User grouping and resource allocation can significantly improve the performance of massive multi-input multioutput (MINO) systems. Correlation based user grouping is a low-complexity method to avoid users of high inte...
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Predicting wireless environment parameters as correctly and timely as possible is of practical importance to optimal resource allocation in wireless systems. For example, as the realtime wireless condition information...
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In Distributed Virtual Environments (DVEs), how to guarantee consistency and fairness is a primary concern for improving user experience. As the scale of DVE systems grows fast, multi-server architecture has been wide...
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Real-time embedded systems have become widely used in many fields such as control, monitoring and aviation. They perform several tasks under strict time constraints. In such systems, deadline miss may lead to catastro...
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ISBN:
(纸本)9781509006809
Real-time embedded systems have become widely used in many fields such as control, monitoring and aviation. They perform several tasks under strict time constraints. In such systems, deadline miss may lead to catastrophic results so that all jobs need to be scheduled appropriately to ensure that they meet their deadline times. This paper presents an efficient dynamic scheduling algorithm during run-time to schedule periodic tasks in multiprocessor environments and uniprocessor as well using a dynamic average estimation. Dynamic average estimation refers to changing in different probability distributions when a task is added or removed from them. It is not always available a value of Worst-Case Execution Time (WCET) in many real-time applications such as multimedia where data has a great variation. The proposed approach selects which task or a set of tasks must be picked up for execution. A simulation system was developed to show validation of the proposed approach.
No specific implementation methods of traffic scheduling have been defined in the Time-Sensitive Network (TSN) protocols. Most research on traffic scheduling algorithms in TSN is for static network traffics. In this p...
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No specific implementation methods of traffic scheduling have been defined in the Time-Sensitive Network (TSN) protocols. Most research on traffic scheduling algorithms in TSN is for static network traffics. In this paper, a window based scheduling algorithm adaptive to network traffic fluctuations is proposed. An optimal period for Gate Control List (GCL) is firstly derived to guarantee the schedulability of control traffics. Then a dynamic window adjustment algorithm is proposed to maximize the bandwidth of best effort traffics. A simulated network is implemented based on OMNeT++/NeSTiNg simulation framework to verify the effectiveness of the proposed algorithm. The simulation results show that the proposed scheduling algorithm significantly improves the delay and jitter performance under dynamic traffic configuration. The time-sensitive services can be guaranteed even when the traffic changes dynamically.
In the context of least slack first scheduling, switching may frequently be caused. The extra overheads of preemptions among tasks debase the performance of soft real-time systems significantly. In this paper, we pres...
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ISBN:
(纸本)9781424414970;1424414970
In the context of least slack first scheduling, switching may frequently be caused. The extra overheads of preemptions among tasks debase the performance of soft real-time systems significantly. In this paper, we present a novel scheduling algorithm, named dynamic fuzzy threshold least slack first (DFTLSF) scheduling, which solved the switching problem when use least slack first scheduling algorithm in tasks. The notion of dynamic fuzzy threshold coefficient was defined to fuzzy the threshold dynamicly. The slack time of the running task is reduced to its fuzzy threshold to avoid thrashing. Comparing to the traditional least slack first scheduling algorithm, the simulation results show that, the dynamic fuzzy preemption make the switching number of the novel algorithm smaller and the missed deadline percentage decreased.
Integrated radar and communications (IRC) waveform can be applied to perform radar and communication tasks simultaneously in a multifunctional integration system (MFIS). In order to solve the task scheduling problems ...
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ISBN:
(数字)9798350355895
ISBN:
(纸本)9798350355901
Integrated radar and communications (IRC) waveform can be applied to perform radar and communication tasks simultaneously in a multifunctional integration system (MFIS). In order to solve the task scheduling problems of MFIS based on dynamic aperture partition antenna, a novel adaptive task scheduling algorithm based on IRC waveform is proposed in this paper. Firstly, the task scheduling optimization problem is established under the conditions of two-dimensional time and aperture resource constraints after establishing the multi-task models. Secondly, the proposed adaptive task scheduling algorithm makes use of the special IRC waveform characteristics of MFIS, function of time window of task and aperture resource to achieve the different kinds of tasks scheduling effectively. Finally, the simulation results show that the proposed algorithm increases the successful scheduling ratio and resource utilization ratio compared with the traditional multiple task parallel scheduling algorithms.
New cellular networks standardized by 3GPP as LTE and LTE-A aim to provide scalable resources optimizing both system performance and user satisfaction. In order to achieve this goal the scheduler algorithm becomes of ...
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New cellular networks standardized by 3GPP as LTE and LTE-A aim to provide scalable resources optimizing both system performance and user satisfaction. In order to achieve this goal the scheduler algorithm becomes of upmost importance. Thus a number of different resource allocation algorithms have been suggested. However, the uplink has been little covered due to the fact it poses more specific constraints and it is quite complicated to balance the trade-off between channel state information, system throughput and user perceived throughput. In this paper we propose a new resource allocation algorithm that tries to balance the advantages of two previously suggested ones. We also introduce a new parameter, the user ratio, which allows us to explicitly quantify the trade-off between fairness, system throughput and user throughput for different channel conditions.
Most of earlier grid scheduling algorithms were based on centralized scheduler. Relying on central scheduler yields not only central point of failure, also, it is not possible because of scalability and political issu...
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Most of earlier grid scheduling algorithms were based on centralized scheduler. Relying on central scheduler yields not only central point of failure, also, it is not possible because of scalability and political issues in present day gigantic grid systems. Hence, meta-schedulers came into limelight. However many authors recognizes limitations of hierarchical grid scheduling and proposed peer-to-peer (P2P) techniques, which have potential for grid scheduling. In this paper, new decentralized scheduling algorithm is proposed for P2P grid systems. In this method, an independent task sovereignly selects a most suitable grid node based on local information of immediate neighbors. A vital feature of this method is that it can schedule both computation intensive and communication intensive tasks to make grid system's workload balanced.
In this work, we present a Genetic Algorithm (GA) based method for pipeline scheduling optimization. The objective is to minimize the circuit area under both data initiation interval and pipeline latency constraints. ...
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In this work, we present a Genetic Algorithm (GA) based method for pipeline scheduling optimization. The objective is to minimize the circuit area under both data initiation interval and pipeline latency constraints. In the initialization, the scheduler generates a series of solutions between As Soon As Possible (ASAP) and As Late As Possible (ALAP) interval. Afterwards a Linear Programming (LP) algorithm is applied for transforming unfeasible solutions to feasible solutions, which are input to GA for searching the optimization result. In the experiments, our proposed algorithm achieves an average of 29.74% area improvement by comparing with ASAP and ALAP methods.
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