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How to place your apps in the fog: State of the art and open challenges

SOFTWARE-PRACTICE & EXPERIENCE 2020年第5期50卷 719-740页

作者： Brogi, Antonio Forti, Stefano Guerrero, Carlos Lera, Isaac Univ Pisa Dept Comp Sci I-56127 Pisa Italy Univ Balearic Isl Dept Comp Sci Palma De Mallorca Balearic Island Spain

Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency-sensitive and bandwidth-hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open-source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

关键词： application deployment application placement fog computing optimization algorithms service placement

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Multi-objective optimization of thermoelectric cooler using genetic algorithms

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AIP ADVANCES 2019年第9期9卷 095105-095105页

作者： Lu, Tianbo Zhang, Xiang Zhang, Jianxin Ning, Pingfan Li, Yuqiang Niu, Pingjuan Tianjin Polytech Univ Minist Educ Engn Res Ctr High Power Solid State Lighting Appl Tianjin 300387 Peoples R China Tianjin Polytech Univ Sch Mech Engn Tianjin 300387 Peoples R China

The thermoelectric cooler (TEC) is a kind of cooling equipment which used to dissipate heat from the devices by Peltier effect. The cooling capacity (Q(c)) and coefficient of performance (COP) are both significant performance parameters of a thermoelectric cooler. In this article, three-dimensional numerical simulations are carried out by finite element analysis based on the temperature-dependent materials properties. The experimental and geometrical parameters have important effects on the TEC performance which have been analysed, such as electrical current, geometric configuration of thermoelectric leg, Thomson effect, thermal contact resistances and electrical contact resistances. The results show when the Thomson effect is ignored, the maximum difference in the cooling capacity is 7.638 W while the maximum difference in the COP is 0.09. When contact effect is not considered, the maximum difference in the cooling capacity is 22.06 W while the maximum difference in the COP is 0.75. Furthermore, the cooling capacity and COP have also been simultaneously optimized according to the multi-objective genetic algorithm. The best optimal value is obtained making use of TOPSIS (technique for order preference by similarity to an ideal solution) method from Pareto frontier. Investigated on these optimal design parameters which were anticipated to provide real guidance in industry.

关键词： Evolutionary computation Finite-element analysis Ceramics Thermoelectricity Cooling capacity Contact impedance optimization algorithms Electronic devices Thermodynamic states and processes

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A Framework for Search and Application Agnostic Interactive optimization

A Framework for Search and Application Agnostic Interactive ...

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2020 Conference on Artificial Life, ALIFE 2020

作者： Powers, Joshua Pell, Sarah Bongard, Josh University of Vermont United States

In domains where measures of utility for automatically-designed artefacts (or agents performing subjective tasks) are difficult or impossible to mathematically describe (such as ‘be interesting’), human interactive search algorithms are an attractive alternative. However, despite notable achievements, they are still designed around a specific search method, resulting in a lack of problem generality: applying a new search algorithm requires an excessive amount of redesign such that an altogether new interactive method is formed in the process. This leads to missed opportunities for human interactive methods to utilize the power of state of the art optimization algorithms. Here, we introduce for the first time a framework for human interactive optimization that is agnostic to both the search method and the application problem. Using 13 different search methods on 24 fitness functions commonly found in evolutionary algorithm benchmarks, we show that our approach works on the majority of tested applications: many of the search methods, provided with access to the fitness functions, performed no better than our framework, which employs surrogate human participants who act as less informed and erroneous representations of the fitness function. In this way, our framework for interactive optimization provides a scalable solution by facilitating the integration of numerous types of current state of the art or future search algorithms. Future work will involve generalizing this method to admit multi-objective optimization methods and validation with human participants. © 2020 Massachusetts Institute of Technology.

关键词： optimization algorithms

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Second-order MCSCF optimization revisited. I. Improved algorithms for fast and robust second-order CASSCF convergence

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JOURNAL OF CHEMICAL PHYSICS 2019年第19期150卷 194106-194106页

作者： Kreplin, David A. Knowles, Peter J. Werner, Hans-Joachim Univ Stuttgart Inst Theoret Chem Pfaffenwaldring 55 D-70569 Stuttgart Germany Cardiff Univ Sch Chem Main BldgPk Pl Cardiff CF10 3AT S Glam Wales

