Management Plane Research Articles

Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation

Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.

Quantum key distribution (QKD) for wireless networks with software‐defined networking

AbstractNetwork management has been significantly transformed by software‐defined networking (SDN), which consolidates control and improves adaptability. As a result of this paradigm shift, security concerns have emerged concurrently. Within SDN environments, maintaining service path integrity is of utmost importance because malicious entities can exploit network flows to gain access to data, resulting in security breaches. Thus, QKD networks require a management plane that can control and manage the QKD resources. Quantum key distribution networks can be controlled and managed separately by SDN, allowing quantum key forwarding to be separated from their control and management. The paper provides an overview of QKD networks enabled by SDN, along with an overview of its architecture, interfaces, and protocols. This paper elaborates on the overall architecture and related interfaces and protocols of QKD networks assisted by SDN. Then, three important paradigms for simulation are presented using three use cases.

Improving Software Defined Network controllers in a multi-vendor environment

Software Defined Networking (SDN) is an emerging networking paradigm with the potential to foster innovation through programmable networks. SDNs are characterized by the separation of control and data planes where, in a logically centralized controller, it's possible to make routing decisions on behalf of forwarding elements. For this, there are different protocols proposed and used like Open Flow, Forward and Control Element Separation (FORCES), Path Computation Element Protocol (PCEP), Network Configuration Protocol (NETCONF), and Interface to Routing System (IRS), among others. Border Gateway Protocol (BGP) is one such protocol that enables the network programmability promised by SDNs. The controllers used in BGP as control plane protocols leverage RESTCONF (RFC 8040) as management plane protocol to interact with physical routers, switches, and network service firewalls. Therefore, the aim of this work is to improve the performance of SDN controllers using BGP enabling a multi-vendor environment without requiring infrastructure upgrade showcasing how interoperability can be achieved. The proposed system is implemented using OpenDaylight controllers. The performance of the prototype is evaluated using Latency, Throughput, CPU consumption, and TTL (Time to Live). Based on the simulation results it is observed that the SDN controllers that use BGP show average throughput of 49.6 Gbps, maximum latency of 7 μs, and average CPU consumption 89% and maximum TTL value of 600 msec. The overall performance of the proposed system shows better results when compared with previous works.

Influence of Human Resource Management on Work Efficiency of Healthcare Professionals

Background: World Health Organization (WHO) defines human resource pl anning in the healthcare industry as "The process of estimating the number of people, knowledge, skills, and attitudes needed to achieve a set of goals".Human resource planning is the most important part of the work of organizational management. Objective: The aim of the study was to point out the importance of timely and optimal planning of human resources and their impact on the work efficiency of healthcare professionals. Methods: This research is a comparative, descriptive-analytical study which used a questionnaire created according to the Likert model. The questionnaire containes statements related to factors affecting work efficiency, work analysis, position systematization, human resource planning, professional selection, work process design, management and manager's style, etc. Results and Discussion: A total of 450 respondents (healthcare professionals, both docotrs and nurses) participated in this research. Participation (inclusion) critaria in the research is employment in healthcare institutions in Canton Sarajevo, of different ages, of both genders. The planning process begins with a quality job analysis and human resource analysis to get an answer to several very important questions. How many people are needed to realize the set goals of the organization? What kind of people are needed: what skills, knowledge, and abilities? How to attract the necessary people (internal, external filling, selection procedure). How to adjust the current employee’s training so that they can adequately respond to all work tasks in the future, more demanding business environment of the health organization? The lack of human resources that have the necessary skills and knowledge, and the burden of inadequate resources (disabled workers), have slowed down the development of many companies. We are experiencing significant negative manifestation on the existing staff through reduced work efficiency and reduced quality of health care provided. Due to the above, it is necessary to timely plan human resources as an integral part of planning, business, and development of the entire organization. The planning of human resources in a health institution is influenced by the following factors: a) institutional factors b) level of health care c) type of health care institutiond) size of health care institution e) infrastructure of the existing institution. Conclusion: Human resource planning is a fundamental management activity from which all other activities arise,and contents of human resource management planes are based.

Unboxing trustworthiness through quantum internet

The unreliable nature of IoT systems drives practitioners to implement heavyweight fault tolerance mechanisms to identify those untrustworthy nodes that are misbehaving erratically and, thus, ensure that the sensed data from the IoT domain are correct. Quantum Internet might be a promising assistance to minimize traffic congestion and avoid worsening reliability due to the link saturation effect by using a quantum consensus layer. In this regard, the purpose of this paper is to explore and simulate the usage of quantum consensus architecture in one of the most challenging natural environments in the world where researchers need a responsive sensor network: the remote sensing of permafrost in Antarctica. More specifically, this paper describes the use case of permafrost remote sensing in Antarctica, proposes the usage of a quantum consensus management plane to reduce the traffic overhead associated with fault tolerance protocols, and discusses, by means of simulation, possible improvements to increase the trustworthiness of a holistic telemetry system by exploiting the complexity reduction offered by the quantum parallelism. Collected insights from this research can be generalized to current and forthcoming IoT environments.

