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IEEE 23rd International Conference on High Performance Switching and Routing (IEEE HPSR)

作者： Li, Xinqiao Liu, Mingyuan Cheng, Nan Liu, Kang Quan, Wei Guo, Liang Qin, Yajuan Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Peng Cheng Lab Dept New Networks Shenzhcn 518040 Peoples R China Xidian Univ State Key Lab ISN Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China China Acad Informat & Commun Technol Beijing 100191 Peoples R China

ISBN: (纸本)9781665406079

With the continuous development of autonomous vehicles, telemedicine, digital media and other time-sensitive applications, a soaring number of network services have high demand for the quality of service (QoS) with extra low delay and jitter. Traditional network architecture only offers best-effort services which cannot meet the stringent delay and jitter requirements. In this paper, we propose a resilient-priority queue scheduling algorithm (RPQ) for delay-sensitive services. RPQ can guarantee stable delay in a fine-grained manner. Particularly, on the premise of meeting the delay requirements of high priority streams, RPQ can give consideration to the delay requirements of lower priority streams depending on its resilient scheduling mechanism. We implement RPQ on programmable switch. The experimental results show that RPQ not only guarantees QoS with low delay and low jitter for delay-sensitive streams but also improves network throughput by comparing with the existing solutions, i.e., SP-PIFO and WRR.

关键词： queue scheduling delay-sensitive QoS programmable data planes

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Mixed Priority queue scheduling Based on Spectral Clustering in Spatial Crowdsourcing

Mixed Priority Queue Scheduling Based on Spectral Clustering...

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IEEE International Conference on Web Services (ICWS) / IEEE World Congress on Services (IEEE SERVICES)

作者： Ma, Yue Ni, Runbo Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong Univ Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665416818

With the ubiquity of GPS-enabled smart devices equipped with high-fidelity sensors and increased availability of the wireless network, spatial crowdsourcing has been recently proposed as a general framework to employ smart device carriers as workers to provide services and perform location-sensitive tasks. In this paper, we focus on the task assignment in spatial crowdsourcing, which aims to find the optimal strategy to assign each task to a proper worker such that the total number of completed tasks is maximized and the traveling time cost is minimized, while the workers can return to their initial locations before deadlines after performing the assigned tasks. Finding the optimal global assignment turns out to be intractable since it does not simply imply optimality for an individual worker, as a typical nearest-neighbor heuristic does not render a satisfactory result in general. In spatial crowdsourcing, we model the task assignment problem as a multiple objective joint optimization problem, which focuses on maximizing accomplished task rate and minimizing travel time cost rate simultaneously, and solves it with a mixed priority queue scheduling algorithm. We also introduce a spectral clustering algorithm in spatial crowdsourcing for the first time to divide the task network into different subdomains, considering the task clustering phenomena in real scenarios. Experiments on synthetic and real-world networks demonstrate the efficiency and effectiveness of our method in the task assignment of spatial crowdsourcing and provide insights into its application in practice.

关键词： Spectral Clustering Task Assignment queue scheduling Priority Index Spatial Crowdsourcing

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Pickup and delivery problem with electric vehicles and time windows considering queues

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 167卷

作者： Zhou, Saiqi Zhang, Dezhi Yuan, Wen Wang, Zhenjie Zhou, Likun Bell, Michael G. H. Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Province Peoples R China Shanghai Steel Union Logist Co Ltd Shanghai 200441 Peoples R China Univ Sydney Inst Transport & Logist Studies Sydney NSW 2006 Australia

The electric vehicle, as a green and sustainable technology, has gained tremendous development and application recently in the logistics distribution system. However, the increasing workload and limited infrastructure capacity pose challenges for electric vehicles in the pickup and delivery operating system, including task allocation, electric vehicle routing, and queue scheduling. To address these issues, this paper introduces a pickup and delivery problem with electric vehicles and time windows considering queues, which considers queue scheduling for multiple electric vehicles when operating at the same site. A novel mixed integer linear programming model is proposed to minimize the cost of travel distance and queue time. An adaptive hybrid neighborhood search algorithm is developed to solve the moderately large-scale problem. Experimental results demonstrate the effectiveness of the model and adaptive hybrid neighborhood search algorithm. The competitive performance of the developed algorithm further confirmed by finding 9 new best solutions for the pickup and delivery problem with electric vehicles and time windows benchmark instances. Moreover, the results and sensitivity analysis of objective weight costs highlight the impact and importance of considering queues in the studied problem and obtain some management insights.

