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End-Edge-Cloud Heterogeneous Resources scheduling Method Based on RNN and Particle Swarm Optimization

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1664-1676页

作者： Wu, Haijie Shen, Wangbo Lin, Weiwei Li, Wei Li, Keqin South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Dept New Networks Shenzhen 518055 Peoples R China Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia State Univ New York New Paltz Dept Comp Sci New Paltz NY 12561 USA

Task scheduling in cloud computing is a challenging but crucial task for ensuring service quality and load balance. Mainstream scheduling algorithms, such as heuristic algorithms and reinforcement learning, have made progress in this area. However, online task scheduling algorithms, such as reinforcement learning, can pose computational challenges in scenarios with limited computational power and heterogeneous resources. Heuristic algorithms, which are more suitable for offline scheduling where the types and quantities of tasks are known in advance, also require substantial computational resources for online scheduling. In this work, we propose the end-edge-cloud (EEC) heterogeneous resources scheduling method (EHRSM) based on a recurrent neural network (RNN) model and particle swarm optimization (PSO). EHRSM uses an RNN model trained on a dataset generated by dynamic programming to recognize and cache online tasks, efficiently transforming online task scheduling into offline scheduling. Additionally, a PSO algorithm with Cantor expansion (CE) for coding optimization is used to complete the offline scheduling. Experimental results show that the method is effective in converting online scheduling to offline scheduling, reducing the average task completion time and waiting time. Compared with existing online scheduling methods, EHRSM reduces task completion time by up to 48.24%.

关键词： Processor scheduling scheduling Job shop scheduling Cloud computing Collaboration Servers Training scheduling algorithms Recurrent neural networks Performance evaluation Cantor expansion end-edge-cloud particle swarm optimization recurrent neural network

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scheduling algorithms Based on Resource Fragmentation for Advance Reservation Tasks

Scheduling Algorithms Based on Resource Fragmentation for Ad...

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4th International Conference on Communication Systems and Network Technologies (CSNT)

作者： Li, Bo Pei, Yijian Wu, Hao Zhao, Xian Shen, Bin Yunnan Univ Sch Informat Sci & Engn Kunming Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781479930708

Advance reservation is an important method to guarantee the quality of service in Grid-like distributed systems. However, reserved jobs will make resource into fragments and decrease utilization. In order to minimize the negative effects of advance reservations, the authors analyzed the generation of resource fragments during reservation and investigated their influence on advance reservation requests in a quantitative way. Based on the quantification, two new scheduling algorithms, Resource Fragment-aware Best Fit (FSB) and Resource Fragment-aware Worst Fit (FSW), were proposed and their performances were investigated via comprehensive simulations. In simulation, mean job size, deadline factor, system load and sever number were chosen as control factors, and the performances of the algorithms were analyzed in terms of job acceptance rate, resource utilization and slowdown. We also compared FSB and FSW with Best Fit, First Fit, Min_LIP and Min_TIP. The simulations show that FSW and FSB can provide higher job acceptance rate, especially under heavy system load.

关键词： grid-computing advance reservation co-allocation scheduling algorithms resource fragments

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Efficient Prefetch and Issue scheduling Approaches for Simultaneous Multithreading Applied to Superscalar RISC-V Processor

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IEEE ACCESS 2025年 13卷 29177-29189页

作者： Ribo, Hananya Greenberg, Shlomo Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel Sami Shamoon Coll Engn Comp Sci Dept IL-8410802 Beer Sheva Israel

Thread level parallelism (TLP) is a common approach to achieve parallelism where Instruction level parallelism (ILP) is insufficient. Hardware multithreading is a prevalent approach in the micro-architecture layer for tolerating long events such as memory access, mispredictions, and accelerators latencies by utilizing idle cycles and avoiding CPU stalling. Multithreading architectures are commonly used across many processors and various embedded edge devices to improve performance. This work suggests a new 2-way superscalar in-order Multithreading (MT) micro-architecture applied to a superscalar RISC-V processor. This work also proposes efficient prefetch-scheduling and Issue-scheduling algorithms for Simultaneous Multithreading (SMT) processors. The suggested micro-architecture was implemented on an FPGA chip. The results show that the proposed prefetch-scheduling and issue-scheduling algorithms outperform the 2-way superscalar Fine-Grained Multi-Threaded (FGMT) and the Coarse-Grained Multi-Threaded (CGMT) approaches. The proposed architecture is evaluated using the standard MiBench and RISCV benchmarks, demonstrating an average processor core utilization improvement of up to 52% in terms of IPC using four threads.

