Performance Evaluation of the NDN Data Plane Using Statistical Model Checking

Khoussi, Siham; Nouri, Ayoub; Shi, Junxiao; Filliben, James J.; Benmohamed, Lotfi; Battou, Abdella; Bensalem, Saddek

doi:10.1007/978-3-030-31784-3_31

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Third, we plan to create a pilot deployment inside a large scale CDN to understand better the trade-offs of applying an Internet architecture with a difference design philosophy to a massively large-scale, complex, and distributed system. Finally, we plan to make use of a high-performance version of the NDN forwarder, ndndpdk [24], [22] that is under active development and can reach up to 100Gbps of forwarding speed. HTTP and NDN file retrieval performance.…”

Section: Discussionmentioning

confidence: 99%

Named Data Networking for Content Delivery Network Workflows

Thelagathoti

Mastorakis

Shah

et al. 2020

2020 IEEE 9th International Conference on Cloud Networking (CloudNet)

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In this work we investigate Named Data Networking's (NDN's) architectural properties and features, such as content caching and intelligent packet forwarding, in the context of Content Delivery Network (CDN) workflows. More specifically, we evaluate NDN's properties for PoP (Point of Presence) to PoP and PoP to device connectivity. We use the Apache Traffic Server (ATS) platform to create a CDN-like caching hierarchy in order to compare NDN with HTTPbased content delivery. Overall, our work demonstrates that several properties inherent to NDN can benefit content providers and users alike through in-network caching of content, fast retransmission, and stateful hop-by-hop packet forwarding. Our experimental results demonstrate that HTTP delivers content faster under stable conditions due to a mature software stack. However, NDN performs better in the presence of packet loss, even for a loss rate as low as 0.1%, due to packet-level caching in the network and fast retransmissions from close upstreams. We further show that the Time To First Byte (TTFB) in NDN is consistently lower than HTTP (~ 100ms in HTTP vs. ~ 50ms in NDN), a vital requirement for CDNs. Unlike HTTP, NDN also supports transparent failover to another upstream when a failure occurs in the network. Finally, we present implementation-agnostic (implementation choices can be Software Defined Networking, Information Centric Networking, or something else) network properties that can benefit CDN workflows.Named Data Networking (NDN) [1] is a future Internet architecture that focuses on content distribution and utilizes content names for most in-network operations such as packet forwarding and content caching. The synergy between CDNs and the NDN architecture is that both aim to optimize content delivery. Further, CDNs and NDN both utilize similar

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Section: Discussionmentioning

confidence: 99%

Named Data Networking for Content Delivery Network Workflows

Thelagathoti

Mastorakis

Shah

et al. 2020

2020 IEEE 9th International Conference on Cloud Networking (CloudNet)

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“…Improving collaboration between capabilities has also been pointed out as a benefit of decomposition in [64]. Finally, the traceability of the atomic capabilities that contribute to a certain composite service was mentioned as a benefit in Information-Centric Networks (ICN) such as Named Data Networks (NDN) [161] [162].…”

Section: B) Composite Capabilitiesmentioning

confidence: 99%

“…This is because we didn't pick primary studies based on these sub-aspects' keywords. For the upcoming years, content-centric networks such as NDN and CCN [161] [162] are gaining momentum with their data caching and interest tracing capabilities, which could revolutionize service composition and decomposition in IoT platforms. Finally, for the QoS aspect, cost, security, and reliability were mentioned in 56% of the primary studies; although they weren't the main subject of this study, this shows their importance and involvement when discussing other service composition sub-aspects.…”

