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On Efficient Service function Chaining in Hybrid Software Defined networks

IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2022年第2期19卷 1614-1628页

作者： Ren, Cheng Li, Hao Li, Yaxin Wang, Yu Xiang, Haiyun Chen, Xuxiang Southwest Petr Univ Sch Elect Engn & Informat Chengdu 610500 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610054 Peoples R China Univ South Brittany Lab Math Atlantic Brittany F-56000 Vannes France Southwest Petr Univ Network & Informat Ctr Chengdu 610500 Peoples R China

To minimize operating expenses (OPEX) and enhance flexibility for network service provisioning, network function virtualization (NFV) is used to chain an ordered sequence of virtualized network functions (VNFs), also known as service function chain (SFC), which can be placed on commodity servers. As a highly complementary to NFV, software-defined networking (SDN) can offer an agile way of VNF orchestration with the capability of fine-granularity network control over flows. However, one-step migration to SDN is impossible in ISP networks. Thus, in this paper we resort to hybrid SDN to implement SFC provisioning. By jointly optimizing SDN deployment that largely determines capital expenditures (CAPEX) and VNF placement that decides OPEX, we manage to make a tradeoff between CAPEX and OPEX, and to find out an appropriate SDN deployment rate for network operator. We first formulate the problem as an integer linear programming (ILP) model P, then, reformulate it with column generation technique and decomposition theory to develop a distributed approximation algorithm CGPD which obtains a tight upper bound of optimal solution to P. In order to better decrease CAPEX, a dynamic programming-based heuristic EOES giving importance to effective resource sharing is further designed based on a hidden Markov model. The simulation results indicate that, the difference between P and CGPD is marginal. CGPD outperforms EOES in terms of total cost, average delay and bandwidth utilization, and EOES on the other hand demands 20% SDN deployment rate which is half of that of CGPD.

关键词： network function virtualization service provisioning software-defined networking hybrid SDN

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Role of network Slicing in Software Defined networking for 5G: Use Cases and Future Directions

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IEEE WIRELESS COMMUNICATIONS 2022年第1期29卷 112-118页

作者： Babbar, Himanshi Rani, Shalli AlZubi, Ahmad Ali Singh, Aman Nasser, Nidal Ali, Asmaa Chitkara Univ Inst Engn & Technol Rajpura Punjab India King Saud Univ Riyadh Saudi Arabia Univ Europea Atlantico Santander Spain Univ Int Cuanza Kuito Angola Alfaisal Univ Riyadh Saudi Arabia Queens Univ Kingston ON Canada

network slicing is expected to be critical in the deployment of 5G mobile networks and systems. On top of a single physical infrastructure, the technology enables operators to operate several virtual networks. As the 5G commercialization was recently deployed, network function virtualization (NFV) and software-defined networking (SDN) will drive network slicing. In this article, we present an overview of SDN in 5G, and the motivation, role, and market growth of network slicing. We then discuss usage scenarios of SDN in network slicing for 5G. The proposed architecture comprises the three usage scenarios: enhanced mobile broadband (eMBB) provides the support to varying types of services used;ultra-reliable low-latency communications (URLLC) provides a certain class of applications such as higher bandwidth, high definition video streaming, mobile TV, and so on;massive machine type communications (mMTC) throws light on the types of services used to connect huge numbers of devices. Finally, challenges and solutions based on network slicing in 5G are presented.

关键词： Technological innovation 5G mobile communication network slicing Ultra reliable low latency communication Power industry network function virtualization Virtual private networks

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DRL-D: Revenue-Aware Online Service function Chain Deployment via Deep Reinforcement Learning

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2022年第4期19卷 4531-4545页

作者： Fan, Qilin Pan, Pan Li, Xiuhua Wang, Sen Li, Jian Wen, Junhao Chongqing Univ Sch Big Data & Software Engn Chongqing 400000 Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400000 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 400000 Peoples R China Haihe Lab Informat Technol Applicat Innovat Tianjin 300072 Peoples R China Binghamton Univ State Univ New York Binghamton Dept Elect & Comp Engn Binghamton 13902 NY USA

