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Availability Model for Data Center networks With Dynamic Migration and Multiple Traffic Flows

IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2023年第3期20卷 2975-2989页

作者： Zhu, Juxing Huang, Ning Wang, Junliang Qin, Xiaopeng Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Sci & Technol Reliabil & Environm Engn Beijing 100191 Peoples R China China Telecom Res Inst Dept Cloud Network Operat Guangzhou 510630 Peoples R China Huawei Technol Cooperat Data Commun Prod Line Architecture & Design Dept Beijing 100095 Peoples R China

The evaluation of application availability is a crucial step in ensuring functionality of the data center network (DCN). The application of data center network is no more a chain of physical devices, but a chain of virtualized network function (VNF) dynamically installed in servers, i.e., the service function chain (SFC), due to the adaptation of network function virtualization (NFV). When dynamic migration is considered, the application availability evaluation would encounter problems since the state of VNF components is not independent. And in the application deployed in DCN, there is multiple traffic flow because of the serialization of 1+1 (active-standby) and N-way (load-sharing) redundant VNF, however only single flow is considered in current availability model. When dynamic migration and multiple traffic flows are considered, the application availability evaluation would encounter the following problems: 1) The application behavior is not described in the existing availability model;2) The availability model could not quantify the influence of different traffic of each flow. This paper proposes an availability model, which consist of network evolution model (NEM) of DCN and the availability evaluation algorithm based on NEM (AEA-NEM), to evaluate the availability of different applications deployed on DCN. The NEM of DCN describes the dynamical process of migration and switch of three kinds of VNF (normal, 1+1 and N-way) in application, and the AEA-NEM evaluate the availability of application with multiple traffic flows. The simulation results show that, the steady state availability of the proposed model is consistent with it of generalized stochastic Petri net (GSPN) model. And the model could evaluate the application availability with dynamic migration and multiple traffic flows in DCN. Based on the current VNF placement and redundant strategy, the most available designing scheme is analyzed.

关键词： Data centers Maintenance engineering Data center network network function virtualization application reliability network evolution model service function chain

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

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SOFTWARE-PRACTICE & EXPERIENCE 2023年第2期53卷 413-437页

作者： Santos, Guto Leoni Endo, Patricia Takako Sadok, Djamel Kelner, Judith Univ Fed Pernambuco Recife PE Brazil Univ Pernambuco Recife PE Brazil

The 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.

关键词： intrastructure monitoring Kubernetes network function virtualization service function chain

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AoI-Aware Energy-Efficient SFC in UAV-Aided Smart Agriculture Using Asynchronous Federated Learning

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 1222-1242页

作者： Akbari, Mohammad Syed, Aisha Kennedy, W. Sean Erol-Kantarci, Melike Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Nokia Bell Labs Ottawa ON K2K 2T6 Canada

In the midst of rising global population and environmental challenges, smart agriculture emerges as a vital solution by integrating advanced technologies to optimize agricultural practices. Through data-driven insights and automation, it tackles the necessity for sustainable resource management, enhancing productivity and resilience in the face of complex food security and ecological concerns. The prospects of utilizing the Internet of Things (IoT) for smart agriculture are tremendous, where many IoT devices can be deployed for local environment monitoring, precision farming, autonomous irrigation, and, soil management. In some use cases like smart monitoring and agrochemical applications, UAV-enabled mobile-edge computing (MEC) is proposed as an enabler to provide IoT nodes with additional resources by hosting their computation functions. From the implementation perspective, to flexibly manage the computation functions in UAVs and/or MEC server, the emerging network function virtualization (NFV) can be utilized. However, efficient orchestration of the virtualized functions would be a challenge. In this paper, we consider a decentralized UAV-aided MEC system for smart agricultural applications in which the processing nodes benefit from the NFV technology. We aim to propose a method for efficiently orchestrating the NFVs while some important metrics are minimized, i.e., the age of information (AoI) and total network energy consumption. Especially, we consider the case in which the network state is not fully observable to the orchestrator or the observations are exposed to uncertainties. Consequently, the problem is formulated as a decentralized partially observable Markov decision process (DEC-POMDP). As the formulated problem is NP-complete, we exploit some structural features of the proposed scheme to introduce the concept of symmetry and simplify the problem. Then, a novel decentralized federated learning-based solution is proposed to solve the problem. Simulation

