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Memory Priority scheduling Algorithm for Cloud Data Center Based on Machine Learning Dynamic Clustering Algorithm

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第4期21卷 3485-3492页

作者： Liang, Bin Wu, Di Shiyou Univ Sch Comp Sci Xian 710065 Peoples R China Shaanxi Univ Sci & Technol Haojing Coll Xian 712046 Peoples R China

As the cloud data center (CDC) landscape continues to broaden, CDC resource utilization as the benchmark for assessing scheduling methodologies. Concurrently enhancing both CPU and memory utilization stands as a paramount priority. However, prevalent algorithms tend to solely prioritize CPU utilization while neglecting memory efficiency, ultimately escalating energy expenditure. This article initiates by conducting a comprehensive examination of memory utilization repercussions on CDCs. Subsequently, it facilitates the dynamic clustering of physical machines and virtual machine deployments, ensuring a balanced utilization profile. Furthermore, it introduces the memory priority scheduling algorithm for CDC based on machine learning dynamic clustering algorithm (PMPD). Comparative evaluations against other algorithms underscore the prowess of PMPD in concurrently optimizing CPU and memory utilization, thereby minimizing the number of active PMs and diminishing energy consumption of CDCs.

关键词： Virtual machines Clustering algorithms Heuristic algorithms Resource management Energy consumption scheduling algorithms Data centers Cloud computing Prediction algorithms Machine learning algorithms Cloud data center (CDC) clustering algorithm machine learning virtual machine (VM)

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Priority-Dominated Traffic scheduling Enabled ATS in Time-Sensitive Networking

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第1期22卷 470-484页

作者： Zhang, Lihui Sun, Gang Liu, Rulin Quan, Wei Yu, Hongfang Niyato, Dusit Univ Elect Sci & Technol China Minist Educ Key Lab Opt Fiber Sensing & Commun Chengdu 611731 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410003 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Jurong West 639815 Singapore

Time-Sensitive Networking (TSN) employs shaping mechanisms such as Time-Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF), which depend heavily on precise time synchronization and complex Gate Control Lists (GCL) configurations, limiting their effectiveness in large-scale mixed traffic networks like those in vehicular systems. In response, IEEE 802.1Qcr protocol introduces the Asynchronous Traffic Shaping (ATS) mechanism, based on Urgency-Based Schedulers (UBS), to asynchronously address diverse traffic needs and ensure low and predictable latency. Nonetheless, no traffic scheduling algorithm exists that can be directly applied to ATS shapers in generic large-scale traffic scenarios to solve for fixed end-to-end (E2E) delay constraints and the number of priority *** this paper, we propose an urgency-based fast flow scheduling algorithm (UBFS) to address the issue. UBFS leverages domain-specific optimizing strategies with a focus on traffic delay urgency inspired by greedy algorithm for priority allocation across hops and flows, complemented by preprocessing for scenario solvability and dynamic verification to ensure scheduling feasibility. We benchmark UBFS against the method with both scalability and solution quality in typical network topology and demonstrate that UBFS achieves more rapid scheduling within seconds across linear, ring, and star topologies. Notably, UBFS significantly outperforms the baseline algorithm in scheduling efficiency in mixed and large-scale traffic environments, scheduling a larger number of flows. UBFS also reduces time costs by 2-10 times in delay-sensitive environments and by more than 10 times in large-scale scenarios, effectively balancing time efficiency, performance and scalability, thereby enhancing its applicability in real-world industrial settings.

关键词： Delays Job shop scheduling Scalability Heuristic algorithms Dynamic scheduling Costs Quality of service Synchronization scheduling algorithms Network topology Time-sensitive networking traffic scheduling asynchronous traffic shaping QoS high applicability

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Behavior scheduling for Multi-Robot Path Planning in Unknown Environment With Communication Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10417-10428页

作者： Lu, Hui Zhao, Meng Zhou, Ping Mao, Kefei Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Inst Unmanned Syst Beijing 100191 Peoples R China

