122 resources related to Cluster Computing
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No organizations are currently tagged "Cluster Computing"
Cluster Computing, Grid Computing, Edge Computing, Cloud Computing, Parallel Computing, Distributed Computing
With technically co-sponsored by IEEE ComSoc(Communications Society), IEEE ComSocCISTC(Communications & Information Security Technical Community), and IEEE ComSocONTC(Optical Networking Technical Community), the ICACT(International Conference onAdvanced Communications Technology) Conference has been providing an open forum forscholars, researchers, and engineers to the extensive exchange of information on newlyemerging technologies, standards, services, and applications in the area of the advancedcommunications technology. The conference official language is English. All the presentedpapers have been published in the Conference Proceedings, and posted on the ICACT Websiteand IEEE Xplore Digital Library since 2004. The honorable ICACT Out-Standing Paper Awardlist has been posted on the IEEE Xplore Digital Library also, and all the Out-Standing papersare subjected to the invited paper of the "ICACT Transactions on the Advanced Communications Technology" Journal issue by GIRI
Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models. The application of computational intelligence techniquesinto industrial design, interactive design, media design, and engineering design are also within the scope.
Conference Theme:AI Empowering the Future Education, the scope of the conference includes: Computer science,Data Science,Educational Technology etc.
International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) is a premier international forum for scientists and researchers to present the state of the art of data mining and intelligent methods inspired from nature, particularly biological, linguistic, and physical systems, with applications to computers, circuits, systems, control, robotics, communications, and more.
No periodicals are currently tagged "Cluster Computing"
2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), 2018
This paper focuses on priority based processing of streaming data. One of the greatest challenges in big data analytics is responding to a bursty input load. The common solutions are to use dynamic resource provisioning techniques, however, these techniques may not respond quickly enough to the change in the load. Another option is to overprovision, but this results in wasted ...
2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 2018
Internet today is an integral part of an organization's working. It is vital to monitor Internet traffic closely in order to detect threats and malicious activities which may not only impact the reputation of an organization but also lead to data loss. One way of achieving this goal is to monitor the logs of critical applications like proxy server which ...
2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), 2019
Customer satisfaction is an essential area of the industry in this 21st century sometimes as known as the information age. However, the perception of customer expectation remains a problem in today's businesses. The internet has enabled people to spread out their thoughts through Social Media (SM) platforms, forums, news comments, and blogs. Consequently, those platforms are generating exponentially the immense ...
2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019
In our previous work, we explored the possibility of applying machine learning technique to graph partition. We use some metrics to describe the graph, rank the execution time of some graph algorithm and feed them into the machine learning models. We proved that decision tree and KNN and good models of this problem. In the paper, we go on to ...
2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 2018
Big data technology (synonymous with Apache Hadoop) is growing rapidly due to its low cost, scalable data processing capabilities, and high fault tolerance. After the release of Hadoop 1.0, the organizations started using this technology but faced some difficulties. At this stage, Hadoop Framework was premature in fulfilling the expectations of different organizations, which looked for an alternative of Apache ...
Similarity and Fuzzy Logic in Cluster Analysis
Reconfigurable Distributed MIMO for Physical-layer Security - Zygmunt Haas - IEEE Sarnoff Symposium, 2019
Rebooting Computing: Parallelism in Computing
A Bayesian Approach for Spatial Clustering - IEEE CIS Webinar
Q&A with Michael Garner: IEEE Rebooting Computing Podcast, Episode 11
Rebooting Computing: Changing Computing
DARPA's Vision for the Future of Computing: IEEE Rebooting Computing 2017
From the Quantum Moore's Law toward Silicon Based Universal Quantum Computing - IEEE Rebooting Computing 2017
On-chip Passive Photonic Reservoir Computing with Integrated Optical Readout - IEEE Rebooting Computing 2017
Q&A with Elie Track: IEEE Rebooting Computing Podcast, Episode 2
How Bio-Design Automation Can Help Reboot Computing: Lessons, Challenges, and Future Directions - IEEE Rebooting Computing 2017
Special Evening Panel Discussion: AI, Cognitive Information Processing, and Rebooting Computing - IEEE Rebooting Computing 2017
Verification in the Nanoscale Era of Computing - IEEE Rebooting Computing 2017
Quantum Accelerators for High-Performance Computing Systems - IEEE Rebooting Computing 2017
IEEE Future Directions: Rebooting Computing
Building a Quantum Computing Community and Ecosystem: Jerry Chow at IEEE Rebooting Computing 2017
Asynchronous Ballistic Reversible Computing: IEEE Rebooting Computing 2017
Towards Truly Adiabatic Operation: IEEE Rebooting Computing 2017
Q&A with Arvind Kumar: IEEE Rebooting Computing Podcast, Episode 4
This paper focuses on priority based processing of streaming data. One of the greatest challenges in big data analytics is responding to a bursty input load. The common solutions are to use dynamic resource provisioning techniques, however, these techniques may not respond quickly enough to the change in the load. Another option is to overprovision, but this results in wasted computing resources. This paper describes a technique that can be used in cases where resources are statically provisioned. This technique enables users to prioritize certain input data items so that in cases where the load suddenly increases, the high priority items are given precedence over low priority items. This technique is implemented on the Spark Streaming engine.
Internet today is an integral part of an organization's working. It is vital to monitor Internet traffic closely in order to detect threats and malicious activities which may not only impact the reputation of an organization but also lead to data loss. One way of achieving this goal is to monitor the logs of critical applications like proxy server which contains crucial information related to Internet activity. Log data is often huge and is ever growing. Also, forensic analysis of an event requires not only current data but also historical one. This poses a big problem of efficient and fast storage and retrieval of data. Traditional RDBMS technologies fail in such situations but with the advent of big data technologies like Apache Hadoop and Apache Spark this task has now become feasible. In this paper, we propose a Spark based system for analysis of Squid proxy logs. Using this system we generate statistics like top domains accessed, top users etc for studying traffic behavior within organization and detect malicious activity. We further study the variation in proposed system's performance with increase in data volume and variation in spark parameters like number of executors, number of executor cores and executor memory. From our experimental study we conclude that log analysis with Spark is extremely fast with no significant performance variation observed with increase in data volume. The challenging task, however, is selecting spark parameters for getting optimal performance.
