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scheduling Design and Performance Analysis of Carrier Aggregation in Satellite Communication Systems

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第8期70卷 7845-7857页

作者： Al-Hraishawi, Hayder Maturo, Nicola Lagunas, Eva Chatzinotas, Symeon Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-4365 Esch Sur Alzette Luxembourg

Carrier Aggregation is one of the vital approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in cellular networks, its application to satellite systems has not been thoroughly explored yet. Carrier aggregation can offer an enhanced and more consistent quality of service for users throughout the satellite coverage via combining multiple carriers, utilizing the unused capacity at other carriers, and enabling effective interference management. Furthermore, carrier aggregation can be a prominent solution to address the issue of the spatially heterogeneous satellite traffic demand. This paper investigates introducing carrier aggregation to satellite systems from a link layer perspective. Deployment of carrier aggregation in satellite systems with the combination of multiple carriers that have different characteristics requires effective scheduling schemes for reliable communications. To this end, a novel load balancing scheduling algorithm has been proposed to distribute data packets across the aggregated carriers based on channel capacities and to utilize spectrum efficiently. Moreover, in order to ensure that the received data packets are delivered without perturbing the original transmission order, a perceptive scheduling algorithm has been developed that takes into consideration channel properties along with the instantaneous available resources at the aggregated carriers. The proposed modifications have been carefully designed to make carrier aggregation transparent above the medium access control (MAC) layer. Additionally, the complexity analysis of the proposed algorithms has been conducted in terms of the computational loads. Simulation results are provided to validate our analysis, demonstrate the design tradeoffs, and to highlight the potentials of carrier aggregation applied to satellite communication systems.

关键词： Satellites Resource management Logic gates Complexity theory scheduling algorithms Load management Broadband communication Carrier aggregation load balancing packet scheduling scheduling algorithms satellite communication

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Chance-constrained Static Schedules for Temporally Probabilistic Plans

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2022年 75卷 1323-1372页

作者： Fang, Cheng Wang, Andrew J. Williams, Brian C. MIT Comp Sci & Artificial Intelligence Lab 32 Vassar St Cambridge MA 02139 USA

Time management under uncertainty is essential to large scale projects. From space exploration to industrial production, there is a need to schedule and perform activities. given complex specifications on timing. In order to generate schedules that are robust to uncertainty in the duration of activities, prior work has focused on a problem framing that uses an interval-bounded uncertainty representation. However, such approaches are unable to take advantage of known probability distributions over *** this paper we concentrate on a probabilistic formulation of temporal problems with uncertain duration, called the probabilistic simple temporal problem. As distributions of-ten have an unbounded range of outcomes, we consider chance-constrained solutions, with guarantees on the probability of meeting temporal constraints. By considering distributions over uncertain duration, we are able to use risk as a resource, reason over the relative like-lihood of outcomes, and derive higher utility solutions. We first demonstrate our approach by encoding the problem as a convex program. We then develop a more efficient hybrid algorithm whose parent solver generates risk allocations and whose child solver generates schedules for a particular risk allocation. The child is made efficient by leveraging existing interval-bounded scheduling algorithms, while the parent is made efficient by extracting conflicts over risk allocations. We perform numerical experiments to show the advantages of reasoning over probabilistic uncertainty, by comparing the utility of schedules generated with risk allocation against those generated from reasoning over bounded uncertainty. We also empirically show that solution time is greatly reduced by incorporating conflict-directed risk allocation.

