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A Dynamic Multi-Objective Approach for Dynamic Load Balancing in Heterogeneous Systems

ieee TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2020年第10期31卷 2421-2434页

作者： Cabrera, Alberto Acosta, Alejandro Almeida, Francisco Blanco, Vicente Univ La Laguna HPC Grp Escuela Super Ingn & Tecnol San Cristobal La Laguna 38270 Tenerife Spain

Modern standards in High Performance Computing (HPC) have started to consider energy consumption and power draw as a limiting factor. New and more complex architectures have been introduced in HPC systems to afford these new restrictions, and include coprocessors such as GPGPUs for intensive computational tasks. As systems increase in heterogeneity, workload distribution becomes a more core problem to achieve the maximum efficiency in every computational component. We present a Multi-Objective Dynamic Load Balancing (DLB) approach where several objectives can be applied to tune an application. These objectives can be dynamically exchanged during the execution of an algorithm to better adapt to the resources available in a system. We have implemented the Multi-Objective DLB together with a generic heuristic engine, designed to perform multiple strategies for DLB in iterative problems. We also present Ull Multiobjective Framework (UllMF), an open-source tool that implements the Multi-Objective generic approach. UllMF separates metric gathering, objective functions to be optimized and load balancing algorithms, and improves code portability using a simple interface to reduce the costs of new implementations. We illustrate how performance and energy consumption are improved for the implemented techniques, and analyze their quality using different DLB techniques from the literature.

关键词： Load management Heuristic algorithms Measurement Linear programming Energy consumption Task analysis Optimization Dynamic load balancing energy efficiency iterative algorithms heterogeneous computing

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Optimal Deployment of Fog Nodes, Microservices and SDN Controllers in Time-Sensitive IoT Scenarios

Optimal Deployment of Fog Nodes, Microservices and SDN Contr...

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2021 ieee Global Communications conference (GLOBECOM)

作者： Juan Luis Herrera Jaime Galán-Jiménez Paolo Bellavista Luca Foschini Jose Garcia-Alonso Juan M. Murillo Javier Berrocal University of Extremadura Spain University of Bologna Italy

ISBN: (纸本)9781728181059

The application of Internet of Things (IoT)-based solutions to intensive domains has enabled the automation of real-world processes. The critical nature of these domains requires for very high Quality of Service (QoS) to work properly. These applications often use computing paradigms such as fog computing and software architectures such as the Microservices Architecture (MSA). Moreover, the need for transparent service discovery in MSAs, combined with the need for network scalability and flexibility, motivates the use of Software-Defined networking (SDN) in these infrastructures. However, optimizing QoS in these scenarios implies an optimal deployment of microservices, fog nodes, and SDN controllers. Moreover, the deployment of each of the different elements affects the optimality of the others, which calls for a joint solution. In this paper, we motivate the joining of these three optimization problems into a single effort and we present Umizatou, a holistic deployment optimization solution that makes use of Mixed Integer Linear programming. Finally, we evaluate Umizatou over a healthcare case study, showing its scalability in topologies of different sizes.

关键词： Software architecture Scalability Microservice architectures Quality of service Medical services Topology Mixed integer linear programming

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On the Detection of Community Smells Using Genetic programming-based Ensemble Classifier Chain 15

On the Detection of Community Smells Using Genetic Programmi...

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15th ACM/ieee International conference on Global Software Engineering (ICGSE)

作者： Almarimi, Nuri Ouni, Ali Chouchen, Moataz Saidani, Islem Mkaouer, Mohamed Wiem Univ Quebec ETS Montreal Montreal PQ Canada Rochester Inst Technol Rochester NY USA

ISBN: (纸本)9781450370936

Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as suboptimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an automated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell instances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89% achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.

关键词： Community smells Social debt Socio-technical factors Multi-label learning Genetic programming Search-based software engineering

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DeepBurning-GL: an Automated Framework for Generating Graph Neural network Accelerators 20

DeepBurning-GL: an Automated Framework for Generating Graph ...

