Coverage redundancy problem is one of the significant problems in wireless sensor networks. To reduce the energy consumption that arises when the high number of sensors is active, various coverage control protocols (s...
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ISBN:
(纸本)9781424464043
Coverage redundancy problem is one of the significant problems in wireless sensor networks. To reduce the energy consumption that arises when the high number of sensors is active, various coverage control protocols (sleep scheduling algorithms) have been proposed. In these protocols, a subset of nodes necessary to maintain sufficient sensing coverage are kept active while the others are put into a sleep mode to reduce the energy consumption. In this paper, we study the coverage redundancy problem in a sensor network where the locations of nodes and the distances between nodes are neither known nor could be easily calculated. We define a neighbor graph as the graph formed by the neighbors of a node and analyze the effect of different levels of connectivity in neighbor graphs on the coverage redundancy of sensor nodes. Moreover, we apply our results to a lightweight deployment-aware scheduling algorithm and demonstrate the improvement in the performance of the algorithm.
A sensor grid is an integration of two technologies: wireless sensor networks and the grid. The sensors deployed in a WSN monitor a phenomenon of interest. The information gained from the WSN is processed in the grid ...
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ISBN:
(纸本)9781467348836;9781467348812
A sensor grid is an integration of two technologies: wireless sensor networks and the grid. The sensors deployed in a WSN monitor a phenomenon of interest. The information gained from the WSN is processed in the grid and is used by the users of applications. Multipurpose WSNs have become very popular where the deployed WSNs support more than one application. The research in this extended abstract focuses on WSNs supporting multiple applications. In this work, we focus on allocation, which is a process of determining the sensor nodes that will be selected for executing the requests corresponding to an application. scheduling, which determines the order in which the application requests submitted to the WSN are executed is performed to improve the mean response time to the users of the applications. Our previous works propose various scheduling algorithms for WSNs hosting multiple applications. In this research, various static and dynamic allocation algorithms are proposed with an attempt to balance the energy consumption amongst the sensor nodes and hence improve the network lifetime of the WSN. Network lifetime is the time when the energy of any sensor node in the WSN falls below a predefined threshold. The proposed algorithms use varying degree of information about the energy consumption at the major energy consuming components of the sensor nodes: the CPU component and the radio component. Simulation experiments are performed to evaluate the performance of the proposed algorithms. This extended abstract presents the preliminary results obtained from the experimentation done so far. The simulation experiments demonstrate that by performing dynamic allocation and by using information about the total energy consumption at the sensor nodes, the lifetime of the WSN can be significantly improved.
The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices. A typical dilemma is: in order to provide complex...
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ISBN:
(纸本)9781577358763
The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices. A typical dilemma is: in order to provide complex intelligent services (e.g. smart city), we need inference results of multiple ML models, but the cost budget (e.g. GPU memory) is not enough to run all of them. In this work, we study underlying relationships among black-box ML models and propose a novel learning task: model linking. Model linking aims to bridge the knowledge of different black-box models by learning mappings (dubbed model links) between their output spaces. Based on model links, we developed a scheduling algorithm, named MLink. Through collaborative multi-model inference enabled by model links, MLink can improve the accuracy of obtained inference results under the cost budget. We evaluated MLink on a multi-modal dataset with seven different ML models and two real-world video analytics systems with six ML models and 3,264 hours of video. Experimental results show that our proposed model links can be effectively built among various black-box models. Under the budget of GPU memory, MLink can save 66.7% inference computations while preserving 94% inference accuracy, which outperforms multi-task learning, deep reinforcement learning-based scheduler and frame filtering baselines.
We present a sequencing problem given on JIT (Just In Time) manufacturing environments, with the objective of minimizing the variation of manufacturing rates (ORV: Output Rate Variation). Specifically, we propose an e...
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ISBN:
(纸本)9783642406423;9783642406430
We present a sequencing problem given on JIT (Just In Time) manufacturing environments, with the objective of minimizing the variation of manufacturing rates (ORV: Output Rate Variation). Specifically, we propose an extension based on requiring to the sequences the preservation of the production mix throughout the products manufacturing. To solve the ORV and the extended problem, we propose algorithms based on BDP (Bounded Dynamic Programming) and we perform two computational experiments based on instances from the literature.
In this paper we consider deterministic differential equation models for the varying number of flows in a network. These arise naturally as limits of stochastic models under joint scaling of flow arrival rates and net...
