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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Yatong Wang Yuchen Pei Yuqi Zhao School of Computer Science Central China Normal University Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the widespread adoption of 5G and Internet of Things (IoT) technologies, the low latency provided by edge computing has great importance for real-time processing. However, managing numerous simultaneous service requests poses a significant challenge to maintaining low latency. Current edge server task scheduling methods often fail to balance multiple optimization goals effectively. This paper introduces a novel task-scheduling approach based on Evolutionary Computing (EC) theory and heuristic algorithms. We model service requests as task sequences and evaluate various scheduling schemes during each evolutionary process using Large Language Models (LLMs) services. Experimental results show that our task-scheduling algorithm outperforms existing heuristic and traditional reinforcement learning methods. Additionally, we investigate the effects of different heuristic strategies and compare the evolutionary outcomes across various LLM services.

关键词： scheduling algorithms Heuristic algorithms Reinforcement learning scheduling Real-time systems Servers Internet of Things Resource management Low latency communication Optimization

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Multi-User scheduling Based on Improved CNN for Massive MIMO Industrial IoT Systems

Multi-User Scheduling Based on Improved CNN for Massive MIMO...

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： ChuXiang Sun YongPan Zhang Su Pan School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Digital Operations Promotion Department China Telecom Jiangsu branch Nanjing China

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

The rapid development of the 5th Generation Mobile System (5G) brings many challenges. Since the number of nodes in the Internet of Things (IoT) system, 5G adopts millimeter wave and massive multiple-input multiple-output (MIMO) technology. Due to the large number of IoT nodes, a substantial computational load will be generated in the massive MIMO system when applying the traditional multi-user scheduling algorithms. In this paper, a multi-user scheduling algorithm based on the improved Convolutional Neural Networks (CNN) is proposed. This algorithm uses the predictive characteristics of the neural network to calculate the channel capacity. The improved particle swarm optimization (IPSO) algorithm is used to find the optimal network structure parameters of the CNN to acquire the CNN with high prediction accuracy and achieve low computational complexity in the scenario with massive users accessed.

关键词： Job shop scheduling 5G mobile communication scheduling algorithms Massive MIMO Prediction algorithms Throughput Convolutional neural networks Particle swarm optimization Computational complexity Optimization

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GACO: Resource Aware scheduling in Multi Cloud Environment using Hybrid Meta-Heuristic Algorithm

GACO: Resource Aware Scheduling in Multi Cloud Environment u...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Santhosh Kumar Medishetti Charan Reddy Tamma Rakesh Venigalla Pranideep Reddy Meka Phani Vishnu Addepalli Baji Babu Indurthi VIT-AP University Amaravathi India Nalla Narasimha Reddy Education Society’s Group of Institution India George Mason University Virginia United States of America George Washington University Virginia United States of America

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

A significant issue with multi-cloud environments is the continued complexity that arises from the dynamic and heterogeneous scheduling of tasks across clouds because multi-clouds have large variability in terms of their used resources. In this paper we propose a new paradigm for Task scheduling (TS) for multi cloud environment using Genetic Ant Colony Optimization (GACO), a hybrid algorithm that synergizes the exploration capabilities of Genetic Algorithm (GA) with the exploitation strengths of ACO. The GACO algorithm aims to optimize task allocation by balancing multiple objectives, including minimizing makespan, reducing energy consumption, and lowering overall operational costs. By leveraging the global search properties of GA and the local optimization capabilities of ACO, GACO effectively navigates the complex search space to find near-optimal scheduling solutions. Extensive simulations conducted in diverse multi-cloud scenarios demonstrate that GACO outperforms traditional scheduling algorithms, leading to significant improvements in resource utilization, energy efficiency, and overall system performance. This work contributes to the field of Cloud Computing (CC) by providing a robust and scalable solution for TS in multi-cloud environments, ensuring efficient resource allocation and enhancing the quality of service.

