MapReduce has become ubiquitous for processing large data volume jobs. As the number and variety of jobs to be executed across heterogeneous clusters are increasing, so is the complexity of scheduling them efficiently to meet required objectives of performance. This report presents a survey of some of the MapReduce scheduling algorithms proposed for such complex scenarios. A taxonomy is provide...

“Big Data” computing increasingly utilizes the MapReduce programming model for scalable processing of large data collections. Many MapReduce jobs are I/O-bound, and so minimizing the number of I/O operations is critical to improving their performance. In this work, we present ThemisMR, a MapReduce implementation that reads and writes data records to disk exactly twice, which is the minimum amou...

MapReduce algorithms can be difficult to write and test due to the accidental complexities involved with existing MapReduce implementations. Furthermore, the configuration details involved in running MapReduce algorithms within a cloud present a set of new challenges. Our research reveals that many details of cloud configuration can be hidden from programmers in an automated and transparent man...

MapReduce has become a popular paradigm for large scale data processing in the cloud. The sheer scale of MapReduce deployments make task assignment in MapReduce an interesting problem. The scale of MapReduce applications presents unique opportunity to use data driven algorithms in resource management. We present a learning based scheduler that uses pattern classification for utilization oriente...

Decision tree is one of the most widely used classification methods. For massive data processing, MapReduce is a good choice. Whereas, MapReduce is not suitable for iterative algorithms. The programming model of Spark is proposed as a memory-based framework that is fit for iterative algorithms and interactive data mining. In this paper, C4.5 is implemented on both MapReduce and Spark. The resul...

Energy considerations are important for Internet datacenters operators, and MapReduce is a common Internet datacenter application. In this work, we use the energy efficiency of MapReduce as a new perspective for increasing Internet datacenter productivity. We offer a framework to analyze software energy efficiency in general, and MapReduce energy efficiency in particular. We characterize the pe...

Big Data, the analysis of large quantities of data to gain new insight has become a ubiquitous phrase in recent years. Day by day the data is growing at a staggering rate. One of the efficient technologies that deal with the Big Data is Hadoop, which will be discussed in this paper. Hadoop, for processing large data volume jobs uses MapReduce programming model. Hadoop makes use of different sch...

The amount of information in spatial databases is growing as more data is made available. Spatial databases mainly store two types of data: raster data (satellite/aerial digital images), and vector data (points, lines, polygons). The complexity and nature of spatial databases makes them ideal for applying parallel processing. MapReduce is an emerging massively parallel computing model, proposed...

MapReduce has proven to be one of the most useful paradigms in the revolution of distributed computing, where cloud services and cluster computing become the standard venue for computing. The federation of cloud and big data activities is the next challenge where MapReduce should be modified to avoid (big) data migration across remote (cloud) sites. This is exactly our scope of research, where ...

MapReduce is an implementation for processing large scale data parallelly. Actual benefits of MapReduce occur when this framework is implemented in large scale, shared nothing cluster. MapReduce framework abstracts the complexity of running distributed data processing across multiple nodes in cluster. Hadoop is open source implementation of MapReduce framework, which processes the vast amount o...