A new improved implementation of the second-order multiconfiguration self-consistent field optimization method of Werner and Knowles [ J. Chem. Phys. 82, 5053 (1985)] is presented. It differs from the original method by more stable and efficient algorithms for minimizing the second-order energy approximation in the so-called microiterations. Conventionally, this proceeds by alternating optimizations of the orbitals and configuration (CI) coefficients and is linearly convergent. The most difficult part is the orbital optimization, which requires solving a system of nonlinear equations that are often strongly coupled. We present a much improved algorithm for solving this problem, using an iterative subspace method that includes part of the orbital Hessian explicitly, and discuss different strategies for performing the uncoupled optimization in a most efficient manner. Second, we present a new solver in which the orbital-CI coupling is treated explicitly. This leads to quadratic convergence of the microiterations but requires many additional evaluations of reduced (transition) density matrices. In difficult optimization problems with a strong coupling of the orbitals and CI coefficients, it leads to much improved convergence of both the macroiterations and the microiterations. Third, the orbital-CI coupling is treated approximately using a quasi-Newton approach with Broyden-Fletcher-Goldfarb-Shanno updates of the orbital Hessian. It is demonstrated that this converges almost as well as the explicitly coupled method but avoids the additional effort for computing many transition density matrices. The performance of the three methods is compared for a set of 21 aromatic molecules, an Fe(II)-porphine transition metal complex, as well as for the [Cu2O2(NH3)(6)](2+), FeCl3, Co-2(CO)(6)C2H2, and Al4O2 complexes. In all cases, faster and more stable convergence than with the original implementation is achieved. Published under license by AIP Publishing.

关键词： Density-matrix optimization algorithms optimization problems Isomerization Slater determinant Transition metals

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Expand or Oversize? Planning Internet Access Network in a Demand Growth Scenario

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2020年第4期28卷 1820-1838页

作者： Liborio, Matheus Pereira Ekel, Petr Iakovlevitch Lyrio, Renata de Mello Bernardes, Patricia Soares, Gustavo Luis Machado-Coelho, Thiago Melo Pontificia Univ Catolica Minas Gerais BR-30535012 Belo Horizonte MG Brazil Federat Ind Minas Gerais BR-35501001 Belo Horizonte MG Brazil Univ Fed Minas Gerais BR-31270901 Belo Horizonte MG Brazil

Internet network design specialists are looking for technologies and strategies to deliver network service under increased demand conditions. The choice of strategies is based on applying optimization and decision-making methods to select the most appropriate cable network design considering criteria established by the problem definition. However, this definition is itself a decision problem that has not received analysis in the literature. In particular, one of the most important questions is the necessity to define an expansion strategy. The first alternative (expansion) is to design a network to serve consumers with Internet demand equal to or greater than the predefined one to expand the network annually to serve consumers that reach the predefined Internet speed. The second alternative (oversizing) is to design a network to serve consumers with future Internet demand (after 5 years) at or above that the predefined one. Considering this, the objective of this research is to define the most advantageous strategy of expansion planning to attend a 5 years forecasted Internet demand, considering: (1) the possibility of utilizing a Gigabit-capable Passive Optical Network technology;(2) the application of the minimal Steiner tree and Dijkstra algorithms in planning procedures;(3) the influence of economic and technological factors on the demand forecast;(4) the aggressive, moderate, and conservative scenarios in decision-making. The results show that the over-dimensioning strategy reduces network investment by between 30 and 41%, but that this reduction does not always lead to a market investment ratio higher than that observed in the expansion strategy.

关键词： Internet forecast model Geoprocessing optimization algorithms Decision-making Network planning strategies

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Optimal Task Offloading and Resource Allocation in Mobile-Edge Computing With Inter-User Task Dependency

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第1期19卷 235-250页

作者： Yan, Jia Bi, Suzhi Zhang, Ying Jun Tao, Meixia Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (e.g., on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and task execution time. The problem is challenging due to the combinatorial nature of the offloading decisions among all tasks and the strong coupling with resource allocation. To tackle this problem, we first assume that the offloading decisions are given and derive the closed-form expressions of the optimal offloading transmit power and local CPU frequencies. Then, an efficient bi-section search method is proposed to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decisions follow an one-climb policy, based on which a reduced-complexity Gibbs Sampling algorithm is proposed to obtain the optimal offloading decisions. We then extend the investigation to a general multi-user scenario, where the input of a task at one WD requires the final task outputs from multiple other WDs. Numerical results show that the proposed method can significantly outperform the other representative benchmarks and efficiently achieve low complexity with respect to the call graph size.

