Faculty Publications
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Publications by NITK Faculty
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Item Enhanced Framework for IoT Applications on Python Based Cloud Simulator (PCS)(Institute of Electrical and Electronics Engineers Inc., 2016) Jaiswal, A.; Domanal, S.; Guddeti, G.R.As innovation develops, more human-made devices are able to communicate with each other by means of Internet. This enables the Internet of Things (IoT) era to emerge. The amount of information generated by IoT applications can overpower computer infrastructures which are not prepared for such a huge data hence they need more CPU cycles. Distributed computing offers a solution at infrastructure level that eases such problems by offering highly scalable computing platforms. This necessitates arranging the framework on demand to meet invariant changes which applications require, in a pay-per-use mode. Current methodologies empowering IoT applications are area specific or concentrate just on communication between devices, therefore they can not be effectively deployed to different domains. To address this issue, in this paper, we present a data centric framework for advancement of IoT applications executed in python based cloud simulator. The framework handles association with information sources, information filtering and use of cloud resources including provisioning, load balancing, and planning thus enabling developers to concentrate on the application logic and encouraging the advancement of loT applications. © 2015 IEEE.Item Load Balancing in Cloud Environment Using a Novel Hybrid Scheduling Algorithm(Institute of Electrical and Electronics Engineers Inc., 2016) Domanal, S.; Guddeti, G.R.We propose a hybrid scheduling algorithm for load balancing in a distributed environment by combining the methodology of Divide-And-Conquer and Throttled algorithms referred to as DCBT. Our algorithm plays an important role in distributing the incoming load in an efficient manner so that it maximizes resource utilization in a cloud environment. Further, load balancer plays an important role in cloud environment by assigning incoming tasks to Virtual Machines (VM) intelligently. The main aim of the proposed DCBT is to reduce the total execution time of the tasks and thereby maximizing the resource utilization. Further, the proposed DCBT algorithm is analyzed using Cloud Sim simulator and also in customized distributed environment using python. Experimental results demonstrate that the proposed algorithm gives better efficiency in both Cloud Sim and customized environments. The proposed DCBT utilizes the Virtual Machines more efficiently while reducing the execution time of the tasks allocated to Request Handlers (RH) by 9.972% in comparison to the Modified Throttled algorithm. © 2015 IEEE.Item Fast Convergence to Near Optimal Solution for Job Shop Scheduling Using Cat Swarm Optimization(Springer Verlag service@springer.de, 2017) Dani, V.; Sarswat, A.; Swaroop, V.; Domanal, S.; Guddeti, G.R.M.Job Shop Scheduling problem has wide range of applications. However it being a NP-Hard optimization problem, always finding an optimal solution is not possible in polynomial amount of time. In this paper we propose a heuristic approach to find near optimal solution for Job Shop Scheduling Problem in predetermined amount of time using Cat Swarm Optimization. Novelty in our approach is our non-conventional way of representing position of cat in search space that ensures advantage of spatial locality is taken. Further while exploring the search space using randomization, we never explore an infeasible solution. This reduces search time. Our proposed approach outperforms some of the conventional algorithms and achieves nearly 86% accuracy, while restricting processing time to one second. © 2017, Springer International Publishing AG.Item GWOTS: Grey Wolf Optimization Based Task Scheduling at the Green Cloud Data Center(Institute of Electrical and Electronics Engineers Inc., 2018) Natesha, B.V.; Sharma, N.; Domanal, S.; Guddeti, R.M.Task Scheduling is a key challenging issue of Infrastructure as a Service (IaaS) based cloud data center and it is well-known NP-complete problem. As the number of users' requests increases then the load on the cloud data center will also increase gradually. To manage the heavy load on the cloud data center, in this paper, we propose multiobjective Grey Wolf Optimization (GWO) technique for task scheduling. The main objective of our proposed GWO based scheduling algorithm is to achieve optimum utilization of cloud resources for reducing both the energy consumption of the data center and total makespan of the scheduler for the given list of tasks while providing the services as requested by the users. Our proposed scheduling algorithm is compared with non meta-heuristic algorithms (First-Come-First-Serve (FCFS) and Modified Throttle (MT)), and meta-heuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO)). Experimental results demonstrate that the proposed GWO based scheduler outperforms all algorithms considered for performance evaluation in terms of makespan for the list of tasks, resource utilization and energy consumption. © 2018 IEEE.Item An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment(Elsevier B.V., 2018) Domanal, S.; Guddeti, G.In this paper, we propose a novel efficient and cost optimized scheduling algorithm for a Bag of Tasks (BoT) on Virtual Machines (VMs). Further, in this paper, we use artificial Neural Network to predict the future values of Spot instances and then validate these predicted values with respect to the current (actual) values of Spot instances. On-Demand and Spot are the key instances which are procured by the cloud customers and hence, in this paper, we use these instances for the cost optimization. The key idea of our proposed algorithm is to efficiently utilize the cloud resources (mainly VMs instances, Central Processing Unit (CPU) and Memory) and also to optimize the cost of executing the BoT in the heterogeneous Infrastructure as a Service (IaaS) based cloud environment. Experimental results demonstrate that our proposed scheduling algorithm outperforms state-of-the-art benchmark algorithms (Round Robin, First Come First Serve, Ant Colony Optimization, Genetic Algorithm, etc.) in terms of Quality of Service (QoS) parameters (Reliability, Time and Cost) while executing the BoT in the heterogeneous cloud environment. Since the obtained results are in the form of ordinal, hence we carried out the statistical analysis on both predicted and actual Spot instances using the Spearman's Rho Test. © 2018 Elsevier B.V.