Customer satisfaction is an essential area of the industry in this 21st century sometimes as known as the information age. However, the perception of customer expectation remains a problem in today's businesses. The internet has enabled people to spread out their thoughts through Social Media (SM) platforms, forums, news comments, and blogs. Consequently, those platforms are generating exponentially the immense amounts of data. The extraction of opinions from those big data can actively allow to rate organizations, learn the consumer needs, and adjust the business's strategies. This paper presents a concept of building a rating system, using Big Data Analytics (BDA) techniques, that apply the existing Sentiment Analysis (SA) algorithms to gain insight into reviews gathered from SM applications. The system will allow to list the various categories of services and evaluate them based on the obtained the customers' reactions. Also, this study aims to manage a large volume of information to rank the institutions and provide a practical solution for competitive, marketing analysis, and track the improvement of customer satisfaction within both the public and private sectors to boost the excellent service delivery in Rwanda.
In our previous work, we explored the possibility of applying machine learning technique to graph partition. We use some metrics to describe the graph, rank the execution time of some graph algorithm and feed them into the machine learning models. We proved that decision tree and KNN and good models of this problem. In the paper, we go on to investigate more metrics to describe the graph after partitioning. We found that AverageDegreeNotCut is also an important metric. We improve the precision score of original machine learning models by 4.9 percent.
Big data technology (synonymous with Apache Hadoop) is growing rapidly due to its low cost, scalable data processing capabilities, and high fault tolerance. After the release of Hadoop 1.0, the organizations started using this technology but faced some difficulties. At this stage, Hadoop Framework was premature in fulfilling the expectations of different organizations, which looked for an alternative of Apache Hadoop. Therefore, Hadoop vendors improvised the open source core Apache Hadoop release and made it enterprise ready. Hadoop Distributions presented a unified product: an added apache repository, new tools, security and central administration all together so that the organizations did not have to spend time assembling all these essentials into a single functional piece. The most accepted Hadoop Distributions existing in the market today are Cloudera CDH, Hortonworks HDP, and MapR M5. This paper discusses different features of these Hadoop vendor distributions and makes comparisons based on the functionality and contribution of each vendor in the big data market.
Real-time streaming applications with multiple heterogeneous data streams have become increasingly popular especially in IoT applications; however, many issues still exist, especially in deploying and maintaining these large amounts of data streams. Using Spark Structured Streaming, this paper introduces a Spark Streaming framework for multiple heterogeneous data streams which allows the deployment of multiple heterogeneous data stream processing in a single Spark application; reducing deployment difficulty, coding redundancy, monitoring difficulties, and solving the problem of inefficient job queueing in multi-stream applications.
Have you ever Wondered how data can be used as a weapon as well as useful information?. Welcome to the year of 2018 where large datasets can be manipulated using the hadoop cluster interface, so that the users can store, read, transfer any type of data including large datasets. Imagine a lawyer who must be having huge amounts of case files. It isnot humanly impossible to remember the dates, file numbers, The status at a single time of a case which was closed several years ago here we use the concept of big data where you can retrieve such information within seconds. The technical advantages of organizing such data can be helpful not only in certain high profile cases where it is necessary that a particular order or organization is present at the most basic level so as to get information that maybe the turning point that is needed to win a particular case but also assist as a alibi for any vocal statements given during a particular session, these data entered in a registry as mentioned above can be lost over time so the storage of these important data can be done by using a Hadoop cluster which stores large datasets and can be referred at will i.e Lawyers as well as lawmakers will be able to successfully get facts in the argument without any Data Unavailability. The Apche ambari is a Hadoop interface used to manipulate and analyze distributed data across number of clusters. The Apache spark can be used to reduce, separate and filter data according to the users requirements.
Apache Spark has become the de-facto processing framework for big data analytics. The main challenges for big data analytics is to manage diverse varieties, store huge volumes and process data at high speed. Apache Spark provides a number of advantages over MapReduce due to its in-memory processing. The default parameters for running the Spark applications are based on commodity hardware and may not provide one-size-fits-all solution for every configuration and environment. Tuning resource allocation for Spark applications is of paramount importance to achieve optimal performance. This article discusses various parameters/options such as caching, broadcast variables, repartitioning and number of executors that may be tuned according to the environment resulting in better performance of Spark applications.
There is a trend in the use of big data today, and subsequently, increased requirements for systems able to store and manipulate these huge quantities of data. However, the adoption of these Big Data management systems can have positive as well as negative effects on the data security of service consumers.This paper aims to highlight the Big Data security problems, as well as the recent statistics showing the number of vulnerabilities that attack these systems, which prevent them to work safely. It allows analyzing a set of statistics in order to shed light on the importance of big data security. For finally present a proposal solution that can improve the security level of Hadoop Big Data management system, while justifying its feasibility.
Every day millions of customers do shopping through e-commerce web sites around the world. Huge amounts of click stream data are generated from customers' use of e-commerce websites. Learning the behavior of users from click data and estimating the intention to buy has become an important need. Within the scope of this research, a methodology is proposed on the estimation of the purchase intention before finalizing the sessions of the customers. The proposed methodology was tested by the ACM RecSys Symposium on e-commerce data set published in 2015 and it was found that successful results could be obtained.
No standards are currently tagged "Cluster Computing"