Throughput Optimization and Delay Guarantee VNF Placement for Mapping SFC Requests in NFV-Enabled Networks

Yue, Yi; Cheng, Bo; Wang, Meng; Li, Biyi; Li, Xuan; Chen, Junliang

doi:10.1109/tnsm.2021.3087838

Cited by 38 publications

(20 citation statements)

References 40 publications

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“…where µ * (q) i→ (t) and µ * (q) →i (t) are defined in (6). Furthermore, in (29b) (in Appendix C), we show…”

Section: Appendix B Multicast Network Stability Regionmentioning

confidence: 63%

“…With the advent of software defined networking (SDN) and network function virtualization (NFV), network services can be deployed as a sequence of software functions or service function chains (SFCs) that can be flexibly interconnected and elastically executed at distributed cloud locations [4]. A number of studies have investigated the problem of joint SFC placement and routing with the objective of either maximizing accepted service requests [5], [6], or minimizing overall resource cost [7], [8]. Nonetheless, these solutions exhibit two main limitations: first, April 20, 2022 DRAFT the problem is formulated as a static optimization problem without considering the dynamic nature of service requests, a critical aspect in next-generation AgI services; second, due to the combinatorial nature of the problem, the corresponding formulations typically take the form of (NP-hard) mixed integer linear programs, and either heuristic solutions or loose approximation algorithms are developed, compromising the quality of the resulting solution.…”

Section: A Related Workmentioning

confidence: 99%

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Decentralized Control of Distributed Cloud Networks with Generalized Network Flows

Cai¹,

Llorca²,

Tulino³

et al. 2022

Preprint

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Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications such as augmented reality, multiplayer gaming, and industrial automation. While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast nature, leading to unnecessary traffic redundancy and network congestion. In this paper, we establish a unified framework for distributed cloud network control with generalized (mixed-cast) traffic flows that allows optimizing the distributed execution of the required packet processing, forwarding, and replication operations. We first characterize the enlarged multicast network stability region under the new control framework (with respect to

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“…where µ * (q) i→ (t) and µ * (q) →i (t) are defined in (6). Furthermore, in (29b) (in Appendix C), we show…”

Section: Appendix B Multicast Network Stability Regionmentioning

confidence: 63%

Section: A Related Workmentioning

confidence: 99%

Decentralized Control of Distributed Cloud Networks with Generalized Network Flows

Cai¹,

Llorca²,

Tulino³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…With the advent of software defined networking (SDN) and network function virtualization (NFV), network (and, by extension, AgI) services can be deployed as a sequence of software functions or service function chains (SFCs) instantiated over distributed cloud locations. A number of studies have investigated the problem of joint SFC placement and routing over multi-hop networks with the objective of either minimizing overall operational cost [10]- [13], or maximizing accepted service requests [14]- [16]. Nonetheless, these solutions exhibit two main drawbacks.…”

Section: A Related Workmentioning

confidence: 99%

“…First, the problem is formulated as a static optimization problem without considering the dynamic nature of service requests. In addition, when it comes to delay performance, these studies mainly focus on propagation delay [12]- [15], while neglecting queuing delay, or using simplified models (e.g., M/M/1) to approximate it [16]. Second, due to the combinatorial nature of the problem, the corresponding formulations typically take the form of (NPhard) mixed integer linear programs and either heuristic or loose approximation algorithms are developed, compromising the quality of the resulting solution.…”

Section: A Related Workmentioning

confidence: 99%

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Ultra-Reliable Distributed Cloud Network Control with End-to-End Latency Constraints

Cai¹,

Llorca²,

Tulino³

et al. 2022

Preprint

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We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as augmented information (AgI) services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets' lifetime and formalize the delay-constrained least-cost dynamic network control problem. To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a near-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing the reliable cloud network control (RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.

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DRL‐based customised resource allocation for sub‐slices in 6G network slicing

2024

Trans Emerging Tel Tech

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Abstract6G network services demand significant computer resources. Network slicing offers a potential solution by enabling customized services on shared infrastructure. However, dynamic service needs in heterogeneous environments pose challenges to resource provisioning. 6G applications like extended reality and connected vehicles require service differentiation for optimal quality of experience (QoE). Granular resource allocation within slices is a complex issue. To address the complexity of QoE services in dynamic slicing, a deep reinforcement learning (DRL) approach called customized sub‐slicing is proposed. This approach involves splitting access, transport, and core slices into sub‐slices to handle service differentiation among 6G applications. The focus is on creating sub‐slices and dynamically scaling slices for intelligent resource allocation and reallocation based on QoS requirements for each sub‐slice. The problem is formulated as an integer linear programming (ILP) optimization problem with real‐world constraints. To effectively allocate sub‐slices and dynamically scale resources, the Advantage Actor‐Critic (A2C)‐based Network Sub‐slice Allocation and Optimization (NS‐AO) algorithm is proposed. Experimental results demonstrate that the proposed algorithm outperforms the state of the art in terms of training stability, learning time, sub‐slice acceptance rate, and resilience to topology changes.

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Throughput Optimization and Delay Guarantee VNF Placement for Mapping SFC Requests in NFV-Enabled Networks

Cited by 38 publications

References 40 publications

Decentralized Control of Distributed Cloud Networks with Generalized Network Flows

Decentralized Control of Distributed Cloud Networks with Generalized Network Flows

Ultra-Reliable Distributed Cloud Network Control with End-to-End Latency Constraints

DRL‐based customised resource allocation for sub‐slices in 6G network slicing

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