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39th ieee/ACM International conference On Computer Aided Design (ICCAD)

作者： Liang, Shengwen Liu, Cheng Wang, Ying Li, Huawei Li, Xiaowei Chinese Acad Sci State Key Lab Comp Architecture Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665423243

Building FPGA-based graph learning accelerators is very time-consuming due to the low-level RTL programming and the complicated design flow of FPGA development. It also requires the architecture and hardware expertise from the Graph Neural network (GNN) application developers to tailor efficient accelerator designs on FPGAs. This work proposes an automation framework, DeepBurning-GL, which is compatible with state-of-the-art graph learning frameworks such as Deep Graph Library so that the developers can easily generate application-specific GNN accelerators from the software-described models. First, DeepBurning-GL employs a GNN performance analyzer to locate the performance bottleneck of specific GNN applications and decide the major design architectures and parameters that meet the user-specified constraints. Second, DeepBurning-GL provides a series of pre-built design templates such as computing templates and memory templates, which can be parameterized and fused to generate the final accelerator design. It also includes an optimizer that conducts automatic optimization by adjusting the accelerator architectural parameters. In evaluation, we use DeepBurning-GL to generate customized accelerators on three different FPGA platforms for various GNN models and workloads. The experimental results show that the generated accelerators achieve 179.4X and 40.1X energy-efficiency boost over the CPU and GPU solutions on average and deliver a 6.28X speedup and 6.73X energy-efficiency improvement on average compared to the latest GNN accelerator HyGCN on Alveo U50.

关键词： graph neural networks automated framework FPGA

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Towards Flying Mobile Edge Computing 34

Towards Flying Mobile Edge Computing

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34th International conference on Information networking (ICOIN)

作者： VanDung Nguyen Tran Trong Khanh Pham Van Nam Ngo Thien Thu Hong, Choong Seon Huh, Eui-Nam Kyung Hee Univ Dept Comp Sci & Engn Global Campus Yongin 17104 South Korea

ISBN: (纸本)9781728141985

With the development of mobile edge computing (MEC), many approaches are proposed to improve the computation performances, such as network congestion, transmission latency, and quality of Internet of Things (IoTs) services. Although ground MEC system architectures are developed, there are still unsolved problems, such as depending on the ground infrastructure. Therefore, unmanned aerial vehicle (UAV) assisted MEC is considered to address these problems. However, the computation performance is limited by the UAV hover model, which is affected by the UAV's velocity, acceleration, and a variable altitude. Nowadays, a terrestrial-satellite network (STN) plays a role in important 5G network development. The characteristics of an STN integrated with 5G are a low delay, high bandwidth, and ubiquitous coverage. Hence, 5G high-speed satellite-terrestrial assisted mobile edge computing (SMEC) is considered as a flying mobile edge computing, which will be discussed in terms of reasons, advantages, and open issues in this paper.

关键词： MEC unmanned aerial vehicle UAV-assisted MEC terrestrial-satellite network STN-assisted MEC

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Robust Optimal Tracking Control for Linear Systems via Adaptive Dynamic programming method 16

Robust Optimal Tracking Control for Linear Systems via Adapt...

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16th ieee International conference on Control and Automation (ICCA)

作者： Xiao, Zhenfei Li, Jinna Ding, Jinliang Liu, Shuai Wang, Guoliang Liaoning Shihua Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China

ISBN: (纸本)9781728190938

This paper takes two-player systems as an example to study the robust optimal tracking control problem for linear discrete-time (DT) multi-player systems with constant uncertainty. To this end, by using adaptive dynamic programming (ADP) method and game theory, the optimal feedback control problem of the dynamic aspect was translated into a two-player cooperative game problem. Thus, we developed a novel off-policy cooperative game Q-learning algorithm first to learn the feedback controllers through the measured data along the system trajectories. Then the steady-state control inputs can be obtained by utilizing the Lagrange equation and correlative parameters learned from the proposed algorithm. Finally, the control inputs of the linear DT systems with uncertainty can be calculated by combining the feedback controllers and the steady-state control inputs. Simulation results are given to verify the effectiveness of the proposed method.

关键词： Linear systems

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Asymmetric Information Acquisition Games 59

Asymmetric Information Acquisition Games

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59th ieee conference on Decision and Control (CDC)

作者： Singh, Vartika Kavitha, Veeraruna Indian Inst Technol IEOR Bombay Maharashtra India

ISBN: (纸本)9781728174471

We consider a stochastic game with partial, asymmetric and non-classical information, where the agents are trying to acquire as many available opportunities/locks as possible. Agents have access only to local information, the information updates are asynchronous and our aim is to obtain relevant equilibrium policies. Our approach is to consider optimal open-loop control until the information update, which allows managing the belief updates in a structured manner. The agents continuously control the rates of their Poisson search clocks to acquire the locks, and they get rewards at every successful acquisition;an acquisition is successful if all the previous stages are successful and if the agent is the first one to complete. However, none of them have access to the acquisition status of the other agents, leading to an asymmetric information game. Using standard tools of optimal control theory and Markov decision process (MDP) we solved a bi-level control problem;every stage of the dynamic programming equation of the MDP is solved using optimal control tools. We finally reduced the game with an infinite number of states and infinite-dimensional actions to a finite state game with one-dimensional actions. We provided closed-form expressions for Nash Equilibrium in some special cases and derived asymptotic expressions for some more.

