Supriya Kinger

Analysis of Job Scheduling Algorithms in Cloud Computing

Cloud computing is flourishing day by day and it will continue in developing phase until computers and internet era is in existence. While dealing with cloud computing, a number of issues are confronted like heavy load or traffic while computation. Job scheduling is one of the answers to these issues. It is the process of mapping task to available resource. In section (1) discuss about cloud computing and scheduling. In section (2) explain about job scheduling in cloud computing. In section (3) existing algorithms for job scheduling are discussed, section (4) existing algorithms are compared and lastly section (5) conclusion and future work are discussed.

Modified Caesar Cipher for Better Security Enhancement

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.

Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

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, its the duty of the scheduler to make cloud efficient to solve clients 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 systems increased efficiency for task scheduling.

Temperature aware resource scheduling in Green Clouds

Green Cloud Computing is a very hot topic now days as it deals with efficient usage of resources. Cloud Computing uses the concept of virtualization for efficient usage of cloud resources. As number of jobs are submitted to virtual machines (VMs) on a physical machine, the temperature of the physical machine increases. This may destroy the system and affects the performance also. It also has negative impact on our environment because of CO2 emissions. So temperature aware resource scheduling can provide green enhancements within cloud computing environment. In this paper, it is proposed that, current and critical temperature of physical machines must be considered before distributing workload among nodes. Lot of research work has already been done on providing better cooling system. But these systems consume a lot of energy in cooling the nodes. This paper proposes the technique for temperature aware resource scheduling and compares it with other scheduling techniques i.e. FCFS and Round-Robin scheduling. Proposed algorithm is better than existing methods as it considers critical temperature of a physical node before making Virtual Machine (VM) allocation decision, thereby reducing the need for cooling systems in a cloud environment.

Increasing Cloud Usage: A Shift towards Green Clouds

Cloud computing is being adopted widely and usage of internet services is increasing day by day, which gives rise to built large scale data centers. In cloud computing, a lot of research has been done on energy saving programs for reducing the energy consumption of large scale data centers. As large scale data centers consumes large amount of electrical power which affects the operating cost, performance as well as environment. This paper surveys the different energy saving techniques in network, protocols, algorithms and hardware infrastructure for energy- efficient operations.

Hybrid Approach for Resource Scheduling in Green Clouds

Task scheduling is very effective issue of green Cloud computing which is used for minimization of energy consumption and for minimization of execution time. To save the power, temperature aware resource scheduler is used. It contains all features that satisfy its users and fill all task requirements.Cloud computing uses the concept of virtualization for energy efficient programs. Temperature aware resource scheduling can provide green enhancement within Cloud computing environment. In proposed algorithm the scheduler allocate the task to that machine which is far from its critical temperature as well as it consume less power. The proposed work is on a hybrid approach for both temperature and power aware resource scheduling.

Fuzzy Rule based Enhanced Homomorphic Encryption in Cloud Computing

a days internet services are one of the most popular services for transferring data and information. Cloud Computing is also based on internet services used for storing, transferring and many more operations related to data & work. As it is based on internet services, security is one of the major concerns in this. Numbers of methods or algorithms were implemented for securing data and information. And when the data or information is to be used it has to be decrypted so that it could be used for further process. This becomes a major concern as time consumption increased. So, to solve this issue a new fully homomorphic environment has been introduces in cloud computing. This resolves the time consumption issue but introduce a new issue that is storage space consumption. This work is introduces a new enhanced fuzzy based fully homomorphic environment. As fuzzy is a artificial intelligent system, here it is rule based intelligent system which work on 27 defined rules and helps to achieve this objective of reduces space consumption issue by using lossless compression. Comparative analysis can be done & proves that this enhanced environment is very secure and reduces burden of the network as well.

Dependency Mapper based Efficient Job Scheduling and Load Balancing in Green Clouds

Cloud computing is usually recognized as a technology which has significant impact on IT. However, cloud computing still has many crucial problems. In a cloud computing system, Load balancing is the most central issue in the system i.e. to distribute the load in an efficient manner. It plays a very important role in the realization of efficient and robust cloud computing platform. In this paper, new load balancing mechanisms have proposed based on character/ nature of jobs along with priority consideration. Furthermore Virtualization is considered in more practical way to avoid the wastage of resources over the network. Finally the performance of the proposed algorithm is analyzed and compared with existing Load balancing and Scheduling policies.