity solutions has been developed by cloud providers and in academic researches, some existing issues have not been fully addressed. infrastructure automatic reactive replication cost GoGrid [10] infrastructure manual replication performance OnApp [35] ”
Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices (2019, methodology): “CloudScale [83], PRESS [48], AGILE [70] and the work by Gmach et al.”
Workload characterization and prediction in the cloud: A multiple time series approach (2012, background): “Also related to this study are the works on capacity management and virtual server placement [23], [24], [25], [26], [27], [28], which typically employ some workload modeling and prediction techniques.”
CloudScale: elastic resource scaling for multi-tenant cloud systems (2011, methodology): “In [24], we described our application-agnostic, light-weight online resource demand predictor and showed that it can achieve good prediction accuracy for a range of real world applications.”
Autopilot: workload autoscaling at Google (2020, methodology): “job performance model), neural networks (as in [18]), recurrent neural networks as in [9, 20] or a custom forecast as in [13] which demonstrates thatMarkov-chain based prediction performs better than methods based on autoregression or autocorrelation.”
Elasticity in Cloud Computing: State of the Art and Research Challenges (2018, methodology): “In order to achieve the second purpose, various techniques are used to inspect the repetitive patterns in time series: pattern matching [108], [112], Fast Fourier Transform (FFT) [108], auto correlation [152], histogram [108].”