International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Jan. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 82
Paper Published : 1209
No. of Authors : 3038
  Journal Paper

Paper Title
Maks: Server Health Monitoring using Kafka

Abstract
Big Data Analytics has immense potential to change the way Server Health Monitoring is being done. Use of Big data analytics can help Network Administrators to become more proactive and well informed. This paper proposes a design for Big Data Platform based Server Health Monitoring system MAKS (Monitoring and Alerting system using Kafka and Spark). Big Data can not only provide high server availability and hence low down time, it can also help in achieving more secured cyber space. Hadoop is deployed for numerous real world problem, but since Hadoop has its own technical limitations in handling real-time streaming data, a much faster application is required to deal with requirements of fast event driven based approach of companies. Hadoop fulfills the requirement to store data in HDFS and to execute analysis, whereas Kafka is the one that delivers high speed in terms of transportation and data distribution to several locations. Spark streaming integrated with Kafka provides a very efficient solution to this problem. The motive of this paper is to propose a design model, using Spark and Kafka for Serve health Monitoring. Keywords - Server Health Monitoring, Alerting, Kafka, Big Data, Spark.


Author - Amit Kumar Srivastava, Crs Kumar

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| Published on 2018-08-09
   
   
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