48 research outputs found
Online Load Balancing for Network Functions Virtualization
Network Functions Virtualization (NFV) aims to support service providers to
deploy various services in a more agile and cost-effective way. However, the
softwarization and cloudification of network functions can result in severe
congestion and low network performance. In this paper, we propose a solution to
address this issue. We analyze and solve the online load balancing problem
using multipath routing in NFV to optimize network performance in response to
the dynamic changes of user demands. In particular, we first formulate the
optimization problem of load balancing as a mixed integer linear program for
achieving the optimal solution. We then develop the ORBIT algorithm that solves
the online load balancing problem. The performance guarantee of ORBIT is
analytically proved in comparison with the optimal offline solution. The
experiment results on real-world datasets show that ORBIT performs very well
for distributing traffic of each service demand across multipaths without
knowledge of future demands, especially under high-load conditions
Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem
Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various
fields in both theory and application. Because the CluMRCT is NP-Hard, the
approximate approaches are suitable to find the solution for this problem.
Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of
the most efficient approximation algorithms to deal with many different kinds
of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT
problems. In the proposed MFEA, we focus on crossover and mutation operators
which create a valid solution of CluMRCT problem in two levels: first level
constructs spanning trees for graphs in clusters while the second level builds
a spanning tree for connecting among clusters. To reduce the consuming
resources, we will also introduce a new method of calculating the cost of
CluMRCT solution. The proposed algorithm is experimented on numerous types of
datasets. The experimental results demonstrate the effectiveness of the
proposed algorithm, partially on large instance
Joint Transaction Transmission and Channel Selection in Cognitive Radio Based Blockchain Networks: A Deep Reinforcement Learning Approach
To ensure that the data aggregation, data storage, and data processing are
all performed in a decentralized but trusted manner, we propose to use the
blockchain with the mining pool to support IoT services based on cognitive
radio networks. As such, the secondary user can send its sensing data, i.e.,
transactions, to the mining pools. After being verified by miners, the
transactions are added to the blocks. However, under the dynamics of the
primary channel and the uncertainty of the mempool state of the mining pool, it
is challenging for the secondary user to determine an optimal transaction
transmission policy. In this paper, we propose to use the deep reinforcement
learning algorithm to derive an optimal transaction transmission policy for the
secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that
allows the secondary user to learn the optimal policy. The simulation results
clearly show that the proposed deep reinforcement learning algorithm
outperforms the conventional Q-learning scheme in terms of reward and learning
speed
Diet Quality Index and food choice motives in Vietnam: The roles of sensory appeal, mood, convenience, and familiarity
Food choices that shape human diets and health are influenced by various socio-economic factors. Vietnam struggles to meet many nutrition targets where links between food choice and diet have not been widely explored. This study assesses the food choice motives, based on a 28-item food choice questionnaire (FCQ), and the diet quality of 603 adults in three sites (urban, peri-urban, and rural) in northern Vietnam. We assess diet quality using the Diet Quality Index–Vietnam (DQI-V) which consists of variety, adequacy, moderation, and balance components. Using factor analysis, we grouped FCQ items into five factors: health focus, sensory appeal, mood ethics, convenience, and familiarity. The structural equation modeling indicates that food choice motives significantly impact the DQI-V and its components but in different directions. The results show that sensory appeal has a positive association with the overall DQI-V score, while having a negative impact on the variety component. Findings present a potential trade-off issue for interventions and policies related to food products. Nutrition knowledge is positively associated with all elements of diet quality across all three study sites. Vietnamese agrobiodiversity could be better utilized to increase dietary diversity. Differentiated policies are necessary to address the poor dietary diversity and adequacy in northern Vietnam
Phase Shift Design for RIS-Aided Cell-Free Massive MIMO with Improved Differential Evolution
This paper proposes a novel phase shift design for cell-free massive
multiple-input and multiple-output (MIMO) systems assisted by reconfigurable
intelligent surface (RIS), which only utilizes channel statistics to achieve
the uplink sum ergodic throughput maximization under spatial channel
correlations. Due to the non-convexity and the scale of the derived
optimization problem, we develop an improved version of the differential
evolution (DE) algorithm. The proposed scheme is capable of providing
high-quality solutions within reasonable computing time. Numerical results
demonstrate superior improvements of the proposed phase shift designs over the
other benchmarks, particularly in scenarios where direct links are highly
probable.Comment: 5 pages, 2 figures. Accepted by IEEE WC