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Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement. Authors: Xiaoqiao Meng, Vasileios Pappas, Li Zhang Presented By:Manoj Raj Penmetcha. Introduction.

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Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

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  1. Improving the Scalability of Data Center Networkswith Traffic-aware Virtual Machine Placement Authors:Xiaoqiao Meng, Vasileios Pappas, Li Zhang Presented By:Manoj Raj Penmetcha

  2. Introduction • The paper presents novel approaches to reduce the latencies and increase the efficiency of networks by viewing the placement of VMs within the topology of the datacenter • Apart from the standard improvisations on the network routing algorithms and network topology, the paper present approaches to increase the effective bandwidth between VM pairs with high data transfer and reduce the latency between such pairs

  3. Introduction • Here it uses a 2 Tier approach Algorithm • First it clusters the slots available on the physical machines using the switch distance as the clustering measure and then cluster the VMs using the cost matrix as the clustering measure. • Then the algorithm tries to match the clusters first and eventually the members within the matched clusters.

  4. Advantages • Approach is toward the placement of the VMs and not towards the scaling of the network hardware. This is useful where the network hardware is not being currently used to the full potential due to bottlenecks. • The beneficial topology conditions and other network parameters which would lead to useful application of the optimization problem are also studied.

  5. Advantages • Apart from the solution to the optimization problem, the effect of traffic models, topology models such as the Global Traffic Model, Partitioned Traffic Model are also presented.

  6. Disadvantages • The paper completely ignore the effect of network related optimization on the result of the optimization of placement of VMs. • The min cut algorithm used for clustering the slots available on the physical machines is of high polynomial complexity (O(n^4)) , which is not ideal for large datacenters.

  7. Disadvantages • It has been proved that the TVMPP has polynomial time solutions only for the global traffic model, but the real traffic model in a datacenter is a hybrid model varying from the global traffic model, appropriate optimizations for a real scenario have not been aptly discussed

  8. Reference • Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement. by Xiaoqiao Meng, Vasileios Pappas, Li Zhang IBM T.J. Watson Research Center 19 Skyline Drive, Hawthorne, NY 10532

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