Service Function Chain Placement Research Articles

Enhancing SFC Placement with Parallelized Functions in MEC Using Deep Reinforcement Learning

This study presents an innovative architecture leveraging Deep Reinforcement Learning (DRL) to optimize online service delivery in Mobile Edge Computing (MEC) networks. Our proposed method, the DRL-based dynamic Service Function Chain (SFC) placement with parallelized Virtual Network Functions (VNFs), aims to maximize Long-Term Cumulative Reward (LTCR). By parallelizing VNFs, our approach achieves computational acceleration, enhancing the efficiency of online service provisioning. Furthermore, we enhance future request processing capabilities by extracting the distribution of initialized VNFs. Through comprehensive simulations, our proposed architecture demonstrates a notable improvement, with approximately a 9.5% increase in the average number of accepted requests.

Multi-objective Service Function Chain placement in 5G cellular networks based on meta-heuristic approach

With the emergence of new applications driven by the popularization of mobile devices, the next generation of mobile networks faces challenges to meet different requirements. Virtual Network Functions (VNFs) have been deployed to minimize operational costs and make network management more flexible. In this sense, strategies for VNF placement can impact different metrics of interest. Invoking and visiting VNFs in a specific execution order may be required for different use cases, resulting in a complete network service called Service Function Chain (SFC). The SFC placement problem is to define a feasible path in the physical infrastructure whose nodes and edges meet the computational and bandwidth requirements for the VNFs and virtual links, respectively. It has already been proved that this process is NP-hard and it is difficult to find an optimal solution to this problem. Therefore, in this paper, we propose the use of meta-heuristics to solve the SFC placement problem in cellular networks. We consider a triathlon competition leading to different mobility patterns. We collected real data about the competitors to simulate their movements through the scenario as well as the measured signal quality of the network. We formulate the SFC placement problem as a multi-objective problem where we try to minimize the placement cost and the total SFC delay. To solve the problem, we propose the use of two algorithms, NSGA-II and GDE3, which compare two different greedy approaches that prioritize the different optimization metrics considered in this work. Our results show that the meta-heuristics provide better results for each of the metrics. For all competition stages, GDE3 presented a slightly lower placement costs than NSGA-II, while NSGA-II had a lower delay in some scenarios.

Dynamic service function chains placement based on parallelized requests in edge computing environment

AbstractThe advent of Network Function Virtualization (NFV) technology brings flexible traffic engineering to edge computing environments. Online services in NFV are chained as Service Function Chains (SFCs), which consist of ordered sequences of Virtual Network Functions (VNFs). The SFC Placement (SFCP) problem is solved under Quality of Service (QoS) requirements and limited resource availability by directing traffic to the required VNFs. However, SFC assembly leads to high latency and network congestion by increasing the count of VNFs, which parallelized SFC can overcome this problem. With parallelizing an SFC request, independent VNFs are activated simultaneously and computational acceleration is realized by reducing the SFC length. Any pair of VNFs that do not conflict with traffic can be activated simultaneously. Most VNFs are deployed on distributed servers for load balancing, which makes SFC parallelization challenging. Meanwhile, the cost of merging/duplicating packets for parallelized SFCs between different servers is not negligible. Hence, in this article, Distributed Parallel Chaining (DPC) is proposed which is an algorithm based on Deep Reinforcement Learning (DRL) approaches. The DPC algorithm solves the SFCP problem to maximize the Long‐Term Expected Cumulative Reward (LTECR). DPC incorporates an Asynchronous Advantage Actor‐Critic (A3C) algorithm as a new approach based on DRL to increase the admission ability of future SFC requests by maximizing LTECR. The evaluation results show the effectiveness of the proposed algorithm from different aspects. Specifically, compared to the best existing approaches, DPC can reduce SFC latency by 8.7%.

A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the destination application in multiaccess edge computing (MEC) for processing. Since there are usually multiple identical VNF instances in the network and the network environment of IoT changes dynamically, placing the SFC for the IoT request flow is a significant challenge. This paper decomposes the dynamic SFC placement problem of the IoT-MEC network into two subproblems: VNF placement and path determination of routing. We first formulate these two subproblems as Markov decision processes. We then propose a meta reinforcement learning and fuzzy logic-based dynamic SFC placement approach (MRLF-SFCP). The MRLF-SFCP contains an inner model that focuses on making SFC placement decisions and an outer model that focuses on learning the initial parameters considering the dynamic IoT-MEC environment. Specifically, the approach uses fuzzy logic to pre-evaluate the link status information of the network by jointly considering available bandwidth, delay, and packet loss rate, which is helpful for model training and convergence. In comparison to existing algorithms, simulation results demonstrate that the MRLF-SFCP algorithm exhibits superior performance in terms of traffic acceptance rate, throughput, and the average reward.