关键词： Dynamic programming

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Integrating On-Load-Tap Changer in Service Restoration for a Distribution network with Multiple PV Systems

Integrating On-Load-Tap Changer in Service Restoration for a...

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International conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Sokleap Heang Vannak Vai Samphors Eng Dara Eam Research and Innovation Center Institute of Technology of Cambodia Phnom Penh Cambodia Faculty of Electrical Engineering Institute of Technology of Cambodia Phnom Penh Cambodia

ISBN: (纸本)9781665480772

In order to improve power system reliability, service restoration was operated to optimal radial topology when the line, transformer, and feeder outage occurred in the power system to restore maximum out-of-service loads with minimize switch operation and return the system to a normal state. In this paper, service restoration accomplished Sequential opening Breach (SOB) by switching the status of several sectionalizing switches (SS) from closed to open. The problem includes four different objective functions: 1) maximizing the total load restored; 2) minimizing the number of switching operations; 3) minimizing power loss and 4) minimizing voltage deviation. Some tie-switches (TS) also needed to be changed from open to close to isolate the fault location due to the shorter service restoration during the broken portion made possible by the rising penetration of PV systems at the loads. To confirm the proposed method, the standard distribution system of ieee 69 buses is tested with different load demands. The simulation results validated the effectiveness of the proposed method.

关键词： Simulation Instruments Switches Distribution networks Fault location Transformers Linear programming

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Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize the Total network Power?

Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize ...

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ieee International conference on Communications (ICC)

作者： Ö zlem Tuğ fe Demir Meysam Masoudi Emil Bjö rnson Cicek Cavdar Department of Computer Science KTH Royal Institute of Technology Kista Sweden Department of Electrical Engineering Link&#x00F6 ping University Link&#x00F6 ping Sweden

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683484

Previous works on cell-free massive MIMO mostly consider physical-layer and fronthaul transport aspects. How to deploy cell-free massive MIMO functionality in a practical wireless system is an open problem. This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN). We aim to minimize the end-to-end power consumption by jointly considering the radio, optical fronthaul, virtualized cloud processing resources, and spectral efficiency requirements of the user equipments. The considered optimization problem is cast in a mixed binary second-order cone programming form and, thus, the global optimum can be found using a branch-and-bound algorithm. The optimal power-efficient solution of our proposed cell-free system is compared with conventional small-cell implemented using V-CRAN, to determine the benefits of cell-free networking. The numerical results demonstrate that cell-free massive MIMO increases the maximum rate substantially, which can be provided with almost the same energy per bit. We show that it is more power-efficient to activate cell-free massive MIMO already at low spectral efficiencies (above 1 bit/s/Hz).

关键词： Wireless communication Power demand Spectral efficiency Electric breakdown Heuristic algorithms conferences Cloud radio access networks

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An Adaptive Segmentation Technique to Detect Brain Tumors Using 2D Unet

An Adaptive Segmentation Technique to Detect Brain Tumors Us...

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ieee International conference on Bioinformatics and Biomedicine (ieee BIBM)

作者： Aledhari, Mohammed Razzak, Rehma Kennesaw State Univ Dept Comp Sci Marietta GA 30060 USA

ISBN: (纸本)9781728162157

The UNet is one of the most well-known convolutional neural network (CNN) architectures used for biomedical image segmentation. Unfortunately, the 2D variant is typically discouraged for volumetric brain tumor segmentation due to slices being correlated with one another. Thus, 3D-Unets have become prevalent in the annual Multimodal Brain tumor Segmentation Challenge (BRaTS) hosted by the Perelman School of Medicine of University of Pennsylvania (UPenn). However, with unique data preprocessing and generator techniques, 2D-Unets may achieve competitive accuracy and performance with 3D-Unets. Furthermore, the addition of residual blocks (R) and squeeze-and-excitation (SE) blocks in the upsampling portion of 2D-Unets could further speed up performance and minimize computational costs without sacrificing f1 or Jaccard's similarity score. This reveals that 2D-UNets for 3D biomedical image segmentation are still valuable. This paper involves the detailed comparison between 2D-Unets and 2D-SE-RUNets for the purposes of segmenting a whole high-grade glioma (HGG) using the metrics of Jaccard's similarity, recall, specificity, and precision. Results indicate that the 2D-SE-RUNet model is superior to the traditional 2D UNet due to efficieny, which can benefit those looking to save computational costs and time.

关键词： brain tumor segmentation Unet convolutional neural network

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