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Handling Uncertainty: Pareto-Efficient BoT Scheduling on Hybrid clouds

Handling Uncertainty: Pareto-Efficient BoT Scheduling on Hyb...

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international conference on parallel Processing (ICPP)

作者： M. Reza Hoseinyfarahabady Hamid R.D. Samani Luke M. Leslie Young Choon Lee Albert Y. Zomaya Centre for Distributed and High Performance Computing The University of Sydney NSW Australia

Coping with uncertainty is a challenging and complex problem particularly in hybrid cloud environments-private cloud plus public cloud. Conflicting goals of minimizing the cost and performance, unknown prior knowledge about task running times, and a lack of estimation tools are just a few of the challenges that resource management systems in those environments encounter. The aim in this paper is to find Pareto-optimal schedules for large-scale Bag-of-Tasks (BoT) applications that meet user defined constraints, such as deadline or budget or some tradeoff between them. BoT applications are common in science and engineering and consist of many independent tasks. To achieve the user's chosen Pareto-optimal schedule, we develop a dynamic resource allocation process for hybrid clouds. We also present a hybrid approach to estimating task running times that incorporates several estimators with a feedback control system to cope with the inherent uncertainty in such estimation. Through extensive experiments on a test bed hybrid cloud, using Amazon EC2 as a public cloud, we show that the proposed approach can achieve near optimality with little overhead, and consistently achieves a solution within 2% of the user's chosen Pareto-optimal schedule. Further, we demonstrate that our approach performs better than an extended List scheduling approach by reducing both the total cost and time needed to run the application by almost 20% and 5% on average, respectively.

关键词： Estimation cloud computing Resource management Schedules Approximation algorithms Uncertainty Scheduling

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IoT based Transformer Protection and Load Sharing Automation

IoT based Transformer Protection and Load Sharing Automation

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： R.K. Ragavapriya S. Sakthivel R.Sharan Prasannaa U. Sudhir Electrical & Electronics Engineering Sri Ramakrishna Engineering College Coimbatore India

The transformer is a passive component, which steps-up or step-down voltage from the primary to secondary coil and then serves what a load needs. The main aim is to give protection to the transformer under overload conditions by sharing the load and can also monitor the performance of the transformer. When overloading appears, the efficiency lowers down and the winding gets overheated and can catch fire easily. Thus by sharing the load on transformer, the transformer is protected and this will be connecting to another transformer in parallel through an embedded controller. The controller compares the load and the first transformer with a value that can be taken as a reference. When the load goes above the reference value, the second transformer will assist the main transformer with extra load. Therefore, the two transformers work efficiently and damage is prevented, also monitoring the performance of transformers which helps in the protection of domestic supply and power transformers concerning load using the Internet of Things (IoT), a communication protocol to transmit and display the monitored results in an output device through cloud. The proposed model holds the advantages such as supply without disturbances, deviating the damages of short circuit, absence of overload and heat issues, etc.

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Shifting the bioinformatics computing paradigm: A case study in parallelizing genome annotation using MAKER and Work Queue

Shifting the bioinformatics computing paradigm: A case study...

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IEEE international conference on Computational Advances in Bio and Medical Sciences (ICCABS)

作者： Andrew Thrasher Douglas Thain Scott Emrich Zachary Musgrave Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

Next generation sequencing technologies have enabled various entities, ranging from large sequencing centers to individual laboratories, to sequence organisms of choice and analyze them on demand. Sequencing and analysis, however, is only part of the equation: to learn about a certain organism, scientists need to annotate it. Each of these problems is highly parallel at a basic level of computation; however, only a few applications support single parallelization frameworks such as MPI. Because of the overall increasing demand for computational analysis and the inherent parallelism available in these problems, applications should easily run on clusters, clouds, and/or grids (even simultaneously); this would enable labs of various sizes to harness the computing power available to them without forcing them to invest in a particular type of batch system. Here we describe modifications made to one particular tool, MAKER. MAKER is a tool for genome annotation that is provided as both a serial application and as an MPI application. We make modifications to enable it to run without MPI and to utilize a wide variety of distributed computing platforms. Further, our proposed parallel framework allows for easy explicit data transfer, which helps overcome a major limitation of bioinformatics tools that generally rely on a shared filesystem. The distributed computing framework we chose to utilize can be used, even during early stages of development, to run bioinformatics tools on clusters, grids, and clouds. We present an evaluation of our modifications using the Caenorhabditis japonica genome comprising 180 megabases of data and achieve a speedup of 45× using 50 workers.

