Faculty Publications
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Publications by NITK Faculty
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Item Optimal load balancing in cloud computing by efficient utilization of virtual machines(2014) Domanal, S.G.; Guddeti, G.R.M.Load balancing is the major concern in the cloud computing environment. Cloud comprises of many hardware and software resources and managing these will play an important role in executing a client's request. Now a day's clients from different parts of the world are demanding for the various services in a rapid rate. In this present situation the load balancing algorithms built should be very efficient in allocating the request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur in the cloud environment. In the present work, a novel VM-assign load balance algorithm is proposed which allocates the incoming requests to the all available virtual machines in an efficient manner. Further, the performance is analyzed using Cloudsim simulator and compared with existing Active-VM load balance algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all available virtual machines without under/over utilization. © 2014 IEEE.Item A novel bio-inspired load balancing of virtualmachines in cloud environment(Institute of Electrical and Electronics Engineers Inc., 2015) Ashwin, T.S.; Domanal, S.G.; Guddeti, G.R.M.Load Balancing plays an important role in managing the software and the hardware components of cloud. In this present scenario the load balancing algorithm should be efficient in allocating the requested resource and also in the usage of the resources so that the over/underutilization of the resources will not occur in the cloud environment. In the present work, the allocation of all the available Virtual Machines is done in an efficient manner by Particle Swarm Optimization load balancing algorithm. Further, we have used cloudsim simulator to compare and analyze the performance of our algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all the available virtual machines uniformly i.e, without any under/over utilization and also the average response time is better compared to all existing scheduling algorithms. © 2014 IEEE.Item Resource usage prediction based on ARIMA-ARCH model for virtualized server system(GEOMATE International Society geomate@gi-j.com, 2017) Mohan, B.R.; Guddeti, G.R.M.Performance degradation is unavoidable in server systems and this is because of factors such as shrinkage of system resources, data corruption, and numerical error accumulation. The resource shrinkage leads to the system failure due to the error propagation. Thus the resource prediction is useful to the administrator of the system so that an accidental outage can be avoided. It has been observed in past that most of the failures occur due to the exhaustion of free physical memory, so here free physical memory of a server consolidation setup is observed. It is also found that most of the studies in this direction were using the measurement-based approach with time series models for prediction. This paper reviews the effectiveness of such models and it examines whether volatility is present in the data or not. It checks whether Gauss-Markov assumptions about homoscedasticity holds good for the ordinary least square estimators of such models or not. This paper applies a combination of AutoRegressive Integrated Moving Average - AutoRegressive Conditional Heteroskedastic (ARIMA-ARCH) model to predict resource usage. Experimental results demonstrate that the goodness of fit of the ARIMA-ARCH Model has improved when compared to the linear ARIMA model. © Int. J. of GEOMATE.