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ISBN:
(纸本)1424403499
In this paper we consider deterministic differential equation models for the varying number of flows in a network. These arise naturally as limits of stochastic models under joint scaling of flow arrival rates and network capacities. We compare these dynamics under (i) coordinated multipath routing and (ii) parallel, uncoordinated routing. We show that for identical traffic demands, parallel uncoordinated routing can be unstable while balanced multipath routing is stable. In other words, coordination can strictly increase the schedulable region, that is the set of demand vectors for which the system is stable. We also show that, even when uncoordinated multipath routing stabilises the system, coordination can bring further benefits, as it naturally minimises network costs at equilibrium.
This paper presents a novel FPGA-based switch design that achieves high algorithmic performance and an efficient FPGA implementation. Crossbar switches based on virtual output queues (VOQs) and variations have been ra...
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ISBN:
(纸本)9781665420105
This paper presents a novel FPGA-based switch design that achieves high algorithmic performance and an efficient FPGA implementation. Crossbar switches based on virtual output queues (VOQs) and variations have been rather popular for implementing switches on FPGAs, with applications to network-on-chip (NoC) routers and network switches. The efficiency of VOQs is well-documented on ASICs, though we show that their disadvantages can outweigh their advantages on FPGAs. Our proposed design uses an output-queued switch internally for simplifying scheduling, and a queue balancing technique to avoid queue fragmentation and reduce the need for memory-sharing VOQs. Our implementation approaches the scheduling performance of the state-of-the-art, while requiring considerably fewer FPGA resources.
We present a new integer linear formulation for the problem of minimizing the total completion time on a single parallel-batching machine. The new formulation is strong, in the sense that it delivers a sharp lower bou...
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We present a new integer linear formulation for the problem of minimizing the total completion time on a single parallel-batching machine. The new formulation is strong, in the sense that it delivers a sharp lower bound, and compact, i.e. polynomial in size, contrasted to recent successful models for the same problem that have exponential size and require to be handled by column generation. The new model is promising: combined with a rounding procedure, it allows to deliver good solutions with small, certified optimality gaps for instances with up to 50 jobs, and we believe it is susceptible of further improvements. Copyright (C) 2022 The Authors.
In this paper we present a reconfigurable System on Chip design framework that generates an architectural design along with binding and scheduling algorithm, specific to the input application in Kahn Process Network s...
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ISBN:
(纸本)9780769533070
In this paper we present a reconfigurable System on Chip design framework that generates an architectural design along with binding and scheduling algorithm, specific to the input application in Kahn Process Network specification. The likelihood that tasks and communication channels may have many potential physical manifestations is explicitly recognised and embraced, to assist the design exploration process. The architectural design, binding and scheduling problems are formulated as a Integer Linear Programming problem, with physical constraints such as available logic resources, computation time and memory footprints to guide the design space exploration.
Cellular Automata (CA) are discrete dynamical systems composed of a transition rule and many cells that can assume a set of states, the rule updates a cell according to the states of cells in the proximity of that cel...
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ISBN:
(纸本)9781538674499
Cellular Automata (CA) are discrete dynamical systems composed of a transition rule and many cells that can assume a set of states, the rule updates a cell according to the states of cells in the proximity of that cell. The study of CA rules to decide the distribution of program tasks to system processors focus on training a CA rule over a specific instance and use this rule to solve several others examples. The best performing CA-based scheduler to this Task scheduling problem uses a stochastic CA with a random cell update. Here we study different probabilistic distributions to be used in the model update, we regard and mix two components: the uniform distribution and a distribution that increases the probability of states that appeared more often in the neighbourhood of a cell. We investigate five models, in which we vary the influence of each component in the mix. The results endorse that the best scheduling result is found by variations where the two components importance is similar, these variations outperformed the state-of-art CA-based model.
Generalized Processor Sharing (GPS) is a powerful fluid model and there are practical scheduling algorithms that can perfectly emulate it. GPS has been widely used as the reference model to schedule guaranteed perform...
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ISBN:
(纸本)9781424488650
Generalized Processor Sharing (GPS) is a powerful fluid model and there are practical scheduling algorithms that can perfectly emulate it. GPS has been widely used as the reference model to schedule guaranteed performance traffic. However, there has not been a way to apply GPS to best effort traffic. In this paper, we propose a bandwidth allocation scheme called Queue Length Proportional (QLP) for crossbar switches without speedup, so as to use GPS to schedule best effort traffic. QLP dynamically obtains a feasible bandwidth matrix as the GPS scheduling criteria. In QLP, the amount of service that each flow receives is proportional to the length of its backlogged queue. We analytically prove that QLP is strongly stable and hence provides 100% throughput for any admissible traffic, no matter whether the traffic distribution is uniform or non-uniform. Moreover, we show that QLP is feasible, which means the allocated bandwidth does not exceed the available capacity. Finally, we perform simulations to verify the theoretical results and to measure the performance of QLP.
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