Load Balancing in Software Defined Mobile Networks

Ahmad, Ijaz; Karunarathna, Suneth Namal; Ylianttila, Mika; Gurtov, Andrei

doi:10.1002/9781118900253.ch13

Cited by 8 publications

(12 citation statements)

References 6 publications

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“…This process will be time consuming, but the system is perceived by the user as still functioning, even with the increase in delay that task offloading represents. Similarly, load balancing techniques that can effectively distribute tasks among available resources can also increase availability of critical resources [36]. Although highly related, it must be noted that a system with high availability is not necessarily reliable, thus achieving the expected high reliability of 6G networks does not guarantee meeting the availability criteria.…”

Section: A Dependability In 6g Networkmentioning

confidence: 99%

Towards dependable 6G networks

Rodríguez¹,

Ahmad²,

Huusko³

et al. 2023

Preprint

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<p>6G communication networks must be highly dependable due to the huge number applications, services, and public infrastructures that will be dependant on it. However, the integration of vast number of technologies, ranging from physical layer up to the application layer, and diverse services ranging from entertainment to mission critical public services need investigation of the infrastructure from dependability point of view. We take four dependability concepts such as reliability, availability, safety and security, and discuss the dependability of 6G networks. Since intelligence and distribution of control functions and elements make the core of future networks, specific diligence is given federated learning and edge intelligence. This article, in summary, provides interesting insights into existing challenges and advocates future research through highlighting the most important research directions to make 6G dependable.</p>

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Section: A Dependability In 6g Networkmentioning

confidence: 99%

Towards dependable 6G networks

Rodríguez¹,

Ahmad²,

Huusko³

et al. 2023

Preprint

Self Cite

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“…A control algorithm constantly controls the load distribution on several paths to achieve an optimal or near-optimal load distribution and to minimise the local load maxima. Dynamic load control, as has been investigated in previous research on layer 3 ISP networks or campus networks [13,15,17,18], usually differentiates between -Flow control: The algorithm for controlling the data flow on a single path increases or decreases throughput. -Fairness control: This algorithm regulates the fair distribution of the reduction or increase in throughput among different data flows.…”

Section: Fig 1 Typical Automation Communication Network Setupmentioning

confidence: 99%

“…Fairness control is of minor importance for automation networks control data (CD), as explained in Chapter 3. Regarding distribution and flow control, more research has been conducted on distribution control [13,15,17,18] than on the flow control subtask. Some network characteristics play a crucial role in flow control.…”

Section: Fig 1 Typical Automation Communication Network Setupmentioning

confidence: 99%

“…𝑇 𝐷𝑇𝑃 𝐴𝑇𝑆 = 𝑛 max 𝜇 (𝑇 𝑆&𝐹 + 𝑇 𝑇𝑟 + 𝑇 𝑄 + 𝑇 𝑁𝐶 ) + ∑ 𝑇 𝐿𝑃𝐺 𝑖 𝑛 max 𝜇 𝑖=1 (15) where 𝑇 𝐷𝑇𝑃 𝐴𝑇𝑆 is the sum of the dead times of the ATS path for the highest priority CD. If the maximum ICI is must be considered, a single additional network cycle length is added.…”

Section: From Distribution Controllermentioning

confidence: 99%

“…In addition to the advantages of redundancy, the availability of multiple paths has led to the use of load-sharing and load-balancing concepts since the late 1990s. These non-TSN networks are based on Open Systems Interconnection (OSI) layer 3 routing technology and dedicated load balancing methods, such as ISP networks [13], campus networks [14], and access networks of mobile connectivity [15]. In factory automation applications, networks with smaller spatial extensions are used to transport information between automation controllers (AC) and devices, such as drives, sensors, and actuators.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Flow Control for Network Load Balancing in IEEE Time-Sensitive Networks for Automation

Weichlein¹,

Zhang²,

Li³

et al. 2022

Preprint

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<p>IEEE time-sensitive networks (TSN) offer redundant paths for automation networks that are essential preconditions for network load balancing (NLB) or distribution. They also provide several traffic shapers and schedulers with different impacts on the data flow control. The selection of the right traffic shaper or scheduler for an automation network is challenging. Their influence depends on various network parameters such as network extension, network cycles, application cycles, and the amount of data per traffic class and network cycle. In this study, the data flow control for network load balancing in an automation TSN using different traffic shapers and schedulers was investigated. The effects of the network parameters on the shapers and schedulers were derived and imported into the data flow control model of the automation network. The sample networks were simulated, and performance comparisons were performed. The results show that enhancements for scheduled traffic (EST), strict priority queuing (SPQ), and the combination of SPQ with frame preemption are better scheduler selections in connection with larger networks, fast network cycles, and fast application cycles. The cyclic queuing and forwarding (CQF) shaper and asynchronous traffic shaper (ATS) are only alternatives for data load control in small networks or in conjunction with slow applications.</p>

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Data Flow Control for Network Load Balancing in IEEE Time Sensitive Networks for Automation

Weichlein

Zhang

et al. 2023

IEEE Access

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IEEE time sensitive networks (TSN) offer redundant paths for automation networks that are essential preconditions for network load balancing (NLB) distribution. They also provide several traffic shapers and schedulers with different impacts on the data flow control. The selection of the right traffic shaper or scheduler for an automation network is challenging. Their influence depends on various network parameters such as network extension, network cycles, application cycles, and the amount of data per traffic class and network cycle. In this study, data flow control for NLB in automation TSN using different traffic shapers and schedulers was investigated. The effects of the network parameters on the shapers and schedulers were derived and imported into the data flow control model of the automation network. The sample networks were simulated, and performance comparisons were made. The results show that the enhancements for scheduled traffic (EST), strict priority queuing (SPQ), and the combination of SPQ with frame preemption (FP) are better scheduler selections in connection with larger networks, fast network cycles, and fast application cycles. The cyclic queuing and forwarding (CQF) shaper and asynchronous traffic shaper (ATS) are rather an alternative for load control in small networks or in conjunction with slow applications. INDEX TERMS Automation networks, data flow control, load balancing, Time Sensitive Networks I. INTRODUCTION

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Load Balancing in Software Defined Mobile Networks

Cited by 8 publications

References 6 publications

Towards dependable 6G networks

Towards dependable 6G networks

Data Flow Control for Network Load Balancing in IEEE Time-Sensitive Networks for Automation

Data Flow Control for Network Load Balancing in IEEE Time Sensitive Networks for Automation

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