关键词： Instruction sets Pipelines Multithreading Hardware Prefetching Computer architecture scheduling scheduling algorithms Multicore processing Registers Hardware multithreading prefetch scheduling issue scheduling superscalar RISC-V in-order pipeline

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CECF: A DNN-Based Energy-Efficient Cloud-Edge Collaboration Framework for Intelligent Workload scheduling in 6G-Enabled Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Lu, Yao Liu, Lu Panneerselvam, John Gu, Jiayan Garraghan, Peter Min, Geyong Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF England Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230000 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England

The rapid growth of Internet of Vehicle (IoV) devices and Artificial Intelligence (AI) applications has accelerated the adoption of Cloud and Edge Computing. The advent of sixth-generation mobile communication technology (6G) further facilitates the deployment of Cloud-Edge collaborative computing in large-scale Intelligent Transportation Systems (ITS). Effective ITS must efficiently handle both latency-sensitive tasks (e.g., obstacle detection, traffic signal recognition) and computationally intensive tasks (e.g., path optimization, traffic flow prediction). However, existing Cloud-Edge collaborative frameworks struggle to accurately classify diverse workloads and provide efficient low-latency processing, leading to energy inefficiencies and task failures. To address these challenges, this paper introduces a Deep Learning-based Cloud-Edge Collaboration Framework (CECF) designed to optimize energy conservation in Cloud and Edge environments. CECF employs a DNN-based classifier to categorize workloads for processing in the Cloud or Edge. The classified tasks are managed by a dedicated Cloud scheduler (DSGA) and an Edge scheduler (EA-DFPSO), respectively. To enhance scheduling efficiency for highly variable Cloud tasks, DSGA incorporates a novel self-adaptive mutation algorithm and a random point fixed distance crossover method. Extensive evaluations using real-world workload traces demonstrate that CECF achieves up to a 8.5% improvement in system reliability and reduces energy consumption by 35.88% compared to baseline approaches.

关键词： Cloud computing Servers Energy consumption Collaboration Energy efficiency Image edge detection Heuristic algorithms scheduling algorithms Real-time systems Virtual machines Cloud-edge collaboration computing Internet of Vehicles (IoV) deep learning intelligent transportation systems

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Learning-Augmented scheduling

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IEEE TRANSACTIONS ON COMPUTERS 2024年第11期73卷 2548-2562页

作者： Zhao, Tianming Li, Wei Zomaya, Albert Y. Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia

The recent revival in learning theory has provided us with improved capabilities for accurate predictions. This work contributes to an emerging research agenda of online scheduling with predictions by studying makespan minimization in uniformly related machine non-clairvoyant scheduling with job size predictions. Our task is to design online algorithms that use predictions and have performance guarantees tied to prediction quality. We first propose a simple algorithm-independent prediction error metric to quantify prediction quality. Then we design an offline improved 2-relaxed decision procedure approximating the optimal schedule to effectively use the predictions. With the decision procedure, we propose an online O(min {log eta,log m})-competitive static scheduling algorithm assuming a known prediction error. We use this algorithm to construct a robust O(min {log eta,log m})-competitive static scheduling algorithm that does not assume a known error. Finally, we extend these static scheduling algorithms to address dynamic scheduling where jobs arrive over time. The dynamic scheduling algorithms attain the same competitive ratios as the static ones. The presented algorithms require just moderate predictions to break the Omega(log m) competitive ratio lower bound, showing the potential of predictions in managing uncertainty.

