Routing-Led Evolutionary Algorithm for Large-Scale Multi-Objective VNF Placement Problems

• URL: http://arxiv.org/abs/2512.15339v1
• Date: Wed, 17 Dec 2025 11:37:07 GMT
• Title: Routing-Led Evolutionary Algorithm for Large-Scale Multi-Objective VNF Placement Problems
• Authors: Peili Mao, Joseph Billingsley, Wang Miao, Geyong Mi, Ke Li,
• Abstract summary: We study how to discover the optimal placement of virtual network functions in large scale data centers.<n>We propose a novel parallel metaheuristic, fast objective functions of the networks and new efficient data structures.<n>Using these new concepts, we are able to find high quality solutions for data centres with up to 64,000 servers.
• Score: 7.607144511202278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern data centers contain thousands of servers making them major consumers of electricity. To minimize their environmental impact, it is critical that we use their resources efficiently. In this paper we study how to discover the optimal placement of virtual network functions in large scale data centers. We propose a novel parallel metaheuristic, fast heuristic objective functions of the QoS and new memory efficient data structures for large networks. We further identify a simple, fast heuristic that can produce competitive solutions to very large problem instances. Using these new concepts, we are able to find high quality solutions for data centres with up to 64,000 servers.

Related papers

• Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
  This paper introduces a novel framework for detecting instability in smart grids using only stable data.<n>It achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks.<n>Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.
  arXiv  Detail & Related papers  (2025-01-27T20:48:25Z)
• I've Got 99 Problems But FLOPS Ain't One [70.3084616806354]
  We take an unconventional approach to find relevant research directions, starting from public plans to build a $100 billion datacenter for machine learning applications. We discover what workloads such a datacenter might carry and explore the challenges one may encounter in doing so, with a focus on networking research. We conclude that building the datacenter and training such models is technically possible, but this requires novel wide-area transports for inter-DC communication, a multipath transport and novel datacenter topologies.
  arXiv  Detail & Related papers  (2024-07-01T10:33:46Z)
• Solving Large-scale Spatial Problems with Convolutional Neural Networks [88.31876586547848]
  We employ transfer learning to improve training efficiency for large-scale spatial problems. We propose that a convolutional neural network (CNN) can be trained on small windows of signals, but evaluated on arbitrarily large signals with little to no performance degradation.
  arXiv  Detail & Related papers  (2023-06-14T01:24:42Z)
• Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks [82.58968700765783]
  Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G) This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
  arXiv  Detail & Related papers  (2022-05-27T13:09:29Z)
• Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures [9.178035808110124]
  JellyBean is a framework for serving and optimizing machine learning inference on heterogeneous infrastructures. We show that JellyBean reduces the total serving cost of visual question answering by up to 58%, and vehicle tracking from the NVIDIA AI City Challenge by up to 36%.
  arXiv  Detail & Related papers  (2022-05-10T07:32:32Z)
• Machine Learning Empowered Intelligent Data Center Networking: A Survey [35.55535885962517]
  This paper comprehensively investigates the application of machine learning to data center networking. It covers flow prediction, flow classification, load balancing, resource management, routing optimization, and congestion control. We design a quality assessment criteria called REBEL-3S to impartially measure the strengths and weaknesses of these research works.
  arXiv  Detail & Related papers  (2022-02-28T05:27:22Z)
• Power Modeling for Effective Datacenter Planning and Compute Management [53.41102502425513]
  We discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features.
  arXiv  Detail & Related papers  (2021-03-22T21:22:51Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• Proximity-based Networking: Small world overlays optimized with particle swarm optimization [0.0]
  Small world networks can be incredibly useful in the dissemination and lookup of information within an internet network. We propose a networking scheme that incorporates geographic location in chord for the organization of peers within each node's partitioned key space. The flexibility of our proposed schemes enables a variety of swarm models, and agents.
  arXiv  Detail & Related papers  (2020-06-03T01:40:46Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)
• Routing-Led Placement of VNFs in Arbitrary Networks [21.790919473654153]
  Virtualisation technology allows for the creation of services by connecting component parts known as virtual network functions (VNFs) Current research on this problem has focussed on placing VNFs and considered routing as a secondary concern. We propose a novel routing-led algorithm and analyse each of the component parts over a range of different topologies.
  arXiv  Detail & Related papers  (2020-01-30T21:06:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.