Grid Computing is the technology of dividing computer networks with different and heterogeneous resources based on distribution computing. Grid computing has no limitation due to its geographical domain and the type of undercover resources. Generally, a grid network can be considered as a series of several big branches, different kinds of microprocessors, thousands of PC computers and workstations in all over the world. The goal of grid computing is to apply available computing resources easily for complicated calculations vie sites which are distributed geographically. In another words, the least cost for many users is to support parallelism, minimize the time of task operation and so on in scientific, trade and industrial contexts. To reach the goal, it is necessary to use an efficient scheduling system as a vital part for grid environment. Generally, scheduling plays very important role in grid networks. So, selecting the type of scheduling algorithm has an important role in optimizing the reply and waiting time which involve as two important factors. As providing scheduling algorithms which can minimize tasks runtime and increase operational power has remarkable importance in these categories. In this paper, we discuss about scheduling algorithms which involve independent algorithms such as Minimum Execution Time, Minimum Completion Time, Min-min, Max-min and XSuffrage.

The Question Answering Systems (QASs) use method of information retrieval and Information extraction to retrieves documents that contain special answers to the question. One of the existence problems is finding the desired information from this very high variety. For this reason, it is necessary to find ways for organizing, classification and retrieving of information. Question classification plays an important role in providing a correct answer on QASs because giving a bunch of formulated questions to provide the correct answer from among the many documents will be highly effective. The aim of classification is selecting suitable label for questions based on the expected response. In this paper, we investigate the effect of automatically classifying questions on machine learning algorithms. In this paper, we will explain different types of algorithms and compare and evaluate them and next we will investigate the existence algorithms' weakness and advantage in question classification. As a result, in the past most classification was done based on sets of words that many studies show that to maximize the efficiency of the classification of algorithms we require semantics and in the questions we should looking for feature that be close to the meaning of questions. A great deal of research proposed to analysis and to classify emotions and to extract knowledge from them and to classify them using semantic and linguistic knowledge but it still requires a lot of research and development.