Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

Thyagaturu, Akhilesh S.; Shantharama, Prateek; Nasrallah, Ahmed; Reisslein, Martin

doi:10.1109/access.2022.3194913

Cited by 11 publications

(7 citation statements)

References 224 publications

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“…However, general-purpose hardware cannot achieve the same performance as special-purpose application-specific devices. Thus, previous research reviewed not only the deployment options for virtualized network functions, including VMs and containers in public clouds, but also the possible software and hardware acceleration technologies to improve the performance of their network operations [11], [36]- [38].…”

Section: A Related Surveysmentioning

confidence: 99%

“…Key Contributions [11], [36]- [38] Focusing on NFV, these works survey the hardware and software acceleration options for virtual network functions, including those deployed in the cloud, but do not consider the approaches to improve the cloud integration of network acceleration technologies. [13] This survey categorizes the hardware acceleration technologies available today, not limited to networking but including any aspect of computing and communication.…”

Section: Surveysmentioning

confidence: 99%

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Empowering Cloud Computing With Network Acceleration: A Survey

Rosa,

Foschini,

Corradi

2024

IEEE Commun. Surv. Tutorials

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Modern interactive and data-intensive applications must operate under demanding time constraints, prompting a shift toward the adoption of specialized software and hardware network acceleration technologies. This specialization, however, poses significant scalability, flexibility, security, and economic sustainability challenges for application developers. Cloud computing holds the potential to overcome these obstacles by offering the cost-effective option to access specialized acceleration technologies through standard cloud interfaces. Nevertheless, that integration is still challenging for cloud providers. In the cloud, physical resources are hidden behind a virtualization layer, whereas acceleration technologies make applications directly interact with the hardware. To bridge this gap, recent literature explores the possibility of empowering cloud platforms with accelerated networking as a commodity, thus offering the innovative option of Network Acceleration as a Service. This survey reviews these recent research efforts by adopting popular technologies like XDP, DPDK, and RDMA as a reference. To organize the surveyed research in a comprehensive framework, we identify four key aspects that pose critical problems to the integration of acceleration options in cloud computing -access interfaces, virtualization techniques, serviceability, and security -and systematically discuss the associated challenges. Then, we present the issues to be further addressed and outline the most promising research directions for the full integration of network acceleration within next-generation cloud computing platforms.

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Section: A Related Surveysmentioning

confidence: 99%

Section: Surveysmentioning

confidence: 99%

Empowering Cloud Computing With Network Acceleration: A Survey

Rosa,

Foschini,

Corradi

2024

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

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“…Once a service application is selected for placement at an H-DU, virtual machines are configured by the application's resource requirements (e.g., RAM, CPU, data storage, I/O, etc.) [50]. However, various services demand different amounts of computational resources, as determined by the service provider.…”

Section: B Collaborative-based Ran Approachmentioning

confidence: 99%

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-1 Approach

Abd,

Findley,

Kim

2024

IEEE Access

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Next-generation sensor and radio access networks (NG-SRANs) namely, Hydra radio access networks (H-RANs) represent a significant evolution in the telecommunications and sensor ecosystem landscape in anticipation of 6G deployment and beyond. H-RAN's vision derives its strength from integrating various technologies and networks into a single central network with the widespread incorporation of artificial intelligence (AI) technologies throughout the network. As a result, H-RAN's unique features and characteristics can serve as a baseline for innovating new applications and significantly enhance the overall functions of conventional open radio access networks (O-RANs). However, among the many improvements and innovations that the H-RAN architecture promises in its functionality, this paper focuses on the initial access implementation "task 1 " approach. Our solution contains several novelties that enhance both overhead and model accuracy. To this end, we define a novel intelligent perception network inspired by the knowledge distribution idea for collaborative H-RAN networks. We develop sparse multi-task learning (SMTL) as part of the AI/ML D-engine for federated learning to perform multiple tasks simultaneously. The SMTL is designed to select the optimal solution from a list of recommended solutions, namely "tasks". In the simulation, figures of merit include metrics such as top-k validation accuracy, beam selection accuracy, throughput ratios, beam sweep time, latency, and initial access times, which are used to evaluate the performance and efficiency of the proposed technologies. Simulation results demonstrate that by exploiting contextual information from distributed collaborative SRUs, and UE sends its own sensing information via a physical random-access channel in addition to using SMTL, our H-RAN-based initial access scheme can achieve 82.9% throughput of an exhaustive beam search (EBS) based-O-RAN network without any beam search overhead and 96.7% by searching among as few as 5 beams. Compared to the conventional MMW 5G-NR solution, our proposed method significantly minimizes the beam search time needed to reach the desired throughput. INDEX TERMSHydra Radio Access Network (H-RAN), Multi-functional networks, Perceptive networks, heterogeneous data, AI/ML engines, Collaborative-based approach, Sparse multi-task learning (SMTL), Initial access.

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“…In a related development, the interfaces in conventional network nodes are increasingly taking over more computing functions, e.g., at the interfaces between the nodes and the network in the form of smart network interface cards (NICs) [350]- [353]. An interesting future research direction is to expand the functionalities of existing cyber-bio interfaces that interconnect conventional networks with DBMC nanonetworks, e.g., [191], [354]- [357], to incorporate computing functions.…”

Section: ) Emerging Paradigms: Beyond Shannon Communication + Conflue...mentioning

confidence: 99%

Coding in Diffusion-Based Molecular Nanonetworks: A Comprehensive Survey

et al. 2023

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Diffusion-based molecular nanonetworks exploit the diffusion of molecules, e.g., in free space or in blood vessels, for the purpose of communication. This article comprehensively surveys coding approaches for communication in diffusion-based molecular nanonetworks. In particular, all three main purposes of coding for communication, namely source coding, channel coding, and network coding, are covered. We organize the survey of the channel coding approaches according to the different channel codes, including linear block codes, convolutional codes, and inter-symbol interference (ISI) mitigation codes. The network coding studies are categorized into duplex network coding, physical-layer network coding, multi-hop nanonetwork coding, performance improvements of network-coded nanosystems, and network coding in mobile nanonetworks. We also present a comprehensive set of future research directions for the still nascent area of coding for diffusion-based molecular nanonetworks; specifically, we outline research imperatives for each of the three main coding purposes, i.e., for source, channel, and network coding, as well as for overarching research goals.

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Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

Cited by 11 publications

References 224 publications

Empowering Cloud Computing With Network Acceleration: A Survey

Empowering Cloud Computing With Network Acceleration: A Survey

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-1 Approach

Coding in Diffusion-Based Molecular Nanonetworks: A Comprehensive Survey

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