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检索条件"主题词=Scheduling algorithms"

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Improved algorithms for Online Rent Minimization Problem Under Unit-Size Jobs 31

Improved Algorithms for Online Rent Minimization Problem Und...

引用

31st Annual European Symposium on algorithms, ESA 2023

作者： Sun, Enze Yang, Zonghan Zhang, Yuhao The University of Hong Kong Hong Kong Shanghai Jiao Tong University China

ISBN: (纸本)9783959772952

We consider the Online Rent Minimization problem, where online jobs with release times, deadlines, and processing times must be scheduled on machines that can be rented for a fixed length period of T. The objective is to minimize the number of machine rents. This problem generalizes the Online Machine Minimization problem where machines can be rented for an infinite period, and both problems have an asymptotically optimal competitive ratio of O(log(pmax/pmin)) for general processing times, where pmax and pmin are the maximum and minimum processing times respectively. However, for small values of pmax/pmin, a better competitive ratio can be achieved by assuming unit-size jobs. Under this assumption, Devanur et al. (2014) gave an optimal e-competitive algorithm for Online Machine Minimization, and Chen and Zhang (2022) gave a (3e + 7) ≈ 15.16-competitive algorithm for Online Rent Minimization. In this paper, we significantly improve the competitive ratio of the Online Rent Minimization problem under unit size to 6, by using a clean oracle-based online algorithm framework. © Enze Sun, Zonghan Yang, and Yuhao Zhang

关键词： scheduling algorithms

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Governing Resource Failures through Reinforcement Learning scheduling in Fog/Edge Computing: A Review

Governing Resource Failures through Reinforcement Learning S...

引用

IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Aliyu Muhamad Masnida Hussin Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Selangor Darul Ehsan Malaysia

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

The exponential growth of IoT applications due to user demands necessitates the emergence of reliable fog/edge computing systems. Because of the wide-open area of these systems, the tendency for failure in communication channels and computing nodes (resources) is unpredictable, especially in real-time IoT applications. Several studies have aimed at tackling fault and failure issues by employing a variety of resource management strategies. Most studies use heuristics or conventional scheduling approaches that leverage the use of reinforcement learning. The element of reinforcement learning aims to support better governance when resource faults and failures occur. This paper provides an analysis of the implementation of reinforcement learning (RL) into fault-tolerant scheduling in fog/edge computing systems. The efficacy of fault-tolerant scheduling is claimed to be improved through RL with regard to system performance. We grouped the discussion based on three themes: resource failures, reinforcement learning (RL) in general, and RL scheduling for fault tolerance. The discussion could further derive solutions for overcoming the challenges of resource failures in fog/edge systems.

关键词： Fault tolerance Processor scheduling Reviews scheduling algorithms System performance Fault tolerant systems Reinforcement learning

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Hybrid Grey Wolf and Particle Swarm Optimized (HGW-PSO) scheduling in Containerized Cloud Computing Environment

Hybrid Grey Wolf and Particle Swarm Optimized (HGW-PSO) Sche...

引用

Emerging Technology (INCET), International Conference for

作者： S.M. Udhaya sankar Martin Joel Rathnam Aswanth Babu Harikumar G J Keerthi Nadh Department of Computer Science and Engineering(Cyber Security) R.M.K College of Engineering and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam college of Engineering Bengaluru Karnataka India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The concept of containerization consists of encapsulating applications with their libraries with the help of operating system level virtualization, ensuring they are separated from other processes on the same machine. Containers gained popularity in several data centers due to their lightweight nature and simplified deployment. Application containerization technology is a fundamental technology that enables the deployment of numerous containers on a single physical node. A single physical node has the capability to offer several services to users. The adoption of microservice architecture is on the rise as it offers several benefits over monolithic designs, such as simplified deployment, enhanced versatility, and improved accessibility. The rise of containerization has significantly increased the significance of microservices, enhancing their scalability and portability. Many researchers have developed theories and solutions for the microservice container scheduling problem, yet limits remain. The algorithm's tendency to become stuck in local optimization and its slow optimal solution search are examples. In this work we present a proposal for grey wolf and particle swarm optimized (GW-PSO) scheduling in a Containerized Cloud Computing Environment. During the first phase of the search, the GW's global search can direct the PSO away from local optima, preventing early convergence. In the last phases of the search, the PSO can improve GW search along the Pareto optimum frontier. The research findings demonstrate, in comparison to the other 3 methods, the algorithm achieves a 19.06% reduction in network transmission cost, a 15.57% improvement in load balancing performance, and a 6.67% enhancement in service stability.

关键词： Cloud computing scheduling algorithms Scalability Microservice architectures Containers Resource management Proposals

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Hadar: Heterogeneity-Aware Optimization-Based Online scheduling for Deep Learning Cluster

Hadar: Heterogeneity-Aware Optimization-Based Online Schedul...