network function virtualization (NFV) is a promising paradigm where network functions are migrated from dedicated hardware appliances onto software middleboxes to promote service agility and reduce management costs. Benefiting from the NFV, the service function chain (SFC) has emerged as a popular network service form. It allows network traffic to pass through a series of virtual network functions in a specific order required by the business logic to arrange a complex service. However, SFC deployment is facing new challenges in seeking a trade-off between pursuing the objective of high long-term average revenue and making decisions in an online manner. In this paper, we propose DRL-D, a deep reinforcement learning-based approach for the online SFC deployment problem to satisfy different demands of SFC requests within resource constraints of the underlying infrastructure. DRL-D aims to maximize the long-term average revenue by combining the strengths of the graph convolutional network in learning a comprehensive representation of network state and the temporal-difference learning in generating deployment solutions for the SFC requests on the fly. Then a heuristic algorithm and a new prioritized experience replay technique are integrated to optimize the DRL framework and reduce the time complexity. Experimental results demonstrate the superiority of our DRL-D approach when compared with other benchmarks in terms of the long-term average revenue, acceptance ratio, and revenue-to-cost ratio. Performance evaluation shows that DRL-D possesses good robustness under different scales of physical networks and achieves excellent deployment performance within acceptable runtime.

关键词： Approximation algorithms Heuristic algorithms Bandwidth Training Service function chaining Reinforcement learning Optimization network function virtualization service function chain deep reinforcement learning graph convolutional network

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Energy Aware Fuzzy Approach for VNF Placement and Consolidation in Cloud Data Centers

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JOURNAL OF network AND SYSTEMS MANAGEMENT 2022年第3期30卷 1-29页

作者： Khemili, Wided Hajlaoui, Jalel Eddine Omri, Mohamed Nazih Univ Sousse Higher Inst Comp Sci & Commun Technol MARS Res Lab Sousse Tunisia Univ Sousse Natl Sch Engineers Sousse MARS Res Lab Sousse Tunisia

Virtual network function (VNF) is one of the pillars of a Cloud network that separates network functions and their dedicated hardware devices, such as routers, firewalls, and load balancers, to host their services on virtual machines. The VNF is responsible for network services that run on virtual machines and can connect each of them alone or organize themselves into a single enclosure to use all the resources available in that enclosure. This flexibility allows physical and virtual resources to be used in a way that ensures control over power consumption, balance in resource use, and minimizing costs and latency. In order to consolidate VNF groups into a minimum number of virtual machine (VM) with estimation of the association relation to a measure of confidence under the context of possibility theory, we propose a new Fuzzy-FCA approach for VNF placement based on formal concept analysis (FCA) and fuzzy logic in mixed environment based on cloud data centers and Multiple access edge computing (MEC) architecture. Thus, the inclusion of this architecture in the cloud environment ensures the distribution of compute resources to the end user in order to reduce end-to-end latency. To confirm the effectiveness of our solution, we compared it to one of the best algorithms studied in the literature, namely the MultiSwarm algorithm. The results of the series of experiments carried out show the feasibility and efficiency of our algorithm. Indeed, the harvested results confirm the capability of maximizing and balancing the use of resources, of minimizing the latency and the cost of energy consumption. The performance of our solution in terms of average latency represents 16%, a slight increase compared to MultiSwarm, and an average gain, of 49% in run time, compared to the same algorithm.

关键词： Cloud data centers Placement Consolidation Multiple access edge computing network function virtualization Formal concept analysis

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Improved Immersive Content Delivery in a Combined Service-based-Virtualized/Open-RAN network Environment 100

Improved Immersive Content Delivery in a Combined Service-ba...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Cabrera, Rufino Montalban, Jon Jimenez, Erick Iradier, Eneko Angueira, Pablo Muntean, Gabriel-Miro

ISBN: (纸本)9798331517786

The stringent requirements of the latest immersive applications (i.e., 360° video, VR, AR, XR, etc.) combined with the scarcity of radio resources are challenging the service operators of the various telecommunication networks. In the recent literature, HetNets have been proposed as an alternative to overcoming these challenges. In previous work, the authors presented SVORA, an architecture for integrating different radio technologies, combining virtualized/open RAN architectures and SBA-based RAN for efficient spectrum usage. In this work, the authors upgrade the previously proposed solution to deliver immersive video efficiently. The paper presents imeNF, a new set of network functions that support delivering high-bitrate immersive video-based applications with increased quality of service. Simulations-based testing shows how, based on imeNF, a 16k video can be sent with delays of 15 ms, while traditional D-RAN, V-RAN, and O-RAN-based approaches take more than 300 ms. © 2024 IEEE.