关键词： Bioinformatics Genomics Hidden Markov models distributed computing Pipelines Libraries Proteins

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Review of Semi-Structured Document Information Extraction Techniques Based on Deep Learning

Review of Semi-Structured Document Information Extraction Te...

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Machine Learning, cloud computing and Intelligent Mining (MLCCIM), international conference on

作者： Yangchun Li Wei Jiang Shouyou Song Chinese Academy of Cybersapce Studies School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Cybersapce Studies Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Chinese Academy of Cybersapce Studies Ministry of Education Beijing University of Posts and Telecommunications Beijing China

With the advent of global digital transformation, using an intelligent method based on deep learning to extract crucial information from semi-structured documents, as represented by various types of receipts and invoices, has emerged as an imperative measure to ensure business stability, data security, and improved work efficiency. This paper provides a detailed review on deep learning-based techniques for information extraction, with systematic introduction, hierarchical analysis, method comparison, and summary with expectations for future development. The review begins with a comprehensive explication of the defining characteristics of semi-structured documents, along with a detailed introduction to the research background, application areas, and technical challenges related to information extraction from semi-structured documents. Then the review extends to an overview of two developmental stages, i.e. the shift from traditional information extraction to deep learning-based information extraction, followed by discussion about technical architecture and method classification, which elaborates on key technologies in terms of typical datasets, detection and recognition, and information reduction. Lastly, paper summarizes the prospects and development in the field. Future research will focus on strengthening algorithm universal and lightweight, as well as improving information protection capabilities and the diversity of datasets.

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The Application of cloud computing and Support Vector Machine in the Construction of Entrepreneurial Model

The Application of Cloud Computing and Support Vector Machin...

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Sensors, Electronics and Computer engineering (ICSECE), IEEE international conference on

作者： Yali Xiong School of Management Shanghai University Shanghai China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

This paper studies the resource integration service of enterprise innovation team. This paper constructs a parallel support vector machine model based on multi-layer SVM, and develops an application system based on cloud platform innovation and entrepreneurship services. Then this paper studies the time series minimum optimization method based on large-scale data. The MapReduce technique is used to partition the massive training set and construct multi-layer support vector machine. Samples are collected around the optimal surface of each support vector machine. Experiments show that the proposed method is much faster in both time and recognition accuracy than the single or conventional parallel search methods. All the unsuccessful “sub-satisfactory” personnel will conduct the next round of “seeking talents”. After several rounds of screening, a fixed entrepreneurial team match was formed. This algorithm is the “delay receive” operation rule, which makes the algorithm more fast, practical and stable. It can effectively improve the resource allocation effect of the system.

关键词： Support vector machines Training Technological innovation cloud computing Time series analysis Entrepreneurship Vectors Stability analysis Delays Resource management

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cloud-based Assisted Decision Making Scheme for Electric Power Dispatch System

Cloud-based Assisted Decision Making Scheme for Electric Pow...

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2017 2nd international conference on Electrical, Control and Automation engineering (ECAE 2017)

作者： Bingquan Zhu Zhongming Xiang Qifeng Xu Yajie Tang Cuiyang Wang Jian Tang Power Dispatch and Communication Center State Grid Zhejiang Electric Power Company College of Electrical Engineering Zhejiang University Hangzhou Power Supply Branch State Grid Zhejiang Electric Power Company

cloud computing is one of the key enablers for modern electric power dispatch systems, in which many assisted decision making applications are involved. This work focuses on one important decision making scenario, i.e., providing suggested actions for alarming signals from electric component in both transmission and distribution network. Knowledge base is set up to find template to characterize the alarming signal patterns. A real-time template matching scheme is then established to find out the best candidate to aware current fault situation. The effectiveness of the proposed approach is verified using realworld electric component alarming data. The results indicate that the workload of operator is significantly reduced and the assisted decision accuracy is improved.

关键词： cloud computing data mining decision making electric power dispatch center service-oriented architecture

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Optimal task scheduler for multi-core processor

Optimal task scheduler for multi-core processor

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international conference on Software Technology and engineering (ICSTE)

作者： Vinay G. Vaidya Sudhakar Sah Priti Ranadive Center of Research in Engineering Sciences and Technology KPIT Cummins Infosystems Limited Pune Maharashtra India

With the advent of multi-core processors, parallel execution of multiple tasks has become a common practice. Various scheduling algorithms have addressed this issue considering multiprocessor systems. Most of these algorithms target application level parallelism. Task level parallelism within an application can be exploited using shared memory architecture such as multi-core processor or in a distributed computing environment. In this paper, we propose a static scheduling algorithm that generates a schedule that can be optimized for one or all parameters including execution time, number of processors, and efficiency of processors. The input to this scheduler is a task dependency matrix is generated using dependency analysis technique. Efficiency of this scheduler is high because it uses multiple parameters such as task wait time, number of tasks whose dependency gets resolved by scheduling a task, and task execution time. The paper concludes with an example of applying this technique to a set of tasks.

