scholarly journals An Evolutionary Computation Approach to Resource Allocation in Container-based Clouds

2021 ◽  
Author(s):  
◽  
Boxiong Tan
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Evolutionary Computation ◽  
State Of The Art ◽  
Training Procedure ◽  
Nsga Ii ◽  
Multi Objective ◽  
Terminal Set ◽  
On Line ◽  
Better Than

<p>A container-based cloud is a new trend in cloud computing that introduces more granular management of cloud resources. Compared with VM-based clouds, container-based clouds can further improve energy efficiency with a finer granularity resource allocation in data centers. The current allocation approaches for VM-based clouds cannot be used in container-based clouds. The first reason is that existing research lacks appropriate models that can represent the interaction of allocation features. Many critical features, such as VM overhead, are also not considered in the current models. The second reason is that current allocation approaches do not perform well to the three frequently encountered allocation scenarios, offline allocation, on-line allocation, and multi-objective allocation. Current approaches for these scenarios are mostly based on greedy heuristics that can be easily stuck at local optimum, or meta-heuristics that consider a simplified one-level allocation problem. Evolutionary Computation (EC) is particularly good at solving combinatorial optimization problems for both off-line and on-line scenarios. The overall goal of this thesis is to propose an EC approach to the three allocation scenarios in order to improve the performance of container-based clouds. Specifically, we aim to optimize energy consumption in all the scenarios. An additional objective, availability of the application, is considered for the multi-objective scenario. First, this thesis investigates two promising representations, vector-based and group-based. We propose two novel vector-based (e.g., Single- chromosome Genetic Algorithm (SGA) and Dual-chromosome GA (DGA)) and a group-based GA approaches for the off-line allocation scenario. Cor- responding genetic operators and decoding processes are also developed and evaluated. Two contributions have been made. Firstly, a novel offline model has been proposed based on current models with additional features. It can be used to evaluate allocation algorithms. Secondly, two types of problem representation, vector-based and group-based, are investigated and three novel approaches are proposed. The three approaches are compared with state-of-the-art approaches. The results show that all solutions produced by these approaches are better than the state-of-the-art approaches and group-based GA is the best approach. Second, this thesis proposes a novel genetic programming hyper-heuristic (GPHH) and a cooperative coevolution (CCGP)-based approach for the on-line allocation scenario. These hyper-heuristic methods can automatically generate allocation rules. For the GPHH-based approach, we develop a novel training procedure to generate reservation-based rules for allocating containers to VM instances. For the CCGP-based approach, we introduce a new terminal set and develop a training procedure to generate allocation rules for two-level allocations. We analyze both human-designed rules and generated rules to provide insights for algorithm designers. Two contributions have been proposed for the on-line problem. First, the on-line model for the on-line allocation scenario is developed. Second, a novel terminal set and training procedures are developed. The automatically generated heuristics perform significantly better than the manually designed heuristics. Third, this thesis proposes a multi-objective approach that generates a set of trade-off solutions for the cloud providers to choose from. Our novel approach, namely Nondominated Sorting-Group GA (NS-GGA), combines the group-based representation and the NSGA-II framework. The experimental results are compared with existing approaches. The results show that our proposed NS-GGA approach outperforms all other approaches. We propose two novelties. The first novelty is the multi-objective model including objectives of energy consumption and availability. The second novelty is the NS-GGA approach that combines the group-based representation with NSGA-II. The allocation solutions found by NS-GGA dominate the solutions found by other existing approaches.</p>

scholarly journals An Evolutionary Computation Approach to Resource Allocation in Container-based Clouds

2021 ◽  
Author(s):  
◽  
Boxiong Tan
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Evolutionary Computation ◽  
State Of The Art ◽  
Training Procedure ◽  
Nsga Ii ◽  
Multi Objective ◽  
Terminal Set ◽  
On Line ◽  
Better Than

