PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints
Por um escritor misterioso
Last updated 20 abril 2025
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://d3i71xaburhd42.cloudfront.net/a615968237d6f2cc1908f779bd9ff80d28f164d0/10-Table1-1.png)
This work proposes a novel approach that for any known stationary workload and a given state configuration optimally solves the problem of host overload detection by maximizing the mean intermigration time under the specified QoS goal based on a Markov chain model. Dynamic consolidation of virtual machines (VMs) is an effective way to improve the utilization of resources and energy efficiency in cloud data centers. Determining when it is best to reallocate VMs from an overloaded host is an aspect of dynamic VM consolidation that directly influences the resource utilization and quality of service (QoS) delivered by the system. The influence on the QoS is explained by the fact that server overloads cause resource shortages and performance degradation of applications. Current solutions to the problem of host overload detection are generally heuristic based, or rely on statistical analysis of historical data. The limitations of these approaches are that they lead to suboptimal results and do not allow explicit specification of a QoS goal. We propose a novel approach that for any known stationary workload and a given state configuration optimally solves the problem of host overload detection by maximizing the mean intermigration time under the specified QoS goal based on a Markov chain model. We heuristically adapt the algorithm to handle unknown nonstationary workloads using the Multisize Sliding Window workload estimation technique. Through simulations with workload traces from more than a thousand PlanetLab VMs, we show that our approach outperforms the best benchmark algorithm and provides approximately 88 percent of the performance of the optimal offline algorithm.
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://onlinelibrary.wiley.com/cms/asset/a73c2eb2-f41c-4f2f-be2f-359fbb1633b3/dac4068-fig-0002-m.jpg)
Multiobjective virtual machine placement mechanisms using nature
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://d3i71xaburhd42.cloudfront.net/1f047651474d737d427907c45e562691106dbde2/4-Figure3.1-1.png)
PDF] Dynamic Consolidation of Virtual Machines In Cloud Data
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://media.springernature.com/m685/springer-static/image/art%3A10.1186%2Fs13677-023-00462-2/MediaObjects/13677_2023_462_Fig6_HTML.png)
A resource scheduling method for cloud data centers based on
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://0.academia-photos.com/attachment_thumbnails/98600672/mini_magick20230213-1-13xizeg.png?1676275089)
PDF) A Priority based Resource Provisioning by Dynamically
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://0.academia-photos.com/attachment_thumbnails/82263712/mini_magick20220316-25512-yjh8kz.png?1647497124)
PDF) Live consolidation for data centers in cloud environment
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://d3i71xaburhd42.cloudfront.net/a615968237d6f2cc1908f779bd9ff80d28f164d0/12-Figure4-1.png)
PDF] Managing Overloaded Hosts for Dynamic Consolidation of
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://media.springernature.com/m685/springer-static/image/art%3A10.1186%2Fs13677-018-0111-x/MediaObjects/13677_2018_111_Fig2_HTML.png)
Proactive dynamic virtual-machine consolidation for energy
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://i1.rgstatic.net/publication/317299094_Energy-Efficient_Algorithms_for_Dynamic_Virtual_Machine_Consolidation_in_Cloud_Data_Centers/links/5a0cc116aca2729b1f4d6d60/largepreview.png)
PDF) Energy-Efficient Algorithms for Dynamic Virtual Machine
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://ars.els-cdn.com/content/image/1-s2.0-S221509862100104X-gr2.jpg)
A multi-objective approach for energy-efficient and reliable
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13677-022-00309-2/MediaObjects/13677_2022_309_Fig6_HTML.png)
Research on virtual machine consolidation strategy based on
![PDF] Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints](https://media.springernature.com/m685/springer-static/image/art%3A10.1186%2Fs13677-022-00368-5/MediaObjects/13677_2022_368_Fig4_HTML.png)
A systematic review on effective energy utilization management
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Server consolidation techniques in virtualized data centers of
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