关键词： scheduling algorithms

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Adaptive Resource scheduling Algorithm for Multi-Feature Optimization in Personalized Wireless Body Area Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Zheng Ren, Xinxing Lai, Chun Sing Wu, Ruiheng Lin, Jinzhao Li, Zhangyong Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Brunel University of London College of Engineering Design and Physical Sciences UB8 3PH United Kingdom Chongqing University of Posts and Telecommunications Chongqing Engineering Research Center of Medical Electronics and Information Technology Chongqing400065 China

Wireless Body Area Network (WBANs), as a crucial technology in the field of healthcare monitoring, also plays a significant role in personal consumer electronics (CE). However, existing solutions have yet to effectively address challenges such as personalized demands, data heterogeneity, and dynamic link variations caused by diverse environmental factors. To address these issues, this study proposes a novel approach based on the IEEE 802.15.6 standard, which incorporates personalized node characteristics to cater to individual needs and adapt to node heterogeneity. Furthermore, a dynamic scheduling mechanism for node data is introduced, leveraging multi-feature environmental factors to enhance the practicality of WBAN systems. Finally, for real-time scheduling of emotional anomaly data, this study incorporates skin conductance nodes for the first time to evaluate emotional factors, ensuring the efficient transmission of urgent data. Theoretical analysis and simulation results demonstrate that the proposed approach significantly outperforms traditional methods in terms of energy efficiency, latency, throughput, and resource balance. © 1975-2011 IEEE.

关键词： scheduling algorithms

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Data Migration in Large Scale Storage Systems with Varying File Sizes 24

Data Migration in Large Scale Storage Systems with Varying F...

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8th International Conference on algorithms, Computing and Systems, ICACS 2024

作者： Goch, Laszlo Chen, Sixia Department of Mathematics and Computer Science Adelphi University Garden CityNY United States

ISBN: (纸本)9798400718304

In this paper, we consider the problem of data migration in large scale storage systems in the homogeneous setting where a set of files needs to be transferred within a set of disks, and each disk can only engage in one transmission at a time. The objective is to schedule the data transmissions in the fewest possible time *** the file sizes are uniform, the problem is equivalent to edge coloring in multigraphs, which has been studied for decades, and many well-known algorithms have been devised. We consider the problem when the file sizes are non-uniform. We propose two algorithms for the problem - Maximal Compatible Sets (MCS) and the Tetris algorithm, and prove that the Tetris algorithm is a 4-approximation algorithm. We also empirically compare the performance of these algorithms against one of the most recent algorithms for the uniform file size problem, the Greedy-Euler-Color algorithm, and find that the Tetris algorithm performs the best in practice for both uniform and non-uniform file size scenarios. © 2024 Copyright held by the owner/author(s).

关键词： scheduling algorithms

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PTPS: Precision-Aware Task Partitioning and scheduling for SpMV on CPU-FPGA Heterogeneous Platforms

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Gao, Jianhua Zhou, Zhi Huang, Xingze Wang, Juan Wang, Yizhuo Ji, Weixing Beijing Normal University School of Artificial Intelligence Beijing100875 China Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China

The CPU-FPGA heterogeneous computing architecture is extensively employed in the embedded domain due to its low cost and power efficiency, with numerous sparse matrix-vector multiplication (SpMV) acceleration efforts already targeting this architecture. However, existing work rarely includes collaborative SpMV computations between CPU and FPGA, which limits the exploration of hybrid architectures that could potentially offer enhanced performance and flexibility. This paper introduces an FPGA architecture design that supports multi-precision SpMV computations, including FP16, FP32, and FP64. Building on this, PTPS, a precision-aware SpMV task partitioning and dynamic scheduling algorithm tailored for the CPU-FPGA heterogeneous architecture, is proposed. The core idea of PTPS is lossless partitioning of sparse matrices across multiple precisions, prioritizing low-precision SpMV computations on the FPGA and high-precision computations on the CPU. PTPS not only leverages the strengths of CPU and FPGA for collaborative SpMV computations but also reduces data transmission overhead between them, thereby improving the overall computational efficiency. Experimental evaluation demonstrates that the proposed approach offers an average speedup of 1.57x over the CPU-only approach and 2.58x over the FPGA-only approach. © 1982-2012 IEEE.

关键词： scheduling algorithms

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Research on Edge Computing-Based Performance Optimization Strategies for Asset Inventory Systems 14th

Research on Edge Computing-Based Performance Optimization St...