<p>A container-based cloud is a new trend in cloud computing that introduces more granular management of cloud resources. Compared with VM-based clouds, container-based clouds can further improve energy efficiency with a finer granularity resource allocation in data centers. The current allocation approaches for VM-based clouds cannot be used in container-based clouds. The first reason is that existing research lacks appropriate models that can represent the interaction of allocation features. Many critical features, such as VM overhead, are also not considered in the current models. The second reason is that current allocation approaches do not perform well to the three frequently encountered allocation scenarios, offline allocation, on-line allocation, and multi-objective allocation. Current approaches for these scenarios are mostly based on greedy heuristics that can be easily stuck at local optimum, or meta-heuristics that consider a simplified one-level allocation problem. Evolutionary Computation (EC) is particularly good at solving combinatorial optimization problems for both off-line and on-line scenarios. The overall goal of this thesis is to propose an EC approach to the three allocation scenarios in order to improve the performance of container-based clouds. Specifically, we aim to optimize energy consumption in all the scenarios. An additional objective, availability of the application, is considered for the multi-objective scenario. First, this thesis investigates two promising representations, vector-based and group-based. We propose two novel vector-based (e.g., Single- chromosome Genetic Algorithm (SGA) and Dual-chromosome GA (DGA)) and a group-based GA approaches for the off-line allocation scenario. Cor- responding genetic operators and decoding processes are also developed and evaluated. Two contributions have been made. Firstly, a novel offline model has been proposed based on current models with additional features. It can be used to evaluate allocation algorithms. Secondly, two types of problem representation, vector-based and group-based, are investigated and three novel approaches are proposed. The three approaches are compared with state-of-the-art approaches. The results show that all solutions produced by these approaches are better than the state-of-the-art approaches and group-based GA is the best approach. Second, this thesis proposes a novel genetic programming hyper-heuristic (GPHH) and a cooperative coevolution (CCGP)-based approach for the on-line allocation scenario. These hyper-heuristic methods can automatically generate allocation rules. For the GPHH-based approach, we develop a novel training procedure to generate reservation-based rules for allocating containers to VM instances. For the CCGP-based approach, we introduce a new terminal set and develop a training procedure to generate allocation rules for two-level allocations. We analyze both human-designed rules and generated rules to provide insights for algorithm designers. Two contributions have been proposed for the on-line problem. First, the on-line model for the on-line allocation scenario is developed. Second, a novel terminal set and training procedures are developed. The automatically generated heuristics perform significantly better than the manually designed heuristics. Third, this thesis proposes a multi-objective approach that generates a set of trade-off solutions for the cloud providers to choose from. Our novel approach, namely Nondominated Sorting-Group GA (NS-GGA), combines the group-based representation and the NSGA-II framework. The experimental results are compared with existing approaches. The results show that our proposed NS-GGA approach outperforms all other approaches. We propose two novelties. The first novelty is the multi-objective model including objectives of energy consumption and availability. The second novelty is the NS-GGA approach that combines the group-based representation with NSGA-II. The allocation solutions found by NS-GGA dominate the solutions found by other existing approaches.</p>

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

Synthesis of LC-Oscillators Using Rival Multi-Objective Multi-Constraint Optimization Kernels

2015 ◽  
pp. 1-27 ◽  
Author(s):  
Ricardo Póvoa ◽  
Ricardo Lourenço ◽  
Nuno Lourenço ◽  
António Canelas ◽  
Ricardo Martins ◽  
...  
Keyword(s):  
Design Automation ◽  
State Of The Art ◽  
Voltage Controlled Oscillator ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Lc Oscillator ◽  
Lc Oscillators ◽  
Statistical Results ◽  
Multiple Synthesis

This chapter presents a state-of-the-art multi-objective/multi-constraint design automation approach applied to the design of an LC-Voltage Controlled Oscillator and an LC-Oscillator for a 130 nm technology node and leading to sets of design solutions showing figures-of-merit around -192 dBc/Hz and -186 dBc/Hz, respectively. The proposed approach, implemented in AIDA-C, guarantees accuracy by using commercial circuit simulators (HSPICE® and ELDO®) to evaluate the performance of the tentative circuit solutions, where the number of time-consuming circuit simulations is efficiently pruned by the optimization kernel. Three multi-objective optimization algorithms, the NSGA-II, the MOPSO, and the MOSA, are experimented with in the synthesis of the quoted oscillators and compared in terms of performance using statistical results obtained from multiple synthesis runs for each one of the oscillators. The performance of the optimized oscillators is then compared to other state-of-the-art results, showing the benefits of the presented multi-objective design approach.

