Performance Analysis of R-DCN Architecture for Next Generation Web Application Integration

Udeze, C. C.; Okafor, K. C.; Okezie, Christiana C.; Okeke, I. O.; Ezekwe, C. G.

doi:10.1109/icastech.2014.7068061

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…This provides library supports for SDN-NFV as well as robust edge SDN-enabled Fog-Clouds constructed from CloudSimSDN for CCPS. In evaluating the proposed DNRCM, tuning parameters in [49], [51] are employed considering the preliminary findings from the production testbed (UNN DCN). In the case of the Mininet, the ping command is used to confirm reachability in the SDN.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…The work [37] proposed MDCube as a high-performance connectivity construct used to expand BCube containers into a complex DCN [38]. A representative sample of DCN literatures were studied extensively such as DCell [16], FiConn [39], Green DCN [40], HCN-BCN [41], Hedera [42], Jellyfish [43], OSA [44], PCube [45], REWIRE [46], iCautz's topology [47], vL2-WIM [48], R-DCN [49], Spine-leaf DCN [50]. These works made excellent contributions in terms of expandability concerns, latency, and network resilience.…”

Section: Related Research Effortsmentioning

confidence: 99%

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Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

2022

KSII TIIS

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Cyber-physical systems (CPS) have been growing exponentially due to improved clouddatacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling largescale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the nonrecursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

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Section: Evaluation Methodologymentioning

confidence: 99%

Section: Related Research Effortsmentioning

confidence: 99%

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

2022

KSII TIIS

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“…A LaScaDa network constructed from n-port switches is a layered and recursive topology that uses nport switches to create a k-layer LaScaDa network (k >1) by interconnecting n 32 (k−1)-Layer LaScaDa networks. These switches are designated as internal switches asserted [45]. The architecture uses its algorithm to construct the route of the source on the bases on the differences between the coordinates for the source and destination.…”

Section: Layered Scalable Data Center Architecturementioning

confidence: 99%

Legacy Data Center Network (DCN) Reviewed: considering Big Data Stream Mobile Computing (BDSMC) applications

Akawuku¹,

Okonkwo²,

Okofor³

2023

IDOSR JAS

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Recently, Enterprises that operate over vast geographical areas uses multiple data centers to collect, store and process data in real time via energy-efficient acquisition, wirelessly transport clients or users data to the cloud. This paper seeks to technically review existing Data Center Networks (DCN) considering BDSMC applications. In this research, related works on Distributed Data Center Networks will be presented. Within the stream computing ecosystems, there are various network models but the pool of possible DCN topologies/architectures to adopt appears little and unfit for the purpose of BDSMC optimization. However, most related Data center architecture will be reviewed. Investigate efforts on both server centric and switch-centric models adaptable to BDSMC network layer. The extent of work done on distributed spine-leaf re-designed server-centric network construction so as to automatically harvest network interconnection into a ‘stellar’ dual-port server-centric SG network; how classical graph-based interconnection network translate network performance similar to generic works for BDSMC ecosystems. Review stellar transformation using the well-studied generalized hypercube family of interconnection networks for BDSMC ecosystems. The literature was searched from the databases: IEEE Xplore Digital Library, Springer Link Digital Library, and Google Scholar, IET Digital Library, Frontiers Library, ACM Digital Library repositories resulting in 98 papers after several eliminations ranging from year 2000-2022. In conclusion, state-of-the-art dual-port server-centric DCNs (FiConn, DCell, DPillar), etc, while looking at possible architectures with excellent comparative performance for BDSMC ecosystems. Research gaps are revealed for further study. Keywords: Data, Center, Network, Data Stream, Mobile Computing and applications

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“…Also, deployment scenarios of these networks create interference. Most hotspots in Nigerian tertiary institutions are faced with several performance issues as the IEEE 802.11 infrastructures are not optimized for high density services such as cloud services [4]. The purpose of this case study is to ascertain the behaviour and performance of user traffic over the radio environment of CWN.…”

Section: Related Research Effortsmentioning

confidence: 99%

Performance Analysis of WiFi Hotspot Network in Nnamdi Azikiwe University Awka

Nonum¹,

Otasowie²

2016

CCS Archive

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This work centres on the study of campus wifi hotspot networks in Nigeria universities, using the Nnamdi Azikiwe University Awka as a case study. A Campus Wide Area Network (CWN) provides a means of communication and collaboration in data intensive environments. These are vital key factors to building a strong knowledge culture and facilitating collaborative research for any educational institution. Network stumbler (Wifi Network Analyzer) and Iperf were installed on different laptop computers in the respective Access Points (AP) while being monitored from a dedicated server running on Mikrotik and wireshark. This was used in collecting useful data needed for the characterisation of the UNIZIK wifi hotspot network in terms of Received signal strength index (RSSI), Data throughput and latency/network delay. The AP distance from user is carefully measured with a meter tape. Performance analysis carried out on this university wifi hotspots shows that the network offers a delay of 0.1545s by default that increases by a factor of 0.001s; a data throughput of 37.30Mbps that decreases by a factor of 0.25Mbps for any user added to the network. Also, that an RSSI of -35.438dBm was obtained at the AP base station which decreases by a factor of 0.4925dBm for any 1m distance away from the APand finally that a traditional hotspot networks based on IEEE 802.11 series lacks integrated intelligence for services convergence, QoS performance and in most cases suffers from interoperability problem

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Performance Analysis of R-DCN Architecture for Next Generation Web Application Integration

Cited by 7 publications

References 16 publications

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

Legacy Data Center Network (DCN) Reviewed: considering Big Data Stream Mobile Computing (BDSMC) applications

Performance Analysis of WiFi Hotspot Network in Nnamdi Azikiwe University Awka

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