An adaptive hybrid elasticity controller for cloud infrastructures

By Ahmed Ali-Eldin, Johan Tordsson, E. Elmroth. 2012 IEEE Network Operations and Management Symposium, 2012

309 citations recorded by the source corpus.

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Elasticity in Cloud Computing: State of the Art and Research Challenges (2018, methodology): “For example, [115] proposes a model that estimates the resources required for a given workload , the mean response time, and other parameters.”
Distributed denial of service (DDoS) resilience in cloud: Review and conceptual cloud DDoS mitigation framework (2016, background): “[30] describe flash crowd as a surge in traffic known as the Slashdot effect, which results in the service being unreachable to other legitimate users during the period.”
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”
Resource Management in Clouds: Survey and Research Challenges (2015, unknown): “[11] uses adaptive proactive controllers that estimate the future load for a service in order to support service elasticity.”
A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, methodology): “[39, 40] model a cloud-hosted application as a G/G/n queue, in which the number of servers n is variable.”