Machine Learning for Robust Network Design: A New Perspective

With the rapid growth of backbone networks and data center networks, ensuring network robustness under various failure scenarios has become a key challenge in network design. The combinatorial nature of failure scenarios in data plane, control plane, and management plane seriously challenges existing practice on robust network design, which often requires verifying the designed network's performance by enumerating all possible failure combinations. Meanwhile, machine learning (ML) has been applied to many networking problems and has shown tremendous success. In this article, we show a general approach to leveraging machine learning to support robust network design. First, we give a selective overview of current work on robust network design and show that failure evaluation provides a common kernel to improve the tractability and scalability of existing solutions. Then we propose a function approximation of the common kernel based on graph attention network (GAT) to efficiently evaluate the impact of various potential failure scenarios and identify critical failures that may have significant consequences. The function approximation allows us to obtain new models of three important robust network design problems and to solve them efficiently by evaluating the solutions against a pruned set of critical failures. We evaluate our approach in the three use cases and demonstrate significant reduction in time-tosolution with minimum performance gap. Finally, we discuss how the proposed framework can be applied to many other robust network design problems.

AI/ML for beyond 5G systems: Concepts, technology enablers & solutions

5G brought an evolution on the network architecture employing the service-based paradigm, enabling flexibility in realizing customized services across different technology domains. Such paradigm gives rise to the adoption of analytics and Artificial Intelligence/Machine Learning (AI/ML) in mobile communications with the ease of collecting various measurements related to end-users and the network, which can be exposed towards consumers, including 3rd party applications. AI/ML may influence network planning and optimization considering the service life-cycle and introduce new operations provision, paving the way towards 6G. This article provides a survey on AI/ML considering the business, the fundamentals and algorithms across the radio, control, and management planes. It sheds light on the key technologies that assist the adoption of AI/ML in 3rd Generation Partnership Project (3GPP) networks considering service request, reporting, data collection and distribution and it overviews the main AI/ML algorithms characterizing them into user-centric and network-centric. Finally, it explores the main standardization and open source activities on AI/ML, highlighting the lessons learned and the further challenges that still need to be addressed to reap the benefits of AI/ML in automation for beyond 5G/6G mobile systems.

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.

A Security Framework for Increasing Data and Device Integrity in Internet of Things Systems.

The trustworthiness of a system is not just about proving the identity or integrity of the hardware but also extends to the data, control, and management planes of communication between devices and the software they are running. This trust in data and device integrity is desirable for Internet of Things (IoT) systems, especially in critical environments. In this study, we developed a security framework, IoTAttest, for building IoT systems that leverage the Trusted Platform Module 2.0 and remote attestation technologies to enable the establishment of IoT devices' collected data and control plan traffic integrity. After presenting the features and reference architecture of IoTAttest, we evaluated the privacy preservation and validity through the implementation of two proof-of-concept IoT applications that were designed by two teams of university students based on the reference architecture. After the development, the developers answered open questions regarding their experience and perceptions of the framework's usability, limitations, scalability, extensibility, potential, and security. The results indicate that IoTAttest can be used to develop IoT systems with effective attestation to achieve device and data integrity. The proof-of-concept solutions' outcomes illustrate the functionalities and performance of the IoT framework. The feedback from the proof-of-concept developers affirms that they perceived the framework as usable, scalable, extensible, and secure.

A Comprehensive Survey on Knowledge-Defined Networking

Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

Impact of preoperative posterior segment evaluation on surgical intervention in camp patients

