MTS: Bringing Multi-Tenancy to Virtual Networking

• URL: http://arxiv.org/abs/2403.01862v1
• Date: Mon, 4 Mar 2024 09:18:38 GMT
• Title: MTS: Bringing Multi-Tenancy to Virtual Networking
• Authors: Kashyap Thimmaraju, Saad Hermak, Gábor Rétvári, Stefan Schmid,
• Abstract summary: Multi-tenant cloud computing provides great benefits in terms of resource sharing, elastic pricing, and scalability. It also changes the security landscape and introduces the need for strong isolation between the tenants, also inside the network. We present, implement, and evaluate a virtual switch architecture, MTS, which brings secure design best-practice to the context of multi-tenant virtual networking.
• Score: 13.601341555716232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-tenant cloud computing provides great benefits in terms of resource sharing, elastic pricing, and scalability, however, it also changes the security landscape and introduces the need for strong isolation between the tenants, also inside the network. This paper is motivated by the observation that while multi-tenancy is widely used in cloud computing, the virtual switch designs currently used for network virtualization lack sufficient support for tenant isolation. Hence, we present, implement, and evaluate a virtual switch architecture, MTS, which brings secure design best-practice to the context of multi-tenant virtual networking: compartmentalization of virtual switches, least-privilege execution, complete mediation of all network communication, and reducing the trusted computing base shared between tenants. We build MTS from commodity components, providing an incrementally deployable and inexpensive upgrade path to cloud operators. Our extensive experiments, extending to both micro-benchmarks and cloud applications, show that, depending on the way it is deployed, MTS may produce 1.5-2x the throughput compared to state-of-the-art, with similar or better latency and modest resource overhead (1 extra CPU). MTS is available as open source software.

Related papers

• SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass [55.28352410490407]
  SHINE is a scalable hypernetwork that can map diverse meaningful contexts into high-quality LoRA adapters for large language models (LLM)<n>We introduce a pretraining and instruction fine-tuning pipeline, and train our hypernetwork to generate high quality LoRA adapters in a single forward pass.<n>Our work achieves outstanding results on various tasks, greatly saves time, computation and memory costs compared to SFT-based LLM adaptation, and shows great potential for scaling.
  arXiv  Detail & Related papers  (2026-02-06T03:40:31Z)
• Intelligent Load Balancing in Cloud Computer Systems [0.0]
  This research was to design a strategy to dynamically allocate tasks without overloading Cloud nodes.<n>The project involved extensive experiments run on the University of Westminster HPC cluster.
  arXiv  Detail & Related papers  (2025-09-22T19:39:08Z)
• VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use [78.29315418819074]
  We introduce VerlTool, a unified and modular framework that addresses limitations through systematic design principles.<n>Our framework formalizes ARLT as multi-turn trajectories with multi-modal observation tokens (text/image/video), extending beyond single-turn RLVR paradigms.<n>The modular plugin architecture enables rapid tool integration requiring only lightweight Python definitions.
  arXiv  Detail & Related papers  (2025-09-01T01:45:18Z)
• Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval [62.904384887568284]
  Asymmetric retrieval is a typical scenario in real-world retrieval systems. We propose a Prunable Network with self-compatibility, which allows developers to generate compatibleworks at any desired capacity.
  arXiv  Detail & Related papers  (2025-04-16T08:59:47Z)
• Slicing Vision Transformer for Flexible Inference [79.35046907288518]
  We propose a general framework, named Scala, to enable a single network to represent multiple smaller ViTs. S Scala achieves an average improvement of 1.6% on ImageNet-1K with fewer parameters.
  arXiv  Detail & Related papers  (2024-12-06T05:31:42Z)
• WDMoE: Wireless Distributed Mixture of Experts for Large Language Models [68.45482959423323]
  Large Language Models (LLMs) have achieved significant success in various natural language processing tasks. We propose a wireless distributed Mixture of Experts (WDMoE) architecture to enable collaborative deployment of LLMs across edge servers at the base station (BS) and mobile devices in wireless networks.
  arXiv  Detail & Related papers  (2024-11-11T02:48:00Z)
• Ditto: Elastic Confidential VMs with Secure and Dynamic CPU Scaling [35.971391128345125]
  "Elastic CVM" and the Worker vCPU design pave the way for more flexible and cost-effective confidential computing environments. "Elastic CVM" and the Worker vCPU design not only optimize cloud resource utilization but also pave the way for more flexible and cost-effective confidential computing environments.
  arXiv  Detail & Related papers  (2024-09-23T20:52:10Z)
• WDMoE: Wireless Distributed Large Language Models with Mixture of Experts [65.57581050707738]
  We propose a wireless distributed Large Language Models (LLMs) paradigm based on Mixture of Experts (MoE) We decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at base station (BS) and mobile devices. We design an expert selection policy by taking into account both the performance of the model and the end-to-end latency.
  arXiv  Detail & Related papers  (2024-05-06T02:55:50Z)
• HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction [50.40732146978222]
  Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications. We propose a Hierarchical information extraction Network (HiNet) for multi-scenario and multi-task recommendation. HiNet achieves a new state-of-the-art performance and significantly outperforms existing solutions.
  arXiv  Detail & Related papers  (2023-03-10T17:24:41Z)
• Virtualization of Tiny Embedded Systems with a robust real-time capable and extensible Stack Virtual Machine REXAVM supporting Material-integrated Intelligent Systems and Tiny Machine Learning [0.0]
  This paper shows and evaluates the suitability of the proposed VM architecture for operationally equivalent software and hardware (FPGA) implementations. In a holistic architecture approach, the VM specifically addresses digital signal processing and tiny machine learning.
  arXiv  Detail & Related papers  (2023-02-17T17:13:35Z)
• Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design [72.27143788103245]
  SemCom and edge computing are disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. Deep learning (DL)-based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties. We present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse.
  arXiv  Detail & Related papers  (2022-12-13T10:29:41Z)
• A smart resource management mechanism with trust access control for cloud computing environment [3.3504365823045044]
  This article suggests a conceptual framework for a workload management paradigm in cloud settings that is both safe and performance-efficient. A resource management unit is used in this paradigm for energy and performing virtual machine allocation with efficiency. A secure virtual machine management unit controls the resource management unit and is created to produce data on unlawful access or intercommunication.
  arXiv  Detail & Related papers  (2022-12-10T15:00:58Z)
• Towards efficient feature sharing in MIMO architectures [102.40140369542755]
  Multi-input multi-output architectures propose to train multipleworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. We highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices.
  arXiv  Detail & Related papers  (2022-05-20T12:33:34Z)
• Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT [51.68933585002123]
  We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
  arXiv  Detail & Related papers  (2021-10-28T08:14:57Z)
• Combination of Convolutional Neural Network and Gated Recurrent Unit for Energy Aware Resource Allocation [0.0]
  Cloud computing service models have experienced rapid growth and inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Resource allocation in cloud data centers aiming to reduce energy consumption has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs) To solve this issue, can be classified according to the pattern of user requests into sensitive or insensitive classes to latency, and thereafter suitable VM can be selected for migration.
  arXiv  Detail & Related papers  (2021-06-23T05:57:51Z)

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.