The multi-robot system that combines flexibility and autonomy has unique advantages in many scenarios. In unknown environment, the threat posed by the environmental uncertainty to the path planning process cannot be ignored. The robots not only need to avoid obstacles, but also need to maintain communication with other robots to ensure timely negotiation. Inspired by the division of labor in biological community, this paper proposes robot behavior scheduling algorithm for multi-robot path planning in unknown environment with communication constraints. First, to avoid the occurrence of oscillation caused by a single force determining the robot behavior, the constituent elements of robot behavior are described in the form of multiple forces. Then, various robot behaviors are instantiated through a diverse combination of these elements and parameters. Finally, two complementary robot behavior scheduling methods are designed. The experiential potential field jointly constructed by robots is proposed to guide the robots to juggle avoidance, communication and planning task for the first time. The effectiveness of these behavior scheduling methods is verified in theory. The simulation experiment results based on real sensor parameters show that a small amount of robot detours can make the multi-robot system fully connected for 70% of the entire path planning process. Note to Practitioners-The motivation of this article is to avoid communication interruptions among robots when planning paths in unknown environment. When the robots perform the complex tasks, communication interruptions will lead to the inability of sharing environmental maps and allocation plans. The lack of such information may delay the task progress and even cause greater loss. This article focuses on the path planning demands in unknown environment and considers the automatic maintenance and reconstruction of the communication links among robots. Based on the analyses of the motivation in reaching the ta

关键词： Robots Path planning Robot kinematics Robot sensing systems Multi-robot systems scheduling Benchmark testing Propagation losses scheduling algorithms Communication networks Multi-robot system path planning communication constraints behavior scheduling experiential potential field

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Courier: A Unified Communication Agent to Support Concurrent Flow scheduling in Cluster Computing

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第5期36卷 861-876页

作者： Zhang, Zhaochen Zhang, Xu Bao, Zhaoxiang Wei, Liang Tan, Chaohong Dou, Wanchun Chen, Guihai Tian, Chen Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China Jiangsu Future Network Innovat Inst Zhenjiang 212013 Peoples R China

As one of the pillars in cluster computing frameworks, coflow scheduling algorithms can effectively shorten the network transmission time of cluster computing jobs, thus reducing the job completion times and improving the execution performance. However, most of existing coflow scheduling algorithms failed to consider the influences of concurrent flows, which can degrade their performance under a massive number of concurrent flows. To fill the gap, we propose a unified communication agent named Courier to minimize the number of concurrent flows in cluster computing applications, which is compatible with the mainstream coflow scheduling approaches. To maintain the scheduling order given by the scheduling algorithms, Courier merges multiple flows between each pair of hosts into a unified flow, and determines its order based on that of origin flows. In addition, in order to adapt to various types of topologies, Courier introduces a control mechanism to adjust the number of flows while maintaining the scheduling order. Extensive large-scale trace-driven simulations have shown that Courier is compatible with existing scheduling algorithms, and outperforms the state-of-the-art approaches by about 30% under a variety of workloads and topologies.

关键词： scheduling algorithms Cluster computing Data centers Topology Servers Switches scheduling Training Network topology Packet loss Data center network coflow scheduling congestion control

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A Budget-Constrained Workflow scheduling Approach With Priority Adjustment and Critical Task Optimizing in Clouds

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 6907-6921页

作者： Fan, Mingjie Zhao, Xinchao Zuo, Xingquan Ye, Lingjuan Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Minist Educ Key Lab Math & Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China

In the rapidly evolving landscape of cloud computing, scheduling complex scientific workflows poses significant challenges, particularly with constraints like budget considerations. While recent years have seen considerable research focus on budget-constrained cloud workflow scheduling, existing studies primarily concentrate on constructing solutions without delving into potential enhancements. To address this gap, our paper introduces PACP-HEFT, a modified HEFT algorithm integrating priority adjustment and task optimization to minimize makespan. Leveraging the Heterogeneous Earliest-Finish-Time (HEFT) algorithm as its foundation, PACP-HEFT incorporates two priority adjusters informed by task characteristics gleaned from task dependency topology and real-time scheduling data. Additionally, a critical task optimizer conducts thorough analyses and optimizations to reduce overall makespan. Extensive experimentation with real-world workflows underscores PACP-HEFT's superior performance compared to contemporary algorithms.

