دانلود و نمایش مقالات مرتبط با distributed processing::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - distributed processing

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 Efficient distance join query processing in distributed spatial data management systems
فاصله کارآمد عضویت پردازش پرس و جو در سیستم های مدیریت داده های فضایی توزیع شده-2020
Due to the ubiquitous use of spatial data applications and the large amounts of such data these applications use, the processing of large-scale distance joins in distributed systems is becoming increasingly popular. Distance Join Queries (DJQs) are important and frequently used operations in numerous applications, including data mining, multimedia and spa- tial databases. DJQs (e.g., k Nearest Neighbor Join Query, k Closest Pair Query, εDistance Join Query, etc.) are costly operations, since they involve both the join and distance-based search, and performing DJQs efficiently is a challenging task. Recent Big Data develop- ments have motivated the emergence of novel technologies for distributed processing of large-scale spatial data in clusters of computers, leading to Distributed Spatial Data Man- agement Systems (DSDMSs). Distributed cluster-based computing systems can be classified as Hadoop-based or Spark-based systems. Based on this classification, in this paper, we compare two of the most recent and leading DSDMSs, SpatialHadoop and LocationSpark, by evaluating the performance of several existing and newly proposed parallel and dis- tributed DJQ algorithms under various settings with large spatial real-world datasets. A general conclusion arising from the execution of the distributed DJQ algorithms studied is that, while SpatialHadoop is a robust and efficient system when large spatial datasets are joined (since it is built on top of the mature Hadoop platform), LocationSpark is the clear winner in total execution time efficiency when medium spatial datasets are com- bined (due to in-memory processing provided by Spark). However, LocationSpark requires higher memory allocation when large spatial datasets are involved in DJQs (even more so when k and εare large). Finally, this detailed performance study has demonstrated that the new distributed DJQ algorithms we have proposed are efficient, robust and scalable with respect to different parameters, such as dataset sizes, k , εand number of computing nodes.
Keywords: Distance join | Spatial data processing | Space partitioning | SpatialHadoop | LocationSpark | Spatial query evaluation
مقاله انگلیسی
2 A review on big data based parallel and distributed approaches of pattern mining
بررسی رویکردهای موازی و توزیع شده مبتنی بر داده های بزرگ مبتنی بر کاوش الگو-2019
Pattern mining is a fundamental technique of data mining to discover interesting correlations in the data set. There are several variations of pattern mining, such as frequent itemset mining, sequence mining, and high utility itemset mining. High utility itemset mining is an emerging data science task, aims to extract knowledge based on a domain objective. The utility of a pattern shows its effectiveness or benefit that can be calculated based on user priority and domain-specific understanding. The sequential pattern mining (SPM) issue is much examined and expanded in various directions. Sequential pattern mining enumerates sequential patterns in a sequence data collection. Researchers have paid more attention in recent years to frequent pattern mining over uncertain transaction dataset. In recent years, mining itemsets in big data have received extensive attention based on the Apache Hadoop and Spark framework. This paper seeks to give a broad overview of the distinct approaches to pattern mining in the Big Data domain. Initially, we investigate the problem involved with pattern mining approaches and associated techniques such as Apache Hadoop, Apache Spark, parallel and distributed processing. Then we examine major developments in parallel, distributed, and scalable pattern mining, analyze them in the big data perspective and identify difficulties in designing the algorithms. In particular, we study four varieties of itemsets mining, i.e., parallel frequent itemsets mining, high utility itemset mining, sequential patterns mining and frequent itemset mining in uncertain data. This paper concludes with a discussion of open issues and opportunity. It also provides direction for further enhancement of existing approaches.
Keywords: Big data | FIM | HUIM | PSPM | Uncertain data mining
مقاله انگلیسی
3 BDEv 3:0: Energy efficiency and microarchitectural characterization of Big Data processing frameworks
BDEv 3:0: کارایی انرژی و خصوصیات میکروارساختاری چارچوب پردازش داده های بزرگ-2018
As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks becomes a crucial task in order to identify potential performance bottlenecks that may delay the processing of large datasets. While most of the existing works generally focus only on execution time and resource utilization, analyzing other important metrics is key to fully understanding the behavior of these frameworks. For example, microarchitecture-level events can bring meaningful insights to characterize the interaction between frameworks and hardware. Moreover, energy consumption is also gaining increasing attention as systems scale to thousands of cores. This work discusses the current state of the art in evaluating distributed processing frameworks, while extending our Big Data Evaluator tool (BDEv) to extract energy efficiency and microarchitecture-level metrics from the execution of representative Big Data workloads. An experimental evaluation using BDEv demonstrates its usefulness to bring meaningful information from popular frameworks such as Hadoop, Spark and Flink.
