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MapReduce is a programming model used by Google to process large amount of data in a distributed computing environment. It is usually used to perform distributed computing on clusters of computers. Computational processing of data stored on either a file system or a database usually occurs. MapReduce takes the advantage of locality of data, processing data on or near the storage areas, thereby avoiding unnecessary data transmission. The simplicity of the programming model and the automatic handling of node failures hiding the complexity of fault tolerance make MapReduce to be used for both commercial and scientific applications. As MapReduce clusters have become popular these days, their scheduling is one of the important factor which is to be considered. In order to achieve good performance a MapReduce scheduler must avoid unnecessary data transmission. Hence different scheduling algorithms for MapReduce are necessary to provide good performance. This paper provides an overview of four different scheduling algorithms for MapReduce namely; Scheduling algorithm in Hadoop, Longest Approximate Time to End (LATE) MapReduce scheduling algorithm, Self-Adaptive MapReduce(SAMR) scheduling algorithm and Enhanced Self-Adaptive MapReduce scheduling algorithm(ESAMR). An overview of these techniques is provided through this paper. Advantages and disadvantages of these algorithms are identified.

The ability to detect defects on hardwood trees and logs holds great promise for the hardwood forest products industry. At every stage of wood processing, there is a potential for improving value and recovery with knowledge of the location, size, shape, and type of log defects. This paper deals with a new method that processes hardwood laser-scanned surface data for defect detection. The detection method is based on robust circle fitting applied to scanned cross-section data sets recorded along the log length. It can be observed that these data sets have missing data and include large outliers induced by loose bark that dangles from the log trunk. Because of that and because of the nonlinearity of the circle model, which presents both additive and nonadditive errors, we initiated a new robust Generalized M-estimator for which the residuals are standardized via scale estimates calculated by means of projection statistics and incorporated in the Huber objective function, yielding a bounded influence method. Our projection statistics are based on the 2-D radial vectors instead of the row vectors of the Jacobian matrix as advocated in the literature dealing with linear regression. These radial distances allow us to develop algorithms aimed at pinpointing large surface rises and depressions from the contour image levels, and thereby, locating severe external defects having at least a height of 0.5 in and a diameter of 5 in.

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Framed field experiments (experimental games) are widely used to assess factors affecting cooperation in management of the commons. However, there is relatively little attention to how details of the games affect experimental results. This paper presents qualitative and quantitative results from a framed field experiment in which participants make decisions about extraction of a common-pool resource, a community forest. The experiment was conducted in 2017-2018 with 120 groups of resource users (split by gender) from 60 habitations in two Indian states, Andhra Pradesh and Rajasthan. We test whether within-subject treatments (non-communication, communication, and optional election of institutional arrangements (rules)), remuneration methods, and design of the game board affect harvest behavior and groups' tendency to cooperate. We also examine how characteristics of the community and players affect players' choices in the game, with special attention to gender differences. Results reveal participants harvested substantially less than the Nash prediction even in the absence of communication, with men extracting less than women in both states. For male groups in both states, both communication and optional rule election were associated with lower group harvest per round, as compared to the reference non-communication game. For female groups in both states communication itself did not significantly slow resource depletion; but introduction of optional rule election did reduce harvest amounts. For both men and women in Andhra Pradesh and men in Rajasthan, incentivized payments to individual participants significantly lowered group harvest, relative to community flat payment, suggesting such payments stimulated deliberation among game players. Findings have methodological and practical implications for designing behavioral intervention programs to improve common-pool resource governance.