关键词： Task analysis Wireless communication Sensors Resource management Wireless sensor networks Servers Energy consumption Mobile edge computing binary offloading optimization algorithms resource allocation

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Design of Optimal Two-Dimensional FIR Filters with Quadrantally Symmetric Properties Using Vortex Search Algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2020年第10期29卷

作者： Yadav, Suman Yadav, Richa Kumar, Ashwni Kumar, Manjeet Bharati Vidyapeeth Coll Engn Dept Elect & Commun Engn New Delhi 110063 India Indira Gandhi Delhi Tech Univ Women IGDTUW Dept Elect & Commun Engn New Delhi 110006 India Bennett Univ Dept Elect & Commun Engn Greater Noida 201310 Uttar Pradesh India

This research paper presents a new evolutionary technique named vortex search optimization (VSO) to design digital 2D finite impulse response (FIR) filter for improved performance both in pass-band and stop-band regions. Optimum filter coefficients are calculated by minimizing the deviation of actual frequency response from specified or desired response. Efficiency of the designed filter is measured by several parameters, such as maximum pass-band ripple, maximum stop-band ripple, mean attenuation in stop band and time taken, to execute the code. Analysis of the performance of designed filter is correlated with various different algorithms like real coded genetic algorithm, particle swarm optimization, genetic search algorithm and hybrid particle swarm optimization gravitational algorithm. Comparative study shows significant reduction in pass-band error, stop-band error and execution time.

关键词： Digital filter 2D finite impulse response filter vortex search algorithm quadrantally symmetric impulse response optimization algorithms

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Constrained based tunable Q wavelet transform for efficient decomposition of EEG signals

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APPLIED ACOUSTICS 2020年第0期163卷 107234-000页

作者： Khare, Smith K. Bajaj, Varun PDPM Indian Inst Informat Technol Design & Mfg Elect & Commun Engn Jabalpur 482005 India

Electroencephalogram (EEG) signal decomposition by selecting veracious basis function is a tedious task. In this paper, constrained based tunable Q wavelet transform (CTQWT) is proposed for adaptive selection of the optimum tuning parameters (Q and r) to decompose the highly nonstationary EEG signals accurately. The fitness function of mean square error (MSE) is used as a constraint to minimize the reconstruction error of EEG signals. Transformative optimization algorithms (TOA) are used to evaluate the optimum Q and r for the decomposition of the EEG signals. The efficacy of the proposed method is evaluated by comparing the reconstruction error obtained with traditional TQWT and CTQWT using the multiclass sleep stages and two class focal EEG datasets. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Electroencephalogram signals Constrained tunable Q wavelet transform optimization algorithms

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Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm

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FUTURE INTERNET 2022年第12期14卷 365-365页

作者： Taha, Ashraf. A. A. Abouroumia, Hagar. O. O. Mohamed, Shimaa. A. A. Amar, Lamiaa. A. A. SRTA CITY Informat Res Inst Dept Networks & Distributed Syst City Sci Res & Technol Applicat Alexandria 21934 Egypt Alexandria Univ Fac Engn Comp & Commun Dept Alexandria 21544 Egypt

As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption.

关键词： wireless sensor networks optimization algorithms aquila optimizer alive nodes energy consumption

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Primal-Dual algorithms for Convex optimization via Regret Minimization

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IEEE CONTROL SYSTEMS LETTERS 2018年第2期2卷 284-289页

作者： Nam Ho-Nguyen Kilinc-Karzan, Fatma Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA

We consider a large-scale convex program with functional constraints, where interior point methods are intractable due to the problem size. The effective solution techniques for these problems permit only simple operations at each iteration, and thus are based on primal-dual first-order methods such as the Arrow-Hurwicz-Uzawa subgradient method, which utilize only gradient computations and projections at each iteration. Such primal-dual algorithms admit the interpretation of solving the associated saddle point problem arising from the Lagrange dual. We revisit these methods through the lens of regret minimization from online learning and present a flexible framework. While it is well known that two regret-minimizing algorithms can be used to solve a convex-concave saddle point problem at the standard rate of O (1/root T), our framework for primal-dual algorithms allows us to exploit structural properties such as smoothness and/or strong convexity and achieve better convergence rates in favorable cases. In particular, for non-smooth problems with strongly convex objectives, our primal-dual framework equipped with an appropriate modification of Nesterov's dual averaging algorithm achieves O (1/T) convergence rate.

关键词： optimization algorithms large-scale systems numerical algorithms

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