Grid computing has become a real alternative to traditional supercomputing environments for developing parallel applications that harness massive computational resources. However, the complexity incurred in building such parallel Grid-aware applications is higher than the traditional parallel computing environments. It addresses issues such as resource discovery, heterogeneity, fault tolerance and task scheduling. Load balanced task scheduling is very important problem in complex grid environment. So task scheduling which is one of the NP-Complete problems becomes a focus of research scholars in grid computing area. The traditional Min-Min algorithm is a simple algorithm that produces a schedule that minimizes the makespan than the other traditional algorithms in the literature. But it fails to produce a load balanced schedule. In this paper a Load Balanced Min-Min (LBMM) algorithm is proposed that reduces the makespan and increases the resource utilization. The proposed method has two-phases. In the first phase the traditional Min-Min algorithm is executed and in the second phase the tasks are rescheduled to use the unutilized resources effectively.

Offering context aware services to users is one of the main objectives of pervasive computing. A context aware system needs to know the activities being performed by the user. Deciding what a user is doing at a given time poses a number of challenges. One significant challenge is dealing with the variation in the number, order and duration of the constituent steps of an activity. There happens to be considerable variation in these parameters even if the same user is performing the same activity at different times. Though fuzzy finite automata have been used by researchers to overcome this challenge, manual construction of the automata for daily life activities becomes onerous. This paper illustrates how finite automata can be constructed automatically and fuzziness incorporated into it, to recognize user activities in a smart environment. The proposed method is tested with a publicly available dataset and is found to give promising results.

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The design phase of the distributed database environment holds a vital part in affecting the performance. The Peerto-Peer architecture gives a great degree of hope to handle the data in an efficient manner. This work analyses a cluster based Peer-to-Peer architecture named FlexiPeer for the distributed databases to address the fragmentation and allocation phases of database design. This work takes the inspiration of the previous works done based on the predicate based fragmentation and introduces the clustering approach for drafting the database architecture and to allocate the fragmented data across the sites. The performance of the FlexiPeer is studied in a simulated environment.

In the past decade, image encryption is given much attention in research of information security and a lot of image encryption algorithms have been introduced. Due to some intrinsic features of images like bulk data capacity and high data redundancy, the encryption of image is different from that of text; therefore it is difficult to handle them by traditional encryption methods. In the proposed work, a new image encryption algorithm based on Magic Rectangle (MR) is being applied. To begin with, the plain-image is converted into blocks of single bytes and then the block is replaced as the value of MR. Further, the control parameters of Magic Rectangle (MR) are selected randomly by the user. Subsequently the image is being encrypted with public key cryptography algorithms such as RSA, ElGamal etc. The experimental result shows that the proposed algorithm can successfully encrypt/decrypt the images with separate secret keys, and the algorithm has good encryption effect. Cipher text developed by this method will be entirely different when compared to the original image file and will be suitable for the secured transmission over the internet. Thus, this model provides an additional level of security to public key algorithm and efficient utilization of memory.