关键词： Cloud computing scheduling algorithms Navigation System performance Heuristic algorithms Metaheuristics Quality of service Dynamic scheduling Resource management Genetic algorithms

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CLSA-CIM: A Cross-Layer scheduling Approach for Computing-in-Memory Architectures

CLSA-CIM: A Cross-Layer Scheduling Approach for Computing-in...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Rebecca Pelke Jose Cubero-Cascante Nils Bosbach Felix Staudigl Rainer Leupers Jan Moritz Joseph Institute for Communication Technologies and Embedded Systems RWTH Aachen University Germany

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The demand for efficient machine learning (ML) accelerators is growing rapidly, driving the development of novel computing concepts such as resistive random access memory (RRAM)-based tiled computing-in-memory (CIM) architectures. CIM allows to compute within the memory unit, resulting in faster data processing and reduced power consumption. Efficient compiler algorithms are essential to exploit the potential of tiled CIM architectures. While conventional ML compilers focus on code generation for CPUs, GPUs, and other von Neumann architectures, adaptations are needed to cover CIM architectures. Cross-layer scheduling is a promising approach, as it enhances the utilization of CIM cores, thereby accelerating computations. Although similar concepts are implicitly used in previous work, there is a lack of clear and quantifiable algorithmic definitions for cross-layer scheduling for tiled CIM architectures. To close this gap, we present CLSA-CIM, a cross-layer scheduling algorithm for tiled CIM architectures. We integrate CLSA-CIM with existing weight-mapping strategies and compare performance against state-of-the-art (SOTA) scheduling algorithms. CLSA-CIM improves the utilization by up to 17.9 ×, resulting in an overall speedup increase of up to 29.2 × compared to SOTA.

关键词： Cross layer design Power demand Machine learning algorithms scheduling algorithms Random access memory Computer architecture In-memory computing

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Two-Level Dependent Task Elastic scheduling for Mobile Edge Computing

Two-Level Dependent Task Elastic Scheduling for Mobile Edge ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jinxiang Yu Jinkai Wang Lei Huang Yibo Yuan Chengsheng Cai Dongxiao Zhao Jie Feng Qingqi Pei Guangzhou Institute of Technology Xidian University Guangzhou School of Telecommunications Engineering Xidian University Xi’an

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Compared with cloud computing, edge computing can provide users with computing services that lower response latency and reduce bandwidth pressure, enabling jobs to be processed in the proximity of users. Due to diverse user requirements and limited network resources, it is crucial to guarantee user experience through efficient task scheduling. However, the complexity of network environments and the inherent dependencies between different tasks from each job make it challenging. Given the dynamicity of network conditions, computation resources and service requirements in wireless networks, we have provided a two-level elastic scheduling scheme for dependent task processing. First, a novel scheduling architecture is presented to decouple executor deployment and task assignment, facilitating the increase of resource utilization and the flexibility and elasticity of task scheduling. Then, an optimization problem has been formulated to minimize the execution delay of all jobs with consideration of the characteristics of network networks and task dependencies. After that, an enhanced first come first serve strategy has been employed to make task assignments based on the devised task sorting algorithm. Finally, extensive simulations have been done to evaluate the performance of our proposed algorithm, and the results have shown that our proposed algorithm performs better than benchmark algorithms.

关键词： Multi-access edge computing Processor scheduling scheduling algorithms Wireless networks Computer architecture User experience Resource management Global communication Optimization Sorting

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Cloud Computing Task scheduling Techniques and its Trends

Cloud Computing Task Scheduling Techniques and its Trends

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Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： Sushree Bharati Prasant Kumar pattnaik Dipti Dash School of Computer Engg KIIT Bhubaneswar India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Cloud computing has transformed how computa- tional resources are delivered to users online. As the number of users and providers grows, managing resources effectively, especially through task scheduling has become paramount. Ef- fective scheduling aims to optimize resource use and minimize wait times, enhancing the effectiveness of cloud services. This review delves deeply into various task-scheduling algorithms used in cloud environments, examining their strengths, weaknesses, and applicability. Challenges like unpredictable workloads and diverse hardware are explored, along with how these algorithms tackle these issues. Additionally, the paper offers a comparative analysis of scheduling techniques using simulations, shedding light on their performance in workflow-based applications. Over- all, this paper is a valuable resource for those interested in the latest task scheduling advancements in cloud computing.

关键词： Cloud computing scheduling algorithms Computational modeling Market research Ubiquitous computing Software Resource management

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Computation Selection: scheduling Users to Enable Over-the-Air Federated Learning

Computation Selection: Scheduling Users to Enable Over-the-A...