Purpose: To retrospectively study impact of preoperative posterior segment evaluation on surgical intervention in camp patients recruited for cataract surgery in Gujarat India. Methods: Retrospective analysis of six months data collected from hospital electronic medical record (EMR) system of 9820 admitted patients recruited from screening camp for cataract surgery from 1/10/2019 to 31/3/2020 in Tertiary Eye Hospital in Gujarat, India, has been done. Comprehensive clinical evaluation, of both anterior and posterior segment which included detailed history; best corrected visual acuity (BCVA); intraocular pressure measurement with non-contact tonometer (NCT) and when required with Goldman applanation tonometer; slit lamp examination; and fundus examination with + 90 diopter lens as well as indirect ophthalmoscope as and when indicated. In case there was no view of retina, a B-scan ultrasound was done to rule out any posterior segment pathology. Immediate surgical intervention done was assessed and results analyzed in percentage. Results: Cataract surgery was advised for 8390 patients (85.43%). Surgical intervention for management of glaucoma was done for 68 patients (0.692%). Retina intervention was done for 86 patients. Posterior segment evaluation changed immediate surgical plane of management for 154 (1.57%) patients. Conclusion: Comprehensive clinical evaluation is economical and should be mandatory especially in community services as comorbid conditions like glaucoma, diabetic retinopathy, retinal vein occlusion, and other varied posterior segment diseases contribute significantly to visual disability in elderly age group. It is difficult to follow these patients later if manageable comorbidity is not informed about and if indicated dealt simultaneously for visual rehabilitation of patient.

MAGNet: Machine Learning Guided Application-Aware Networking for Data Centers

Modern data centers are witnessing fast-growing east-west traffic on their network infrastructure due to the highly distributed data center applications. Motivated by the heterogeneity of such application workloads, we propose in this paper an extensible network management architecture called MAGNET which enables application-aware intra-data center networking. The crux of MAGNET is the smart endpoint residing within end-hosts, which is empowered by machine learning combined with lightweight workload tracing to detect workload identities and enable workload-dependent packet tagging. The centralized management plane interface of MAGNET allows network functions to interpret packet tags and perform application-aware packet processing. We demonstrate the feasibility of the architecture via prototype implementation and extensive use case evaluation. Our experiments show that the smart endpoint can fingerprint many real-world applications with 99% accuracy only at 12% additional CPU, and that application-aware data plane can potentially bring substantial benefits in terms of security (e.g., via identity-based microsegmentation), CPU usage (e.g., for intrusion detection) and network latency (e.g., via TCP stack customization).

Mitigation Services on SDN for Distributed Denial of Service and Denial of Service Attacks Using Machine Learning Techniques

Software-Defined Networking (SDN) offers an innovative model over the separation of the data plane, control plane and management plane. This separation would result in more effective network management, including cost reductions for hardware and manpower, and the ability to deliver on-demand solutions using programmable SDN approaches. As network policy and on-demand services become more impartial, SDN is becoming more popular. However, there are safety risks associated with the SDN network due to malicious floods such as Distributed Denial of Service (DDoS) attacks and Denial of Service (DoS) attacks directed at the SDN Controller, OpenFlow Virtual Switch (OVS), and end nodes, which must be addressed. Because of these assaults, network throughput is reduced, resulting in a lapse in the availability of network services and a reduction in business operations. The main emphasis of this study is on the detection and mitigation of DDoS and DoS assaults in the SDN network, which is accomplished by the use of both unsupervised and supervised learning approaches. The use of the Dynamic Access Control List (DACL) allows for the performance of mitigation operations in the SDN network, which has been implemented using the mininet. The outcome of the experiment demonstrates that malicious (DDoS and DoS) flood is reduced as a consequence of the mitigation technique.

Scaling Optical Interconnects for Hyperscale Data Center Networks

Due to widespread applications and rapid development of cloud computing and Internet services, hyperscale data centers have experienced an unprecedented demand for networking. Data center networks are required not only to handle fast-growing traffic, which doubles almost every one to two years, but also to provide high flexibility and availability to support rapidly changing businesses as well. Therefore, hyperscale data center networks have become one of the main drivers for optical interconnect technologies in recent years. In this article, technologies for scaling optical interconnects for inter-data center interconnects (DCIs) are presented. We first describe hyperscale data center network architectures and requirements, as well as the differences between carrier’s optical transport networks and DCI optical networks, with the main focus on metro-DCI and campus-DCI networks. The scale and fast growth rates of DCI networks require innovations not only in data plane technologies but also in control and management planes as well. In data planes, high-capacity flexible coherent technology, pluggable wavelength-division-multiplexing (WDM) optical transponders, including 100G direct-detection four-level pulse-amplitude modulation (PAM4) and coherent 400ZR/800ZR, and flex-grid and disaggregated reconfigurable optical add-drop multiplexer (ROADM) optical networks are described. Control and management planes are an integral part of optical transport networks and crucial for effectively operating DCI networks. We show that standard protocols, data models, and modular design of a software platform are essential to building a scalable open and disaggregated optical network, which is fundamental to enabling high automation and intelligence in an optical network. An example control and management platform is discussed in detail. With current fiber capacity approaching the nonlinear Shannon limit, challenges to further scale DCI networks and some potential technologies to support ever-increasing traffic growth are discussed at the end.