关键词： Prediction algorithms Heuristic algorithms Optimal scheduling Dynamic scheduling Single machine scheduling scheduling algorithms Minimization Learning-augmented algorithms scheduling makespan predictions

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Partitioned scheduling With Shared Resources on Imprecise Mixed-Criticality Multiprocessor Systems

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 65-76页

作者： Zhang, Yi-Wen Ma, Jin-Peng Gu, Zonghua Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Hofstra Univ Dept Comp Sci Hempstead NY 11549 USA Umea Univ Dept Appl Phys & Elect S-90187 Umea Sweden

Both resource access protocols and real-time scheduling algorithms have been extensively studied in classic embedded real-time systems. However, there has been relatively little attention given to the resource access protocol and real-time scheduling algorithms in mixed-criticality systems. In this article, we pay attention to the problem of scheduling an imprecise mixed-criticality (IMC) taskset on a multiprocessor platform with shared resources. First, we propose an IMC with MSRP (IMC-MSRP) resource access protocol, which ensures mutually exclusive access to the shared resources for the tasks. Second, we propose the schedulability test based on the IMC-multiprocessor stack resource policy (MSRP) for a given task-to-processor mapping method. Third, we propose a feasible task-to-processor mapping algorithm called resource-aware criticality-unaware worst-fit decreasing (RA-CU-WFD), which first assigns tasks sharing the same resources to the same processor to reduce the global waiting time of the tasks and thus improve the schedulability ratio of the system. And then assigns tasks based on the criticality-unaware worst-fit decreasing (CU-WFD) algorithm. Finally, we conduct experiments using the synthetic tasksets, and the experimental results show that the RA-CU-WFD outperforms the other approaches in terms of the schedulability ratio.

关键词： Task analysis Program processors Job shop scheduling Access protocols scheduling algorithms Quality of service Partitioning algorithms Imprecise mixed-criticality (IMC) multicore partitioned scheduling shared resource

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A survey on the scheduling mechanisms in serverless computing: a taxonomy, challenges, and trends

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 5981-5993页

作者： Ghorbian, Mohsen Ghobaei-Arani, Mostafa Esmaeili, Leila Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran

In recent years, serverless computing has received significant attention due to its innovative approach to cloud computing. In this novel approach, a new payment model is presented, and a microservice architecture is implemented to convert applications into functions. These characteristics make it an appropriate choice for topics related to the Internet of Things (IoT) devices at the network's edge because they constantly suffer from a lack of resources, and the topic of optimal use of resources is significant for them. scheduling algorithms are used in serverless computing to allocate resources, which is a mechanism for optimizing resource utilization. This process can be challenging due to a number of factors, including dynamic behavior, heterogeneous resources, workloads that vary in volume, and variations in number of requests. Therefore, these factors have caused the presentation of algorithms with different scheduling approaches in the literature. Despite many related serverless computing studies in the literature, to the best of the author's knowledge, no systematic, comprehensive, and detailed survey has been published that focuses on scheduling algorithms in serverless computing. In this paper, we propose a survey on scheduling approaches in serverless computing across different computing environments, including cloud computing, edge computing, and fog computing, that are presented in a classical taxonomy. The proposed taxonomy is classified into six main approaches: Energy-aware, Data-aware, Deadline-aware, Package-aware, Resource-aware, and Hybrid. After that, open issues and inadequately investigated or new research challenges are discussed, and the survey is concluded.

关键词： Serverless computing Function as a service scheduling algorithms Energy-aware scheduling Data-aware scheduling Deadline-aware scheduling Package-aware scheduling Resource-aware scheduling Makespan Serverless edge computing Resource management Performance evaluation

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Tile-Weighted Rate-Distortion Optimized Packet scheduling for 360° Virtual-Reality Video Streaming

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IEEE INTELLIGENT SYSTEMS 2024年第4期39卷 60-72页

作者： Wang, Haopeng Dong, Haiwei El Saddik, Abdulmotaleb Univ Ottawa Ottawa ON K1N 6N5 Canada Huawei Technol Canada Ottawa ON K2K 3J1 Canada Univ Ottawa Ottawa ON K1N 6N5 Canada

