Modelling and Analysis of an Integrated Scheduling Scheme with Heterogeneous LRD and SRD Traffic

Jin, Xiaolong; Min, Geyong

doi:10.1109/tcomm.2013.050313.112217

Cited by 11 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… The third proposed model, stay with restarting transmission, has high values of delay, but the target channel selection problem is already solved by staying in the same channel. And it has some applications using in the carrier sense multiple access collision detection (CSMA-CD) protocols to describe a fiber optical bus network, which connected a general finite number of stations [16]. Table 2.…”

Section: Processing Results For the 3 Rd Proposed Modelmentioning

confidence: 99%

Performance Study of Queuing Delay in CRN Using Different Proposed Spectrum Handoff Approaches

Ali

Shokair

Dessouky

et al. 2018

Menoufia Journal of Electronic Engineering Research

View full text Add to dashboard Cite

A Ab bs st tr ra ac ct t Spectrum handoff in cognitive radio networks is to have a seamless switch between channels consequently, a high quality of service for the secondary users. This paper proposes three techniques for spectrum handoff. The first proposed one based on the low priority user leaves the channel to another channel. The second proposed model based on the low priority user stays on the same channel with resuming its transmission when the high priority user finished its transmission. Finally, the third proposed model based on the low priority user stays on the same channel with restarting its transmission when the high priority user finished its transmission. The obtained results indicate that the three proposed system models give different queuing delay performances for each user. The techniques of the models and the obtained results are important for evaluating the total system time, service time and queuing delay for secondary users. Consequently, the quality of service for the secondary users and hence the useful for cognitive radio network design and optimization will be obtained.

show abstract

Section: Processing Results For the 3 Rd Proposed Modelmentioning

confidence: 99%

Performance Study of Queuing Delay in CRN Using Different Proposed Spectrum Handoff Approaches

Ali

Shokair

Dessouky

et al. 2018

Menoufia Journal of Electronic Engineering Research

View full text Add to dashboard Cite

show abstract

“…Extended features: Based on the queue theory, the basic features can be extended to capture better effective features that affect GPS performance. Referring to Jin's work [49], the extended features (EFs) mainly include the minimum guaranteed capacity MU, the proportion of the arrival rate PA, and the utility based on the minimum guaranteed capacity UM for each flow feeding into the GPS server, which can be denoted as EF = (MU, PA, UM).…”

Section: Feature Extraction and Processingmentioning

confidence: 99%

On-Demand Centralized Resource Allocation for IoT Applications: AI-Enabled Benchmark

Zhang,

Liu,

Dong

et al. 2024

Sensors

View full text Add to dashboard Cite

The development of emerging information technologies, such as the Internet of Things (IoT), edge computing, and blockchain, has triggered a significant increase in IoT application services and data volume. Ensuring satisfactory service quality for diverse IoT application services based on limited network resources has become an urgent issue. Generalized processor sharing (GPS), functioning as a central resource scheduling mechanism guiding differentiated services, stands as a key technology for implementing on-demand resource allocation. The performance prediction of GPS is a crucial step that aims to capture the actual allocated resources using various queue metrics. Some methods (mainly analytical methods) have attempted to establish upper and lower bounds or approximate solutions. Recently, artificial intelligence (AI) methods, such as deep learning, have been designed to assess performance under self-similar traffic. However, the proposed methods in the literature have been developed for specific traffic scenarios with predefined constraints, thus limiting their real-world applicability. Furthermore, the absence of a benchmark in the literature leads to an unfair performance prediction comparison. To address the drawbacks in the literature, an AI-enabled performance benchmark with comprehensive traffic-oriented experiments showcasing the performance of existing methods is presented. Specifically, three types of methods are employed: traditional approximate analytical methods, traditional machine learning-based methods, and deep learning-based methods. Following that, various traffic flows with different settings are collected, and intricate experimental analyses at both the feature and method levels under different traffic conditions are conducted. Finally, insights from the experimental analysis that may be beneficial for the future performance prediction of GPS are derived.

show abstract

“…Specifically, in terms of the input process, we model the heterogeneous traffic via SRD Markov-Modulated Poisson Processes (MMPP) and LRD fractional Brownian motion (fBm) processes [19]. In terms of traffic scheduling rules, we use the common PQ-GPS in integrated scheduling [20], so that some traffic flows are served with the strictly high priority, and other traffic flows are handled by the conventional GPS scheduling mechanism. In terms of service capability, the traffic in the PQ queue uses link bandwidth first, then the traffic in the GPS queue allocates link bandwidth according to its own weight after the transmission of the PQ queue.…”

Section: Introductionmentioning

confidence: 99%

BS-HTIS: Buffer Sizing for Heterogeneous Traffic and Integrated System

2021

View full text Add to dashboard Cite

Buffer sizing of switching and routing devices is of significance to guarantee QoS of critical services in Internet of Things (IoT), continuously evolving scheduling mechanism and complex traffic characteristics pose new challenges to the traditional method of static buffer sizing based on the rule-ofthumb. In this paper, the scope of buffer sizing is extended from simple scheduling system under single traffic to integrated scheduling system under heterogeneous traffic input which is more ubiquitous. Voices, videos and other heterogeneous data in IoT are categorized into Short-Range Dependent (SRD) and Long-Range Dependent (LRD) traffic, and the integrated scheduling system is decomposed into Single-Server Single-Queue (SSSQ) systems, not only decoupling the complex dependence between heterogeneous traffic, but also taking into account the impact of SRD and LRD traffic burstiness on buffer size. On this basis, the expressions of relation between the minimum buffer size and overflow probability are derived in detail are presented. The numerical results show that the average rate, burst level and scheduling priority are positively correlated with the required buffer size, and when the overflow probability is set, the minimum buffer size is determined respectively. The achievements of this paper will provide a theoretical guidance for IoT manufacturers and technicians to set the optimal IoT devices buffer size.

show abstract

Modelling and Analysis of an Integrated Scheduling Scheme with Heterogeneous LRD and SRD Traffic

Cited by 11 publications

References 40 publications

Performance Study of Queuing Delay in CRN Using Different Proposed Spectrum Handoff Approaches

Performance Study of Queuing Delay in CRN Using Different Proposed Spectrum Handoff Approaches

On-Demand Centralized Resource Allocation for IoT Applications: AI-Enabled Benchmark

BS-HTIS: Buffer Sizing for Heterogeneous Traffic and Integrated System

Contact Info

Product

Resources

About