Secure and Intelligent Service Function Chain for Sustainable Services in Healthcare Cyber Physical Systems

The gradual integration of Internet of Things (IoT) devices has made the healthcare cyber physical systems (CPSs) ubiquitous and complex. Since IoT devices are heterogeneous, it is crucial to virtualize these devices in a general way so as to orchestrate and allocate their equipped functions and resources in a general manner. Network function virtualization (NFV) is the key networking technology towards the device virtualization. Within NFV, service function chain (SFC) is the crucial issue. While in real networking environment, physical device accidentally fails. How to guarantee the SFC, deployed on top of one or more failed devices, recover to work has not been well addressed yet. Hence, we research this issue, aiming at realizing the secure and sustainable SFC placement and scheduling in an intelligent manner. An intelligent SFC placement and scheduling framework, labeled as <i>Sec-SFC-Intell</i>, is proposed in this paper. When receiving a SFC request, the <i>Sec-SFC-Intell</i> will deploy the SFC on top of the healthcare CPSs. When the failure of certain one physical device is detected, the affected SFC will be re-placed and re-scheduled in order to recover to run and serve. In order to prove the feasibility and highlight the merits of <i>Sec-SFC-Intell</i>, simulation work is conducted. Compared with two compared benchmarks, <i>Sec-SFC-Intell</i> has an apparent SFC acceptance advantage of nearly <inline-formula><tex-math notation="LaTeX">$10\%$</tex-math></inline-formula>.

Dynamic SFC placement scheme with parallelized SFCs and reuse of initialized VNFs: An A3C-based DRL approach

Mobile Edge Computing (MEC) is a well-known network architecture that extends cloud computing to the network edge. Compared with cloud computing, Network Function Virtualization (NFV) can provide flexible services in MEC for mobile users. Virtual Network Functions (VNFs) have emerged as software-based hardware middleboxes by NFV technology to host real-time applications. Basically, the combination of multiple VNF instances is defined as a Service Function Chain (SFC), which can provide dynamic service requirements in the MEC. Despite the rapid growth of MEC and the widespread support of service providers for SFC, many issues are still challenging and need to be addressed. In MEC scenarios with limited resources, the effective placement of SFCs with the aim of resource efficiency remains a challenging problem. Motivated by the scalability shortcomings of existing schemes to solve dynamic placement of SFCs, we propose Deep Reinforcement Learning (DRL)-based approaches to solve this problem, i.e., Asynchronous Advantage Actor-Critic (A3C). The proposed scheme is based on the reuse of initialized VNFs to improve the Quality of Service (QoS), which is developed with the aim of maximizing the long-term cumulative reward. In addition, a parallel processing approach of SFCs is included in the proposed scheme, which can split the traffic in each flow into sub-flows. This shares the processing load by instantiating duplicate instances of each VNF type in the SFC. The simulation results guarantee the efficiency of the proposed scheme and improves the average performance between 6% and 24% compared to the state-of-the-art clustering methods.

Automatic Performance-Optimal Offloading of Network Functions on Programmable Switches

In network function virtualization (NFV), network functions (NFs) are chained as a service function chain (SFC) to enhance NF management with low cost and high flexibility. Recent NFV solutions indicate that the packet processing performance of SFCs can be significantly improved by offloading NFs to programmable switches. However, such offloading requires a deep understanding of heterogeneous NF properties (e.g., resource consumption) to achieve the maximum SFC performance. Unfortunately, none of existing solutions provide automatic analysis of NF properties. Thus, network administrators have to manually examine the source codes of NFs and profile various NF properties, which is time-consuming and laborious. In this paper, we propose LightNF, a system that simplifies NF offloading in programmable networks. LightNF automatically dissects comprehensive NF properties by means of code analysis and performance profiling while eliminating manual efforts. It then leverages its analysis results of NF properties in its SFC placement so as to make the performance-optimal offloading decisions. We have implemented LightNF on Tofino-based hardware programmable switches. We perform extensive experiments to evaluate LightNF with a real-world testbed and large-scale simulation. Our experiments show that LightNF outperforms state-of-the-art solutions with an orders-of-magnitude reduction in per-packet processing latency and 9.5 improvement in SFC throughput.

