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Developing resource consolidation frameworks for moldable virtual machines in clouds Author: Liang He, Deqing Zou, Zhang Zhang, etc Presenter: Weida Zhong
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abstract Using Genetic Algorithm to consolidate moldable Virtual Machines Developing a reconfiguration algorithm to lower the transition overhead that transiting the Cloud to the optimized system state needs
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contents System Hierarchy and workload models Genetic Algorithm Reconfiguring virtual clusters 1. Categorizing changes in system state 2. Transiting system states 3. Calculating transition time Experimental studies
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System Hierarchy and workload models
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The cloud system aims to maintain a steady level of Quality of Service (QoS) delivered by every VC. The desired QoS is expressed as that the total service rate of all VMs in a VC cannot be less than a certain figure
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contents System Hierarchy and workload models Genetic Algorithm Reconfiguring virtual clusters 1. Categorizing changes in system state 2. Transiting system states 3. Calculating transition time Experimental studies
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Classical genetic algorithm procedure initialization Evaluation/ fitness computing reproduction Next generation Stop? crossover mutation begin end Yes No
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Genetic Algorithm
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Schematic diagram new Current active
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Genetic Algorithm
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Genetic Algorithm -- crossover
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Genetic Algorithm -- mutation 1. determining index i, j, k The ratio of the probability of selecting the major resource type to other resource types is set to be R :1 (R is the number of resource types in the system) Select VC Select Node Select Resource
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Genetic Algorithm -- mutation
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Genetic Algorithm – fitness function
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contents System Hierarchy and workload models Genetic Algorithm Reconfiguring virtual clusters 1. Categorizing changes in system state 2. Transiting system states 3. Calculating transition time Experimental studies
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Reconfiguring virtual clusters GA S cost? VM Creation VM Deletion VM Migration Changing Capacity
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Categorizing changes in system state
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Transiting system states --- VM operations during the transition number of request average execution time of a request duration that the current request has been run
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Transiting system states --- VM operations during the transition releasingallocating
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Transiting system states --- Performing VM operations without dependency
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Transiting system states --- Performing VM operations with dependency
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Algorithm 2(cont.)
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Algorithm 4. Reconfiguring the cloud
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Calculating transition time A Directed Acyclic Graph (DAG) can be constructed based on the dependencies between the VM operations as well as between source nodes and mapping destination nodes.
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Calculating transition time If the VM operations in all nodes form a single DAG, calculating the transition time of the reconfiguration plan for the cloud can be transformed to compute the critical path in the DAG. If there are several DAG graphs, the time of the longest critical path is the transition time of the reconfiguration plan
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contents System Hierarchy and workload models Genetic Algorithm Reconfiguring virtual clusters 1. Categorizing changes in system state 2. Transiting system states 3. Calculating transition time Experimental studies
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The simulation experiments about the effectiveness of the GA algorithm The performance of the cloud reconfiguration method
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Performance of GA --- impact of the number of physical nodes
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Performance of GA --- impact of free capacity
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Performance of GA --- impact of the number of VCs
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Performance of the cloud reconfiguration
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Conclusion Develop a resource consolidation framework for moldable virtual machines in clouds A Genetic Algorithm is developed to compute the optimized system state A cloud reconfiguration algorithm is developed to transfer the cloud from the current state to the optimized one
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