1. Presented by: Katya Rodriguez, Ahmed Alsuwat, and Saud Tawi 3.11.15 Kun-Ting Chen, Chien Chen, Po-Hsian Wang

2.  Introduction  Related Works and Motivations  Problem  Network-Aware Bipartite Matching Load Algorithm  Contribution to the Field  Questions?

3.  Cloud data centers use virtualization-based technology for the sole purpose of consolidating hardware resource usage. ◦ This provides application hosting for multiple service providers.  Without proper allocation, the loads of different resources may become unbalanced among different physical hosts.

4.  Currently, the existing algorithms for load balancing search for a VM to begin a migration.  The selection of the next migration doesn’t occur until after the previous migration has completely ended. ◦ Also known as sequential migration

5.  It proposes a different method of migrating data centers in a VM.  This method doesn’t degrade application performance.  Speed up VM migration for data centers by using a network-topology that is aware of parallel migration. ◦ Network-Aware Bipartite Matching Load-Balancing Algorithm

6.  Many of the previously proposed load balancing schemes for cloud computing measure the load on physical hosts differently.  Zhao and Huang use the number of VMs of a host as their load measurement.  Mimicking animal behavior of honeybees for load balancing.

7.  Considering the demands of the VM and heterogeneous capacity in each host. ◦ Have overloaded hosts below their threshold by migrating VMs sequentially.  Even though currently the modern cloud administrators migrate multiple VMs concurrently, migration still has a chance to degrade into sequential migration.

8. Dimension of resources consumed by a VM = vm  VM host = h a Trigger set = TR Vector of system threshold along each dimension of resources = t Vectors of capacity and resource utilization along n dimensions of resources = u

9. Standard deviation of resource usage of the hosts is least in the case of Network-Aware Bipartite matching (NABM). Standard deviation of VectorDot (VD) and NOLB is greater than the standard deviation of (NABM).

10. ‣Network-Aware Bipartite matching (NABM) allows the system to strike a balanced state quicker than allowed by VectorDot (VD). ‣The graph states that as the number of hosts is increased, the time taken by NABM to reach a balanced state remains more or less the same, but the time taken by VD increased almost proportionally.

11. ‣VM migrations per round is one in the case of VD, but in the case of NABM, average VM migrations increase in proportion to the addition of the number of hosts. ‣This result unveils that performance of parallel VM migration load management system improves as more hosts are added to the system, but it does not happen in the case of sequential load management system.

12. ‣This result implies that parallel VM migration system handles overloaded hosts more effectively than sequential system. ‣This is because the algorithm of NABM does not break down when hosts are overburdened. Instead, it removes the queues quickly and allows the users to get information from servers without delays.

14. ‣If network parameter value goes beyond.6, then performance level may not remain the same even under parallel VM migration.

15.  Viability and usefulness of the parallel VM migration for data centers in managing load.  Existing load management systems of virtual data centers do not ensure reliable throughput, swift handing of job queues by servers and fast attainment of balanced state.

16.  Handles multiple migrations of VMs at one time ◦ Reducing downtime ◦ Increasing service efficiency  By adapting parallel VM migration, the data can be migrated from one center to another very quickly. ◦ It enhances the ability of the cloud computing technology to foil data theft attempts.

17.  A sustained balanced state, under parallel VM migration, cuts down the cost of system maintenance arising out of frequent system breakdowns, hotspots and over usage of resources.