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BiTE: a dynamic bi-level traffic engineering model for load balancing and energy efficiency in data center networks

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Abstract

With the recent significant growth of virtualization and cloud services, the data center network (DCN) as the underlying infrastructure is more important. The increasing and changing volume of workloads highlights critical issues such as load balancing and energy efficiency in data centers. Large path diversity in DCNs introduces multipath forwarding as a promising approach to improve load distribution. However, the over-provisioned DCNs consume large amounts of power while the network is under full capacity most of the time. Accordingly, this paper proposes BiTE, a dynamic bi-level traffic engineering (TE) scheme in a hierarchical Software Defined Networking (SDN)-based DCN to strike a balance between load balancing and energy efficiency objectives. BiTE consists of decision-making problems at two levels modeled as a multi-period bi-level optimization problem, where each decision maker optimizes one of objectives. According to the inherent complexity of bi-level programming, a co-evolutionary metaheuristic algorithm is proposed for solving BiTE. BiTE performance is evaluated in comparison to NSGA-II algorithm and four previously proposed TE schemes in terms of several load balancing and energy saving metrics under different scenarios. The results show that BiTE performs well in traffic load balancing while preserves the energy efficiency. We apply the Analytic Hierarchy Process (AHP) method to multi-criteria analyze and rank the performance of studied TE mechanisms. AHP results for different scenarios indicate that BiTE is in first or second place in terms of the overall performance score among six studied approaches.

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Notes

  1. Network Functions Virtualization

  2. Spanning Tree Protocol

  3. Capital Expenditure

  4. Operational Expenditure

  5. Shortest Path Bridging

  6. Transparent Interconnection of Lots of Links

  7. Intermediate System to Intermediate System

  8. SegmenT Routing based Energy Efficient TE

  9. Source Packet Routing in Networking

  10. Explicit Congestion Notification

  11. We define TMt as the traffic demands that are received during time period t.

  12. For simplicity, we just use the interdict word referring to interdicting from participating in traffic routing.

  13. We skipped drawing the figure at the beginning of (i + 2)th period, one can imagine at this time, the load on network links is similar to ith period with 4% decrease on 1-9-18-11-2 path, and the interdicted links are such as (i + 1)th period.

  14. Two objective functions are addressed at the same time

  15. We use polling and updating words interchangeably in this paper.

  16. Depth-First Search

  17. Non-dominated Sorting Genetic Algorithm

  18. As explained, TPM and Hou-MOPSO do not turn off any network link.

  19. Maximum Link Utilization

  20. We consider each individual as a feasible path in implementation.

  21. The problem constraints depend on different objectives

  22. The solutions are feasible for the same set of constraints

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Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran

    Negar Rikhtegar, Manijeh Keshtgari & Omid Bushehrian

  2. Laboratoire d’Informatique de Paris 6, Sorbonne Universities, Université Pierre et Marie Curie – Paris 6, Paris, France

    Guy Pujolle

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  1. Negar Rikhtegar
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Rikhtegar, N., Keshtgari, M., Bushehrian, O. et al. BiTE: a dynamic bi-level traffic engineering model for load balancing and energy efficiency in data center networks. Appl Intell 51, 4623–4648 (2021). https://doi.org/10.1007/s10489-020-02003-9

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