关键词： Internet of Things UAV-aided mobile edge computing (UAV-aided MEC) age of information network function virtualization federated reinforcement learning

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Towards a QoS-aware network virtual deployment for network-as-a-service

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 623-639页

作者： Diaz, Gladys Chahbar, Mohammed L2TI Sorbonne Paris Nord University 99 avenue J-B Clément Villetaneuse Saint Denis93430 France

The digital environment is constantly evolving with a growing diversity of network access technologies, such as ADSL, WiFi, 5G, LiFi, Zigbee, and the deployment of innovative services such as mobility, location, and telemetric services, as well as new applications such as smart parking, smart cities, machine-to-machine communication, and pervasive gaming. A few years ago, the services offered were dependent on the type of network, such as voice for telecommunications networks, data for computer networks, and audio/video for broadcast networks. However, service providers now need to adapt and anticipate changing consumption patterns, such as user-centric services, in their offerings. The challenge lies in how to quickly and efficiently deploy new services in this rapidly evolving technological landscape. The primary aim of this paper is to examine the impact of virtualization on the network deployment process in this new landscape. We concentrate on the integration of virtualization into the network Deployment Process (called Virtual network Virtual Deployment-VNVD). VNVD considers the properties of flexibility, adaptability, and dynamicity, which are crucial for network-as-a-Service. In this context, Software-Defined networking and network function virtualization play a significant role in the design of new network architectures. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： network function virtualization

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An Approach to network Service Placement Reconciling Optimality and Scalability

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2023年第3期20卷 2218-2229页

作者： Taghavian, Masoud Hadjadj-Aoul, Yassine Texier, Geraldine Huin, Nicolas Bertin, Philippe IRT BCOM Adv Connect Lab F-35510 Cesson Sevigne France IMT Atlantique IRISA Adopnet F-35510 Cesson Sevigne France Univ Rennes CNRS INRIA IRISA F-35000 Rennes France Infrastruct Innovat & Engn Lab F-35510 Cesson Sevigne France

The inevitable transition from physical dedicated hardware devices towards lightweight containerized reusable software modules with network function virtualization (NFV) introduces countless opportunities while presenting several unprecedented challenges. Satisfying NFV expectations in post-5G networks heavily depends on the efficient placement of network services. In this paper, after modeling the placement problem and proposing the exact resolutions using Integer Linear Programming (ILP) and Column Generation (CG), we propose our deterministic placement solution, capable of obtaining optimal results with the scalability of a heuristic-grade approach. Our method is organized as a Branch and Bound (BnB) structure, applying Artificial Intelligence (AI) search strategies (especially A*) to address the problem of network service placement. We believe that it is suitable for a range of applications in online placement scenarios, whether we concentrate on the quality of the results or on the strict time constraints. We are interested in the popular objective of Service Acceptance (SA) maximization and have carried out several extensive evaluations. The obtained results confirm the effectiveness of our solution.

关键词： Quality of service Scalability Bandwidth Costs Virtual links Substrates Search problems network function virtualization network service placement branch and bound artificial intelligence

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RuleDRL: Reliability-Aware SFC Provisioning With Bounded Approximations in Dynamic Environments

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第5期16卷 3651-3664页

作者： Zeng, Yue Qu, Zhihao Guo, Song Tang, Bin Ye, Baoliu Li, Jing Zhang, Jie Nanjing Univ Dept Comp Sci & Technol State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing 211100 Peoples R China HongKong Polytech Univ Dept Com puting Hung Hom Hong Kong Peoples R China