DSPPV: Dynamic service function chains placement with parallelized virtual network functions in mobile edge computing

This study configures an architecture based on Deep Reinforcement Learning (DRL) with the aim of providing online services to end users in Mobile Edge Computing (MEC) networks. Network Function Virtualization (NFV) technology can provide these services in MEC by turning hardware middleboxes to Virtual Network Functions (VNFs) for mobile users. Network services are chained as an ordered sequence of VNFs named Service Function Chains (SFCs), which are provided by directing traffic to the required VNFs. Meanwhile, the SFC placement problem is challenging for provisioning online service requests under limited resource availability and improving quality of service. We propose DRL-based Dynamic SFC Placement method with Parallelized VNFs (DSPPV) to solve this problem that seeks to maximize long-term expected cumulative reward. By sharing VNFs in parallel, DSPPV can achieve computational acceleration in providing online services. Any pair of VNFs that do not conflict on traffic can process the packet simultaneously, so configuring SFC with parallelized VNFs will reduce the SFC length and thus reduce the latency. In addition, DSPPV increases the ability to process future requests by extracting the distribution of initialized VNFs. The performed simulations show the effectiveness of the proposed architecture. Specifically, the obtained numerical results show that the average number of accepted requests has improved between 4% and 15%.

Service Function Chains multi-resource orchestration in Virtual Mobile Edge Computing

Next-Generation Networks (NGNs) provide efficient services to improve the overall network performance, especially in Internet of Things (IoT) networks. However, service placement requires a significant cost in terms of multi-resource chaining guarantees. Therefore, the balance between improving network performance and optimizing resources orchestration is still a fundamental issue to assure the benefits of NGNs with optimal resources’ utilization. In this paper, we present new approaches for Service Function Chain (SFC) orchestration in Virtual Mobile Edge Computing (VMEC) environments, where IoT devices are used as on-demand virtual edge servers. We propose Optimal SFC Placement in VMEC over AI-IoT (OSPV) algorithm to select the optimal virtual mobile edge (VME) according to heterogeneous computing resources constraints. Moreover, to deal with OSPV complexity issues in dense IoT networks, we propose an Efficient SFC Placement in VMEC over AI-IoT (ESPV) algorithm that selects sufficient VME nodes for services operations and deployments. Furthermore, recent Deep Learning (DL) techniques such LSTM and GRU are implied to predict mobility and energy consumption sequences of IoT devices. These instances introduce feasible sets of IoT nodes, where the optimization algorithms should operate. Results show the efficiency of the DL instances and prove that the prediction awareness prevents the selected VME from failure and disconnection during the communication. Moreover, the proposed optimization algorithms are implemented and evaluated under different computing scenarios. Then, they are compared according to total allocated servers and SFC placement time. Optimization results show that both algorithms are efficient in terms of predetermined key performance indicators compared to the state of the art.

Position-aware packet loss optimization on service function chain placement

The advent of Network Function Virtualization (NFV) and Service Function Chains (SFCs) unleashes the power of dynamic creation of network services using Virtual Network Functions (VNFs). This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term. However, the study shows with a set of test-bed experiments that packet loss at certain positions (i.e., different VNFs) in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge, this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions. This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming (MILP) and proved that it is NP-hard. In this study, Palosis proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost. Extensive experiment results show that Palos can achieve up to 42.73% improvement on packet dropping cost and up to 33.03% reduction on average SFC latency when compared with two other state-of-the-art schemes.

Cost-Aware Placement and Enhanced Lifecycle Management of Service Function Chains in a Multidomain 5G Architecture

Service providers highly rely on network softwarization for addressing the demanding use cases of the 5G verticals. In order for the 5G vertical scenarios to be seamlessly executed, where the coverage should be provided in large areas, the required infrastructure capacity and availability can only be supported by cross-operator cooperation. In this context, we employ a novel federated core-edge 5G architecture, where 5G vertical services are represented as service function chains (SFCs) and can be offered on the infrastructure owned either by the local operator or a foreign one. Furthermore, we propose two online SFC placement methods that aim to efficiently place the SFCs across the architecture, taking into account the deployment cost. A simulation of the optimal solution, based on an integer linear programming (ILP) approach, is evaluated against a hop-based heuristic placement method that runs on our experimental 5G platform. Our experimental results demonstrate that the optimized placement produces the most cost-effective solution. Our heuristic algorithm introduces a near-optimal solution, but with a higher deployment cost. Compared with the ILP approach, the heuristic solution provides lower complexity and execution time. Finally, in order to further augment the placement techniques, we propose enhanced lifecycle management methods that use live migration and scaling actions to further decrease the execution cost and adapt to real-time incoming traffic, respectively.