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Abeda Sultana Fei Xu Xu Yuan Li Chen Nian-Feng Tzeng School of Computing and Informatics University of Louisiana at Lafayette USA School of Computer Science and Technology East China Normal University China Department of Computer and Information Sciences University of Delaware USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

With the wide adoption of deep neural network (DNN) models for various applications, enterprises, and cloud providers have built deep learning clusters and increasingly deployed specialized accelerators, such as GPUs and TPUs, for DNN training jobs. To arbitrate cluster resources among multi-user jobs, existing schedulers fall short, either lacking fine-grained heterogeneity awareness or hardly generalizable to various scheduling policies. To fill this gap, we propose a novel design of a task-level heterogeneity-aware scheduler, Hadar, based on an online optimization framework that can express other scheduling algorithms. Hadar leverages the performance traits of DNN jobs on a heterogeneous cluster, characterizes the task-level performance heterogeneity in the optimization problem, and makes scheduling decisions across both spatial and temporal dimensions. The primal-dual framework is employed, with our design of a dual subroutine, to solve the optimization problem and guide the scheduling design. Extensive trace-driven simulations with representative DNN models have been conducted to demonstrate that Hadar improves the average job completion time (JCT) by 3× over an Apache YARN-based resource manager used in production. Moreover, Hadar outperforms Gavel [1], the state-of-the-art heterogeneity-aware scheduler, by 2.5× for the average JCT, shortens the queuing delay by 13%, and improves FTF (Finish-Time-Fairness) by 1.5%.

关键词： Deep learning Training Runtime scheduling algorithms Prototypes Artificial neural networks Optimal scheduling

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Research on Bus scheduling Algorithm Based on Integer Programming

Research on Bus Scheduling Algorithm Based on Integer Progra...

引用

Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Sheng Liu Minna Huang Tong Zhang Nan Jing Intelligent Transportation Information Co. Ltd. Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

To address challenges inherent in the scheduling of public bus transportation, such as disparities in peak and off-peak operational demands, amalgamated single and double shift operations, this study endeavors to model and optimize the daily operational schedules for bus routes, grounded in empirical data reflecting actual operational requirements. The formulation of the bus scheduling problem entails the integration of various parameters including planned trip quantities, peak-hour road conditions, station dwell durations, inter-departure intervals, and driver shift change dynamics. In the optimization process, a hybrid algorithm, combining Genetic Algorithm (GA) and Tabu Search (TS), is proposed. GA serves as a heuristic for a global exploration of the solution space, while TS is for a detailed exploration of local regions. Leveraging the operational parameters of actual bus schedules in Nanjing, the proposed hybrid algorithm is applied to refine the scheduling plan for a specific bus route. The actual scheduling results demonstrate that, in comparison to stand-alone implementations of GA, greedy algorithms, and manually crafted schedules, the hybrid GA-Tabu algorithm yields a noteworthy 7.88 percent improvement in the utilization rate of working hours. Furthermore, the departure frequency seamlessly adapts to peak periods, aligning with passenger demand patterns and augmenting the overall system efficiency. The proposed hybrid GA-Tabu algorithm proves efficacious in enhancing system efficiency, catering to passenger demands, and ensuring compliance with driver work hour regulations. Besides, its applicability showcases a degree of generality within the realm of public bus transportation scheduling.

关键词： Greedy algorithms Schedules scheduling algorithms Roads Manuals Dynamic scheduling Regulation

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TRAVEL: Deterministic Transmission scheduling for Satellite-Terrestrial Integrated Networks

TRAVEL: Deterministic Transmission Scheduling for Satellite-...

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IEEE International Conference on Communications in China (ICCC)

作者： Weiting Zhang Peixi Liao Dong Yang Ping Dong Haixia Peng Hongke Zhang School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

In this paper, we investigate a deterministic transmission scheduling problem for satellite-terrestrial integrated networks (STIN), in which satellite networks supplement terrestrial networks endowed with deterministic mechanisms (e.g., cycle specified queuing and forwarding) to improve scheduling success ratio and reduce overall transmission delay (i.e., network revenue) while maintaining users’ quality of service (QoS). We propose a fixed-mobile-satellite integrated architecture and formulate the transmission scheduling problem as a two-hierarchical routing and queuing problem. Then, a deep reinforcement learning-based Transient Routing And Varied quEue aLgorithm (TRAVEL) is developed to address the two-hierarchical decision problem. Specifically, according to the intrinsic properties of routing and queuing, we decouple the decision problem into two sub-problems, namely route planning at the macro level and queue selection at the micro level. The proposed TRAVEL can realize efficient decision making to perform the differential transmission scheduling of intricate tasks. Simulation results demonstrate that the TRAVEL delivers robust and effective performance, thereby enhancing network operation revenue in terms of different traffic proportion.

关键词： Satellites scheduling algorithms Simulation Decision making Quality of service Routing Space-air-ground integrated networks

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A dual-kernel scheduling scheme based on TrustZone

A dual-kernel scheduling scheme based on TrustZone

引用

Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Jintao Qiu Department of Basic Software East China Institute of Computing Technology China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

With the continuous improvement of the performance of embedded hardware platforms, in the fields of aviation and automotive electronics, an important development direction is to concentrate functions including real-time control, information visualization and human-computer interaction into the same hardware platform. Because of its preemptive design and customized drivers for real-time scenarios, RTOS can meet the requirements of real-time control for hard real-time. However, in terms of the functions related to user experience such as human-computer interaction, the mainstream general operating system (GPOS) such as Windows and Linux is obviously a more suitable choice. In TZDKS architecture, with the help of TrustZone technology under AArch64 architecture, RTOS and GPOS are integrated into the same SoC platform, which can run real-time tasks and common tasks at the same time. However, the Idle scheduling scheme of TZDKS makes the responsiveness of GPOS completely sacrificed when the real-time kernel load is heavy. To solve this problem, this paper uses Sporadic Server algorithm to replace Idle scheduling scheme, and significantly improves the response capability of GPOS while ensuring hard real-time tasks in RTOS.

