Online Scheduling for Dynamic VM Migration in Multicast Time-Sensitive Networks

Yu, Qinghan; Wan, Hai; Zhao, Xibin; Gao, Yue; Gao, Ming

doi:10.1109/tii.2019.2925538

Cited by 44 publications

(7 citation statements)

References 29 publications

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“…Other authors have discussed dynamic VM migrations to solve the time-sensitive network problem in cloud computing. The proposed approach is divided into two phases: the offline scheduling phase and the online scheduling phase [17]. The offline phase is based on a search graph using a tree at a minimum distance to increase reusable rescheduling results, while the online phase aims to accelerate the rescheduling process according to a heuristic scheduling approach to reuse the results of the offline phase.…”

Section: Literature Reviewmentioning

confidence: 99%

A machine learning model for improving virtual machine migration in cloud computing

2023

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These resources are virtualized using virtualization software to make them available to users as a service. In this environment, the migration of virtual machines (VMs) is a significant concern these days. This technique provided by virtualization technology impacts the performance of the cloud. When allocating resources, the distribution of VMs is unbalanced, and their migration from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper presents a machine learning model for migrating virtual machines. The goal is to improve the selection of virtual machines and migration processes by reducing energy consumption and the number of VMs migrations. Numerical results demonstrate that the proposed solution can significantly enhance the goals addressed.

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Section: Literature Reviewmentioning

confidence: 99%

A machine learning model for improving virtual machine migration in cloud computing

2023

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“…Most academic researches in this area focus on the scheduling of messages. For example, a computational efficient solution to the fully deterministic 802.1Qbv scheduler was presented in [9]; a bandwidth-efficient TSN scheduler was investigated through a size based queueing method in [10]; an asynchronous traffic scheduling algorithm which achieves both low delay and low implementation complexity was proposed in [11]; and an online scheduling approach was proposed to deal with the dynamic virtual machine migrations in multicast TSN networks in [12]. In addition, the authors proposed a simple hardware enhancement of switches to increase the schedulability and throughput of time-triggered traffics in [13].…”

Section: B Related Workmentioning

confidence: 99%

“…That is, the average number of packets that can be received δ-on time is equal to the sum probability for each of the packets to be received δ-on time. Since the probability P (x k ) for each of the packets being received δ-on-time has been given in Proposition 1, we can calculate the on-time reception rate ̺ M = κ M /M by combing the results in Proposition 1, Theorem 1, and equation (12).…”

Section: Proof See Appendix Bmentioning

confidence: 99%

On-Time Communications Over Fading Channels

Li¹,

Dong²,

Shim³

2022

Preprint

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“…The simulation findings guaranteed targeted services for emergency data with minimal effect on the scheduled traffic. The authors in [18], [19] recommended adapting GCL-timings according to the network resources availability and instantaneous traffic intensity, while [20], [21] proposed a scheduling algorithm with online updating to efficiently integrate incoming flows. [13], [22], [23] on the other hand, recommended joint routing and scheduling algorithms targeting suitable use of network resources and proper load balancing.…”

Section: Related Workmentioning

confidence: 99%

Critical Offset Optimizations for Overlapping-Based Time-Triggered Windows in Time-Sensitive Network

et al. 2021

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Deterministic and low latency communications are increasingly becoming essential requirements for several safety-critical applications, such as automotive and automation industries. The timesensitive networking (TSN) is an element of the new IEEE 802.1 standards that introduced Ethernet-based amendments to support these applications. One of these enhancements was presented in IEEE 802.1Qbv to define time-aware shaping (TAS) technique for time-triggered (TT) traffic scheduling. The TAS mechanism is a window-based scheduling using a gating system controlled by the gate control list (GCL) schedules in all nodes. Although several scheduling algorithms have been proposed to investigate the effects of windowrelated parameters on network performance, the offset difference ( ) between the same-class windows in the adjoining nodes has not been optimized yet. These optimizations are extremely crucial to implement less pessimistic latency schedules. In this paper, we propose an optimized flexible window-overlapping scheduling (OFWOS) algorithm that optimizes TT window offsets based on latency evaluations considering the overlapping between different priority windows at the same node. First, we formulate the GCL timings as mathematical forms under variable s between the same-priority windows. Then, an analytical model is implemented using the network calculus (NC) approach to express the worst-case end-to-end delay ( ) for TT flows and evaluated using a realistic vehicular use case. The OFWOS model optimizes under all overlapping situations between TT windows at the same node. In comparison with latest works of 3-hop and 30-hop TSN connections, the OFWOS reduces the bounds by 8.4% and 32.6%, respectively which accomplishes less pessimistic end-to-end latency bounds.

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Online Scheduling for Dynamic VM Migration in Multicast Time-Sensitive Networks

Cited by 44 publications

References 29 publications

A machine learning model for improving virtual machine migration in cloud computing

A machine learning model for improving virtual machine migration in cloud computing

On-Time Communications Over Fading Channels

Critical Offset Optimizations for Overlapping-Based Time-Triggered Windows in Time-Sensitive Network

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