Keywords: Big Data processing, performance evaluation, energy efficiency, microarchitectural characterization
مقاله انگلیسی
4 Optimal Task Allocation in Near-Far Computing Enhanced C-RAN for Wireless Big Data Processing
تخصیص وظیفه بهینه در C-RAN پیشرفته محاسبات نزدیک- دور برای پردازش داده های بزرگ بی سیم-2018
With the increasing popularity of user equipments, the corresponding UE generated big data (UGBD) is also growing substantially, which makes both UEs and current network structures struggle to process those data and applications. This article proposes a near-far computing enhanced C-RAN (NFC-RAN) architecture that can better process big data and its corresponding applications. NFC-RAN is composed of near edge computing (NEC) and far edge computing (FEC) units. NEC is located in the remote radio head,, which can quickly respond to delay-sensitive tasks from the UEs, while FEC sits next to a baseband unit pool, which can do other computation-intensive tasks. Task allocation between NEC and FEC is introduced in this article. Also, WiFi indoor positioning is illustrated as a case study of the proposed architecture. Moreover, simulation and experiment results are provided to show the effectiveness of the proposed task allocation and architecture.
Keywords: Big Data, distributed processing, indoor radio, radio access networks, wireless LAN
مقاله انگلیسی
5 Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security
استفاده از سیستم چند عاملی و هوش مصنوعی برای نظارت و بهبود امنیت و عملکرد ابر-2017
Cloud Computing is one of the most intensively developed solutions for large-scale distributed processing. Effective use of such environments, management of their high complexity and ensuring appropriate levels of Quality of Service (QoS) require advanced monitoring systems. Such monitoring systems have to support the scalability, adaptability and reliability of Cloud. Most of existing monitoring systems do not incorporate any Artificial Intelligence (AI) algorithms for supporting the change inside the task stream or environment itself. They focus only on monitoring or enabling the control of the system as a part of a separated service. An effective monitoring system for the Cloud environment should gather information about all stages of tasks processing and should actively control the monitored environment. In this paper, we present a novel Multi-Agent System based Cloud Monitoring (MAS-CM) model that supports the performance and security of tasks gathering, scheduling and execution processes in large scale service-oriented environments. Such models are explicitly designed to control the performance and security objectives of the environment. In our work, we focus on prevention of unauthorized task injection and modification, optimization of scheduling process and maximization of resource usage. We evaluate the effectiveness of MAS-CM empirically using an evolutionary driven implementation of Independent Batch Scheduler and FastFlow framework. The obtained results demonstrate the effective ness of the proposed approach and the performance improvement.
Keywords: Cloud computing | Cloud monitoring | Multi-agent systems | Cloud security | Genetic algorithms | Artificial neural networks | Independent batch scheduling
مقاله انگلیسی
6 Smart distribution system volt/VAR control using distributed intelligence and wireless communication
سیستم توزیع هوشمند کنترل volt/var با استفاده از هوش توزیع شده و ارتباطات بی سیم-2015
This study presents a smart volt/VAR control (VVC) technique for smart distribution systems, which is designed to be integrated into a distributed real-time analysis and control framework implemented with the use of multiple processing units equipped with wireless communication transceivers. The distributed processing units collaborate to perform power flow analysis based on smart meter measurements for controlling and coordinating the switched capacitor banks and voltageregulating transformers. The objective is to maintain acceptable voltage levels along the distribution feeder, minimise system losses, and limit the number of switching operations. GNU-Octave simulations are employed as a means of evaluating the performance of the proposed smart VVC technique with respect to loss reduction and number of switching operations. The network simulator ns-3 is used to simulate the distributed processing units that execute the proposed smart VVC technique. Worldwide interoperability for microwave access (WiMAX) and long-term evolution (LTE) communication networks are employed in the ns-3 simulations in order to provide data connectivity among the distributed processing units. The performance of the communication network is evaluated in terms of the execution time of the smart VVC technique, the average packet delay and the average packet delivery ratio.
مقاله انگلیسی
7 همگن سازی: یک مکانیسم برای پردازش های توزیع شده در میان یک شبکه محلی
سال انتشار: 2004 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 16
پردازش توزیع شده در یک محیط شبکه ای از رفتار غیر قابل پیش بینی افزایش سرعت به دلیل ماهیت ناهمگونی سخت افزار و نرم افزار در دستگاه های راه دور ناشی شده است. این ها قابل بحث است که عملکرد بهتر از یک سیستم توزیع شده را با توزیع وظیفه به روش هوشمندانه به دست آوریم، به طوری که ماهیت ناهمگونی سیستم هیچ تأثیری بر میزان سرعت نداشته باشد. این مقاله همگن سازی را یک تکنیکی معرفی می کند که، توزیع و تعادل حجم کار را به گونه ای تنظیم کرده است تا کاربر بتواند بیشترین سرعت را از محیط توزیع شده به دست آورد. علاوه بر ارائه عملکرد بهتر، همگن سازی برای کاربر کاملا شفاف است و کاربر ان نیازی به تعامل با سیستم برای حفاظت از مزایا ندارد.
کليدواژگان: همگرايي | پردازش توزیع | جاوا | معماری پویا مثلثی (TDA)
مقاله ترجمه شده
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 1859 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 1859 :::::::: افراد آنلاین: 48