Concurrency and Computation: Practice and Experience最新文献_第9页

A cooperative multi‐agent offline learning algorithm to scheduling IoT workflows in the cloud computing environment 云计算环境下物联网工作流调度的协同多智能体离线学习算法

Concurrency and Computation: Practice and Experience Pub Date : 2022-07-08 DOI: 10.1002/cpe.7148

Hadi Gholami, Mohammad Taghi Rezvan

{"title":"A cooperative multi‐agent offline learning algorithm to scheduling IoT workflows in the cloud computing environment","authors":"Hadi Gholami, Mohammad Taghi Rezvan","doi":"10.1002/cpe.7148","DOIUrl":"https://doi.org/10.1002/cpe.7148","url":null,"abstract":"Regarding the problem of workflow scheduling in cloud environments, users want the workflow to be processed at a suitable time while cloud providers want to increase resource utilization. This article proposes a cooperative multi‐agent offline learning algorithm called CMOL for minimizing makespan and energy consumption. This algorithm schedules a workflow that is represented by a directed acyclic graph (DAG) and assigns them to virtual machines (VMs). Multiple parallel agents interact and cooperate based on an algorithm in three steps of research, improvement, and selection to meet the imposed constraints of deadline and energy. Depending on the number of DAG levels, there is the same number of specialist agents who use strategies to create a Pareto feasible solution and simultaneously gain experience in the first two steps. The parallel agents exploit the extracted knowledge to improve the solution obtained by ensembling their experience in the selection step. To compare the efficiency of CMOL, two algorithms based on multi‐agent systems and one algorithm based on single‐agent are developed. The performance of the four algorithms is investigated on different real‐world workflows and compared on various sizes. Computational results reveal the competitiveness of CMOL and its relative superiority compared with others.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90486005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Adaptive group‐aware topic model for venue recommendation 场地推荐的自适应群体感知主题模型

Concurrency and Computation: Practice and Experience Pub Date : 2022-07-06 DOI: 10.1002/cpe.7130

Ruichang Li, Xiang-wu Meng, Yujie Zhang

引用次数: 0

A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm 一种基于雾的医疗保健系统任务调度的新方法，使用混合自然启发算法

Concurrency and Computation: Practice and Experience Pub Date : 2022-07-06 DOI: 10.1002/cpe.7155

B. Wang, Peng Wu, Maryam Arefzaeh

{"title":"A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm","authors":"B. Wang, Peng Wu, Maryam Arefzaeh","doi":"10.1002/cpe.7155","DOIUrl":"https://doi.org/10.1002/cpe.7155","url":null,"abstract":"The goal of the healthcare system is to offer a dependable and well‐organized solution for improving human's wellbeing. Examining a patient's history can assist clinicians in considering the patient's wants when building a healthcare system and providing service, resulting in increased patient satisfaction. Thus, healthcare is becoming a more competitive sector. Massive data volume, latency, response time, and security susceptibility are all difficulties resulting from this substantial increase in healthcare systems. As a famous distributed structure, fog computing might thus aid in the resolution of such problems. Processing parts are situated among end devices and cloud components in a fog computing infrastructure and run programs. This design is well suited to real‐time and low‐latency applications, like healthcare systems. Because task scheduling is an NP‐hard optimization issue in fog‐based medical healthcare systems, this work proposes a hybrid genetic algorithm and particle swarm optimization (GA‐PSO) strategy. A powerful single‐objective optimization technique is the GA‐PSO. Individuals in a novel generation are formed in GA‐PSO through mutation and crossover operations in GA‐PSO, which uses a redefined local optimization swarm. Hence, it may avoid local minimums and perform well in global searches. The study's goal in fog‐based medical healthcare systems is to lower the makespan and overall response time. The suggested technique is simulated in MATLAB and compared to the GA and PSO methods. The empirical findings confirmed the improved makespan, making the approach appropriate for medical and real‐time systems applications.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"89 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76760907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

