In Polyamorous scheduling, we are given an edge-weighted graph and must find a periodic schedule of matchings in this graph which minimizes the maximal weighted waiting time between consecutive occurrences of the same...
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In order to solve the shortcomings of traditional methods such as high Hamming loss value and low demand supply rate, a multi-product supply chain scheduling method based on hybrid genetic algorithm was designed. Firs...
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In order to solve the dynamic job scheduling problem in current intelligent machining systems, the author proposes a multi-agent electromechanical production line dynamic scheduling based on iterative algorithms. The ...
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This study addresses a critical chain project scheduling (CCPS) problem regarding the feeding buffer. The main contribution of this study lies in determining the critical chain when the feeding buffer is considered al...
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scheduling problems refer to the decision-making process of allocating tasks to resources, usually scarce and in high demand, to optimize different performance measures. We consider the class of shop scheduling proble...
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scheduling problems refer to the decision-making process of allocating tasks to resources, usually scarce and in high demand, to optimize different performance measures. We consider the class of shop scheduling problems arising in the context of manufacturing systems, which are often NP-hard and challenging to solve. Exact methods have limitations in finding optimal solutions in reasonable computation times, even for instances of moderate size. Therefore, in real-life production environments, finding high-quality solutions is often satisfactory, even if they are not optimal. We contribute to the solution of shop scheduling problems with the design and implementation of the SSP-3M framework, oriented by three main guidelines: versatility, extensibility, and independence of the optimization method. These characteristics reduce the gap between scheduling theory and practice in real-life environments and improve the integration of the scheduling framework with other process planning or functions such as Computer-aided Process Planning, Advanced Planning and scheduling, Integrated Process Planning and scheduling, and Computer Integrated Manufacturing. The problem and solution representations adopted in our framework design make it possible to handle six shop scheduling problem variants, illustrating its versatility: job shop, flow shop, permutation flow shop, generalized flow shop, flexible flow shop, and flexible job shop. SSP-3M is open-source and can be used by any interested party. Our experimental evaluation shows that it can successfully be integrated with external optimization methods. We claim that SSP-3M is a good choice for companies that need free and quick-to-develop solutions to shop scheduling problems.
The shift towards agile microservice architecture has enabled significant benefits for IT companies but has also resulted in increased complexity for Cloud orchestration tools. Traditional tools were designed for cent...
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The shift towards agile microservice architecture has enabled significant benefits for IT companies but has also resulted in increased complexity for Cloud orchestration tools. Traditional tools were designed for centralized data centers and are ineffective for locating microservices in geographically-distributed edge-like infrastructures. This paper presents Phare, a decentralized scheduling algorithm designed to optimize the placement of microservices by satisfying their computing and communication demands while minimizing deployment costs. Phare employs a heuristic-based approach to solve the NP-Hard scheduling problem, prioritizing the microservices with the more stringent requirements and placing them on the most convenient computing facilities, based on the concept of affinity, contributing to the field by providing a more holistic approach to resource scheduling in edge computing. We validate our approach against Firmament, the state-of-the-art workload scheduling algorithm for component-based applications, on simulated edge infrastructures with hundreds of clusters. Phare achieves up to a 10x reduction in terms of deployment costs compared to Firmament while providing a much lower scheduling latency.
With the advent of the 5G era and the accelerated development of edge computing and Internet of Things technologies, the number of tasks to be processed by mobile devices continues to increase. Edge nodes become incap...
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With the advent of the 5G era and the accelerated development of edge computing and Internet of Things technologies, the number of tasks to be processed by mobile devices continues to increase. Edge nodes become incapable of facing massive tasks due to their own limited computing capabilities, and thus the cloud and edge collaborative environment is produced. In order to complete as many tasks as possible while meeting the deadline constraints, we consider the task scheduling problem in the cloud-edge and edge-edge collaboration scenarios. As the number of tasks on edge nodes increases, the solution space becomes larger. Considering that each edge node has its own communication range, we design an edge node based clustering algorithm(ENCA), which can reduce the feasible region while dividing the edge node set. We transform the edge nodes inside the cluster into a bipartite graph,and then propose a task scheduling algorithm based on maximum matching(SAMM). Our ENCA and SAMM are used to solve the task scheduling problem. Compared with the other benchmark algorithms, experimental results show that our algorithms increase the number of the tasks which can be completed and meet the latest deadline constraints by 32%–47.2% under high load conditions.
For efficient and faster working of computer, researchers tries to optimize different scheduling algorithms of operating system. Along with modernization of computer, the speed of hardware (like processor, RAM, and ha...
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In this paper, we devise a deep SARSA reinforcement learning (DSRL) user scheduling algorithm for a base station (BS) that uses a high-altitude platform station (HAPS) as a backup to serve multiple users in a wireless...
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In this paper, we devise a deep SARSA reinforcement learning (DSRL) user scheduling algorithm for a base station (BS) that uses a high-altitude platform station (HAPS) as a backup to serve multiple users in a wireless cellular network. Considering a realistic scenario, we assume that only the outdated channel state information (CSI) of the terrestrial base station (TBS) is available in our defined user scheduling problem. We model this user scheduling problem using a Markov decision process (MDP) framework, aiming to maximize the sum-rate while minimizing the number of active antennas at the HAPS. Our performance analysis shows that the sum-rate obtained with our proposed DSRL algorithm is close to the optimal sum-rate achieved with an exhaustive search method. We also develop a heuristic optimization method to solve the user scheduling problem at the BS. We show that for a scenario where perfect CSI is not available, our proposed DSRL algorithm outperforms the heuristic optimization method.
Canvas-based attention scheduling was recently pro-posed to improve the efficiency of real-time machine perception systems. This framework introduces a notion of focus locales, referring to those areas where the atten...
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ISBN:
(数字)9798350358414
ISBN:
(纸本)9798350358421
Canvas-based attention scheduling was recently pro-posed to improve the efficiency of real-time machine perception systems. This framework introduces a notion of focus locales, referring to those areas where the attention of the inference system should “allocate its attention”. Data from these locales (e.g., parts of the input video frames containing objects of interest) are packed together into a smaller canvas frame which is processed by the downstream machine learning algorithm. Compared with processing the entire input data frame, this practice saves resources while maintaining inference quality. Previous work was limited to a simplified solution where the focus locales are quantized to a small set of allowed sizes for the ease of packing into the canvas in a best-effort manner. In this paper, we remove this limiting constraint thus obviating quantization, and derive the first spatiotemporal schedulability bound for objects of arbitrary sizes in a canvas-based attention scheduling framework. We further allow object resizing and design a set of scheduling algorithms to adapt to varying workloads dynamically. Experiments on a representative AI-powered embedded platform with a real-world video dataset demonstrate the improvements in performance and inform the design and capacity planning of modern real-time machine perception pipelines.
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