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IEEE International Symposium on Information Theory

作者： Bobak Nazer Krishna Narayanan ECE Department Boston University Boston MA USA ECE Department Texas A&M University College Station TX USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Recent work has argued that federated learning over wireless channels can be accelerated by a factor of $K$ (the numbers of users), by using computation over multiple-access channels to directly average the gradients. This implicitly presumes that timely channel state information is available at the transmitters, which may not be feasible for large $K$ . This paper presents a simple scheduling algorithm that only uses channel state information at the receiver to activate a subset of the users for computation, and accelerates averaging by a factor of $K^{2/3}$ .

关键词： Schedules Protocols Federated learning scheduling algorithms Transmitters Wireless networks Receivers

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A Web Service for HTCondor scheduling System

A Web Service for HTCondor Scheduling System

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Heng Zhang Xiaotian Zhang Guorui Chen Yunpeng Zhang Shandong Institute of Advanced Technology (SDIAT) Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

As a high throughput computing (HTC) task scheduling system, the HTCondor (hereinafter called Condor) has been widely applied in the field of high-energy physics computing and analysis, due to its open-source, installation-friendly and better scheduling algorithm. Many research institutes deployed Condor on their computing clusters to complete the resource management and task scheduling. However, Condor is lack of a user-friendly web interface. Some beginners or students, who are not familiar with Condor, have to spend time to figure out the rules about how to submit and manage their computing or analysis tasks using condor-specified commands. Fortunately, Condor provides several application programming interfaces (APIs), which makes it possible to develop relevant application or service according to the actual environment. Herein, by utilizing the Condor python interface combined with the currently popular VUE front-end architecture, we developed a web service for Condor system. Instead of using condor commands, users can submit, query and control their HTC tasks on a web browser. This work brings great convenience to Condor system users, especially for beginners and students. Besides, it also provides a real-time monitor function, which makes more convenient and intuitive for users and system managers to monitor the cluster resources and tasks' status in real time.

关键词： scheduling algorithms Web pages Production Throughput Real-time systems scheduling Service-oriented architecture Resource management Monitoring Python

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Towards Secure and Efficient Data scheduling for Vehicular Social Networks

arXiv

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arXiv 2024年

作者： Xia, Youhua Zhang, Tiehua Jin, Jiong He, Ying Yu, Fei Shenzhen518107 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The College of Electronics and Information Engineering Tongji University Shanghai China The School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia

Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly centers on crafting cooperative scheduling algorithms tailored for vehicular networks. Notwithstanding, the intricacies of orchestrating scheduling in vehicular social networks both effectively and efficiently remain a formidable task. This paper introduces an innovative learning-based algorithm for scheduling data transmission that prioritizes efficiency and security within vehicular social networks. The algorithm first uses a specifically constructed neural network to enhance data processing capabilities. After this, it incorporates a Q-learning paradigm during the data transmission phase to optimize the information exchange, the privacy of which is safeguarded by differential privacy through the communication process. Comparative experiments demonstrate the superior performance of the proposed Q-learning enhanced scheduling algorithm relative to existing state-of-the-art scheduling algorithms in the context of vehicular social networks. © 2024, CC BY.

关键词： scheduling algorithms

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A probabilistic analysis on general probabilistic scheduling problems

A probabilistic analysis on general probabilistic scheduling...

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IEEE International Symposium on Information Theory

作者： Daiki Suruga Graduate School of Mathematics Nagoya University Furocho Chikusaku Nagoya Japan

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

The scheduling problem is a key class of optimization problems and has various kinds of applications both in practical and theoretical scenarios. In the scheduling problem, probabilistic analysis is a basic tool for investigating performance of scheduling algorithms, and therefore has been carried out by plentiful amount of prior works. However, probabilistic analysis has several potential problems. For example, current research interest in the scheduling problem is limited to i.i.d. scenarios, due to its simplicity for analysis. This paper provides a new framework for probabilistic analysis in the scheduling problem and aims to deal with such problems. As a consequence, we obtain several theorems including a theoretical limit of scheduling problems which can be applied to general, non-i.i.d. probability distributions. Several information theoretic techniques, such as information-spectrum method, turned out to be useful to prove our results. Since the scheduling problem has relations to many other research fields, our framework hopefully yields other interesting applications in the future. The full paper is available at arXiv [1].

关键词： scheduling algorithms Probabilistic logic scheduling Probability distribution Optimization Information theory

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