Agile dynamic provisioning of multi-tier Internet applications

By Bhuvan Urgaonkar, Prashant J. Shenoy, Abhishek Chandra, P. Goyal, Timothy Wood. TAAS, 2008

553 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/ec8e9d987bd5d077f87b4b5310448ec834d17b85

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting (2011, methodology): “Limitations in related work: The work in [2] and [3] do not relate the placement mechanism to an overall utility value to the Cloud provider.”
MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving (2019, background): “A key challenge of provisioning model serving in the cloud is: how should a serving system choose from a bewildering array of cloud services to provide low-latency, cost-effective inference at scale? Unfortunately, there is no general guideline given by the cloud providers, nor has it been studied i”
An adaptive hybrid elasticity controller for cloud infrastructures (2012, background): “[9] propose a hybrid control mechanism that incorporates both a proactive controller and a reactive controller.”
Workload characterization and prediction in the cloud: A multiple time series approach (2012, background): “Other applications include elastic resource provisioning: a provider can use our model to predict how workload patterns will change, and dynamically scale the resources to meet the demand [26], [30].”
A survey on techniques for improving the energy efficiency of large-scale distributed systems (2014, background): “Workload consolidation [Chase and Doyle 2001; Chase et al. 2001], investigated in following works [Doyle et al. 2003; Urgaonkar et al. 2008; Verma et al. 2008; Kalyvianaki et al. 2009; Kusic et al. 2008; Jung et al. 2009; Srikantaiah et al. 2008], consists in running multiple tasks on the same…”
A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, methodology): “Due to the limitation of models with a single queue, we will focus on the use of queuing networks to formulate a single application tier as n parallel servers [83], [85], or a whole multi-tier application [84], [89].”
Elasticity in Cloud Computing: State of the Art and Research Challenges (2018, methodology): “[55], [57], [103], [59] use queuing theory in reactive mode while [103], [114], [115], [116], [117] adhere to the queuing theory principles in predictive mode.”
Swayam: distributed autoscaling to meet SLAs of machine learning inference services with resource efficiency (2017, background): “[31] and explicitly models the provisioning costs in each tier for autoscaling.”
Adaptive resource provisioning for read intensive multi-tier applications in the cloud (2011, methodology): “[17] presents a predictive and reactive approach using queuing theory to address dynamic provisioning for multi-tier applications.”