scholarly journals Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 811 ◽  
Author(s):  
Yongmao Xiao ◽  
Qingshan Gong ◽  
Xiaowu Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Model ◽  
Low Cost ◽  
Minimum Cost ◽  
Processing Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Set Up

The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods.

scholarly journals Multi-objective Building Design Optimization under Operational Uncertainties Using the NSGA II Algorithm

Buildings ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 88
Author(s):  
Shobhit Chaturvedi ◽  
Elangovan Rajasekar ◽  
Sukumar Natarajan
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Design Optimization ◽  
Building Design ◽  
Building Energy ◽  
Global South ◽  
Efficiency Measure ◽  
Nsga Ii ◽  
Multi Objective ◽  
Energy Efficiency Measure

Operational uncertainties play a critical role in determining potential pathways to reduce the building energy footprint in the Global South. This paper presents the application of a non-dominated sorting genetic (NSGA II) algorithm for multi-objective building design optimization under operational uncertainties. A residential building situated in a mid-latitude steppe and desert region (Köppen climate classification: BSh) in the Global South has been selected for our investigation. The annual building energy consumption and the total number of cooling setpoint unmet hours (h) were assessed over 13,122 different energy efficiency measures. Six Pareto optimal solutions were identified by the NSGA II algorithm. Robustness of Pareto solutions was evaluated by comparing their performance sensitivity over 162 uncertain operational scenarios. The final selection for the most optimal energy efficiency measure was achieved by formulating a robust multi-criteria decision function by incorporating performance, user preference, and reliability criteria. Results from this robust approach were compared with those obtained using a deterministic approach. The most optimal energy efficiency measure resulted in 9.24% lower annual energy consumption and a 45% lower number of cooling setpoint unmet h as compared to the base case.

scholarly journals Multiobjective Environmentally Sustainable Optimal Design of Dedicated Connected Autonomous Vehicle Lanes

2021 ◽  
Vol 13 (6) ◽  
pp. 3454
Author(s):  
Yu Lin ◽  
Hongfei Jia ◽  
Bo Zou ◽  
Hongzhi Miao ◽  
Ruiyi Wu ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Travel Time ◽  
Heterogeneous Traffic ◽  
System Level ◽  
Network Equilibrium ◽  
Nsga Ii ◽  
The Sustainable Development ◽  
Multi Objective ◽  
Upper Level

The emergence of connected autonomous vehicles (CAVs) is not only improving the efficiency of transportation, but also providing new opportunities for the sustainable development of transportation. Taking advantage of the energy consumption of CAVs to promote the sustainable development of transportation has attracted extensive public attention in recent years. This paper develops a mathematical approach to investigating the problem of the optimal implementation of dedicated CAV lanes while simultaneously considering economic and environmental sustainability. Specifically, the problem is described as a multi-objective bi-level programming model, in which the upper level is to minimize the system-level costs including travel time costs, CAV lane construction cost, and emission cost, whereas the lower level characterizes the multi-class network equilibrium with a heterogeneous traffic stream consisting of both human-driven vehicle (HVs) and CAVs. To address the multi-objective dedicated CAV lane implement problem, we propose an integrated solution framework that integrates a non-dominated sorting genetic algorithm II (NSGA-II) algorithm, diagonalized algorithm, and Frank–Wolfe algorithm. The NSGA-II was adopted to solve the upper-level model, i.e., hunting for the optimal CAV lanes implementation schemes. The diagonalized Frank–Wolfe (DFW) algorithm is used to cope with multi-class network equilibrium. Finally, numerical experiments were conducted to demonstrate the effectiveness of the proposed model and solution method. The experimental results show that the total travel time cost, total emission cost, and total energy consumption were decreased by about 12.03%, 10.42%, and 9.4%, respectively, in the Nguyen–Dupuis network as a result of implementing the dedicated CAV lanes.

scholarly journals A Generalized SDP Multi-Objective Optimization Method for EM-Based Microwave Device Design

