The Wide-Area Virtual Service Migration Problem: A Competitive Analysis Approach

  • Authors:

  • Marcin Bienkowski;Anja Feldmann;Johannes Grassler;Gregor Schaffrath;Stefan Schmid

  • Affiliations:

  • Institute of Computer Science, University of Wroc aw, Poland;Telekom Innovation Laboratories (T-Labs), TU Berlin, Berlin, Germany;Telekom Innovation Laboratories (T-Labs), TU Berlin, Berlin, Germany;Telekom Innovation Laboratories (T-Labs), TU Berlin, Berlin, Germany;Telekom Innovation Laboratories (T-Labs), TU Berlin, Berlin, Germany

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2014

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Abstract

Today's trend toward network virtualization and software-defined networking enables flexible new distributed systems where resources can be dynamically allocated and migrated to locations where they are most useful. This paper proposes a competitive analysis approach to design and reason about online algorithms that find a good tradeoff between the benefits and costs of a migratable service. A competitive online algorithm provides worst-case performance guarantees under any demand dynamics, and without any information or statistical assumptions on the demand in the future. This is attractive especially in scenarios where the demand is hard to predict and can be subject to unexpected events. As a case study, we describe a service (e.g., an SAP server or a gaming application) that uses network virtualization to improve the quality of service (QoS) experienced by thin client applications running on mobile devices. By decoupling the service from the underlying resource infrastructure, it can be migrated closer to the current client locations while taking into account migration costs. We identify the major cost factors in such a system and formalize the wide-area service migration problem. Our main contributions are a randomized and a deterministic online algorithm that achieve a competitive ratio of $O(\log {n})$ in a simplified scenario, where $n$ is the size of the substrate network. This is almost optimal. We complement our worst-case analysis with simulations in different specific scenarios and also sketch a migration demonstrator.