Expert Cloud is a new class of Cloud computing which enables the users to achieve their requirements from a collection containing Experts and skills created by the Human Resources (HRs). The acquisition of these skills and Experts from this collection is possible by using the internet and Cloud computing concepts without consideration of the HRs location. The load balancing in Cloud computing means equal load distribution among resources, Virtual Human Resources (VHRs) and servers. The effective load distribution in a heterogeneous environment such as Cloud is an important challenge. The increase in the number of users, the differences of request types and also different resources capabilities and capacities cause that some resources become overload and some others become idle. This paper presents a dynamic load balanced task scheduling algorithm in Expert Cloud. In this method, we utilize the Genetic Algorithm (GA) as a ranking for making distinction among the VHRs capabilities. In the proposed method, interval estimation and specification matrix are used to allocate the VHRs and also to determine the service rate. The load balancing and mapping process are modeled based on Simple Exponential Smoothing and Probability Theory. This statistical load balancing model allows allocating the VHRs based on service rate and Poisson model. Thus, each task is delivered to the VHR; which is capable to execute it. The simulation results have shown that the Expert Cloud could reduce the execution and tardiness time and improve VHR utilization. The cost of using resources as an effective factor is also observed.

A knowledge of Cloud properties and spatiotemporal variations of Clouds is especially crucial to understand the radiative forcing of climate. This research aims to study Cloudiness in Iran using the most recent satellite data, powerful databases, and regional and seasonal analyses. In this study, three data series were used for the spatiotemporal variations of Cloudiness in the country: A) Cloudiness data of 42 synoptic stations in the country during the statistical period from 1970 to 2005, B) Cloud Optical Thickness (COT) of Terra and Aqua MODIS sensors for 2003-2015, and C) Total Cloud Cover (TCC) of ECMWF Database, ERA-Interim version, for 1979-2015. The values obtained in the country were located via the kriging geostatistical method by RMSE. The results showed that the highest TCC occurs during the winter months. At this time of the year, the Cloud cover is reduced from North to South and from West to East. Besides, COT showed that in the cold months of the year, the highest COT is observed in January and the lowest in March. The west and central Zagros highlands have the highest COT. Incorporating COT and TCC results showed that the two factors of height and approximation and access to moisture sources contribute significantly to the regional differences of Cloudiness in Iran.