No abstract

Hadoop cluster is specifically designed to store and analyze a large amount of data in distributed environment. With ever increasing use of Hadoop clusters, a scheduling algorithm is required for optimal utilisation of cluster resources. The existing scheduling algorithms are limited to one or more of the following crucial problems such as limited utilization of computing resources, limited applicability towards heterogeneous cluster, random scheduling of non-local map tasks, and negligence of small jobs in scheduling. In this paper, we propose a novel job aware scheduling algorithm that overcomes the above limitations. In addition, we analyze the performance of the proposed algorithm using MapReduce WordCount benchmark. The experimental results show that the proposed algorithm increases the resource utilization and reduces the average waiting time compared to existing Matchmaking scheduling algorithm.

Automatically creating the description of an image using any natural languages sentence like English is a very challenging task. It requires expertise of both image processing as well as natural language processing. This paper discuss about different available models for image captioning task. We have also discussed about how the advancement in the task of object recognition and machine translation has greatly improved the performance of image captioning model in recent years. In addition to that we have discussed how this model can be implemented. In the end, we have also evaluated the performance of model using standard evaluation matrices.

No abstract

No abstract