As a key enabling technology for 5G, network function virtualization abstracts services into software-based service function chains (SFCs), facilitating mission-critical services with high-reliability requirements. However, it is challenging to cost-effectively provide reliable SFCs in dynamic environments due to delayed rewards caused by future SFC requests, limited infrastructure resources, and heterogeneity in hardware and software reliability. Although deep reinforcement learning (DRL) can effectively capture delayed rewards in dynamic environments, its trial-and-error exploration in a vast solution space with massive infeasible solutions may lead to frequent constraint violations and traps in poor local optima. To address these challenges, we propose a RuleDRL algorithm that combines the capability of DRL to capture delayed rewards and the strength of rule-based schemes to explore high-quality solutions without violating constraints. Specifically, we first formulate the reliable SFC provision problem as an integer nonlinear programming problem, which is proven to be NP-hard. Then, we jointly design DRL and rule-based schemes that are coupled to make the final decision and establish a bounded approximation ratio in general cases. Extensive trace-driven simulations show that RuleDRL can save the total cost by up to 65.67% and improve the SFC acceptance ratio by up to 82%, compared to the state-of-the-art solution.

关键词： network function virtualization edge computing 5G deep reinforcement learning

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SFC Design and VNF Placement Based on Traffic Volume Scaling and VNF Dependency in 5G networks

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 1791-1814页

作者： Zhihao Zeng Zixiang Xia Xiaoning Zhang Yexiao He School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China

The development of Fifth-Generation(5G)mobile communication technology has remarkably promoted the spread of the Internet of Things(IoT)*** a promising paradigm for IoT,edge computing can process the amount of data generated by mobile intelligent devices in less time *** function virtualization(NFV)that decouples network functions from dedicated hardware is an important architecture to implement edge computing,deploying heterogeneous Virtual network functions(VNF)(such as computer vision,natural language processing,intelligent control,etc.)on the edge service *** the NFV MANO(Management and Orchestration)framework,a Service function Chain(SFC)that contains a set of ordered VNFs can be constructed and placed in the network to offer a customized network ***,the procedure of NFV orchestration faces a technical challenge in minimizing the network cost of VNF placement due to the complexity of the changing effect of traffic volume and the dependency on *** this end,we jointly optimize SFC design and VNF placement to minimize resource cost while taking account of VNF dependency and traffic volume ***,the problem is formulated as an Integer Linear Programming(ILP)model and proved NPhard by reduction from Hamiltonian Cycle *** we proposed an efficient heuristic algorithm called Traffic Aware and Interdependent VNF Placement(TAIVP)to solve the *** with the benchmark algorithms,emulation results show that our algorithm can reduce network cost by 10.2%and increase service request acceptance rate by 7.6%on average.

关键词： network function virtualization virtual network function service function chain resource allocation routing

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Metronome: Adaptive and Precise Intermittent Packet Retrieval in DPDK

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IEEE-ACM TRANSACTIONS ON networkING 2023年第3期31卷 979-993页

作者： Faltelli, Marco Belocchi, Giacomo Quaglia, Francesco Pontarelli, Salvatore Bianchi, Giuseppe Univ Roma Tor Vergata Dept Civil & Comp Engn I-00133 Rome Italy Consorzio Nazl Interuniv Telecomunicaz CNIT I-16124 Genoa Italy Sapienza Univ Rome Dept Comp Sci I-00185 Rome Italy Univ Roma Tor Vergata Dept Elect Engn I-00133 Rome Italy

The increasing performance requirements of modern applications place a significant burden on software-based packet processing. Most of today's software input/output accelerations achieve high performance at the expense of reserving CPU resources dedicated to continuously poll the network Interface Card. This is specifically the case with DPDK (Data Plane Development Kit), probably the most widely used framework for software-based packet processing today. The approach presented in this paper, descriptively called Metronome, has the dual goals of providing CPU utilization proportional to the load, and allowing flexible sharing of CPU resources between I/O tasks and applications. Metronome replaces DPDK's continuous polling with an intermittent sleep&wake mode, and revolves around a new multi-threaded operation, which improves service continuity. Since the proposed operation trades CPU usage with buffering delay, we propose an analytical model devised to dynamically adapt the sleep&wake parameters to the actual traffic load, meanwhile providing a target average latency. Our experimental results show a significant reduction of the CPU cycles, improvements in power usage, and robustness to CPU sharing even when challenged with CPU-intensive applications.