An automated brain tumor segmentation framework using a novel fruit fly UNet 基于果蝇UNet的自动脑肿瘤分割框架

Concurrency and Computation: Practice and Experience Pub Date : 2022-07-06 DOI: 10.1002/cpe.7171

Ravi Boda, Reni K. K Cherian, Vinit Kumar

引用次数: 0

Heterogeneous graph prompt for Community Question Answering

Concurrency and Computation: Practice and Experience Pub Date : 2022-07-04 DOI: 10.1002/cpe.7156

Huanghai Liu, Ying Qin

引用次数: 1

Adaptive façade for building energy efficiency improvement by arithmetical optimization algorithm 基于算术优化算法的建筑节能自适应优化

Concurrency and Computation: Practice and Experience Pub Date : 2022-07-01 DOI: 10.1002/cpe.7152

Leyan Shi, Somayeh Pouramini

{"title":"Adaptive façade for building energy efficiency improvement by arithmetical optimization algorithm","authors":"Leyan Shi, Somayeh Pouramini","doi":"10.1002/cpe.7152","DOIUrl":"https://doi.org/10.1002/cpe.7152","url":null,"abstract":"To improve the energy efficiency in buildings, a computational optimization method is suggested in this article. The method is designed based on the structure of the adaptive façade. This type of façade system can adjust its visible and thermal transmissivity for actively changing weather conditions. The main concept of this system is the optimization process. This process integrates the DesignBuilder as the building energy simulation tool with an optimization method, called arithmetical optimization algorithm, through the MLE+ toolkit. It is notable that the proposed approach can be applied to different building types and is also not involved in any specific optimization tool. In this regard, two case studies, a typical single office room, and a medium office building are selected to show the capability of the presented approach to improve the energy effectiveness of the building. Based on the results, the energy consumption is decreased by 15.0%–29.1% for the first case study and 14.3%–22.4% for the second case compared to the static façades. The adaptive façades' potentiality is indicated by the important achievements in this study.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86861774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Multi‐objective reliability‐based workflow scheduler: An elastic and persuasive task scheduler based upon modified‐flower pollination algorithm in cloud environment 基于多目标可靠性的工作流调度程序:一种基于改进的云环境下花朵授粉算法的弹性和有说服力的任务调度程序

Concurrency and Computation: Practice and Experience Pub Date : 2022-07-01 DOI: 10.1002/cpe.7150

Neha Miglani, Gaurav Sharma, Savita Khurana

{"title":"Multi‐objective reliability‐based workflow scheduler: An elastic and persuasive task scheduler based upon modified‐flower pollination algorithm in cloud environment","authors":"Neha Miglani, Gaurav Sharma, Savita Khurana","doi":"10.1002/cpe.7150","DOIUrl":"https://doi.org/10.1002/cpe.7150","url":null,"abstract":"This research article formulates contemporary approach named multi‐objective reliability‐based workflow scheduler. Numerous strategies have been proposed in the past to prioritize and map the tasks to cloud resources. Though the recent studies lead to efficient solutions however they are restrained in terms of performance due to lack of resource consideration based on utilization rate and reliability index. It is crucial to consider reliability parameter while mapping tasks onto the virtual machines and not just the reliability value, but the cost incurred must also be minimized. To this end, the proposed strategy has been categorized into four modules, (i) scrutiny of reliable VMs, (ii) task ranking, (iii) optimizing the task re‐ordering using flower pollination optimization, and (iv) task mapping onto the VM. It intends to map task onto the most suitable machine in terms of makespan, efficiency, and incurred cost. In the experimental setup, four scientific workflows have been considered namely, LIGO, Genome, Cybershake, and Montage, they have been tested on the proposed approach while making comparison with the existing approaches namely FPA, GWO, and GA. The simulation results justified the claims by allocating resources to the cloudlets efficiently and stabilizing all the aforementioned parameters by attaining performance measures adequately.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76255047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Object detection and estimation: A hybrid image segmentation technique using convolutional neural network model 目标检测与估计:一种基于卷积神经网络模型的混合图像分割技术

