4.6 Article

Novel Framework of Risk-Aware Virtual Network Embedding in Optical Data Center Networks

Journal

IEEE SYSTEMS JOURNAL
Volume 12, Issue 3, Pages 2473-2482

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2017.2673828

Keywords

Optical data center network (ODCN); risk detection and isolation; virtual network embedding

Funding

  1. National Natural Science Foundation of China [61401082, 61471109, 61502075, 61672123, 91438110, U1301253]
  2. Fundamental Research Funds for Central Universities [N161604004, N161608001, N150401002]
  3. Liaoning BaiQianWan Talents Program
  4. Liaoning Province Doctor Startup Fund [201501166]
  5. China Post-Doctoral Science Foundation Project [2015M580224]
  6. National High-Level Personnel Special Support Program for Youth Top-Notch Talent

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The traffic between geographically distributed data centers (DCs) becomes bandwidth hungry. Since the optical interconnection has a high capacity, the optical data center network (ODCN)-where DCs are located at the edge of the optical backbone-emerges. By virtualization, the virtual networks- representing service requirements-are embedded onto the same part of the substrate ODCN. Each virtual network has virtual machine (VM) nodes interconnected by virtual links (VLs). Therefore, a virtual network embedding (VNE) operation includes two components: 1) the VM mapping for putting a VM into the server of an appropriate DC and 2) the VL mapping for establishing one substrate path to support inter-VM communications. In this paper, we focus on a risk-aware VNE framework because a blind VNE operation would result in severe information leakage among coresident VMs in the server. By evaluating VM threat and vulnerability, risky VMs are identified according to experimental results. To perform physical isolation between risky and security VMs, a risk-aware VNE heuristic algorithm is proposed. The simulation results show that our heuristic algorithm performs better than the benchmark in terms of maintaining ODCN security and earning rental revenue. There is also a good match between our algorithm solution and the problem bound.

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