Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Load balancing in cloud computing using modified throttled algorithm(IEEE Computer Society help@computer.org, 2013) Domanal, S.G.; Guddeti, G.Load balancing is one of the critical components for efficient operations in the cloud computing environment. In recent years many clients from all over the world are demanding the various services at rapid rate. Many algorithms have been designed to carry out the client's request towards the cloud nodes. Accordingly the cloud computing platform will dynamically configure its servers and these servers may be present physically or virtually in the computing environment. Hence, selecting the virtual machines or servers has to be scheduled properly by using an appropriate load balancing approach. In the present work, a local optimized load balancing approach is proposed for distributing of incoming jobs uniformly among the servers or virtual machines. Further, the performance is analyzed using CloudAnalyst simulator and compared with existing Round Robin and Throttled algorithms. Simulation results have demonstrated that the proposed algorithm has distributed the load uniformly among virtual machines. Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.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 Windows malware detection based on cuckoo sandbox generated report using machine learning algorithm(Institute of Electrical and Electronics Engineers Inc., 2016) Shiva Darshan, S.L.S.; M.a, M.A.A.; Jaidhar, C.D.Malicious software or malware has grown rapidly and many anti-malware defensive solutions have failed to detect the unknown malware since most of them rely on signature-based technique. This technique can detect a malware based on a pre-defined signature, which achieves poor performance when attempting to classify unseen malware with the capability to evade detection using various code obfuscation techniques. This growing evasion capability of new and unknown malwares needs to be countered by analyzing the malware dynamically in a sandbox environment, since the sandbox provides an isolated environment for analyzing the behavior of the malware. In this paper, the malware is executed on to the cuckoo sandbox to obtain its run-time behavior. At the end of the execution, the cuckoo sandbox reports the system calls invoked by the malware during execution. However, this report is in JSON format and has to be converted to MIST format to extract the system calls. The collected system calls are structured in the form of N-Grams, which help to build the classifier by using the Information Gain (IG) as a feature selection technique. A comprehensive experiment was conducted to perceive the best fit classifier among the chosen classifiers, including the Bayesian-Logistic-Regression, SPegasos, IB1, Bagging, Part, and J48 defined within the WEKA tool. From the experimental results, the overall best performance for all the selected top N-Grams such as 200, 400, and 600 goes to SPegasos with the highest accuracy, highest True Positive Rate (TPR), and lowest False Positive Rate (FPR). © 2016 IEEE.Item A Machine Learning Approach for Load Balancing in a Multi-cloud Environment(Springer Science and Business Media Deutschland GmbH, 2022) Divakarla, D.; Chandrasekaran, K.A multi-cloud environment makes use of two or more cloud computing services from different cloud vendors. A typical multi-cloud environment can consist of either only private clouds or only public clouds or a combination of both. Load balancing mechanism is essential in such a computing environment to distribute user requests or network load efficiently across multiple servers or virtual machines, ensuring high availability and reliability. Scalability is also achieved by sending requests only to those servers that are healthy and available to take up the computing workload and thus providing the flexibility to scale up and scale down to satisfy QoS requirements as well, in order to save costs. In our proposed model, a time series-based approach as well as predictive load balancing has been experimented and the results are presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.