关键词： network function virtualization

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Tapping into NFV Environment for Opportunistic Serverless Edge function Deployment

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IEEE TRANSACTIONS ON COMPUTERS 2022年第10期71卷 2698-2704页

作者： Zhang, Lu Feng, Weiqi Li, Chao Hou, Xiaofeng Wang, Pengyu Wang, Jing Guo, Minyi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

Even with network function virtualization (NFV), many commodity network servers have spare cycles. Despite that they are small and irregularly occur, spare cycles are fit for deploying short-lived serverless computing functions at the network edge. In this work, we perform detailed analyses of the benefits and limitations of co-locating serverless functions on NFV-ready servers. We propose NEMO, a novel platform that enables efficient serverless edge function deployment in the NFV environment. NEMO can intelligently harvest spare cycles of network functions to warm up the serverless functions and speed up the function invocation in an agile manner. Besides, NEMO can judiciously manage the thread conflict in a resource-limited environment. We build a prototype of NEMO. Our thorough evaluations show that NEMO can harvest up to 41% spare cycles and achieve about 12.5 similar to 25X performance improvement compared with straightforward co-location.

关键词： network function virtualization serverless functions spare cycle

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Fairness-Aware VNF Sharing and Rate Coordination for High Efficient Service Scheduling

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第12期33卷 4597-4611页

作者： Yi, Bo Wang, Xingwei Huang, Min Das, Sajal K. Li, Keqin Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA State Univ New York Dept Comp Sci New York NY 12561 USA

network service provisioning becomes flexible and programmable with the help of network function Virfitualization (NFV), since NFV abstracts various service functions into software components called Virtual network function (VNF) and VNFs can be flexibly and quickly composed to form new services. It is commonly known that sharing the same VNF among different services can improve the resource utilization. However, we should be aware that such sharing also leads to serious resource preemption. In addition, VNF sharing aggravates the generation of the performance bottleneck, which then causes the rate mismatch problem between the upstream and downstream VNFs belonging to the same service chain. In this article, we propose a dynamic and flexible algorithm to jointly address the VNF sharing resource allocation and the rate coordination between the upstream and downstream VNFs. Specifically, 1) the VNFs are shared among different service chains with a fairness factor considered for the purpose of reducing the resource preemption probability and improving the resource utilization;2) the backpressure indicator of each VNF is defined to judge its pressure condition, based on which we can dynamically adjust the processing rates between it and its downstream or upstream VNFs by maximizing the idle resource utilization. The experimental results indicate that the proposed algorithm outperforms the other methods in terms of the average delay, the flow completion time, the throughput and the backlog, etc. Meanwhile, the proposed algorithm achieves more stable performance than the other methods.

关键词： Resource management Noise measurement Heuristic algorithms network function virtualization Software Computer science Mathematical models network function virtualization performance bottleneck rate coordination service chain traffic scheduling

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FIPAM: Fuzzy Inference Based Placement and Migration Approach for NFV-Based IoTs

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2022年第4期19卷 4298-4309页

作者： Tariq, Muhammad Arslan Farooq, Muhammad Umar Zeeshan, Muhammad Hassan, Ali Akhunzada, Adnan Natl Univ Sci & Technol Coll Elect & Mech Engn Islamabad 44000 Pakistan Univ Malaysia Sabah Fac Comp & Informat Sabah 88400 Malaysia

The advancement and spread of the Internet-of-Things (IoT) have massively been increased over a decade. With the widespread of IoT networks, it is becoming difficult to acquire and execute real-time data. network function virtualization (NFV) provides a flexible and efficient solution for IoT-based applications and service management. NFV creates a virtualized environment that can run a large number of micro-services for different IoT applications by using the virtual network functions (VNFs) through placement and chaining. In this paper, we propose a novel fuzzy inference-based placement and migration (FIPAM) approach for placement and migration/chaining of VNFs to ensure that resource allocation is carefully carried out during VNF orchestration and embedding. Firstly, we formulate the VNF chaining and placement problem. Secondly, we propose a lightweight VNF placement solution that considers the underlying network conditions while making the placement decisions. A novel usage of fuzzy inference is proposed to optimize the chaining mechanism along with the dynamic instantiation of VNFs to meet specific service needs. Simulation results are shown to validate the superiority of the proposed algorithm over existing schemes.

