Addressing the requirements of Industrial Internet of Things (IIoT) in Industry 4.0, the Time Slotted Channel Hopping (TSCH) protocol of the IEEE 802.15.4e amendment has been proposed. However, the lack of a defined s...
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ISBN:
(数字)9798350311617
ISBN:
(纸本)9798350311624
Addressing the requirements of Industrial Internet of Things (IIoT) in Industry 4.0, the Time Slotted Channel Hopping (TSCH) protocol of the IEEE 802.15.4e amendment has been proposed. However, the lack of a defined scheduling procedure in the standard remains an open research area. Existing reinforcement learning-based scheduling proposals demonstrate great potential for this technique due to the ongoing observations within the network environment. Beneficial for real-world scenarios where network conditions are volatile and unpredictable. This work presents QL- TSCH-plus, an enhancement of the existing QL- TSCH scheduler that reduces energy consumption by adapting the Action Peeking mechanism to a distributed scheme. Instead of continuously listening to neighboring nodes communication, QL- TSCH-plus allows nodes to broadcast the learned transmission slots for updating the Action Peeking Tables and allocating reception slots, reducing energy use by up to 47% compared to QL-TSCH. This novel approach also maintains reliability and timeliness, demonstrating significant potential for efficient scheduling in TSCH networks, making it suitable for the IIoT.
In modern embedded platforms, safety-critical functionalities that must be certified correct to very high levels of assurance may co-exist with less critical software that are not subject to certification requirements...
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In modern embedded platforms, safety-critical functionalities that must be certified correct to very high levels of assurance may co-exist with less critical software that are not subject to certification requirements. Upon such platforms one seeks to satisfy two, sometimes contradictory, goals: (i) being able to certify the safety-critical functionalities under very conservative assumptions, and (ii) ensuring high utilization of platform resources even when actual run-time behavior does not live up to such pessimistic expectations. This paper describes efforts at designing scheduling algorithms that balance these two requirements, when scheduling recurrent tasks that are triggered by external events of unknown exact frequency.
Maximizing the utility of limited Earth observing satellite resources is a difficult ongoing problem. Dynamic Targeting is an approach to this challenge that intelligently plans and executes primary sensor observation...
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ISBN:
(纸本)9798350384581;9798350384574
Maximizing the utility of limited Earth observing satellite resources is a difficult ongoing problem. Dynamic Targeting is an approach to this challenge that intelligently plans and executes primary sensor observations based on information from a lookahead sensor. However, current implementations have failed to account for realistic satellite operational constraints and have used static utility for repeat observations of the same target. To address these limitations, we implement a more general Dynamic Targeting framework that comprises a physics-based slew model, a dynamic model of observation utility, and an algorithm for gathering high-utility observations. To demonstrate this framework, we also supply complex dynamic utility models that are applicable to many missions and new algorithms for intelligently scheduling observations with slewing restrictions and changing utility, including a greedy algorithm and a depth-first search algorithm. To evaluate these algorithms, we test their performance across simulated runs through two datasets and compare to the performance of an algorithm representative of most scheduling algorithms aboard Earth science missions today as well as an intractable upper bound. We show that our algorithms have great potential to improve science return from Earth science missions.
In this paper, a new scheduling algorithm has been introduced based on dynamic genetic algorithm, which is more efficient in comparison with the previous similar algorithms and has more reliability. SQEFG is a new pro...
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This paper proposes an improved scheduling algorithm that optimizes the allocation of the real-time services and elastic flows in EPON by spreading the real-time service periods lowering the delay and thus fulfilling ...
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As the computing facilities growing fastly with advance technologies, side by side problems in engineering & science become so perplexed and require large computing power to handle these problems. In Grid environm...
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As the computing facilities growing fastly with advance technologies, side by side problems in engineering & science become so perplexed and require large computing power to handle these problems. In Grid environment, it is a challenge or a more complexive to schedule the independent tasks. An efficient task scheduling algorithm is needed to utilize the total available power of grid system for requested tasks to execute them. In this paper, we propose an Adaptive Ranking Task scheduling (ARTS) algorithm for scheduling the tasks in grid environment. Objective of proposed work is to minimize the average completion time of submitted tasks as compared with other scheduling methods. Internally ARTS draw up two important basic factors concerned with type of gridlets whether data centric or computation centric and assigning tasks to the resources on the basis of organization rank (OR) value in a grid environment.
Wireless communication systems are predicted to appear massively in the future human life. In case of 5G communications, high expectations in terms of Quality of Service and reliability are foreseen. However, the deve...
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ISBN:
(纸本)9781479966653
Wireless communication systems are predicted to appear massively in the future human life. In case of 5G communications, high expectations in terms of Quality of Service and reliability are foreseen. However, the development of future systems has to cope with a better exploitation of the available resources. As a matter of fact, system coexistence and frequency sharing are two of the most promising approaches to achieve spectrum efficiency. Cognitive Radio (CR) is a key enabler of these approaches, which are also arising in Satellite Communications (SatComs). In this paper, a carrier allocation based scheduling algorithm inspired by genetic algorithms (GA) is proposed by taking into account different environment conditions and service requirements of the users of the cognitive satellite system.
Advance resource reservations allow clients to have exclusive access to Grid resources. The goal is to deliver higher levels of Quality of Service. However, in practice, the support for advance reservations in Grids h...
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With the advent of SMT multicore (Simultaneous Multithreading) processors co-scheduling of jobs on the same core, thus sharing core resources like L1, L2 caches there results a per process performance degradation depe...
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In a mobile computing or shared I/O environment, one data service can satisfy multiple requests. The data broadcast problem is one example of data service that has been extensively studied. Previous work in data broad...
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ISBN:
(纸本)1601320841
In a mobile computing or shared I/O environment, one data service can satisfy multiple requests. The data broadcast problem is one example of data service that has been extensively studied. Previous work in data broadcast typically focuses on how to shorten the average request waiting time or how to broadcast the data items to serve as many requests as possible in a limited time, but does not consider how to meet the deadlines of requests. In addition, such work typically considers individual data requests, as opposed to the multiple data requests associated with a transaction. In this paper, we discuss how to serve data in order to complete more transactions with multiple data requests before their deadlines. We assume that transactions can read one or more data items and the data access sequence does not matter. We propose a new data server scheduling strategy for read-only transactions with deadlines that considers the importance of transactions, and we introduce a new cost model to measure the system performance. The simulation results show that our novel algorithm achieves better performance for transactions with deadlines than existing scheduling algorithms.
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