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14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Zhang, Erxi Xu, Hao Chen, Feng Jiang, Anbo Department of Digitalization Changshu Institute of Technology Jiangsu Province Changshu215500 China

ISBN: (纸本)9789819626243

This study focuses on an asset inventory system for Internet of Things (IoT) technologies, proposing a performance optimization strategy based on edge computing. We have established a novel system model that leverages heterogeneous computing resources to achieve reduced data processing and transmission delays. During the research, we introduced a task scheduling strategy optimized by a deep learning algorithm, which can intelligently analyze the characteristics of asset data and automatically adjust the task distribution between the cloud and edge nodes, thereby effectively alleviating the computational burden on the central cloud server. Additionally, we incorporated a fast caching structure based on capacity constraints to enhance the access performance of asset information. Experimental results confirm that our optimization strategy significantly improves system performance, particularly in high-concurrency scenarios, where the average response time is shortened by 30% and resource utilization is increased by 40%. Our research holds significant theoretical and practical implications for the application of edge computing in industrial IoT and smart city domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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BBR-Based Congestion Control and Packet scheduling for Bottleneck Fairness Considered Multipath TCP in Heterogeneous Wireless Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第1期70卷 914-927页

作者： Wei, Wenjia Xue, Kaiping Han, Jiangping Xing, Yitao Wei, David S. L. Hong, Peilin Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Univ Sci & Technol China Sch Cyber Secur Hefei 230027 Peoples R China Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA

Aiming at improving the performance of Multipath TCP (MPTCP) in heterogeneous wireless network environments, in this paper, by taking the advantages Bottleneck Bandwidth and Round-trip propagation time (BBR) and considering bottleneck fairness, we propose BBR-based Congestion Control and Packet scheduling scheme, called BCCPS. The proposed BCCPS first considers a BBR-based congestion control algorithm, MPTCP-BBR, for MPTCP by adaptively adjusting the sending rate of each subflow according to the real probing rate rather than the loss information to enhance the goodput while keeping bottleneck fairness with regular TCP. Then, considering that the different sending rates of the subflows in heterogeneous wireless networks will result in the problem of out-of-order (OFO) delivery, a fine-grained packet scheduling scheme is proposed to keep in-order delivery and so as to reduce the application layer completion time. The performance of the proposed scheme is evaluated via both NS-3 and real network scenarios. Experimental results show that our proposed scheme far outperforms existing MPTCP schemes in heterogeneous wireless environment.

关键词： scheduling algorithms Bandwidth Delays Wireless networks Packet loss Throughput scheduling BBR bottleneck fairness congestion control multipath TCP packet scheduling

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Birkhoff's Decomposition Revisited: Sparse scheduling for High-Speed Circuit Switches

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IEEE-ACM TRANSACTIONS ON NETWORKING 2021年第6期29卷 2399-2412页

作者： Valls, Victor Iosifidis, George Tassiulas, Leandros Yale Univ Elect Engn & Inst Network Sci New Haven CT 06520 USA Delft Univ Technol Dept Software Technol NL-2628 Delft Netherlands

Data centers are increasingly using high-speed circuit switches to cope with the growing demand and reduce operational costs. One of the fundamental tasks of circuit switches is to compute a sparse collection of switching configurations to support a traffic demand matrix. Such a problem has been addressed in the literature with variations of the approach proposed by Birkhoff in 1946 to decompose a doubly stochastic matrix exactly. However, the existing methods are heuristic and do not have theoretical guarantees on how well a collection of switching configurations (i.e., permutations) can approximate a traffic matrix (i.e., a scaled doubly stochastic matrix). In this paper, we revisit Birkhoff's approach and make three contributions. First, we establish the first theoretical bound on the sparsity of Birkhoff's algorithm (i.e., the number of switching configurations necessary to approximate a traffic matrix). In particular, we show that by using a subset of the admissible permutation matrices, Birkhoff's algorithm obtains an epsilon-approximate decomposition with at most O (log(1/epsilon)) permutations. Second, we propose a new algorithm, Birkhoff+, which combines the wealth of Frank-Wolfe with Birkhoff's approach to obtain sparse decompositions in a fast manner. And third, we evaluate the performance of the proposed algorithm numerically and study how this affects the performance of a circuit switch. Our results show that Birkhoff+ is superior to previous algorithms in terms of throughput, running time, and number of switching configurations.