关键词： Instruction sets Kernel Central Processing Unit Task analysis Linux Power demand Data centers Computers and information processing network function virtualization data centers cloud computing

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Runtime Adaptation Framework for Fulfilling Availability and Continuity Requirements of network Services

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2023年第3期20卷 2259-2282页

作者： Azadiabad, Siamak Khendek, Ferhat Toeroe, Maria Concordia Univ Dept Elect & Comp Engn Montreal PQ H3X 3Y5 Canada Ericsson Inc Montreal PQ H4S 0B6 Canada

The network function virtualization (NFV) framework is an enabler for the automation of network Service (NS) management. In the context of NFV, an NS is realized by interconnecting Virtual network functions (VNF) using Virtual Links (VL). Availability and continuity are among the important characteristics of an NS. These characteristics depend on the availability of the VNFs and VLs composing the NS, which are usually selected at NS design time. VNFs and VLs utilize the resources of the underlying infrastructure, and their availability (partially) depends on the availability of these resources. To design an NS to fulfill availability and continuity requirements, the availability required from the resources is constrained at design time. However, the characteristics of these resources may change at runtime due to the dynamicity of NFV infrastructure. Thus, impacting the availability of the VNFs and the VLs, which in turn may impact the availability and continuity of the NS. To fulfill these requirements at runtime despite the changes in the infrastructure, the NS should be adapted. In this paper, we propose a framework for the runtime adaptation of NSs that reacts to changes and adapts the NS configuration so that it can fulfill its availability and continuity requirements during the NS lifetime. We also propose a method to develop machine learning models that are used within the framework to determine the required adjustments at runtime. We implemented the proposed framework, the method for developing the machine learning models, a testbed, and NSs to assess the feasibility and validity of our approach through experiments.

关键词： Runtime network function virtualization Data models Training Software Reinforcement learning Predictive models network service (NS) network function virtualization (NFV) virtual network function (VNF) service availability service continuity runtime adaptation machine learning deep learning neural networks

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SAP: Subchain-Aware NFV Service Placement in Mobile Edge Cloud

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2023年第1期20卷 319-341页

作者： Doan, Tung V. V. Nguyen, Giang T. T. Reisslein, Martin H. P. Fitzek, Frank H. P. Tech Univ Dresden Deutsch Telekom Chair Commun Networks D-01187 Dresden Germany Tech Univ Dresden Ctr Tactile Internet Human In The Loop D-01187 Dresden Germany Tech Univ Dresden Chair Hapt Commun Syst D-01187 Dresden Germany Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

Existing network function virtualization (NFV) service placements that reuse already deployed network functions either reuse an entire Service function Chain (SFC) or only individual network functions while ignoring the chain configuration cost for configuring the SFC traffic steering and ignoring the reliability of the network functions. Also, the Mobile Edge Cloud (MEC) frameworks that are required to implement an NFV service placement should ideally seamlessly cooperate with the various existing NFV Management and Orchestration (MANO) frameworks. However, the existing MEC frameworks lack multi-MANO support. We formulate the novel Subchain-Aware NFV service Placement (SAP) optimization model that accounts for the configuration cost for stitching together reused network functions to an SFC and strives to reuse existing subchains of consecutive network functions (with already deployed SFC traffic steering), while accounting for the recovery cost of network functions with limited reliability. We develop Tabu-SAP, a Tabu search approach to solve the SAP optimization problem. Furthermore, we introduce the novel Automated Provisioning framework for MEC (APMEC) with open-source OpenStack implementation to enable the deployment of Tabu-SAP in real networks;APMEC supports multiple MANOs through a loose coupling MANO-MEC design. Our Tabu-SAP evaluations indicate an around eightfold increase of the number of supported SFCs compared to the state-of-the-art reuse of individual network functions, while substantially reducing the total cost, which includes the chain configuration cost. Also, for long SFCs of seven or more network functions, the Tabu-SAP total cost is less than 10% higher than the optimal solution (which requires over ten times longer execution time).

关键词： Noise measurement Costs Reliability Software reliability Cloud computing network function virtualization Hardware Configuration cost multi-access edge computing (MEC) network function placement network function virtualization (NFV) reuse service function chain (SFC)

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