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Energy-efficient and quality-aware VM consolidation method

https://doi.org/10.1016/j.future.2019.08.004

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers

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CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

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Energy Efficient Allocation of Virtual Machines in Cloud Computing Environments Based on Demand Forecast

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Energy Efficiency in Large Scale Distributed Systems

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An Intelligent and Adaptive Threshold-Based Schema for Energy and Performance Efficient Dynamic VM Consolidation

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Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center

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Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

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Power-efficient assignment of virtual machines to physical machines

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Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

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DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

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Multi-Capacity Combinatorial Ordering GA in Application to Cloud resources allocation and efficient virtual machines consolidation

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Bayesian network-based Virtual Machines consolidation method

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Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method

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A survey on virtual machine migration and server consolidation frameworks for cloud data centers

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The Effects of Hotspot Detection and Virtual Machine Migration Policies on Energy Consumption and Service Levels in the Cloud

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Solving bin Packing Problem with a Hybrid Genetic Algorithm for VM Placement in Cloud

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Markov Prediction Model for Host Load Detection and VM Placement in Live Migration

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Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters

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2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach

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Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing

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2015 IEEE 8th International Conference on Cloud Computing

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1000 Islands: Integrated Capacity and Workload Management for the Next Generation Data Center

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2008 International Conference on Autonomic Computing

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A Cooperative Multi Agent Learning Approach to Manage Physical Host Nodes for Dynamic Consolidation of Virtual Machines

inferredcites →https://doi.org/10.1109/seaa.2013.23openalex-snapshot

LiRCUP: Linear Regression Based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Centers

inferredcites →https://doi.org/10.1109/stc-csit.2017.8098750openalex-snapshot

Consolidation of virtual machines using simulated annealing algorithm

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Stochastic Load Balancing for Virtual Resource Management in Datacenters

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Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints

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Energy Aware Consolidation Algorithm Based on K-Nearest Neighbor Regression for Cloud Data Centers

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Optimized pre-copy live migration for memory intensive applications

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Proceedings of the ACM Symposium on Cloud Computing

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Distributed Autonomous Virtual Resource Management in Datacenters Using Finite-Markov Decision Process

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Implementation and performance analysis of various VM placement strategies in CloudSim

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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