关键词： Human computer interaction Visualization scheduling algorithms Computer architecture Real-time systems Hardware User experience scheduling Servers Kernel

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Multi-Temporal Resource Slicing and scheduling Algorithm for 6G Networks Based on MPNN-DQN

Multi-Temporal Resource Slicing and Scheduling Algorithm for...

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International Conference on Communication Technology (ICCT)

作者： YiFei Wang XiaoRong Zhu Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

This paper proposes a dynamic network slicing and resource scheduling algorithm for 6G networks, combining Message Passing Neural Networks (MPNN) and Deep Q-Learning (DQN). The algorithm uses MPNN to extract features from network nodes and links, updating their states through a message-passing mechanism. DQN then predicts the Q-value of each action based on these features, enabling fine-grained resource allocation. This approach dynamically adjusts both intra-slice and inter-slice resource allocations, significantly enhancing network resource utilization and service quality. Extensive simulations demonstrate the algorithm's ability to maintain stable resource allocation over longer time scales while improving real-time service performance on shorter time scales. Results indicate that the algorithm significantly outperforms existing methods in resource utilization, task completion rate, and service quality.

关键词： 6G mobile communication Q-learning scheduling algorithms Heuristic algorithms Network slicing Dynamic scheduling Prediction algorithms Feature extraction Stability analysis Resource management

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Optimal Self-scheduling for Fast EV Charging Station on Highway: A Tunnel Case Study

Optimal Self-Scheduling for Fast EV Charging Station on High...

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International Conference on Environment and Electrical Engineering (EEEIC)

作者： Erfan Shirdare Mostafa Kermani Luigi Martirano Cristina Moscatiello Luigi Calcara Daniela de Nigris Giuseppe Bianchi Carlotta Cerani Vito Distante Department of Electrical Engineering Sapienze University of Rome Rome Italy ANAS Rome Italy

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

Highways are one of the main locations where installing an Electric Vehicle Charging Station (EVCS) is crucial. This paper introduces a novel concept to overcome the main challenge in this regard, which is the lack of enough electrical transmission systems close to the roads. The main idea is to leverage surplus power generated from nearby tunnels that have exclusive power supply. By capitalizing on excess power from the tunnel infrastructure, this study explores the feasibility of ultra-fast EVCSs facilities. The research investigates contingent upon the available power supply, with dual objectives: foremost, to optimize the accommodation capacity for EVs at the charging station, and secondly, to minimize the wait times for charging sessions. Employing advanced scheduling algorithms and mathematical optimization techniques, the study delineates optimal self-scheduling strategies for managing the charging infrastructure efficiently. This research contributes insights into the potential synergies of transportation infrastructure for the development of robust and efficient EVCSs along highways.

关键词： scheduling algorithms Roads Transportation Europe Optimal scheduling Power system stability Electric vehicle charging Vehicle dynamics Thermal stability Fire safety

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ALNS Framework for Platform-Based Vehicle scheduling in Logistics Industrial Park CPSs

ALNS Framework for Platform-Based Vehicle Scheduling in Logi...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Botong Liu Yuqiao Wang Yang Liu Wenming Zhe Yan Cheng JD Logistics Beijing China Southeast University Nanjing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The limitation of platform resources is usually the bottleneck of loading/unloading operations in logistics. Hence, the strategies for utilizing platforms significantly impact the efficiency of logistics, which further affects the throughput of logistics industrial parks. In order to improve the efficiency of logistics management and operation, platform-based vehicle scheduling is studied in this work, which aims to appropriately assign the vehicles for loading/unloading to specific platforms and time ranges, thereby optimizing the usage of platforms and avoiding unnecessary waiting. The recent trend of Industrial 4.0 has further promoted the applications of the cyber-physical system (CPS) infrastructure in modern logistics systems, allowing the scheduling based on the real-time states of entities and resources. However, this also poses new challenges such that the decisions to support large-scale scheduling tasks need to be made within limited time. In order to tackle these challenges and enable real-time decision making, this work proposes an innovative scheduling algorithm based on the adaptive large neighborhood search (ALNS) framework to optimize the operation time in the logistics industrial park with high efficiency. The proposed algorithm is compared against the Gurobi professional solver based on real-world data for vehicle scheduling in logistics indus-trial parks, where it is demonstrated to significantly outperform Gurobi on the efficiency and scalability in both day-ahead and real-time scenarios without any degradation of solution quality.

关键词： Degradation Job shop scheduling scheduling algorithms Scalability Decision making Throughput Market research Real-time systems Informatics Logistics

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