A prediction-Based VM consolidation approach in IaaS Cloud Data Centers

Mahdhi, Tarek; Mezni, Haithem

doi:10.1016/j.jss.2018.09.083

Cited by 26 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The susceptibility to failure of hosts must also be taken into account while making reallocation decisions. A VMC algorithm based on the Differential Evolution metaheuristic focused on enhancing the energy‐efficiency and quality was proposed by Li et al 9 Mahdhi and Mezni 10 adopted the kernel density estimation technique to predict the VM resource usage. A temperature model that captures the thermal features of processors was proposed by Wang et al 11 Furthermore, the future resource demands of the VMs are estimated using a Markov model, which is considered by the VMC algorithm.…”

Section: Related Workmentioning

confidence: 99%

Nature‐inspired resource management and dynamic rescheduling of microservices in Cloud datacenters

Joseph

Chandrasekaran

2021

Concurrency and Computation

View full text Add to dashboard Cite

Distributed Cloud environments are now resorting to Cloud applications composed of heterogeneous microservices. Cloud service providers strive to provide high quality of service (QoS) and response time is one of the key QoS attributes for microservices. The dynamism of microservice ecosystems necessitates runtime adaptations and microservices rescheduling to avoid performance degradation. Existing works target rescheduling in hypervisor‐based systems, while ignoring the influence of configuration parameters of container‐based microservices. In an effort to address these challenges, this article describes a novel microservice rescheduling framework, throttling and interaction‐aware anticorrelated rescheduling for microservices, to proactively perform rescheduling activities whilst ensuring timely service responses. Based on periodic monitoring of the performance attributes, the framework schedules container migrations. Considering the exponentially large solution space, a metaheuristic approach based on multiverse optimization is developed to generate the near‐optimal mapping of microservices to the datacenter resources. Experimental results indicate that our framework provides superior performance with a reduction of up to 13.97% in the average response time, when compared with systems with no support for rescheduling.

show abstract

Section: Related Workmentioning

confidence: 99%

Nature‐inspired resource management and dynamic rescheduling of microservices in Cloud datacenters

Joseph

Chandrasekaran

2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…This policy places a migrating VM to a candidate PM which causes the minimum available CPU after allocating the VM to that PM. Moges and Abebe [41] proposed a bin-packing heuristic, named Medium-Fit (MF) to efficiency balance energy consumption and SLA violation in a cloud datacenter.…”

Section: Related Workmentioning

confidence: 99%

Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter

RahimiZadeh

Dehghani

2021

Cluster Comput

View full text Add to dashboard Cite

Cloud service providers employ virtualization technologies in order to deliver energy efficiency and optimize resource utilization in cloud datacenters. In this way, Virtual Machine (VM) consolidation is an efficient technique for reducing energy related costs and environmental concerns. This technique consists of placing as much as possible VMs on as less as possible Physical Machines (PM)s by means of live VM migration. However, VM consolidation approaches bring advantages, they cannot efficiently guarantee performance isolation of co-located VMs resulting in operational interference issues. Therefore, it is necessary to be aware of workload performance degradation issues during VMs consolidation in a datacenter. In this paper, we propose a novel joint profit and interference-aware scheduling scheme (PIAS) to efficiently consolidate VMs that host multi-tier application workloads in Infrastructure-as-a-Service (IaaS) cloud datacenter. The PIAS scheme considers profit and cost of power consumption, operational interference of VMs, resource utilization, and service level agreements (SLA)s as well as amount of transferred memory pages during live VM migration for consolidating VMs. In this regard, a stochastic dynamic programming is introduced to model the operational behavior of VM. Also, an optimization problem is introduced to maximize provider's profit and minimize the overall execution cost of application workloads. We simulate a series of experiments based on real workloads traces for different IaaS cloud datacenter configurations. The PIAS scheme on average provides better results than other competitors and the results demonstrate how our approach at least improves profit, energy efficiency, and service down time by 40%, 29%, and 35%, respectively.

show abstract

“…In order to propose a virtual machine consolidation approach, the Kernel Density Estimation technique is used in [27] to estimate resource usage and predict future host's states. Using real web server request traces, Calheiros et al [28] proposed an auto-regressive integrated moving average (ARIMA) model that assesses the accuracy of future workload forecasting.…”

Section: Related Workmentioning

confidence: 99%

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

Djennane

Yacoub²,

Aoudjit

et al. 2021

SWCC

View full text Add to dashboard Cite

Backgroud: The major objective of resource management systems in the cloud environments is to assist providers in making consistent and cost-effective decisions related to the dynamic resource allocation. However, because of the demand changes of the applications and the exponential evolution of the cloud, the resource management systems are constantly called into question with regard to their ability to guarantee an effective resource provisioning. Objective: To tackle these challenges, the future demand prediction is a practical solution that has been adopted in the literature. The prediction has widely relied on the CPU utilization since it is considered as a leading cause of the Quality of Service dropping. Method: The successful application of artificial intelligence techniques in forecasting problems motivated us to use the Kohonen Self Organizing Maps that tries to capture the gathered empirical CPU load time series in regular behaviors to perform an accurate forecast. The proposed solution is a two-step approach that first classifies the collected data and then predicts the future CPU load. Results and conclusion: The experimental results show that our proposed system outperforms other models reported in the literature. In addition, we proved that Self Organizing Maps known for its strength in classification is also effective for prediction.

show abstract

A prediction-Based VM consolidation approach in IaaS Cloud Data Centers

Cited by 26 publications

References 33 publications

Nature‐inspired resource management and dynamic rescheduling of microservices in Cloud datacenters

Nature‐inspired resource management and dynamic rescheduling of microservices in Cloud datacenters

Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

Contact Info

Product

Resources

About