Section: B Trends Gaps and Threats To Validity 1) Trendsmentioning

confidence: 99%

IoT Capabilities Composition and Decomposition: A Systematic Review

et al. 2023

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As billions of IoT devices join the Internet, researchers and innovators increasingly explore IoT capabilities achieved via service composition or reuse of existing capabilities via service decomposition. Many systematic literature reviews (SLRs) were produced on this subject; however, two issues remain to be addressed: i) a reference taxonomy of the different aspects of IoT capabilities composition and decomposition is needed, and ii) many formal questions (e.g., standards role, formal representations applications, state-space explosion countermeasures, etc.), technical questions (e.g., composition process types and automation levels synergies, service decomposition categories, the role of AI/ML, etc.), and QoS questions (e.g., privacy, interoperability, and scalability challenges and solutions, etc.) remain unanswered. We introduce this work by discussing notions of IoT capabilities composition and decomposition in a layered IoT architecture while highlighting the strengths and weaknesses of existing SLRs. We identify unanswered questions through gaps in related work and motivate these questions using the PICOC methodology. We explain the search methodology and organize the topic questions using the proposed reference taxonomy. The identified research questions are answered, and trends and gaps that need additional attention from the research community are highlighted. This effort benefits city planners and end-users of IoT systems as it contributes to a better understanding of the role of composition and decomposition of IoT capabilities in building valueadded services or reusing existing ones for resource optimization. For researchers, this effort contributes a reference taxonomy for the topic and sheds light on important questions while highlighting corresponding trends and gaps requiring further attention.

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“…mpg” might match “/google” or “/google/video”. Though an unbounded namespace raises the question of how to maintain control over the routing table sizes and whether looking up variable-length, hierarchical names can be done at line rate, previous works have shown that it is indeed possible to forward packets at 100 Gbps or more ( So et al, 2013 ; Khoussi et al, 2019 ).…”

Section: Named Data Networkingmentioning

confidence: 99%

Named Data Networking for Genomics Data Management and Integrated Workflows

et al. 2021

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Advanced imaging and DNA sequencing technologies now enable the diverse biology community to routinely generate and analyze terabytes of high resolution biological data. The community is rapidly heading toward the petascale in single investigator laboratory settings. As evidence, the single NCBI SRA central DNA sequence repository contains over 45 petabytes of biological data. Given the geometric growth of this and other genomics repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous as they are not only large in the size but also stored in geographically distributed repositories in various repositories such as National Center for Biotechnology Information (NCBI), DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA’s GeneLab. In this work, we first systematically point out the data-management challenges of the genomics community. We then introduce Named Data Networking (NDN), a novel but well-researched Internet architecture, is capable of solving these challenges at the network layer. NDN performs all operations such as forwarding requests to data sources, content discovery, access, and retrieval using content names (that are similar to traditional filenames or filepaths) and eliminates the need for a location layer (the IP address) for data management. Utilizing NDN for genomics workflows simplifies data discovery, speeds up data retrieval using in-network caching of popular datasets, and allows the community to create infrastructure that supports operations such as creating federation of content repositories, retrieval from multiple sources, remote data subsetting, and others. Named based operations also streamlines deployment and integration of workflows with various cloud platforms. Our contributions in this work are as follows 1) we enumerate the cyberinfrastructure challenges of the genomics community that NDN can alleviate, and 2) we describe our efforts in applying NDN for a contemporary genomics workflow (GEMmaker) and quantify the improvements. The preliminary evaluation shows a sixfold speed up in data insertion into the workflow. 3) As a pilot, we have used an NDN naming scheme (agreed upon by the community and discussed in Section 4) to publish data from broadly used data repositories including the NCBI SRA. We have loaded the NDN testbed with these pre-processed genomes that can be accessed over NDN and used by anyone interested in those datasets. Finally, we discuss our continued effort in integrating NDN with cloud computing platforms, such as the Pacific Research Platform (PRP). The reader should note that the goal of this paper is to introduce NDN to the genomics community and discuss NDN’s properties that can benefit the genomics community. We do not present an extensive performance evaluation of NDN—we are working on extending and evaluating our pilot deployment and will present systematic results in a future work.

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Performance Evaluation of the NDN Data Plane Using Statistical Model Checking

Cited by 5 publications

References 17 publications

Named Data Networking for Content Delivery Network Workflows

Named Data Networking for Content Delivery Network Workflows

IoT Capabilities Composition and Decomposition: A Systematic Review

Named Data Networking for Genomics Data Management and Integrated Workflows

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