Topology-Aware VM Placement for Network Optimization in Cloud Data Centers

Cloud data centers are hosting more and more complicated applications or services. This makes the network bandwidth becoming essential and critical for the normal application conduction or service provision. Hence, it is necessary to consider the network issue appropriately when eploying virtual machines (VMs) to avoid the network bottleneck and to guarantee the quality of services. In this paper, we focus on the VM placement problem for minimizing the maximal link utilization to avoid network congestion. We formulate the VM placement problem by representing the user requests with resource topologies, and prove the problem to be NP-hard. We present a heuristic algorithm based on the graph theory, which takes the resource topologies into account. The basic idea is to divide the requested VMs into servers with low network communication cost by analyzing the resource topology. We conduct extensive simulations, and the results show that our algorithm has a significant performance improvement on reducing network occupation compared to the best-fit strategy and divide-and-conquer strategy.

[1]  Jie Wu,et al.  Let's stay together: Towards traffic aware virtual machine placement in data centers , 2012, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[2]  Ehsan Ahvar,et al.  CACEV: A Cost and Carbon Emission-Efficient Virtual Machine Placement Method for Green Distributed Clouds , 2016, 2016 IEEE International Conference on Services Computing (SCC).

[3]  Nelson Luis Saldanha da Fonseca,et al.  Topology-Aware Virtual Machine Placement in Data Centers , 2015, Journal of Grid Computing.

[4]  Dhiren Patel,et al.  A Comparative Analysis of Virtual Machine Placement Techniques in the Cloud Environment , 2016 .

[5]  Bin Tang,et al.  Near-optimal virtual machine placement with product traffic pattern in data centers , 2013, 2013 IEEE International Conference on Communications (ICC).

[6]  Lisandro Zambenedetti Granville,et al.  Using Empirical Estimates of Effective Bandwidth in Network-Aware Placement of Virtual Machines in Datacenters , 2016, IEEE Transactions on Network and Service Management.

[7]  Jie Wu,et al.  Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center , 2013, Math. Comput. Model..

[8]  Minghua Chen,et al.  Joint VM placement and routing for data center traffic engineering , 2012, 2012 Proceedings IEEE INFOCOM.

[9]  Rolf Stadler,et al.  Resource Management in Clouds: Survey and Research Challenges , 2015, Journal of Network and Systems Management.

[10]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.

[11]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[12]  Weisheng Hu,et al.  Congestion-Aware Embedding of Heterogeneous Bandwidth Virtual Data Centers With Hose Model Abstraction , 2017, IEEE/ACM Transactions on Networking.

[13]  Hua Wang,et al.  An Energy-Aware Ant Colony Algorithm for Network-Aware Virtual Machine Placement in Cloud Computing , 2016, 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS).

[14]  Tao Chen,et al.  Optimized Virtual Machine Placement with Traffic-Aware Balancing in Data Center Networks , 2016, Sci. Program..

[15]  Fang Dong,et al.  AppBag: Application-Aware Bandwidth Allocation for Virtual Machines in Cloud Environment , 2016, 2016 45th International Conference on Parallel Processing (ICPP).