Research and design of a digital twin-driven smart river basin platform

<p>River basin management involves many issues including water resources, water ecology, water environment, and water disasters. Digital twins integrated with GIS, BIM and IoT are applied to river basin management, to establish digital twin data and model integration and visual expression methods, study the digital twin operation mechanism, build smart river basin twin, and design a digital twin-driven smart river basin platform. Taking the joint flood dispatching management and control of the reservoir group in the Duhe River Basin as an example. The application has proven that: compared with the problems of incomplete information, insufficient accuracy, lagging feedback and single expression in traditional basin database management or two-dimensional plane management, the digital twin-driven smart basin platform, on the basis of the integration and interaction of GIS data, BIM data and IoT data, and on the basis of data and model two-way drive, can realize simulation, decision making, optimization and visualization in external environment, and the control effect is better than traditional means. The research and practice of the platform can realize real-time monitoring, diagnosis, analysis, decision making and prediction for river basin management, providing a new solution for its intelligent operation, precise control and safe operation and maintenance.</p>

Research and design of a digital twin-driven smart river basin platform

<p>River basin management involves many issues including water resources, water ecology, water environment, and water disasters. Digital twins integrated with GIS, BIM and IoT are applied to river basin management, to establish digital twin data and model integration and visual expression methods, study the digital twin operation mechanism, build smart river basin twin, and design a digital twin-driven smart river basin platform. Taking the joint flood dispatching management and control of the reservoir group in the Duhe River Basin as an example. The application has proven that: compared with the problems of incomplete information, insufficient accuracy, lagging feedback and single expression in traditional basin database management or two-dimensional plane management, the digital twin-driven smart basin platform, on the basis of the integration and interaction of GIS data, BIM data and IoT data, and on the basis of data and model two-way drive, can realize simulation, decision making, optimization and visualization in external environment, and the control effect is better than traditional means. The research and practice of the platform can realize real-time monitoring, diagnosis, analysis, decision making and prediction for river basin management, providing a new solution for its intelligent operation, precise control and safe operation and maintenance.</p>

Software-Defined Networking: Categories, Analysis, and Future Directions.

Software-defined networking (SDN) is an innovative network architecture that splits the control and management planes from the data plane. It helps in simplifying network manageability and programmability, along with several other benefits. Due to the programmability features, SDN is gaining popularity in both academia and industry. However, this emerging paradigm has been facing diverse kinds of challenges during the SDN implementation process and with respect to adoption of existing technologies. This paper evaluates several existing approaches in SDN and compares and analyzes the findings. The paper is organized into seven categories, namely network testing and verification, flow rule installation mechanisms, network security and management issues related to SDN implementation, memory management studies, SDN simulators and emulators, SDN programming languages, and SDN controller platforms. Each category has significance in the implementation of SDN networks. During the implementation process, network testing and verification is very important to avoid packet violations and network inefficiencies. Similarly, consistent flow rule installation, especially in the case of policy change at the controller, needs to be carefully implemented. Effective network security and memory management, at both the network control and data planes, play a vital role in SDN. Furthermore, SDN simulation tools, controller platforms, and programming languages help academia and industry to implement and test their developed network applications. We also compare the existing SDN studies in detail in terms of classification and discuss their benefits and limitations. Finally, future research guidelines are provided, and the paper is concluded.

Deploying near-optimal delay-constrained paths with Segment Routing in massive-scale networks

With a growing demand for quasi-instantaneous communication services such as real-time video streaming, cloud gaming, and industry 4.0 applications, multi-constraint Traffic Engineering (TE) becomes increasingly important. While legacy TE management planes like MPLS have proven laborious to deploy, Segment Routing (SR) drastically eases the deployment of TE paths and is thus increasingly adopted by Internet Service Providers (ISP). There is now a clear need in computing and deploying Delay-Constrained Least-Cost paths (DCLC) with SR for real-time interactive services requiring both low delay and high bandwidth routes. However, most current DCLC solutions are not tailored for SR. They also often lack efficiency (particularly exact schemes) or guarantees (by relying on unbounded heuristics). Similarly to approximation schemes, we argue that the actual challenge is to design an algorithm providing both performances and strong guarantees. However, conversely to most of these schemes, we also consider operational constraints to provide a practical, high-performance implementation.In this work, we extend and further evaluate our previous contribution, BEST2COP. BEST2COP leverages inherent limitations in the accuracy of delay measurements, accounts for the operational constraint added by SR, and provides guarantees and bounded computation time in all cases thanks to simple but efficient data structures and amortized procedures. We show that BEST2COP is faster than a state-of-the-art algorithm on both random and real networks of up to 1000 nodes. Relying on commodity hardware with a single thread, our algorithm retrieves all non-superfluous 3-dimensional routes in under 100ms in both cases. This execution time is further reduced using multiple threads, as the design of BEST2COP enables a significant speed-up thanks to a highly parallelizable core which also enables a balanced computing load between threads. Finally, we extend BEST2COP to deal with massive-scale ISP by leveraging the multi-area partitioning of these deployments. Thanks to our new topology generator specifically designed to model realistic patterns in such massive IP networks, we show that BEST2COP can solve DCLC-SR in approximately 1 s even for ISP having more than 100 000 routers.