关键词： Pickup and delivery problem Electric vehicle queue scheduling Mixed integer linear programming Adaptive hybrid neighborhood search

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SP-DG: A programmable packet-level scheduling for queuing delay guarantees in time-critical networks

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COMPUTER NETWORKS 2024年 250卷

作者： Dai, Bin Li, Hetao Wang, Yifan Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China

Time-critical applications, such as remote surgery, virtual reality, and automatic driving, impose stringent requirements for minimizing delay and jitter. The queuing delay at forwarding nodes significantly affects application traffic. To tackle this challenge, Time-Sensitive Networking (TSN) techniques have been proposed to ensure deterministic queuing delay. However, existing deterministic solutions, including the Time-Aware Shaper (TAS) and the Cyclic Queuing and Forwarding (CQF) model, encounter limitations due to their reliance on precise synchronization and dedicated forwarding plane devices. In this paper, we present a novel solution: the Strict Priority queuing model with a queuing Delay Guarantees (SP-DG), specifically designed for programmable forwarding planes. This model introduces an upper bound on the queuing delay for packets at each switch, effectively characterizing their distinct queuing delay requirements. By utilizing exponential smoothing to predict traffic intensity, packets are intelligently scheduled to appropriate queues based on their upper-bound queuing delay requirements. Our Netbench simulation and P4 software switch experiments demonstrate that the proposed SP-DG algorithm, compared to the state-of-the-art SP-based queue scheduling algorithms, achieves superior performance in delay-bounded queue scheduling.

关键词： queue scheduling Programmable forwarding plane Strict priority Packet-level

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Clustering Based Priority queue Algorithm for Spatial Task Assignment in Crowdsourcing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第2期17卷 452-465页

作者： Ma, Yue Gao, Xiaofeng Bhatti, Shahzad Sarwar Chen, Guihai Shanghai Jiao Tong Univ Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Univ Educ Dept Informat Sci Div Sci & Technol Lahore 54770 Punjab Pakistan

Spatial crowdsourcing is an increasingly popular category in the era of mobile Internet and sharing economy, where tasks have spatio-temporal constraints and must be completed at specific locations. In this article, we focus on the Multi-Objective Spatio-Temporal task assignment (MOST) problem considering the worker heterogeneity in spatial crowdsourcing and model it as a combinatorial multi-objective optimization (MOO) problem with the goals of maximizing the overall task completion rate and minimizing the average task time cost. Finding the optimal global assignment turns out to be intractable since it does not simply imply optimality for an individual worker, as a typical nearest-neighbor heuristic generally does not render a satisfactory result. We prove that the problem is NP-hard. Subsequently, we formulate an efficient algorithm for the MOST problem - Task Clustering based Mixed Priority queue scheduling (TAMP). First, we improve the spectral clustering algorithm to evenly divide the task network into different subdomains according to tasks' geographical locations, considering the task clustering phenomena in real scenarios. We then design a mixed priority queue strategy considering the geographical influence and temporal urgency, to schedule workers finishing tasks in sequence. Experiments on synthetic and real datasets demonstrate the efficiency of our solution over other methods.

关键词： Spatial crowdsourcing task assignment worker heterogeneity spectral clustering queue scheduling

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Managing queues with Different Resource Requirements

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OPERATIONS RESEARCH 2023年第4期71卷 1387-1413页

作者： Zychlinski, Noa Chan, Carri W. Dong, Jing Techn Israel Inst Technol Fac Ind Engn & Management IL-32000 Haifa Israel Columbia Business Sch Div Decis Risk & Operat New York NY 10027 USA

queueing models that are used to capture various service settings typically assume that customers require a single unit of resource (server) to be processed. However, there are many service settings where such an assumption may fail to capture the heterogeneity in resource requirements of different customers. We propose a multiserver queueing model with multiple customer classes in which customers from different classes may require different amounts of resources to be served. We study the optimal scheduling policy for such systems. To balance holding costs, service rates, resource requirement, and priority-induced idleness, we develop an index-based policy that we refer to as the idle-avoid c mu/m rule. For a two-class two-server model, where policy-induced idleness can have a big impact on system performance, we characterize cases where the idle-avoid c mu/m rule is optimal. In other cases, we establish a uniform performance bound on the amount of suboptimality incurred by the idle-avoid c mu/m rule. For general multiclass multiserver queues, we establish the asymptotic optimality of the idle-avoid c mu/m rule in the many-server regime. For long-time horizons, we show that the idle-avoid c mu/m is throughput optimal. Our theoretical results, along with numerical experiments, provide support for the good and robust performance of the proposed policy.

