Intelligent End-to-End Deterministic Scheduling Across Converged Networks

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zongrong Cheng;Weiting Zhang;Dong Yang;Chuan Huang;Hongke Zhang;Xuemin Sherman Shen
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Abstract

Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.
跨融合网络的智能端到端确定性调度
确定性网络服务在支持具有有限低延迟、抖动和高可靠性的新兴实时应用程序方面起着至关重要的作用。确定性保障渗透到5G、WiFi、卫星、边缘计算等各类网络中。从用户的角度来看,实时应用需要跨融合网络的端到端确定性保证。本文研究了整个融合网络的端到端确定性保证问题,旨在为不同类型的融合网络提供一种可扩展的方法,以满足每个流的端到端有限延迟、抖动和高可靠性需求,同时提高网络调度QoS。特别地,我们建立了全局端到端控制平面,从融合网络中抽象确定性相关资源,并利用抽象资源对确定性流传输进行建模。有了资源抽象,我们的模型可以很好地适用于不同的底层技术。由于我们的模型中存在大量的抽象资源,传统算法很难充分利用这些资源。因此,我们提出了一种基于深度强化学习的端到端确定性相关资源调度(E2eDRS)算法,对网络资源进行端到端调度。通过设置动作组,E2eDRS可以在水平和垂直端到端确定性相关的网络体系结构中支持不同的网络维度。实验结果表明,与MultiDRS和multi - active算法相比,E2eDRS算法水平调度的可调度流数分别平均增加1.33倍和6.01倍。与MultiDRS算法相比,E2eDRS算法可以分别优化2.65倍和3.87倍的服务器负载均衡。对于垂直调度,E2eDRS在可调度流数和服务器负载平衡方面仍然可以表现得更好。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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