Developing resource consolidation frameworks for moldable virtual machines in clouds Author: Liang He, Deqing Zou, Zhang Zhang, etc Presenter: Weida Zhong

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

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

System Hierarchy and workload models

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

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

Classical genetic algorithm procedure initialization Evaluation/ fitness computing reproduction Next generation Stop? crossover mutation begin end Yes No

Genetic Algorithm

Schematic diagram new Current active

Genetic Algorithm

Genetic Algorithm -- crossover

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

Genetic Algorithm -- mutation

Genetic Algorithm – fitness function

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

Reconfiguring virtual clusters GA S cost? VM Creation VM Deletion VM Migration Changing Capacity

Categorizing changes in system state

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

Transiting system states --- VM operations during the transition releasingallocating

Transiting system states --- Performing VM operations without dependency

Transiting system states --- Performing VM operations with dependency

Algorithm 2(cont.)

Algorithm 4. Reconfiguring the cloud

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.

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

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

The simulation experiments about the effectiveness of the GA algorithm The performance of the cloud reconfiguration method

Performance of GA --- impact of the number of physical nodes

Performance of GA --- impact of free capacity

Performance of GA --- impact of the number of VCs

Performance of the cloud reconfiguration

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