vSPACE: VNF Simultaneous Placement, Admission Control and Embedding

Nejad, Mohammad Ali Tahmasbi; Parsaeefard, Saeedeh; Maddah-Ali, Mohammad Ali; Mahmoodi, Toktam; Khalaj, Babak Hossein

doi:10.1109/jsac.2018.2815318

Cited by 65 publications

(12 citation statements)

References 36 publications

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“…Then, to minimize the average response latency of each service instance, they also proposed another heuristic for request scheduling to optimize request scheduling cost. The authors in [16] investigated the problem of resource allocation, particularly the joint task of access control (AC) and VNFforwarding graph embedding (FGE). Two variants of such a problem with respect to VNF splitting and multi-path routing were considered.…”

Section: Related Workmentioning

confidence: 99%

Deterministic Latency Bounded Network Slice Deployment in IP-Over-WDM Based Metro-Aggregation Networks

Taleb

Zhang

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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As a critical enabler of the next generation networks, network slicing can dynamically and flexibly create virtual networks leveraging necessary network resources for services with different quality of service (QoS) requirements. To satisfy the strict QoS requirements of some vertical industries, e.g., industry automation, the Deterministic Networking (DetNet) concept has been recently proposed to investigate deterministic service provisioning with bounds on service latency, loss, and jitter. A critical issue for providing deterministic services is how to allocate the right amount of resources that ensures the QoS requirements within a network slice. In this paper, we first obtain the amount of network resources needed for a network slice, using the stochastic network calculus (SNC) method, to ensure the end-to-end latency requirement under specific traffic demands. We then study the network slice deployment problem in an IP-over-WDM (wavelength division multiplexing) metro aggregation network and propose a heuristic with three different objectives: minimizing traffic hops, minimizing lightpaths and minimizing wavelengths, which can help network service provider to optimize network deployment under different considerations. The simulation results show the comparison of the resource utilization under different strategies.

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Section: Related Workmentioning

confidence: 99%

Deterministic Latency Bounded Network Slice Deployment in IP-Over-WDM Based Metro-Aggregation Networks

Taleb

Zhang

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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“…Another thread of works focuses on an efficient admission policy that maximizes the throughput or revenue of admitted requests [10], [11], [12], [13]. In particular, Sallam et al [10] formulate joint VNF placement and resource allocation problem to maximize the number of fully served flows considering the budget and capacity constraints.…”

Section: Related Workmentioning

confidence: 99%

“…They leverage the submodularity property for a relaxed version of the problem and propose two heuristics with a constant approximation ratio. [11] studies the joint VNF placement and service chain embedding problem, so as to maximize the revenue from the admitted requests. A similar problem is tackled in [13] and [12] but for an online setting where the requests should be admitted and served upon their arrival.…”

Section: Related Workmentioning

confidence: 99%

“…We assume that dummy logical links are ideal, i.e., they have no capacity limit and zero delay and cost. We can now formulate the associated traffic constraints as: (11) where 𝜌(𝑘, 𝑙, •, 𝑞, 𝑡) and 𝜌(𝑘, 𝑙, 𝑞, •, 𝑡) are the fraction of new traffic entering ingress VNF 𝑞 and the fraction of traffic departing from egress VNF 𝑞, respectively, going through logical link 𝑙 at time 𝑡.…”

Section: B Problem Formulationmentioning

confidence: 99%

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Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G

Golkarifard,

Chiasserini,

Malandrino

et al. 2021

Preprint

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5G networks are going to support a variety of vertical services, with a diverse set of key performance indicators (KPIs), by using enabling technologies such as softwaredefined networking and network function virtualization. It is the responsibility of the network operator to efficiently allocate the available resources to the service requests in such a way to honor KPI requirements, while accounting for the limited quantity of available resources and their cost. A critical challenge is that requests may be highly varying over time, requiring a solution that accounts for their dynamic generation and termination. With this motivation, we seek to make joint decisions for request admission, resource activation, VNF placement, resource allocation, and traffic routing. We do so by considering real-world aspects such as the setup times of virtual machines, with the goal of maximizing the mobile network operator profit. To this end, first, we formulate a one-shot optimization problem which can attain the optimum solution for small size problems given the complete knowledge of arrival and departure times of requests over the entire system lifespan. We then propose an efficient and practical heuristic solution that only requires this knowledge for the next time period and works for realistically-sized scenarios. Finally, we evaluate the performance of these solutions using real-world services and large-scale network topologies. Results demonstrate that our heuristic solution performs better than state-of-the-art online algorithms and close to the optimum.

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“…This VNF-FGE problem is branched out into two sub-problems: Virtual Node (i.e., VNF) mapping and Virtual Link (The connection between two consecutive VNFs) mapping, as illustrated in Figure 2. Stage 3 concentrates on minimizing the VNF-FG's overall deployment time by providing a time-slotted strategy for the arrival of VNF, this is called VNF Scheduling (VNF-SCH) [4,6]. Our work in this paper is restricted to stages 2 and 3 only, we consider stage 1 as a predetermined factor provided by the network operator.…”

Section: Introductionmentioning

confidence: 99%

Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning

et al. 2021

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With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by virtualizing network functions and placing them on shared commodity servers. However, one of the critical issues in NFV is the resource allocation for the highly complex services; moreover, this problem is classified as an NP-Hard problem. To solve this problem, our work investigates the potential of Deep Reinforcement Learning (DRL) as a swift yet accurate approach (as compared to integer linear programming) for deploying Virtualized Network Functions (VNFs) under several Quality-of-Service (QoS) constraints such as latency, memory, CPU, and failure recovery requirements. More importantly, the failure recovery requirements are focused on the node-outage problem where outage can be either due to a disaster or unavailability of network topology information (e.g., due to proprietary and ownership issues). In DRL, we adopt a Deep Q-Learning (DQL) based algorithm where the primary network estimates the action-value function Q, as well as the predicted Q, highly causing divergence in Q-value’s updates. This divergence increases for the larger-scale action and state-space causing inconsistency in learning, resulting in an inaccurate output. Thus, to overcome this divergence, our work has adopted a well-known approach, i.e., introducing Target Neural Networks and Experience Replay algorithms in DQL. The constructed model is simulated for two real network topologies—Netrail Topology and BtEurope Topology—with various capacities of the nodes (e.g., CPU core, VNFs per Core), links (e.g., bandwidth and latency), several VNF Forwarding Graph (VNF-FG) complexities, and different degrees of the nodal outage from 0% to 50%. We can conclude from our work that, with the increase in network density or nodal capacity or VNF-FG’s complexity, the model took extremely high computation time to execute the desirable results. Moreover, with the rise in complexity of the VNF-FG, the resources decline much faster. In terms of the nodal outage, our model provided almost 70–90% Service Acceptance Rate (SAR) even with a 50% nodal outage for certain combinations of scenarios.

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vSPACE: VNF Simultaneous Placement, Admission Control and Embedding

Cited by 65 publications

References 36 publications

Deterministic Latency Bounded Network Slice Deployment in IP-Over-WDM Based Metro-Aggregation Networks

Deterministic Latency Bounded Network Slice Deployment in IP-Over-WDM Based Metro-Aggregation Networks

Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G

Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning

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