关键词： scheduling Cloud computing Costs Heuristic algorithms scheduling algorithms Feature extraction Resource management workflow scheduling budget constraint priority adjustment critical task optimizing

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Integrated and Fungible scheduling of Deep Learning Workloads Using Multi-Agent Reinforcement Learning

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第3期36卷 391-406页

作者： Li, Jialun Xiao, Danyang Yang, Diying Mo, Xuan Wu, Weigang Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510640 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

GPU clusters have been widely used to co-locate various deep learning (DL) workloads in a multi-tenant way. Although such resource sharing can significantly reduce training cost, resource contention and interference among co-located workloads make task scheduling very complex and challenging. To simplify the scheduling problem, existing algorithms usually divide the procedure of scheduling into two sub-tasks, i.e., task placement and resource allocation, and allocate resources according to pre-defined and fixed resource demands. However, such a paradigm significantly constrains the selection of potential scheduling solutions. In this article, we present MAIFS, a novel multi-agent reinforcement learning based scheduling algorithm that handles task placement and resource allocation integratedly, and allows fungible resource allocation based on resource sensitivity of DL workloads. The core of MAIFS lies in two mechanisms. The multi-agent attention mechanism is designed to learn and share inter-related resource state features observed from different agents, which enables agents to explore fungible resource allocation solutions. The dynamic coordination graph mechanism is designed for coordinating interactive task placement decisions of agents during integrated scheduling, so as to mitigate potential task conflicts. Simulated experiments using two large scale production DL workload traces and physical deployment experiments based on a Kubernetes based GPU cluster show that MAIFS can outperform state-of-the-art scheduling algorithms by up to 44% in terms of makespan and 46% in terms of job completion time (JCT).

关键词： Resource management scheduling algorithms Graphics processing units Dynamic scheduling Sensitivity Random access memory Job shop scheduling Heuristic algorithms Training Servers GPU cluster task scheduling deep learning workload MARL MLaaS

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Cost and Makespan-Aware Task scheduling With Deep Reinforcement Learning in Multicloud Environments

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Tang, Xuhao Liu, Fagui Wang, Bin Zhang, Mengjiao Jiang, Jun Tang, Quan Wu, Qingbo Chen, C. L. Philip South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Dept New Networks Shenzhen 518066 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

The multicloud environments (MCE) represent a novel paradigm encompassing multiple infrastructure as a service (IaaS) providers, enabling users to tailor and optimize cloud services according to their specific requirements. This approach effectively addresses the limitations of a single cloud environment (SCE) regarding technical constraints, geographical coverage deficiencies, and cost-effectiveness concerns while catering to the increasingly diverse and expanding user demands. In MCE, users must employ appropriate strategies to efficiently allocate diverse tasks across multiple cloud service providers (CSPs) by leveraging the best available resources. Traditional scheduling algorithms are inadequate for addressing the complexities of such MCE. This study introduces a framework for the task scheduling procedure in MCE, treating independent task scheduling as a Markov decision process (MDP). We propose a novel agent environment framework that is designed based on the distinctive characteristics of MCE and enables independent task scheduling. Furthermore, we propose a task scheduling algorithm for MCE based on deep reinforcement learning (DRL) to optimize cost and makespan according to diverse user requirements. The simulation experiments are conducted using both simulated datasets and real-world datasets, demonstrating that our proposed algorithm surpasses the other five algorithms in terms of cost minimization and makespan optimization.

关键词： Costs Cloud computing scheduling algorithms Optimization Heuristic algorithms Decision making Dynamic scheduling Complexity theory Deep reinforcement learning Resource management Cost deep reinforcement learning (DRL) makespan multicloud task scheduling

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Critical-Task-Driven Multi-Objective Evolutionary Algorithm for scheduling Large-Scale Workflows in Cloud Computing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Liu, Xiaolu Yao, Feng Xing, Lining Chen, Huangke Zhao, Wei Zheng, Long Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Xidian Univ Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China

Simultaneously optimizing energy consumption and makespan is essential for deploying workflows on cloud platforms. Meanwhile, it poses various challenges, such as multiple types of decision variables and large-scale decision variables. Until now, some studies have adopted multi-objective evolutionary optimization techniques to handle these challenges. But, most relevant studies regard the optimization problem as a black box and do not fully explore the domain knowledge to strengthen the search efficiency. To further answer the above challenges, this study tailors a critical-task-driven evolutionary algorithm, namely CTMOEA, to optimize both energy consumption and makespan by evolving two types of decision variables: 1) mappings from tasks to resources;and 2) task runtime. In particular, an adaptive incentive mechanism is designed to distinguish decision variables with higher criticality and allocate more computing resources to evolve them, thus handling large-scale decision variables in a targeted manner. Moreover, a critical task remapping mechanism is designed to adaptively remap critical tasks onto the same resources as their successor tasks to eliminate data transfer time between them, thus pursuing a simultaneous reduction in energy consumption and makespan. At last, based on 16 real-world workflow traces, we validate the superior overall performance of the proposed CTMOEA by significantly outperforming five baselines on 13 test instances. Also, we conduct an ablation analysis to demonstrate the contributions of the three problem-specific mechanisms to overall performance.

