SDN in high performance computing for scientific and business environment (SBE)

Krishna, Sabbi Vamshi; Shrivastava, Azad; Wagh, Sunil J.

doi:10.1109/iccids.2017.8272654

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Voice Over IP (VoIP) is a very good example for the realization of FlowVista, as VoIP is driven by high-level business policies. There has been a need for KDN-based high-performance computing solutions for enhanced throughput and better utilization of bandwidth to meet business needs [546]. Enterprise Integration Pattern (EIP) is a combination of design patterns that combines existing and new business applications in an enterprise environment.…”

Section: Business Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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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.

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Section: Business Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…While there exists plenty of research on packet matching, most of this work focuses on preix and range matching [28]. With the prevalence of High-Performance Computing (HPC) applications, the software-deined network technique will be deployed in HPC networks [17], requiring high-performance packet classiication [8]. Field matching schemes allow wildcards for the ields that are not associated with particular matches.…”

Section: Introductionmentioning

confidence: 99%

SIMD-Matcher: A SIMD-based Arbitrary Matching Framework

Wang

Wen

Gratz

et al. 2022

ACM Trans. Archit. Code Optim.

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Packet classification methods rely upon matching packet content/header against pre-defined rules, which are generated by network applications and their configurations. With the rapid development of network technology and the fast-growing network applications, users seek more enhanced, secure, and diverse network services. Hence it becomes critical to improve the performance of arbitrary matching operations. This paper presents SIMD-Matcher, an efficient Single Instruction Multiple Data (SIMD) and cache-friendly arbitrary matching framework. To further improve the arbitrary matching performance, SIMD-Matcher adopts a trie node with a fixed high-fanout, and a varying span for each node depending on the data distribution. The trie node layout leverage cache and modern processor features such as SIMD instructions. To support arbitrary matching, we first interpret arbitrary rules into three fields: value, mask, and priority. Second, to support insertion of randomly positioned wildcards to arbitrary rules, we propose the SIMD-Matcher extraction algorithm to process the wildcard bits. Third, we add an array of wildcard entries to the leaf entries, which stores the wildcard rules and guarantees the correctness of matching result. Experiments show that SIMD-Matcher outperforms GenMatcher under large scale of rule set and key set, in terms of search time, insert time, and memory cost. Specifically with 5M rules, our method achieves a 2.7X speedup on search time, and the insertion time takes~ ∼ 7.3 seconds, gaining a 1.38X speedup; meanwhile the memory cost reduction is up to 6.17X.

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