关键词： scheduling algorithms machine learning algorithms switches Birkhoff decomposition

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A Probabilistic Machine Learning Approach to scheduling Parallel Loops With Bayesian Optimization

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第7期32卷 1815-1827页

作者： Kim, Khu-rai Kim, Youngjae Park, Sungyong Sogang Univ Dept Elect Engn Seoul 04107 South Korea Sogang Univ Dept Comp Sci & Engn Seoul 04107 South Korea

This article proposes Bayesian optimization augmented factoring self-scheduling (BO FSS), a new parallel loop scheduling strategy. BO FSS is an automatic tuning variant of the factoring self-scheduling (FSS) algorithm and is based on Bayesian optimization (BO), a black-box optimization algorithm. Its core idea is to automatically tune the internal parameter of FSS by solving an optimization problem using BO. The tuning procedure only requires online execution time measurement of the target loop. In order to apply BO, we model the execution time using two Gaussian process (GP) probabilistic machine learning models. Notably, we propose a locality-aware GP model, which assumes that the temporal locality effect resembles an exponentially decreasing function. By accurately modeling the temporal locality effect, our locality-aware GP model accelerates the convergence of BO. We implemented BO FSS on the GCC implementation of the OpenMP standard and evaluated its performance against other scheduling algorithms. Also, to quantify our method's performance variation on different workloads, or workload-robustness in our terms, we measure the minimax regret. According to the minimax regret, BO FSS shows more consistent performance than other algorithms. Within the considered workloads, BO FSS improves the execution time of FSS by as much as 22% and 5% on average.

关键词： Frequency selective surfaces Task analysis Heuristic algorithms Dynamic scheduling Optimization scheduling algorithms Copper Parallel loop scheduling Bayesian optimization parallel computing OpenMP

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Advanced Cross-Layer Protocol Architecture through Modified Ideal Link State Routing (MOLSR) 7

Advanced Cross-Layer Protocol Architecture through Modified ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Suseela, M. Sri Uma Praveena, V. Lakshmi, N.D.N. Hemalatha, M. Kumar, Vusse Sandeep Vishnu Institute of Technology Department of Ece Bhimavaram India Sri Vishnu Enginnering College for Women Department of Ece Bhimavaram India

ISBN: (纸本)9798350350067

Due to both the growing need for communication capacity and the ineffective utilization of the currently available spectrum, there is a shortage of spectrum. By leveraging timeserving access to licensed bands, primary consumers can be enhanced without interfering with the unnecessary usage of the current spectrum. For activities like spectrum access and routing, cognitive settings provide a difficult environment since primary users make it difficult to access channels. The problem of routing in cognitive networks is primarily concerned with the job of establishing and preserving wireless multi-hop paths between nodes. Throughout this procedure, it is necessary to decide the frequency that will be used and the number of hops at each path node. In this work, we describe a cross-layer optimization method aimed at accomplishing the previously mentioned objective. For WSN, we proposed to use an adaptive cross-layer optimized subcarrier distribution technique to achieve low energy consumption and optimal performance. A method called the Advanced Cross-Layer Protocol Architecture through Modified Optimal Link Routing (MOLR) protocol was devised in order to accomplish this goal. To distribute the subcarriers to the sensors according to the channel conditions, we provide here a trustworthy scheduling algorithm and proportional algorithms. The study analyzes the performance of the suggested MOLR algorithm and draws conclusions by contrasting the bit error rate and throughput of the simulation results with those of a standard multicarrier (MC) system. First, the Received Signal Strength Indicator (RSSI) and the current queue state are used to adjust each node's data rate through the use of the congestion-aware routing algorithm. Additionally, we can conclude that the suggested protocol is a very energy-efficient way to perform stable multipath routing at optimal levels with comparatively little network congestion during data transmission. © 2024 IEEE.

关键词： scheduling algorithms

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