Ronak Vihol1*, Hiren Patel2 and Nimisha Patel3
Offering “Computing as a utility” on pay per use plan, Cloud computing has emerged as a technology of ease and flexibility for thousands of users over last few years. Distribution of dynamic workload among available servers and efficient utilization of existing resources in datacenter is one of the major concerns in Cloud computing. The load balancing issue needs to take into consideration the utilization of servers, i.e. the resultant utilization should not exceed the preset upper limits to avoid service level agreement (SLA) violation and should not fall beneath stipulated lower limits to avoid keeping some servers in active use. Scheduling of workload is regarded as an optimization problem that considers many varying criterion such as dynamic environment, priority of incoming applications, their deadlines etc. to improve resource utilization and overall performance of Cloud computing. In this work, a Genetic Algorithm (GA) based novel load balancing mechanism is proposed. Though not done in this work, in future, we aim to compare performance of proposed algorithms with existing mechanisms such as first come first serve (FCFS), Round Robin (RR) and other search algorithms through simulations.
Rajan Patel1*,Sumaiya Vhora2 and Nimisha Patel3
Packet drop (grayhole/blackhole) attack is occurs at a network layer to discard the packets in MANET. It is essential to detent and prevent this attack for improving performance of network. This article provides the packet drop attack detection and prevention using RBDR (Rank Based Data Routing) for AOMDV routing protocol. The fields of RBDR are generated with routing information and analysis behavior of network for detecting the malicious paths. The scheme is to identify the malicious paths for preventing the packet drop attack and also able to find the trusted multiple disjoint loop free routes for data delivery in MANET. The simulation is conducted in NS2 using AOMDV reactive routing protocol and analyze with packet loss delivery, average end-to-end delay and packet delivery ratio. The proposed technique can reduce the effect of packet drop attack.
Comparative Evaluation of WOFOST and CERES-Rice Models in Simulating Yield of Rice Cultivars at Navari
Nilesh J. Hadiya, Neeraj Kumar, B. M. Mote, Chiragkumarm. Thumar and D. D. Patil
A field experiment was conducted during kharif season of 2015 to assess the prediction performance of CERES-Rice and WOFOST model for grain and straw yield of three rice cultivars viz., (V1:Jaya, V2: Gurjari and V3: GNR-2) sown under four different environments viz., (D1: 10/07/2015, D2: 25/07/2015, D3: 09/08/2015 and D4: 24/08/2015) with two nitrogen levels N1:75 and N2:100 kg NPK/ha-1.Results showed that the prediction of WOFOST model forgrain yield of rice cultivars under different treatments more close to the corresponding observed value with percent error PE between (18.66%)as camper to CERES-rice model with PE (28.56%), but for straw yield CERES-rice model give more close prediction than WOFOST model with PE (20.99%) and (27.33%) between predicted and observed value.
Vikram Agrawal1* and Dilipsinh Bheda2
In the field of Image mosaicing, much research has been done to fulfil the two major challenges, time complexity and quality improvement. Proposed method is a pre-processing step before actual image stitching carried out. The method aims to find out the overlapping regions in two images. Thus features can be extracted from these overlapping regions and not from the whole images, which result into reduction of computation time. For detecting overlapping portion, gradient based edge extraction method and invariant moments are used. In the deduced region, SIFT features are extraction to determine the matching features. The registration process carried out by RANSAC algorithm and final output mosaic will obtained by warping the images. An optimized approach to calculate the moment difference values is presented to improve time efficiency and quality.
M. A. Lone, S. A. Mir , Imran Khan and M. S. Wani
This article deals with the problem of finding an optimal allocation of sample sizes in stratified sampling design to minimize the cost function. In this paper the iterative procedure of Rosen’s Gradient projection method is used to solve the Non linear programming problem (NLPP), when a non integer solution is obtained after solving the NLPP then Branch and Bound method provides an integer solution.