Comparison of Mobile and Fixed Device Workloads in an Academic Web Server

Song, Yo-Der; Mahanti, Anirban

doi:10.1109/iwmn.2019.8804983

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…system utilisation (11) and normalization constant (12). Following the same procedure as discussed in section 2, the queue-length distribution beomes…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…It is also observed that the service time of packets at network systems can be best explained by long-tail power-law distribution [11]. Besides, several other studies such as the one on reference time of files (time difference between successive requests of a file) as well as the intersession time (time difference between two sessions of a client) endorse the appearance of lognormal distribution [12], inter arrival time of cyberlocker traffic flows on cloud platform advocates hybrid lognormal and gamma distribution [13]. It is important to highlight that the lognormal and power law distributions are connected and appears as an alternative to each other [14].…”

Section: Introductionmentioning

confidence: 95%

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Analysis of Power-law Queues Using Maximum Entropy Framework: An Application to Performance Evaluation of Network Systems

Sharma

2023

Preprint

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Extensive research on traffic of modern communication networks including Internet and wireless establish the presence of power-law behavior. However, developing traffic models capturing power-law characteristics is a difficult task due to analytical intractability. Adopting maximum entropy approach, a theoretical model Pow/Pow/1/∞ has been proposed recently. The paper extends the framework to build two new theoretical models Pow/Pow/1/K and Pow/Pow/K/K where both inter-arrival and service times follow power law distribution. Closed form expressions of queue length distribution and various performance measures are derived in terms of traffic intensity ρ. An explicit expression for state probability distribution of Pow/Pow/K/K is also derived and compared with well-known Erlang loss formula. Both queue length distribution and blocking probability are found to depict power-law implying that increasing buffers have little impact on improving the performance of systems in these cases. Numerical computations reveal that the mean queue length explodes as ρ tends to 0.5 resulting in longer delay. The variance of number of packets also depicts sharp rise as ρ approaches to 0.3. These results are in close agreement with the empirical studies carried out on real traffic traces in the past. The proposed models are useful in designing networks, dimensioning resources and developing congestion control algorithms.

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“…system utilisation (11) and normalization constant (12). Following the same procedure as discussed in section 2, the queue-length distribution beomes…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Analysis of Power-law Queues Using Maximum Entropy Framework: An Application to Performance Evaluation of Network Systems

Sharma

2023

Preprint

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“…These attacks can affect the wireless network security of Fog computing, which can take place between the cloud to things continuum. In general, the users trust the network configurations and data generated by the network traffic, which is usually managed manually by a network administrator [37][38][39]. As Fog nodes placed at the edge of the network, therefore, would be an unmanageable task for the network administrator.…”

Section: Network Securitymentioning

confidence: 99%

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

et al. 2021

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Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.

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Analysis of Power-Law Queues Using Maximum Entropy Framework: An Application to Performance Evaluation of Network Systems

Sharma

2024

Wireless Pers Commun

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Comparison of Mobile and Fixed Device Workloads in an Academic Web Server

Cited by 7 publications

References 14 publications

Analysis of Power-law Queues Using Maximum Entropy Framework: An Application to Performance Evaluation of Network Systems

Analysis of Power-law Queues Using Maximum Entropy Framework: An Application to Performance Evaluation of Network Systems

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

Analysis of Power-Law Queues Using Maximum Entropy Framework: An Application to Performance Evaluation of Network Systems

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