关键词： Program processors Hardware Fires

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Multi-cloud Environments With Quantum cloud computing for Secure Data Transfer using Two-Tier Homomorphic Encryption

Multi-Cloud Environments With Quantum Cloud Computing for Se...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： Manish Chhabra Rajesh E School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Nowadays, data transfer has been vast online and on social media. Hence data security in the various cloud environments has been enabled to send and receive data. While transferring the data, some issues in the cloud environment are the lack of connectivity, control, and the insecurity of the password and the data. To handle this problem, data security and the internet connection have to be enabled for the cloud environments. The method of homomorphic encryption and the transport layer security protocol have been enabled for the analysis. The homomorphic encryption method converts the data to the ciphertext for making the text in the original format. Then the transport layer security protocol is used to authenticate the data, and the data integrity of the communication can be executed. The proposed system performs better than current protocols like Dynamic Level Based Integrity Checking Protocol, TLS protocol, and Lightweight Cryptosystem, making it a potential option for jobs involving safe and effective cloud information processing. Overall, the proposed Two-tier Homomorphic Encryption system exhibits improved execution time, security strength, resource utilization, communication overhead, and user satisfaction. It addresses issues such as connectivity gaps, control deficiencies, and password and data insecurity, making it an effective method for boosting cloud security in quantum cloud computing environments.

关键词： Protocols Quantum computing cloud computing security distributed databases Passwords Information processing Data transfer

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Research and Implementation of a grid-Source Coordination Management Platform Based on Dispatching and Control cloud

Research and Implementation of a Grid-Source Coordination Ma...

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Digital Twins and parallel Intelligence (DTPI), IEEE international conference on

作者： Lina Lu Chao Sun Tianyu Wang Jiali Wu Kaifeng Zhang Zirui Huang Hangzhou Dianzi University Information Engineering School Hangzhou China Guodian Nanjing Automation Co. Ltd. Nanjing China State Grid Zhejiang Electric Power Research Institute Hangzhou China China Ship Dvelopment and Design Center Wuhan China School of Automation Southeast University Hangzhou China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

Ultra-high voltage alternating current and direct current transmission in Zhejiang Province has experienced a rapid development in recent years., which poses a great challenge to the management of grid and source coordination. In this paper., an effective grid and source coordination management platform based on dispatching and control cloud is established to achieve the management and deepening utilization of grid and source coordination data., contributing to the operational safety of the Zhejiang power grid. Meanwhile., the information sharing of the provincial dispatching center and unified power plants can be realized through the platform., which can improve the efficiency of the grid and source coordination management. Several ultrahigh voltage grid DC blocking faults in the Zhejiang power grid were analyzed. The results show that the primary frequency regulation capacity of the Zhejiang power grid has been significantly improved after the effective strategies in the platform were implemented.

关键词： Power quality Power system stability Solids Power grids Dispatching Stability analysis Smart grids

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Load Balancing in cloud-based Flow Security System

Load Balancing in Cloud-based Flow Security System

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2017 2nd international conference on Advanced Materials Science and Environment engineering(AMSEE2017)

作者： Gang HE Yue-jian XIAO Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications

In the context of load balancing [1], Lu et al. proposed a novel class of algorithms called Join-Idle-Queue(JIQ) for distributed load balancing in large systems, where a group of dispatchers distribute jobs to a cluster of parallel servers. Each dispatcher maintains a queue called Idle-Queue for idle servers. When a job arrives to a dispatcher at random, the dispatcher sends it to a server which in the head of the Idle-Queue, or to a random server if the Idle-Queue is empty. Meanwhile conversely,when a server has no jobs, it actively requests to be placed on an Idle-Queue. Although this algorithm was shown to be quite effective [2,3], it is defective in some respects. This paper presents several new improvements, which makes the algorithm more effective, especially in cloud-based Flow Security System(CFS). These improvements focus primarily on these aspects, such as the definition of idle server, data concurrency while choosing an Idle-Queue, etc. The experimental results show that the improved load balancing algorithm can improve the utilization of resources in a certain extent.

关键词： cloud computing Load balancing Join-Idle-Queue algorithm Randomized algorithm

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