A key challenge of 360 degrees virtual-reality (VR) video streaming is ensuring high quality with limited network bandwidth. Currently, most of the studies focus on tile-based adaptive bit-rate streaming to reduce bandwidth consumption, where resources in network nodes are not fully utilized. This article proposes a tile-weighted rate-distortion (TWRD) packet scheduling optimization system to reduce data volume and improve video quality. A multimodal spatial-temporal attention transformer is proposed to predict viewpoint with probability that is used to dynamically weight tiles and their corresponding packets. The packet scheduling problem of determining which packets should be dropped is formulated as an optimization problem solved by a dynamic programming solution. Experiment results demonstrate that the proposed method outperforms the existing methods under various conditions.

关键词： Streaming media scheduling algorithms Distortion Transformers Rate-distortion Bandwidth Visualization Virtual reality

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Privacy-Preserving Link scheduling for Wireless Networks

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IEEE ACCESS 2024年 12卷 109657-109672页

作者： Abbasalizadeh, Maryam Chan, Jeffrey Rayavaram, Pranathi Chen, Yimin Narain, Sashank Univ Massachusetts Lowell Miner Sch Comp & Informat Sci Lowell MA 01854 USA

Wireless communication is now a cornerstone of modern society, propelled by the widespread adoption of IoT devices and sophisticated wireless technologies. As wireless networks grow in complexity, there is an increasing need for efficient scheduling algorithms that can manage resources and adapt to evolving conditions effectively. Link scheduling is critical as it optimizes the use of wireless spectrum and bandwidth, prioritizing high-priority transmissions and minimizing interference. However, traditional link scheduling algorithms have primarily focused on performance, often overlooking the crucial aspect of privacy. This oversight poses significant risks in scenarios where privacy is paramount. To address this issue, we introduce PriLink, a novel link scheduling algorithm that prioritizes privacy without sacrificing performance. PriLink employs a least privilege model, sharing only essential links to protect critical topology details from potential adversaries. Our comprehensive evaluation shows that PriLink not only matches but occasionally surpasses the performance of established benchmarks like Greedy Maximal scheduling and Local Greedy scheduling. Moreover, it offers faster execution times and superior privacy protection. These results highlight PriLink's effectiveness as a robust solution for efficient, real-time, and privacy-preserving link scheduling in dynamic wireless networks.

关键词： Wireless networks Privacy Network topology scheduling scheduling algorithms Wireless communication Interference Link scheduling wireless networks privacy topology concealment

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Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems

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Discover Internet of Things 2025年第1期5卷 1-13页

作者： Zhou, Fei Zhao, Lihong Ding, Xiaomei Wang, Shuqin Anhui Wenda University of Information Engineering Anhui Hefei231201 China

This paper presents an improved Deep Deterministic Policy Gradient (DDPG) algorithm for task scheduling in Mobile Edge Computing (MEC) systems, focusing on reducing latency and energy consumption. The enhanced DDPG incorporates a soft update mechanism for target network parameters and an experience replay buffer to improve stability and training efficiency. A composite reward function, considering both latency and energy consumption, is designed and compared in linear and nonlinear forms. Experimental results demonstrate that the nonlinear reward function achieves faster convergence and better system performance. The improved DDPG outperforms traditional algorithms like Shortest Job First (SJF), Round Robin (RR), and Earliest Deadline First (EDF) in terms of latency and energy consumption across different task loads. At a task load of 10, the improved DDPG achieved a latency of 250 ms and energy consumption of 965 J, significantly lower than other algorithms. Ablation experiments further confirm the positive impact of each enhancement, showing that the combination of soft updates and experience replay buffers significantly reduces both latency and energy consumption. The study concludes that the improved DDPG algorithm provides an effective solution for task scheduling in MEC systems, offering robustness and adaptability in complex network environments. Future research will focus on real-world deployment and further optimization of the algorithm for diverse MEC scenarios. © The Author(s) 2025.

关键词： scheduling algorithms

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