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3065 ◽  
Author(s):  
Ying Liu ◽  
Qingsha S. Cheng ◽  
Slawomir Koziel
Keyword(s):  
Computational Cost ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Uwb Antenna ◽  
Nsga Ii ◽  
Multi Objective ◽  
Iot Applications ◽  
Comparison Results ◽  
Pareto Fronts ◽  
Better Than

In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three objectives is elaborated in detail in this paper. The GSDP method is compared with the NSGA-II method using multi-objective problems in the DTLZ series, and the results show the GSDP method saved computational cost by more than 85% compared to NSGA-II method. A diversity comparison indicator (DCI) is used to evaluate approximate Pareto fronts. The comparison results show the diversity performance of GSDP is better than that of NSGA-II in most cases. We demonstrate the proposed GSDP method using a practical multi-objective design example of EM-based UWB antenna for IoT applications.

Optimal Photovoltaic System Design with Multi-Objective Optimization

2013 ◽  
Vol 4 (4) ◽  
pp. 63-89 ◽  
Author(s):  
Amin Ibrahim ◽  
Farid Bourennani ◽  
Shahryar Rahnamayan ◽  
Greg F. Naterer
Keyword(s):  
Incident Energy ◽  
State Of The Art ◽  
Photovoltaic System ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Generalized Differential ◽  
Maximum Field ◽  
Real World Problems

Recently, several parts of the world suffer from electrical black-outs due to high electrical demands during peak hours. Stationary photovoltaic (PV) collector arrays produce clean and sustainable energy especially during peak hours which are generally day time. In addition, PVs do not emit any waste or emissions, and are silent in operation. The incident energy collected by PVs is mainly dependent on the number of collector rows, distance between collector rows, dimension of collectors, collectors inclination angle and collectors azimuth, which all are involved in the proposed modeling in this article. The objective is to achieve optimal design of a PV farm yielding two conflicting objectives namely maximum field incident energy and minimum of the deployment cost. Two state-of-the-art multi-objective evolutionary algorithms (MOEAs) called Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Generalized Differential Evolution Generation 3 (GDE3) are compared to design PV farms in Toronto, Canada area. The results are presented and discussed to illustrate the advantage of utilizing MOEA in PV farms design and other energy related real-world problems.

scholarly journals A Multi-Objective INLP Model of Sustainable Resource Allocation for Long-Range Maritime Search and Rescue

2019 ◽  
Vol 11 (3) ◽  
pp. 929 ◽  
Author(s):  
Yu Guo ◽  
Yanqing Ye ◽  
Qingqing Yang ◽  
Kewei Yang
Keyword(s):  
Resource Allocation ◽  
Long Range ◽  
Search And Rescue ◽  
Decision Makers ◽  
Rule Base ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multiple Resources ◽  
Use Of Resources ◽  
Maritime Search And Rescue

Maritime search and rescue (SAR) operations play a crucial role in reducing fatalities and mitigating human suffering. Compared to short-range maritime SAR, long-range maritime SAR (LRMSAR) is more challenging due to the far distance from the shore, changeful weather, and less available resources. Such an operation put high requirements on decision makers to timely assign multiple resources, such as aircraft and vessels to deal with the emergency. However, most current researches pay attention to assign only one kind of resource, while practically, multiple resources are necessary for LRMSAR. Thus, a method is proposed to provide support for decision makers to allocate multiple resources in dealing with LRMSAR problem; to ensure the sustainable use of resources. First, by analyzing the factors involved in the whole process, we formulated the problem as a multi-objective optimization problem, the objective of which was to maximize both the probability of completing the tasks and the utilities of allocated resources. Based on the theory of search, an integer nonlinear programming (INLP) model was built for different tasks. Second, in order to solve the non-deterministic polynomial-time hardness (NP-hard) model, by constructing a rule base, candidate solutions can be found to improve the calculation efficiency. Furthermore, in order to obtain the optimal scheme, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to the candidate solution sets to approximate Pareto fronts. Finally, an emergency case of Chinese Bohai Sea was used to demonstrate the effectiveness of the proposed model. In the study, 11 resource allocation schemes were obtained to respond to the emergency, and calculation processes of schemes were further analyzed to demonstrate our model’s rationality. Results showed that the proposed models provide decision-makers with scientific decision support on different emergency tasks.

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