Cloud Computing is an emerging computing paradigm. It aims to share data, calculations, and service transparently over a scalable network of nodes. Since Cloud computing stores the data and disseminated resources in the open environment. So, the amount of data storage increases quickly. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper discusses some of the existing load balancing algorithms in cloud computing and also their challenges.
Encryption is the process of scrambling a message so that only the intended recipient can read it. With the fast progression of digital data exchange in electronic way, Information Security is becoming much more important in data storage and transmission. Caesar cipher is a mono alphabetic cipher. It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter. In this paper, author modified the traditional Caesar cipher and fixed the key size as one. Another thing alphabet index is checked if the alphabet index is even then increase the value by one else alphabet index is odd decrease the key value by one. Encryption and scrambling of the letters in the Cipher Text.
Cloud computing is basically internet based computing while software, information and shared resources are provided to devices and computers on demand, like electricity grid. With the fusion of network technology and traditional computing technology such as distributed computing parallel computing, grid computing a cloud computing product is formed. Task scheduling is the major concern in the field of cloud computing. As the use of cloud computing increases, the burden on the cloud network also increases. So, it's the duty of the scheduler to make cloud efficient to solve client's tasks. This work focuses on the same to achieve the objective of optimized task scheduling where improved genetic algorithm is proposed. Genetic algorithm is artificial intelligent based soft computing technique to optimize the process. Here in this work, genetic algorithm is enhanced using new fitness function based on mean and grand mean values. This optimization can be implemented on both ends, for job scheduling and resource scheduling. This will schedule the whole process and optimize as much as possible. The results analysis also proves the cloud system's increased efficiency for task scheduling.
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