Virtual infrastructures (VIs) consolidated the dynamic provisioning of computing and communication resources. A VI is a set of virtual machines interconnected by virtual links and switches/routers. Infrastructure prov...
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Data centers have networks that provide, for a large number of users, various services such as video streaming, financial services, file storage, among others. Like any network-based system, a Data Center (DC) is subj...
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One of the main objectives in multimodal optimization is to find multiple optima solutions in a search space. Hence, population-based metaheuristics are suitable for this class of problems but their loss of diversity ...
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The Traveling Salesman Problem (TSP) is extensively used to test new algorithms that tackle combinatorial optimiza-tion problems. Bee inspired algorithms are receiving a con-siderable attention from the Swarm Intellig...
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ISBN:
(纸本)9780889869837
The Traveling Salesman Problem (TSP) is extensively used to test new algorithms that tackle combinatorial optimiza-tion problems. Bee inspired algorithms are receiving a con-siderable attention from the Swarm Intelligence field. This paper presents the TSPoptBees, a bee inspired algorithm to solve the TSP. A vast discussion is made in regards to the parameter analysis of the proposed algorithm, showing trends on varying certain parameters and proposing default values for them. The quality is assessed using instances from the TSPLIB and the results show the algorithm is able to provide good quality solutions.
This work proposes an accuracy-driven evaluation model of an approximate adder (AxA) called an approximate parallel prefix adder (AxPPA). It explores the significance of approximate computing (AxC) in optimizing perfo...
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Infrastructure-as-a-service clouds enable customers to use computing resources in a flexible manner to satisfy their needs, and pay only for the allocated resources. One challenge for IaaS customers is the correct pro...
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Infrastructure-as-a-service clouds enable customers to use computing resources in a flexible manner to satisfy their needs, and pay only for the allocated resources. One challenge for IaaS customers is the correct provisioning of their resources. Many users end up underprovisioning, hurting application performance, or overprovisioning, paying for resources that are not really necessary. Memory is an essential resource for any computing system, and is frequently a performance-limiting factor in cloud environments. In this work, we propose a model that enables cloud customers to determine whether the memory allocated to their virtual machines is correctly provisioned, underprovisioned, or overprovisioned. The model uses two metrics collected inside a VM, resident and committed memory, and defines thresholds for these metrics that characterize each provisioning level. Experimental results with Linux guests on Xen, running four benchmarks with different workloads and varying memory capacity, show that the model was able to accurately diagnose memory provisioning in 98% of the scenarios evaluated. Copyright 2013 by the Authors.
The mobile games market has grown in relevancy compared to traditional gaming platforms. The standard architecture for these games requires the processing of game logic and graphics using the device's own hardware...
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Protein Structure Prediction (PSP) problem is an open problem in bioinformatics and, as the problem scales, complexity and processing time increases. In this way, robust methods and massively parallel architectures ar...
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Nowadays, one of the challenges of communications systems is to enhance spectrum utilization and data rate without robustness loses. This situation is more perceptive in digital television applications, since there is...
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This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates f...
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ISBN:
(数字)9798331531591
ISBN:
(纸本)9798331531607
This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.
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