One of the infrastructure applications that cloud computing offers as a service is parallel data processing. MapReduce is a type of parallel processing used more and more by data-intensive applications in cloud computing environments. MapReduce is based on a strategy called "divide and conquer," which uses regular computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used with the proposed fractal network model in the MapReduce application. A well-known model, the cube, is used to compare the fractal network model and its work. Where experiments demonstrated that the fractal model is preferable to the cube model. The fractal model achieved an average speedup of 2.7 and an efficiency rate of 67.7%. In contrast, the cube model could only reach an average speedup of 2.5 and an efficiency rate of 60.4%.

Parallel data processing is one of the specific infrastructure applications categorized as a service provided by cloud computing. In cloud computing environments, data-intensive applications increasingly use the parallel processing paradigm known as MapReduce. MapReduce is based on a strategy called "divide and conquer," which uses ordinary computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used in the MapReduce application using proposed fractal network models. Two fractal network models are offered, and their work is compared with a well-known network model, the hypercube. The first fractal network model achieved an average speedup of 3.239 times while an efficiency ranged from 73-95%. In the second model of the network, the speedup got to 3.236 times while keeping an efficiency of 70-92%. Furthermore, the path-finding algorithm employed in the recommended fractal network models remarkably identified all paths and calculated the shortest and longest routes.

Parallel data processing is one of the specific infrastructure applications categorized as a service provided by cloud computing. In cloud computing environments, data-intensive applications increasingly use the parallel processing paradigm known as MapReduce. MapReduce is based on a strategy called "divide and conquer," which uses ordinary computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used in the MapReduce application using proposed fractal network models. Two fractal network models are offered, and their work is compared with a well-known network model, the hypercube. The first fractal network model achieved an average speedup of 3.239 times while an efficiency ranged from 73-95%. In the second model of the network, the speedup got to 3.236 times while keeping an efficiency of 70-92%. Furthermore, the path-finding algorithm employed in the recommended fractal network models remarkably identified all paths and calculated the shortest and longest routes.

With the fast evolution of digital data exchange, security of information becomes massively important in data storage and transmission. Due to the increasing use of images in an different process, it is essential to protect the confidential image data from unauthorized access. If encryption is not well performed then there may be possibility of stealing the information. Image compression is also essential where images need to be stored, transmitted or viewed quickly and efficiently. The current paper proposes an efficient technique to compress image by using Daubechies wavelet transforms. Moreover, it uses two algorithms which are advanced encryption standard (AES) and Arnold transform method for encryption the image. Experimental results show efficient technique that is simple in implementation and has high degree of security.