关键词： Cloud computing Energy consumption Optimization Costs scheduling algorithms Data centers Runtime Data transfer Data models Computational modeling workflow scheduling evolutionary computation multi-objective optimization large-scale energy efficient

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Packet scheduling in Multi-Flow Carrier Aggregation With QoS Provisioning: Cross-Layer Design and Performance Analysis

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第2期24卷 507-524页

作者： Alorainy, Abdulaziz King Abdulaziz City Sci & Technol KACST Future Generat Connect & Wireless Sensors Res Ins Riyadh 12354 Saudi Arabia

Multi-flow carrier aggregation (CA) is a key solution for improving data rates in heterogeneous networks (HetNets). Efficient data splitting is important to enhance the performance of multi-flow CA. In this paper, packet scheduling in multi-flow CA is investigated via a cross-layer design approach with comprehensive quality of service (QoS) provisioning. In particular, an approximation of the average queuing delay (AQD) in terms of the average buffers' length and the average packet arrivals is presented. Then, using the presented AQD approximation, various packet scheduling schemes are developed in this paper. First, a low-complexity buffer and link aware heuristic packet scheduling scheme is proposed. Next, in order to reduce backhaul signalling overhead, a hybrid packet scheduling scheme that combines heuristic packet scheduling and random packet scheduling is considered. Then, an optimal packet scheduling scheme, albeit of high computational complexity, is proposed as a benchmark for other packet scheduling schemes. Also, the performances of the proposed packet scheduling schemes are analyzed and various data link layer performance parameters such as AQD, packet loss probability (PLP) and statistical queuing delay bound (SDB) are taken into consideration. Moreover, the complexity and the signalling overhead of the proposed packet scheduling schemes are investigated. Presented numerical results demonstrate that the heuristic packet scheduling scheme can achieve a performance that is similar to the performance of the optimal packet scheduling scheme while overcoming complexity and feasibility issues of the optimal packet scheduling scheme. Also, the hybrid packet scheduling scheme reduces signalling overhead while reaping some of the benefits of using buffer and link aware packet scheduling.

关键词： scheduling algorithms Quality of service Delays Complexity theory Macrocell networks Mobile computing Cross layer design Dual connectivity multi-flow carrier aggregation packet scheduling queuing delay

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Response Time Analysis and Optimal Priority Assignment for Global Non-Preemptive Fixed-Priority Rigid Gang scheduling

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第3期36卷 455-470页

作者： Sun, Binqi Kloda, Tomasz Chen, Jiyang Lu, Cen Caccamo, Marco Tech Univ Munich D-85748 Munich Germany Univ Toulouse AAS CNRS INSA F-31000 Toulouse France

Non-preemptive rigid gang scheduling combines the efficiency of parallel execution with the reduced overhead of non-preemptive scheduling. This approach is particularly advantageous for parallel hardware accelerators, such as Google's Edge Tensor Processing Unit (TPU), which is widely used for deep neural network (DNN) inference on embedded systems. This paper studies sporadic global non-preemptive fixed-priority (NP-FP) rigid gang scheduling, which is well-suited for DNN applications in Edge TPU pipelines. Each gang task spawns a fixed number of threads that must execute concurrently across distinct processing units. We introduce the first carry-in limitation technique specifically designed for gang task response time analysis, addressing the unique challenges posed by intra-task parallelism. This technique is formulated as a generalized knapsack problem, and we develop both a linear programming relaxation and a dynamic programming approach to solve it under different time complexities. Additionally, we propose the first optimal priority assignment policy for NP-FP gang schedulability tests. Our proposed schedulability analysis and optimal priority assignment policy are evaluated through extensive experiments, including both synthetic task sets and a case study using DNN benchmarks on commercial off-the-shelf Edge TPU accelerators. The results demonstrate that the proposed approaches effectively enhance the state-of-the-art global NP-FP gang schedulability tests, achieving improvements of up to 57.9% for synthetic task sets and 76.7% for Edge TPU benchmarks. Furthermore, we conduct an ablations study to examine the impact of different algorithmic components in the proposed technique, providing valuable insights for future research.

关键词： Optimal scheduling Artificial neural networks scheduling Time factors Pipeline processing scheduling algorithms Real-time systems Interference Benchmark testing Sun Gang scheduling response time analysis optimal priority assignment Edge TPU real-time system

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