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Cloud computing is one of the innovative computing, which deals with storing and accessing data and programs over the Internet [1]. It is the delivery of computing resources and services, such as storing of data on servers and databases, providing networking facilities and software development platforms over the Internet. It provides the flexibility of resources for everyone. These services are provided via data centers, which are located in various parts of the world [2, 3]. Cloud computing makes access to these resources to everyone on a global scale at a very minimal cost and significantly higher speed. These servers provide services to the users, which would have cost a lot of computational power to them if they had to buy them. The first mention of cloud computing was referenced in a Compaq internal document released in 1996 [4]. Cloud computing was then commercialized in 2006 when Amazon released elastic compute cloud (EC2). Furthermore, Google released Google app engine in 2008 and Microsoft Azure services were launched in October 2008, which increased the competition in the area of cloud computing. Since then these companies have done a lot of development in cloud computing.

Intelligent Systems (ISs) are technologically advanced machines, which perceive and respond to the environment around them. They are usually of various forms ranging from software to hardware. ISs are generally the fusion of Artificial Intelligence (AI), robotics and Internet of things (IoT). In order to strengthen ISs, one of the key technologies is green technology (GT). It refers to the continuously advancing methods and materials, which cover techniques for producing energy to non-toxic cleaning products. It may also be broadened to saving energy, and reducing toxic and waste materials in the environment. The motto of GT can be achieved by using the ISs. In this paper, we present various applications of ISs in GT. Moreover, we discuss various possible solutions using ISs in order to overcome the on-going real-life problems.

Round Robin (RR) scheduling algorithm is a preemptive scheduling algorithm. It is designed especially for time sharing Operating System (OS). In RR scheduling algorithm the CPU switches between the processes when the static Time Quantum (TQ) expires. RR scheduling algorithm is considered as the most widely used scheduling algorithm in research because the TQ is equally shared among the processes. In this paper a newly proposed variant of RR algorithm called Min-Max Round Robin (MMRR) scheduling algorithm is presented. The idea of this MMRR is to make the TQ repeatedly adjusted using Min-Max dispersion measure in accordance with remaining CPU burst time. Our experimental analysis shows that MMRR performs much better than RR algorithm in terms of average turnaround time, average waiting time and number of context switches., Comment: 9 pages, 15 figures. International Journal on Computer Science and Engineering (IJCSE), 2012

Round Robin (RR) Algorithm is considered as optimal in time shared environment because the static time is equally shared among the processes. If the time quantum taken is static then it undergoes degradation of the CPU performance and leads to so many context switches. In this paper, we have proposed a new effective dynamic RR algorithm SMDRR (Subcontrary Mean Dynamic Round Robin) based on dynamic time quantum where we use the subcontrary mean or harmonic mean to find the time quantum. The idea of this approach is to make the time quantum repeatedly adjusted according to the burst time of the currently running processes. Our experimental analysis shows that SMDRR performs better than RR algorithm in terms of reducing the number of context switches, average turnaround time and average waiting time., Comment: 5 pages, 13 figures. Journal of Global Research in Computer Science 2011. arXiv admin note: text overlap with arXiv:1103.3832 by other authors

Security issues in MANET are a challenging task nowadays. MANETs are vulnerable to passive attacks and active attacks because of a limited number of resources and lack of centralized authority. Blackhole attack is an attack in network layer which degrade the network performance by dropping the packets. In this paper, we have proposed a Secure Fault-Tolerant Paradigm (SFTP) which checks the Blackhole attack in the network. The three phases used in SFTP algorithm are designing of coverage area to find the area of coverage, Network Connection algorithm to design a fault-tolerant model and Route Discovery algorithm to discover the route and data delivery from source to destination. SFTP gives better network performance by making the network fault free., Comment: 6 pages, 9 figures

Management of disk scheduling is a very important aspect of operating system. Performance of the disk scheduling completely depends on how efficient is the scheduling algorithm to allocate services to the request in a better manner. Many algorithms (FIFO, SSTF, SCAN, C-SCAN, LOOK, etc.) are developed in the recent years in order to optimize the system disk I/O performance. By reducing the average seek time and transfer time, we can improve the performance of disk I/O operation. In our proposed algorithm, Optimize Disk Scheduling Algorithm (ODSA) is taking less average seek time and transfer time as compare to other disk scheduling algorithms (FIFO, SSTF, SCAN, C-SCAN, LOOK, etc.), which enhances the efficiency of the disk performance in a better manner., Comment: 8 pages, 26 figures

Round Robin (RR) Scheduling is the basis of time sharing environment. It is the combination of First Come First Served (FCFS) scheduling algorithm and preemption among processes. It is basically used in a time sharing operating system. It switches from one process to another process in a time interval. The time interval or Time Quantum (TQ) is fixed for all available processes. So, the larger process suffers from Context Switches (CS). To increase efficiency, we have to select different TQ for processes. The main objective of RR is to reduce the CS, maximize the utilization of CPU and minimize the turn around and the waiting time. In this paper, we have considered different TQ for a group of processes. It reduces CS as well as enhancing the performance of RR algorithm. TQ can be calculated using min-max dispersion measure. Our experimental analysis shows that Group Based Time Quantum (GBTQ) RR algorithm performs better than existing RR algorithm with respect to Average Turn Around Time (ATAT), Average Waiting Time (AWT) and CS., Comment: 7 pages, 16 figures