An overview of queuing delay and various delay based algorithms in networks

Pachuau, Joseph L.; Saha, Anish Kumar

doi:10.1007/s00607-021-00973-3

Cited by 31 publications

(12 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If a new packet arrives while an old one is still waiting or being transmitted, it will stay in the buffer and wait. For an M/M/1 queuing system [36], where the first M denotes the packet arrival rate, the second M denotes the packet service rate, and 1 denotes the number of servers in the queuing system, the queuing delay L q is discussed in [37]. The authors assumed a service rate of 740 kpps (where kpps stands for kilo packets per second), an arrival rate of 726 kpps, a link data rate of 9 Gbps, and a packet size of 1,500 bytes with an extra 25 bytes of header and footer and calculated L q to be 4 ms. Based on this calculation, we assume the queuing delay as 4 ms for 10 Gbps data rate communication.…”

Section: ) Node Latency Modelmentioning

confidence: 99%

Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability

Liang,

Chaudhry,

Erdogan

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

In free-space optical satellite networks (FSOSNs), satellites can have different laser intersatellite link (LISL) ranges for connectivity. As the LISL range increases, the number of satellites from among all the satellites in the constellation that will be needed on the shortest path between a source and a destination ground station decrease, and thereby the number of the LISLs on the shortest path decreases. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 (i.e., the latest version of Starlink's Phase 1) and Kuiper Shell 2 (i.e., Kuiper's biggest shell) constellations for different LISL ranges and different inter-continental connections. We use appropriate system models for calculating the average satellite transmission power (i.e., the average of the transmission power of all satellites on the shortest path) and network latency (i.e., the end-to-end latency of the shortest path). The results show that the mean network latency (i.e., the mean of network latency over all time slots) decreases and mean average satellite transmission power (i.e., the mean of average satellite transmission power over all time slots) increases with an increase in LISL range. For the Toronto-Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2,900 km, the mean network latency and mean average satellite transmission power intersect and are approximately 135 ms and 380 mW, respectively. For an FSOSN with the Kuiper Shell 2 constellation in this inter-continental connection, this LISL range is around 3,800 km, and the two parameters are approximately 120 ms and 700 mW, respectively. For the Toronto-Istanbul and Toronto-London inter-continental connections, the LISL ranges at the intersection are different and vary from 2,600 km to 3,400 km. Furthermore, we analyze outage probability performance of optical uplink/downlink due to atmosphere attenuation and turbulence.INDEX TERMS Free-space optical satellite networks, network latency, optical inter-satellite link, optical uplink/downlink, satellite transmission power, tradeoff, outage probability.

show abstract

Section: ) Node Latency Modelmentioning

confidence: 99%

Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability

Liang,

Chaudhry,

Erdogan

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…Prior studies have adopted the M/M/1 and M/M/N queuing models to schedule IoT network packets in both wired [4,18] and wireless networks [22]. Additionally, studies have shown that both wired [16] and wireless [13] traffic follow Poisson distribution in IoT scenarios.…”

Section: Priority-based and Queuing-theoretic Modelingmentioning

confidence: 99%

Software-defined Dynamic 5G Network Slice Management for Industrial Internet of Things

Min¹,

Shekhar²,

Mahmoudi³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper addresses the challenges of delivering fine-grained Quality of Service (QoS) and communication determinism over 5G wireless networks for real-time and autonomous needs of Industrial Internet of Things (IIoT) applications while effectively sharing network resources. Specifically, this work presents DANSM, a dynamic and autonomous network slice management middleware for 5G-based IIoT use cases, such as adaptive robotic repair. The novelty of our approach lies in (1) its use of multiple M/M/1 queues to formulate a 5G network resource scheduling optimization problem comprising service-level and system-level objectives; (2) the design of a heuristics-based solution to overcome the NP-hard properties of this optimization problem, and (3) how it dynamically and autonomously provisions and manages 5G network slices used to deliver predictable communications to IIoT use cases. The results of experiments evaluating DANSM on our testbed comprising a Free5GC-based core and UERANSIMbased simulations reveal that it can efficiently balance the traffic load in the data plane thereby reducing the end-to-end response time and improve the service performance by finishing 34% more subtasks than Modified Greedy Algorithm (MGA), 64% more subtasks than First Fit Descending (FFD) approach and 22% more subtasks than Best Fit Descending (BFD) approach while minimizing the operational costs.

show abstract

“…Interference and congestion are inherent to this kind of traffic and lead to packet losses [7]. They always provoke delays and energy waste, hence reduction of network efficiency and lifetime [8].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2023

IJWMN

View full text Add to dashboard Cite

Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data collection process must be conducted while copying with packet collisions, nodes' congestion or data redundancy. These issues always result in energy waste which is detrimental to network efficiency and lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks' duty cycling, data aggregation and transmission scheduling in order to minimise message overhead and packet losses. Simulation results show that this solution can mitigate delay while significantly increasing packet delivery and network lifetime.

show abstract

An overview of queuing delay and various delay based algorithms in networks

Cited by 31 publications

References 97 publications

Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability

Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability

Software-defined Dynamic 5G Network Slice Management for Industrial Internet of Things

Untitled

Contact Info

Product

Resources

About