关键词： Internet of Things Heuristic algorithms Bandwidth Servers Security Costs Optimization IoT network function virtualization fuzzy inference systems

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An AI-Empowered Framework for Cross-Layer Softwarized Infrastructure State Assessment

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2022年第4期19卷 4434-4448页

作者： Diamanti, Alessio Vilchez, Jose Manuel Sanchez Secci, Stefano Orange Labs Orange Innovat F-92320 Chatillon France CNAM CEDRIC F-75141 Paris France

network softwarization technologies challenge legacy fault management systems. Coordination and dependency among different novel software components for orchestration, switching, virtual machine and container management creates novel monitoring points, besides novel sources of faults, bugs and vulnerabilities. To cope with the high heterogeneity and granularity of software components, we propose a modular network AI framework to detect anomalies, toward closed-loop automation. We design an AI-empowered anomaly detection framework able to assess the running state and the state deviations of a softwarized infrastructure, monitored through different features grouped depending on their layer and connect-compute stack component. Our framework learns the nominal working conditions of the infrastructure, with respect to which anomalies are detected, and characterized tracing back the layer and component root cause;it includes a network state assessment technique that leverages anomalies characterization through their most visible symptoms. We implement and validate the proposed framework through experimental tests on a containerized IP Multimedia Subsystem platform.

关键词： Automation Resilience Monitoring Anomaly detection Software Principal component analysis Heuristic algorithms network function virtualization network automation artificial intelligence autoencoder autonomic and cognitive management

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function virtualization Can Play a Great Role in Blockchain Consensus

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IEEE ACCESS 2022年 10卷 59862-59877页

作者： Wang, Jun Zhu, Jiang Zhang, Minghui Alam, Iqbal Biswas, Sujit China Elect Technol Grp Corp Res Inst 30 Beijing 100846 Peoples R China Jose Rizal Univ Grad Sch Mandaluyong City Philippines Guangdong Innovat Tech Coll Sch Informat Engn Dongguan 523960 Guangdong Peoples R China Nan Yang Acad Sci NASS Beijing 102400 Peoples R China Univ Surrey Elect & Elect Engn Dept Guildford GU2 7XH Surrey England Univ Dhaka Comp Sci & Engn Dept Faridpur Engn Coll Dhaka 1000 Bangladesh

Bitcoin introduced a cryptocurrency as a form of public ledger consequently that turned into a most popular security technology, Blockchain. Its integrated mining technology lies the key security mechanism. The system allows forming a pool mining group to solve a particular job and share their revenues to their CPU usage while one of them successfully mines a block. To mine a block, a cryptographic puzzle should be solved, which requires significant compute resources that cause huge energy consumption. On the other hand, recent statistics show that low computational energy-restricted Internet of Things (IoT) devices are increasing exponentially. Although it has low energy and limited computation power, it is large in quantity when it is integrated. So we focus on a stochastic geometry theory, which resolves the issue of block mining computation via utilizing multiple mobile IoT devices, given that these IoT devices are Computation Capable Nodes (CCNs). To further normalize this issue, we propose an efficient mathematical solution that uses smart coordination of Virtual network functions (VNFs) for IoT devices to enable their CPU usage efficiently. At the same time, the work and credit point distribution policy is smartly handled through virtual pool mining. The proposal renders network function virtualization technology to configure VNF, and Service function Chain technology is utilized to enable the network flow of such VNFs. New algorithms are presented to solve multiple issues like node discovery, computation offloading, and work credit point distribution. Our goal is to minimize energy consumption within the given time constraint. Implementation results show that although virtual functions for block mining require extensive computations in IoT devices, dividing computation work into small fractions called tasks embedded with VNF, and offloading them to nearby CCNs, tend to minimize the cost and energy consumption of individual shared miners. The overall mining p

关键词： Internet of Things Task analysis virtualization Blockchains Peer-to-peer computing Hardware Business Internet of Things network function virtualization virtual network function service function chain blockchain bitcoin pool mining

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