关键词： queue scheduling different resource requirements coupling competitive analysis asymptotic optimality

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Multi-Agent Deep Reinforcement Learning for Cross-Layer scheduling in Mobile Ad-Hoc Networks

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China Communications 2023年第8期20卷 78-88页

作者： Xinxing Zheng Yu Zhao Joohyun Lee Wei Chen Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical and Electronic Engineering Hanyang UniversityAnsan 15588South Korea

Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and *** this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power *** effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement *** transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention *** the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different *** method is general and can be extended to various types of topologies.

关键词： Ad-hoc network cross-layer scheduling multi agent deep reinforcement learning interference elimination power control queue scheduling actorcritic methods markov decision process

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A Traffic Prioritization Framework For Smart Home IoT Networks using Programmable Data Planes

A Traffic Prioritization Framework For Smart Home IoT Networ...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Datta, Suvrima Venkanna, U. Iiit Naya Raipur Department of Computer Science and Engineering India

ISBN: (纸本)9798350370218

The rapid growth of IoT provides a wide range of applicationsx, such as smart homes, healthcare, industry, etc. However, these applications generate various data that must be processed dynamically to ensure the desired QoS. Fortunately, recent advancements in data plane programmability provide some unique opportunities to dynamically process the loT traffic flow in a fine-grained manner. However, most research lacks of provide a solution to prioritize and schedule loT traffic flows in programmable switches. This paper proposes a dynamic prioritization technique for loT traffic flows by analyzing header field values granularly. Further, a weighted fair queue scheduling algorithm is designed in the programmable switch, which updates queue weights dynamically to reduce the delay for delay-sensitive loT applications. The experimental results show that the proposed solution prioritizes loT traffic flows 99.6% correctly. Additionally, the solution has achieved a throughput of 410.377Mbps for high-priority queues, and high-priority flows experienced a minimum delay of 1.84ms on average. © 2023 IEEE.

关键词： IoT IoT traffic prioritization Next generation networks QoS queue scheduling

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A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2382-e2382页

作者： Liu, Yazhi Yao, Xinyi Yang, Zhigang Li, Wei North China Univ Sci & Technol Coll Artificial Intelligence Tangshan Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan Peoples R China

Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees. The strategy utilizes virtual queues and dynamic weighted round-robin scheduling to achieve traffic partitioning in a programmable data plane. It constructs a multi-queue, multi-class QoS queuing model based on the QoS requirements of different traffic and network conditions. The model is solved by real-time to obtain the ECN marking thresholds and round-robin weights for different queues, in order to achieve dynamic QoS requirements of different network applications. We implemented this strategy in Mininet and BMv2, and compared it with DCQCN, P4QCN, and TCN. The experimental results indicate that this policy demonstrates good performance in terms of queue length, RTT, and throughput, while also ensuring fairness between traffics. Results of the experiment indicate that the proposed approach is superior to DCQCN and P4QCN in the field of performance fluctuation and rapid feedback, and it exhibits notable advantages over TCN, and also ensures the fairness of traffic.

关键词： Programmable data plane queue scheduling P4 Queuing theory QoS

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scheduling Optimization Strategy for Communication queues of Adjustable Load Interactive Services 12

Scheduling Optimization Strategy for Communication Queues of...

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12th International Conference on Power, Energy and Electrical Engineering (CPEEE)

作者： Li, Lei Wang, Chaoliang Du, Yabin Li, Bin State Grid Zhejiang Elect Power Co Ltd Mkt Serv Ctr Hangzhou Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781665420495

Under the traditional communication architecture, in order to guarantee the service quality of demand response business communication transmission, an adjustable load business flow model is analyzed and established, and the transmission delay characteristics are analyzed. Based on this, a fuzzy control theory is proposed to optimize the weighted fair queue. The communication queue scheduling strategy of the scheduling algorithm. The algorithm can dynamically adjust the queue weights according to the real-time indicators of network monitoring. Its effectiveness in ensuring the quality of service requirements of various DR services has been verified in the communication network simulation environment built by OMNeT++ software.

关键词： load interactive business fuzzy control theory queue scheduling service quality

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