Final Year IEEE Big Data Projects In Chennai
Chennai Trainings provide IEEE final year Big Data projects in Chennai with low cost for Bsc, Msc, BE IT, ME IT, BCA, MCA, B Tech, M Tech final year students. We are the best final year Big data project centre in Chennai. We offer best Big data IEEE projects for Engineering students.
IEEE Final Year Big Data Project Center In Chennai
At Chennai Trainings, Chennai we offer best IEEE final year Big data projects for students at affordable cost. We develop real-time Big data projects for students and IEEE based paper in our project Centre Chennai. We are the best final year Big data project centre in Chennai. Chennai Trainings provide various final year IEEE projects like Java, Dot Net, Python, PHP, Hadoop, VLSI and IoT for the final year students all over India.
List of Big Data Projects
1. Deadline-aware MapReduce Job Scheduling with Dynamic Resource Availability
As MapReduce is becoming ubiquitous in large-scale data analysis, many recent studies have shown that the performance of MapReduce could be improved by different job scheduling approaches, e.g., Fair Scheduler and Capacity Scheduler. However, most exiting MapReduce job schedulers focus on the scenario that MapReduce cluster is stable and pay little attention to the MapReduce cluster with dynamic resource availability. In fact, MapReduce cluster resources may fluctuate as there is a growing number of Hadoop clusters deployed on hybrid systems, e.g., infrastructure powered by mix of traditional and renewable energy, and cloud platforms hosting heterogeneous workloads. Thus, there is a growing need for providing predictable services to users who have strict requirements on job completion times in such dynamic environments. In this paper, we propose, RDS, a Resource and Deadline-aware Hadoop job Scheduler that takes future resource availability into consideration when minimizing job deadline misses. We formulate the job scheduling problem as an online optimization problem and solve it using an efficient receding horizon control algorithm. To aid the control, we design a self-learning model to estimate job completion times. We further extend the design of RDS scheduler to support flexible performance goals in various dynamic clusters. In particular, we use flexible deadline time bounds instead of the single fixed job completion deadline. We have implemented RDS in the open-source Hadoop implementation and performed evaluations with various benchmark workloads. Experimental results show that RDS substantially reduces the penalty of deadline misses by at least 36% and 10% compared with Fair Scheduler and Earliest Deadline First (EDF) scheduler, respectively. In a Hadoop cluster running partially on renewable energy, the experimental result shows the green power based resource prediction approach can further reduce the penalty of deadline misses by 16% compared to Auto-Regressive Integrated Moving Average (ARIMA) prediction approach.
2. Handling Big Data Using a Data-Aware HDFS and Evolutionary Clustering Technique
The increased use of cyber-enabled systems and Internet-of-Things (IoT) led to a massive amount of data with different structures. Most big data solutions are built on top of the Hadoop eco-system or use its distributed file system (HDFS). However, studies have shown inefficiency in such systems when dealing with today’s data. Some research overcame these problems for specific types of graph data, but today’s data are more than one type of data. Such efficiency issues lead to large scale problems, including larger space required in data centers, and waste in resources (like power consumption), that in turn lead to environmental problems (such as more carbon emission), as per scholars. We propose a data-aware module for the Hadoop eco-system. We also propose a distributed encoding technique for Genetic Algorithms. Our framework allows Hadoop to manage the distribution of data and its placement based on cluster analysis of the data itself. We are able to handle a broad range of data types as well as optimize query time and resource usage. We performed our experiments on multiple datasets generated via LUBM.
3. PISCES: Optimizing Multi-job Application Execution in MapReduce
Nowadays, many MapReduce applications consist of groups of jobs with dependencies among each other, such as iterative machine learning applications and large database queries. Unfortunately, the MapReduce framework is not optimized for these multi-job applications. It does not explore the execution overlapping opportunities among jobs and can only schedule jobs independently. These issues significantly inflate the application execution time. This paper presents PISCES (Pipeline Improvement Support with Critical chain Estimation Scheduling), a critical chain optimization (a critical chain refers to a series of jobs which will make the application run longer if any one of them is delayed), to provide better support for multi-job applications. PISCES extends the existing MapReduce framework to allow scheduling for multiple jobs with dependencies by dynamically building up a job dependency DAG for current running jobs according to their input and output directories. Then using the dependency DAG, it provides an innovative mechanism to facilitate the data pipelining between the output phase (map phase in the Map-Only job or reduce phase in the Map-Reduce job) of an upstream job and the map phase of a downstream job. This offers a new execution overlapping between dependent jobs in MapReduce which effectively reduces the application runtime. Moreover, PISCES proposes a novel critical chain job scheduling model based on the accurate critical chain estimation. Experiments show that PISCES can increase the degree of system parallelism by up to 68% and improve the execution speed of applications by up to 52%.
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How To Choose Big Data Final Year IEEE Projects?
By choosing application and domain wise, We can select and develop a project as per IEEE final year Big Data project requirements.
What Is Final Year Big Data IEEE Project?
Nowadays, Final year projects are manatory for those who are all pursuing final year in universities and colleges. Especially engineering and science graduate i.e, BE, ME, Bsc, Msc in CS and IT. Final year project will allways show your knolowedge and uniqueness.
Why Big Data Projects?
Big Data is a trending technologies for those who are all studing CS and IT background.
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