SPIDER: An availability‐aware framework for the service function chain placement in distributed scenarios

AbstractThe network function virtualization (NFV) paradigm replaces hardware‐dependent network functions by virtual network functions (VNFs) that can be deployed in commodity hardware, including legacy servers. Consequently, the use of NFV is expected to reduce operating and capital expenses, as well as improve service deployment operation and management flexibility. For many use cases, the VNFs must be visited and invoked following a specific order of execution in order to compose a complete network service, named service function chain (SFC). Nonetheless, despite the benefits from NFV and SFC virtualization technologies, their introduction must not harm network performance and service availability. On the one hand, redundancy is seen by network service planners as a mechanism well established to combat availability issues. At same time, there is a goal to optimize resource utilization in order to reduce operational expenditure. In this article, we share our experience in the design use of a framework, named SPIDER, focused on SFC placement that considers the network infrastructure condition and the required SFC availability to define the placement strategy. The SPIDER monitors the status of infrastructure nodes and links and defines which servers the VNFs should be placed on and the number of redundant replicas needed. We present a proof‐of‐concept of SPIDER using Kubernetes to launch the VNFs as containers. We also use Kubernetes to forward the traffic between the VNFs, composing the service chain. We perform experiments to evaluate the runtime of SPIDER and the SFC delay under different network conditions.

A dynamic planning model for deploying service functions chain in fog-cloud computing

Fog computing allows services to be deployed on computing resources at the edge of the network to address the limitations of centralized cloud systems. However, the adoption of fog computing concepts is in the early stages, and there are still many challenges to benefiting from infrastructure in Fog-Cloud Computing-based Networks (FCCN). One of them is known as Service Function Chain (SFC), which can use network software instances instead of costly dedicated hardware to share resources. Network Function Virtualization (NFV) technology separates hardware middleboxes such as gateways, firewalls, and set-top boxes from hardware and treats them as Virtual Network Functions (VNFs), where they can execute as software instances on decentralized nodes in the FCCN. VNFs are chained together in specific sequences that form SFCs. Meanwhile, deploying VNFs on nodes in the FCCN to accomplish SFC is an NP-Hard problem that can lead to efficient utilization of resources and reduce latency and cost. Recent research has performed SFC placement through heuristic algorithms that often cannot cope with the dynamic behavior of the network. In addition, existing works explicitly ignores SFC placement with the reuse of VNF instances. Hence, in this paper, we address the SFC placement problem by reusing VNFs through Deep Reinforcement Learning (DRL) based approaches. The proposed algorithm as a dynamic planning model can reconcile service costs and Quality of Service (QoS) by considering resource constraints and dynamic distribution analysis of VNFs required in the FCCN. Here, the Asynchronous Advantage Actor-Critic (A3C) algorithm is used as a DRL approach with the aim of maximizing long-term cumulative reward. The simulation results through real-world data traces show that the proposed algorithm effectively improves the system performance and outperform the best result of benchmark methods ranging from 14% to 28% by considering the resource cost.

Multi-objective Optimization Service Function Chain Placement Algorithm Based on Reinforcement Learning

Multi-objective Optimization Service Function Chain Placement Algorithm Based on Reinforcement Learning

A reinforcement learning-based approach for availability-aware service function chain placement in large-scale networks

By decoupling network service functions from custom-built hardware, network function virtualization is transforming network service provisioning, making large scale telecommunications more flexible, scalable, and agile. Service function chaining often requires network traffic to flow through a specific sequence of virtual network functions (VNFs) to comply with network policies. The dependencies between VNFs in such a chain can significantly and negatively impact the availability of an end-to-end service function chain (SFC) when one or more VNFs malfunction or fail. While redundancy strategies can be used to reduce the impact of such failure, this can increase operational expenditure and energy consumption. In this paper, we propose a solution for SFC placement based on reinforcement learning (RL) taking into account SFC availability, operational costs, and energy consumption. The Cand-RL algorithm uses RL based on proximal policy optimization (PPO) to select the suitable candidate node and define the redundancy strategy to meet availability requirements. We compare Cand-RL against two greedy algorithms in a variety of simulated scenarios. The results show that the Cand-RL outperforms the greedy algorithms, achieving a higher acceptance rate and a good balance between availability, placement cost, and energy consumption.