Concurrency and Computation: Practice and Experience Pub Date : 2022-07-01 DOI: 10.1002/cpe.7114

Aarthi Sundaram, C. Sakthivel

{"title":"Object detection and estimation: A hybrid image segmentation technique using convolutional neural network model","authors":"Aarthi Sundaram, C. Sakthivel","doi":"10.1002/cpe.7114","DOIUrl":"https://doi.org/10.1002/cpe.7114","url":null,"abstract":"Object detection from image is more challenging and integral part in the inter‐discipline area of computer vision. The computer vision is highly attractive in many applications like human pose estimation, instance segmentation, recognizing action, disease predictions object prediction and many more applications. The traditional method of detecting objects from the images is done using bounding boxes with labels. It suffers from the overlapping of the boxes with various smaller objects, which leads to accuracy issues in detection problems. Hence, machine learning techniques are used to detect the relevant objects from the image using center point to avoid the nonmaximal suppression in bounding box. To accurately identify images, an U‐Net architecture based object detection method is proposed. In this model, it effectively uses semantic level segmentation and instance segmentation. This system effectively identifies all the objects present in the given image using the efficient hybrid segmentation models and Gromov Hausdroff distance measure. For experimentation, two data sets are used for evaluation of the model to identify all categories of objects from the image. The proposed model achieves an accuracy of 91.8% and reliable when compared to existing effective object detection algorithms like fully convolution network (FCN), YOLO (you only look once) and mask region based‐convolutional neural network (mask R‐CNN) model.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78632972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of deep learning in the detection and practical evaluation of ski resort teaching area 深度学习在滑雪场教学区检测与实际评价中的应用

Concurrency and Computation: Practice and Experience Pub Date : 2022-06-29 DOI: 10.1002/cpe.7151

Xiangxian Chen, Xiang Qi, Pin Lyu

引用次数: 0

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud PGWO - AVS - RDA:基于智能优化和集群的云负载均衡模型

Concurrency and Computation: Practice and Experience Pub Date : 2022-06-27 DOI: 10.1002/cpe.7136

Raghavender Reddy Kothi Laxman, A. Lathigara, Dr Rajanikanth Aluvalu, Uma Maheswari Viswanadhula

{"title":"PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud","authors":"Raghavender Reddy Kothi Laxman, A. Lathigara, Dr Rajanikanth Aluvalu, Uma Maheswari Viswanadhula","doi":"10.1002/cpe.7136","DOIUrl":"https://doi.org/10.1002/cpe.7136","url":null,"abstract":"Load balancing and task scheduling in cloud have gained a significant attention by many researchers, due to the increased demand of computing resources and services. For this purpose, there are various load balancing methodologies are developed in the existing works, which are mainly focusing on allocating the tasks to Virtual Machines (VMs) based on their priority, order of tasks, and execution time. Still, it facing the major difficulties in finding the best tasks for allocation, because the sequence of patterns are normally used to categorize the relevant tasks with respect to the load. Thus, this research work intends to develop an intelligent group of mechanisms for efficiently allocating the tasks to the VMs by finding the best tasks with respect to the scheduling parameters. Initially, the user tasks are given to the load balancer unit, where the Probabilistic Gray Wolf Optimization (PGWO) technique is used to find the best fitness value for selecting the tasks. Then, the Adaptive Vector Searching (AVS) methodology is utilized to cluster the group of tasks for efficiently allocating the tasks with improved Quality of Service (QoS). Finally, the Recursive Data Acquisition (RDA) based scheduler unit can allocate the clustered tasks to the appropriate VMs in the cloud system by analyzing the properties of storage capacity, balancing load of VM, CPU usage, memory consumption, and execution time of tasks. During evaluation, the performance of the proposed load balancing model is validated by using various measures. Then, the obtained results are compared with some state‐of‐the‐art models for proving the betterment of the proposed scheme.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74355142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3