Effect of latency on network and end user domains in Cloud Computing

Bali, Malvinder Singh; Khurana, Shivani

doi:10.1109/icgce.2013.6823539

Cited by 13 publications

(1 citation statement)

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“…Both of these latencies depend on the Round Trip Time (RTT) [19], which defines the time interval between the user requests and the arrival of the corresponding response. Another type of latency existing in the process architecture is the computational latency which is the intracloud latency [20] found among the processing VMs located within a single datacentre. This latency depends on both the software and the hardware components [6,21] such as CPU architecture, runtime environment, and memory, guests and host operating system, instruction set, and hypervisor used.…”

Section: Workload Latencymentioning

confidence: 99%

RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

Lu¹,

Panneerselvam²,

Liu

et al. 2016

Scientific Programming

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Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

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Section: Workload Latencymentioning

confidence: 99%