HARS: A High-Available and Resource-Saving Service Function Chain Placement Approach in Data Center Networks

Network function virtualization (NFV) is a promising technology that decouples network functions from hardware. Connecting virtual network functions (VNFs) in series to form a service function chain (SFC) can flexibly orchestrate and expand network functions. However, there are higher availability requirements for SFCs. This paper aims to solve the SFC placement problem under availability and resource constraints. This paper proposes the sideway cross (SC) backup model, which considers the availability of both VNFs and physical machines (PMs) in a data center. The SC model cross-arranges the backup instances of VNFs to guarantee availability and optimize resource consumption. Then, this paper proposes the heuristic meteor shower optimization (MSO) algorithm to place SFCs. Compared to traditional heuristic algorithms, MSO can improve the execution time by approximately 200&#x0025;. Combined with the SC backup model, MSO can effectively improve the availability and resource overhead. The evaluation results show that the proposed approach can guarantee higher availability and consumes fewer resources. The proposed approach only needs 75&#x0025; of the resources to achieve the same availability as the state-of-the-art models.

Efficient instance reuse approach for service function chain placement in mobile edge computing

The combination of mobile edge computing and network function virtualization has led to the emergence of Virtualized Network Function (VNF) in a broader range of application scenarios. These latency-sensitive and highly dynamic services can be provided by combining multiple VNFs into Service Function Chains (SFCs). However, existing work has conspicuously neglected that online placing SFC with instance reuse can significantly improve resource utilization and save initialization time, which requires considering both the dynamic distribution of required VNFs over time and resource constraints on the edge network. In this paper, we initiate the study of Online SFC placement combined with Instance Reuse. An OSIR algorithm is proposed to gain a tradeoff between service costs and users’ quality of experience. The OSIR is designed based on deep reinforcement learning, which improves the system performance by maximizing the long-term cumulative reward. In OSIR, an SFC queue network is designed to extract the dynamic distribution of required VNFs over time, composed of memory space and the long short-term memory learning approach. The experimental results with real-world data traces show that OSIR can efficiently and effectively improve system performance and outperform the best result of all existing algorithms ranging from 17% to 26%.

Optimizing Service Function Chaining Migration With Explicit Dynamic Path

Network Function Virtualization (NFV) can support customized on- demand network services with flexibility and cost-efficiency. Virtual Network Function (VNF) instances need to be scaled out, scaled in, and reallocated across the NFV infrastructure (NFVI) to avoid a violation of service agreements when the demand traffic changes. However, selecting the new placement of VNFs for migrating a service function chain (SFC) is an issue of efficient NFV control. We propose two novel integer linear programming (ILP) models and two approximation algorithms for SFC placement and migration to maximize the cost-efficiency of an NFV network regarding the changes of service demands and dynamic routing. The ILP models allow us to obtain the optimal solutions of SFC placement and migration with explicit dynamic paths. The approximation migration results provided by our proposed heuristic and reinforcement learning algorithms are close to the optimal solution. Evaluation results carried out with real datasets and synthetic network topologies provide a helpful suggestion of a migration strategy for an NFV service provider to optimize the operating cost of an NFV network in the long term.

A Reinforcement Learning-Based Approach for Availability-Aware Service Function Chain Placement in Large-Scale Networks

A Reinforcement Learning-Based Approach for Availability-Aware Service Function Chain Placement in Large-Scale Networks

Multi-Objective Deep Reinforcement Learning Assisted Service Function Chains Placement

The study of Service Function Chains (SFCs) placement problem is crucial to support services flexibly and use resources efficiently. Solutions should satisfy various Quality of Service requirements, avoid edge resource congestion, and improve service acceptance ratio (SAR). This work presents a novel approach to address these challenges by solving a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-objective SFCs placement</i> problem based on the Pointer Network in multi-layer edge and cloud networks. We design a Deep Reinforcement Learning algorithm, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chebyshev-assisted Actor-Critic SFCs Placement Algorithm</i> , to overcome the limitations of traditional heuristic and evolutionary algorithms. Then, we run this algorithm iteratively with a set of weights to obtain non-dominated fronts, which have much higher hypervolume values than those obtained from other state-of-the-art algorithms. Moreover, running our algorithm individually with selected weights from non-dominated fronts can avoid edge resource congestion and achieve 98% SARs of low-latency services during high-workload periods. Finally, based on both simulation and real testbed experimental results, it is validated that the proposed algorithm fits for pragmatic service deployment while achieving 100% of SARs in the use cases deployed on the testbed.