Rajeev Kumar Gupta

Cloud Server Optimization with Load Balancing and Green Computing Techniques Using Dynamic Compare and Balance Algorithm

Cloud computing is a business oriented concept to provide online IT resources and IT services on demand using pay per use model where main goal of cloud service provider is to use cloud computing resources efficiently and gain profits marginally. One of the challenging areas in cloud computing is frequent optimization of cloud server. It mainly concerns with the load balancing of cloud data centers to improve efficiency of the host machine and minimize number of active host machine to support green computing concept. To balance the load of entire data center, we need to transfer the virtual machines of the overloaded host to the light weighted host using migration techniques. In this paper, we introduce a threshold based Dynamic compare and balance algorithm (DCABA) for cloud server optimization. Unlike the traditional server optimization strategies which consider only load balancing and scheduling of resources based on the usage of CPU, RAM and BW in physical servers, DCABA also minimizes the number of host machines to be powered on, for reducing the cost of cloud services. Our approach can serve the purpose of service cost reduction in cloud industry with effective utilization of available resources.

Balance Resource Utilization BRU Approach for the Dynamic Load Balancing in Cloud Environment by Using AR Prediction Model

Oneofthemajorchallengesforthecloudprovideristheefficientutilizationofthephysicalresources. Toachievethis,thispaperproposedaBalanceResourceUtilization(BRU)approachthatnotonly minimizestheresourceleakagebutalsoincreasestheresourceutilizationandoptimizethesystem performance.Theproposedapproachconsider tworesources i.e.,CPUandmemory,asdecision metrics for loadbalancinganduse three thresholdsnamed lower threshold,upper thresholdand warningthresholdtodefineunderloaded,overloadedandwarningsituations,respectively.Themain conceptof thisapproach is toplaceVMto thePM,whereresourcerequirementof theVMand resourceutilizationofthePMarecomplementstoeachother.Toevadeunnecessarymigrationsdue tothetemporarypeakloadARtimeseriespredictionmodelisused.Theauthors’approachtreatsload balancingproblemfromthepracticalperspectiveandimplementedinOpenStackcloudwithKVM hypervisor.Moreover,proposedapproachresolvetheissueofVMmigrationintheheterogeneous environment. KEywORDS Auto Regression (AR) Model, CPU Load, CPU Utilization, Energy Efficient, Response Time, Virtual Machine, Warning Threshold

Design of Decentralized PSSs for Multimachine Power System via Reduced Order Model

The Power System Stabilizer (PSS) is added to excitation system to enhance the damping during low frequency oscillations. In this paper, the design of decentralized PSSs for 10 machines with 39 buses using fast output sampling method via reduced order model is proposed. In multi-machine power system the order of the states matrix is very large. The main objectives of order reduction is to design a controller of lower order which can effectively control the original high order system so that the overall system is of lower order and easy to understand. The state space matrices of the reduced order system are chosen such that the dominant eigenvalues of the full order system are unchanged. The other system parameters are chosen using the particle swarm optimization with objective function to minimize the mean squared errors between the outputs of the full order system and the outputs of the reduced order model when the inputs are unit step. Design of fast output sampling controllers via reduced order model using Particle Swarm Optimization (PSO) method is proposed for good damping enhancement for various operating points of multi-machine power systems. In fast output sampling technique, the nonlinear model of 10 machine and 39 bus system is linearized at different operating point and a linear model is obtained. A robust fast output sampling feedback gain which realizes output injection gain is obtained using LMI approach. This robust fast output sampling control is applied to non-linear model of a Multi-machine system at different operating points. This method gives very good results in the design of Power System Stabilizers and takes less computation time in operation of power system.

Numeral Gesture Recognition Using Leap Motion Sensor

3-Dimensional gesture tracking and recognition systems are changing traditional methods of human computer interaction with gesture interaction. These systems offer advantage of tracking and recognizing unencumbered gestures which are drawn in free air. In this paper, we present numeral gesture recognition using Leap Motion. Leap motion is a 3D motion sensor which provides unencumbered, 3-dimensional gestures drawn in free air. Researchers has done ton of work related to numeral gestures recognition with the help of Microsoft Kinect 3D sensor, Prima Sense 3D motion sensor and other similar devices. In place of traditional gesture capturing devices, we used Leap Motion to capture free air numeral gestures and applied Geometric Template Matching method to classify gesture numerals ranging from 0-9. The experimental results shows that average classification rate of 70.2% is achieved by Geometric Template Matching.

Tuning of PID Controller for a Linear BLDC Motor Using TLBO Technique

In this paper, an optimum tuning of PID Controller is proposed. A Linear Brushless DC motor is known for higher efficiency & lower maintenance. A newly developed algorithm named, teaching learning based optimization algorithm is applied for the PID Controller tuning of Brushless Linear DC Motor. This algorithm is inspired by the teaching learning process and it works on the effect of influence of a teacher on the output of the learners in a class. Teaching -- Learning-Based Optimization (TLBO) algorithms simulate the teaching -- learning phenomenon of a classroom to solve multidimensional, linear and nonlinear problems with appreciable efficiency. TLBO is population based method. The performance of TLBO algorithm compared Mehdi nasri et.at. Realization of TLBO based PID controller for BLDC motor is economic and easily implemented.

Design of Robust Power System Stabilizer Using Balance Truncation Method

Power system stabilizer are used to provide damping torque for the synchronous generators, to suppress the low frequency oscillations present in the power system, by generating supplementary control signals for the excitation system. Numerous techniques have been proposed to design PSS. The main difficulty in PSS design is that power system encounter continuous variation in the load patterns and consequently the nominal model design does not guarantee satisfactory performance at all operating points. In this paper a different technique is used for model reduction this methods yield encouraging results for the design of power system stabilizers. Using Balance Truncation technique, lower order model is obtained from the higher order model and for this reduced model a stabilizing state feedback gain is obtained. This method is applied to a nonlinear plant model of ten machines at different operating points.