Service Function Chain Placement Research Articles

Enhancing network function parallelism in mobile edge computing using Deep Reinforcement Learning

This paper introduces a Deep Reinforcement Learning (DRL)-based framework to enhance Network Function Parallelism (NFP) in Mobile Edge Computing (MEC). Leveraging Network Function Virtualization (NFV), the proposed framework optimizes service delay by solving a fairness-aware throughput maximization problem for service function chain placement. It aims to maximize the long-term cumulative reward while satisfying Quality of Service (QoS) requirements. The framework also preserves resources for future requests by efficiently managing the initialized network functions distribution. Simulation results demonstrate the superior performance of the proposed framework across various metrics. Specifically, our framework improves the average delay and deployment rate by 1.2% and 2.4% compared to the existing best method.

LPulse: An efficient algorithm for service function chain placement and routing with delay guarantee

Modern network services increasingly depend on the effective orchestration of the Service Function Chain (SFC) with stringent end-to-end delay guarantees. To achieve this, the Delay-Constrained Service Function Chain Placement and Routing (DC-SFCPR) problem must be addressed. This problem involves the optimal selection of nodes for placing network functions and routes that adhere to a specific sequence of service functions, to minimize network bandwidth and CPU costs while strictly adhering to stringent end-to-end delay constraints. The DC-SFCPR problem is NP-hard and existing algorithms either fail to guarantee strict delay constraints or are computationally expensive, making them unsuitable for expanding network topologies. We propose the LPulse algorithm, designed to efficiently solve the DC-SFCPR problem. This algorithm utilizes a layered graph to embed the requirements of service functions, transforming the DC-SFCPR problem into a Delay-Constrained Shortest Path (DCSP) problem. The LPulse algorithm then applies Pulse, a depth-first search framework enhanced with efficient pruning strategies, and incorporates two novel acceleration strategies to solve the DCSP problem. We prove that LPulse ensures the optimality of solutions. Evaluations conducted across various topologies, with node scales ranging from 22 to 10,000, show that LPulse surpasses existing algorithms in both solution quality and speed. For instance, the number of cases meeting strict delay constraints with LPulse is 1.9× that of those solved by deep reinforcement learning algorithms; furthermore, its solving efficiency is 4.9× that of the highest-performing existing optimal algorithm, the LagrangianKsp algorithm.

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.

Correction to “Dynamic service function chain placement in mobile computing: An asynchronous advantage actor‐critic based approach”

Correction to “Dynamic service function chain placement in mobile computing: An asynchronous advantage actor‐critic based approach”

Mobility-aware SFC migration in dynamic 5G-Edge networks

The fifth-generation mobile networks bring an evolution in the development of novel classes of applications, increasing the demand for performance and flexibility to support new classes of services. Edge Computing and Network Function Virtualization are two key technologies that bring data processing closer to the user and allow the replacement of traditional hardware-based network functions with virtualized counterparts, which reduces costs and permits the provision of low-latency services. In this scenario, services are often implemented as a Service Function Chain (SFC), consisting of a set of ordered Virtual Network Functions (VNFs) that provide the required service functionality. During service execution, users can move from one location to another, which impacts service performance. An approach to reduce the impact of user mobility is to migrate services as users move to different places. However, the SFC migration problem imposes several challenges that require complex decision-making. VNFs should be migrated to nodes that have the necessary resources to execute the VNF instances. Also, the migration should be fast and effective, using as few resources as possible to reduce migration costs. In this paper, we propose a proactive and iterative SFC migration strategy to address the performance impact caused by user mobility. Our proposed solution anticipates the movement of users and estimates the impact of their mobility on service delay, proactively triggering service migrations when needed. The proposed algorithm decides which VNFs of the SFC should be migrated and which compute nodes will host the migrating VNFs, fulfilling service, resource, and migration constraints while maintaining a low migration overhead. Our algorithm also considers the dynamic nature of the edge environment in which other SFC placement and migrations might be occurring when an SFC is being migrated, which also introduces new challenges related to the availability and concurrency of resources required for migration.

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 Multi-objective Service Function Chain Placement Based on Deep Reinforcement Learning

Dynamic Multi-objective Service Function Chain Placement Based on Deep Reinforcement Learning

Efficient Service Function Chain Placement over Heterogeneous Devices in Deviceless Edge Computing Environments

Efficient Service Function Chain Placement over Heterogeneous Devices in Deviceless Edge Computing Environments

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, Palos is 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