A QoS-Based Reactive Auto Scaler for Cloud Environment

Kumar, D. Suresh; Gondhi, Naveen Kumar

doi:10.1109/icngcis.2017.22

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the PSO algorithm may fall into the local convergence, and thus it is hard to obtain the overall optimal solution of resource allocation. Moreover, Kumar et al [16] designed a self-scaling model that can dynamically adjust resource provisioning referring to the QoS indicators, which performed the resource correction at the VM level while considering the situations of under-utilization and over-utilization for resources. Although this model can make on-demand resource adjustments for the applications under changeable workloads, it did not well consider the relationship between the QoS and VM rental costs nor take into account future workload changes with complex fluctuations.…”

Section: A Literature Reviewmentioning

confidence: 99%

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

et al. 2020

View full text Add to dashboard Cite

Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make runtime decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services. The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods. INDEX TERMS Cloud-based software services, resource allocation, QoS prediction, workload-time windows, particle swarm optimization, genetic algorithm.

show abstract

Section: A Literature Reviewmentioning

confidence: 99%

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

et al. 2020

View full text Add to dashboard Cite

show abstract

“…For example, the work in Reference presents the ACCRS framework, which adopts static thresholds to trigger reactive elasticity. Several other proposals present similar restrictions under different scenarios, such as Reference : which proposes ElasticDocker and Reference which proposes ( DoCloud ), both proposal to scale up and down docker containers, considering unlimited resources; works with QoS metrics to support unlimited elasticity; which proposes Helpar , an hybrid model for elasticity, but also based on static thresholds and unlimited resources. As can be seen, none of these proposals address critical scenarios in terms of the amount of resources available to perform elasticity, and all follow stochastic models for resource‐computing.…”

Section: Related Workmentioning

confidence: 99%

End‐to‐end elasticity control of cloud‐network slices

Medeiros

Neto

Sampaio

et al. 2019

Internet Technology Letters

View full text Add to dashboard Cite

The design of efficient elasticity control mechanisms for dynamic resource allocation is crucial to increase the efficiency of future cloud-network slice-defined systems. Current elasticity control mechanisms proposed for cloud-or network-slicing, only consider cloud-or network-type resources respectively. In this paper, we introduce the elaSticity in cLOud-neTwork Slices (SLOTS) which aims to extend the horizontal elasticity control to multi-providers scenarios in an end-to-end fashion, as well as to provide a novel vertical elasticity mechanism to deal with critical insufficiency of resources by harvesting underused resources on other slices. Finally, we present a preliminary assessment of the SLOTS prototype in a real testbed, revealing outcomes that suggest the viability of the proposal. K E Y W O R D S5G, cloud-network slicing, end-to-end slicing, resource elasticity

show abstract