6Blocks: 6G-enabled trust management scheme for decentralized autonomous vehicles

In this paper, we propose a 6G-envisioned blockchain (BC)-based scheme for cellular vehicle-to-everything (C-V2X) ecosystems (termed as 6Blocks), which optimizes the edge-resource provisioning of 5G C-V2X nodes via network function virtualization (NFV)-enabled infrastructure. 6Blocks operates in three phases. In the first phase, a secure data aggregation through 6G sensors is performed at the data plane. Then, in the second phase, the aggregated data is sent to the 6G-enabled NFV control plane to leverage resource allocation to connected autonomous smart vehicles (CASVs) through edge nodes via service container operations. In the third phase, management plane operations are designed that includes registration and auction smart contracts (SC) for connected autonomous vehicle owner (CAVO) and CASVs over the BC with public/private pairs fetched from interplanetary file systems (IPFS). The proposed scheme is compared against existing NFV schemes like distant, edge, and SDN-based edge control. At 20 CASVs, 6Blocks achieves an edge-service latency of 0.05ms against 0.76ms, 0.27, and 0.15ms for cloud, edge, and SDN-edge schemes, respectively, with high connection density of 107 nodes, at maximum utilized IPFS bandwidth of 0.2 Mbps. Then, we validate the trust of our model through the automated validation of the internet security protocol and applications (AVISPA) tool and modeled the vehicle and sensor nodes. The obtained results show the efficacy of the proposed scheme against existing state-of-the-art approaches with respect to parameters such as network, security, transaction costs, and auction.

Prospects and Challenges of Photonic Switching in Data Centers and Computing Systems

This Tutorial will discuss the motivation, benefits, and challenges of photonic switching in data centers and cover prospects of future data centers involving emerging new technologies and cross-layer solutions. The primary motivation for considering photonic switching in data centers rises from the need for energy-efficient and scalable intra-data center networks to meet rapid increases in data traffic driven by emerging applications, including machine learning. The data traffic inside the data centers (East-West traffic) is typically significantly greater than that of the traffic coming in and out of the data centers (North-South traffic) (Benson <i>et al.</i> 2010). To accommodate such traffic, today&#x0027;s large-scale data centers employ cascaded stages of many power-hungry electronic packet switches interconnected across the data center network in fixed hierarchical communication topologies. These electronic switches add significant latency and energy consumption while limiting the communication bandwidth. On the other hand, photonic switches can, in principle, support interconnections at very high data rates on many parallel wavelengths while keeping their energy consumption nearly independent of the switch port bandwidth. Numerous research papers have predicted significant benefits in scalability, throughput, and power efficiency from deploying photonic switches in data centers. However, photonic switching is not yet widely deployed in commercial warehouse-scale data centers at the time of writing this Tutorial due to significant challenges. They are related to 1) cross-layer issues involving control and management planes together with data integrity during switching, 2) scalability to &gt;5000 racks (&gt;a quarter-million servers), 3) performance monitoring required for reliable operation, 4) currently existing standards allowing limited power margin (3 dB), and 5) other practical (technology-dependent) issues relating to polarization sensitivity, temperature sensitivity, cost, etc. In telecom, commercial deployments of reconfigurable optical add-drop multiplexers (ROADMs) (Lightwave, 2003)&#x2013;(Perrin, 2015) also had faced similar challenges and took place nearly ten years after the first research network testbed demonstrations in 1997 (Garrett, 2015) with fully implemented network control and management planes. In data centers, the challenges are far more significant due to the scale of the network and the dynamicity of the traffic. We will discuss possible solutions for future data centers involving cross-layer methods, new topologies, and innovative photonic switching technologies. In particular, the Tutorial broadly surveys state-of-the-art photonic switching technologies, architectures, and experimental results, and further covers the details of arrayed-waveguide-grating-router-based switch fabrics offering hybrid switching methods with distributed control planes towards scalable data center networking.