Faculty Publications
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Publications by NITK Faculty
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Item Improved Variable Round Robin Scheduling Algorithm(Institute of Electrical and Electronics Engineers Inc., 2021) Mangukia, A.; Ibrahim, M.; Golamudi, S.; Kumar, N.; Anand Kumar, M.The scheduling strategy used has an impact on system efficiency. It assigns processes in a specified order in order to improve those functions. The Round Robin method is one of several types of scheduling algorithms. In Round Robin, each process is allotted a time quantum (TQ), which indicates that each process consumes the same amount of time as the other processes. There is no precedence among the functions, and the CPU has a relatively short response time. The Time Quantum value cannot be too small because the number of context shifts will increase and hamper performance, while a Time Quantum value that is too large will harm the ART. The suggested study focuses on making the Round Robin approach more practical and efficient. © 2021 IEEE.Item Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO-GSA algorithm(Elsevier Ltd, 2015) Khadanga, R.K.; Satapathy, J.K.In this work we present a novel approach in order to improve the power system stability, by designing a coordinated structure composed of a power system stabilizer and static synchronous series compensator (SSSC)-based damping controller. In the design approach various time delays and signal transmission delays owing to sensors are included. This is a coordinated design problem which is treated as an optimization problem. A new hybrid particle swarm optimization and gravitational search algorithm (hPSO-GSA) algorithm is used in order to find the controller parameters. The performance of single-machine infinite-bus power system as well as the multi-machine power systems are evaluated by applying the proposed hPSO-GSA based controllers (PSS and damping controller). Various results are shown here with different loading condition and system configuration over a wide range which will prove the robustness and effectiveness of the above design approach. From the results it can be observed that, the proposed hPSO-GSA based controller provides superior damping to the power system oscillation on a wide range of disturbances. Again from the simulation based results it can be concluded that, for a multi-machine power system, the modal oscillation which is very dangerous can be easily damped out with the above proposed approach. © 2015 Elsevier Ltd. All rights reserved.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.Item A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment(Institute of Electrical and Electronics Engineers, 2020) Domanal, S.G.; Guddeti, R.M.R.; Buyya, R.In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time. © 2008-2012 IEEE.Item A machine learning algorithm for scheduling a burn-in oven problem(Inderscience Publishers, 2023) Mathirajan, M.; Reddy, S.; Vimala Rani, M.V.; Dhaval, P.This study applies artificial neural network (ANN) to achieve more accurate parameter estimations in calculating job-priority-data of jobs and the same is applied in a proposed dispatching rule-based greedy heuristic algorithm (DR-GHA) for efficiently scheduling a burn-in oven (BO) problem. The integration of ANN and DR-GHA is called as a hybrid neural network (HNN) algorithm. Accordingly, this study proposed eight variants of HNN algorithms by proposing eight variants of DR-GHA for scheduling a BO. The series of computational analyses (empirical and statistical) indicated that each of the variants of proposed HNN is significantly enhancing the performance of the respective proposed variants of DR-GHA for scheduling a BO. That is, more accurate parameter estimations in calculating job-priority-data for DR-GHA via back-propagation ANN leads to high-quality schedules w.r.t. total weighted tardiness. Further, proposed HNN variant: HNN-ODD is outperforming relatively with other HNN variants and provides very near optimal/estimated solution. © © 2023 Inderscience Enterprises Ltd.Item RUSH: Rule-Based Scheduling for Low-Latency Serverless Computing(Institute of Electrical and Electronics Engineers Inc., 2025) Birajdar, P.A.; Anchalia, K.; Satpathy, A.; Addya, S.K.Serverless computing abstracts server management, enabling developers to focus on application logic while benefiting from automatic scaling and pay-per-use pricing. However, dynamic workloads pose challenges in resource allocation and response time optimization. Response time is a critical performance metric in serverless environments, especially for latency-sensitive applications, where inefficient scheduling can degrade user experience and system efficiency. This paper proposes RUSH (Rule-based Scheduling for Low-Latency Serverless Computing), a lightweight and adaptive scheduling framework designed to reduce cold starts and execution delays. RUSH employs a set of predefined rules that consider system state, resource availability, and timeout thresholds to make proactive, latency-Aware scheduling decisions. We implement and evaluate RUSH on a real-world serverless application that generates emoji meanings. Experimental results demonstrate that RUSH consistently outperforms First-Come-First-Served (FCFS), Random Scheduling, and Profaastinate, achieving ? 33% reduction in average execution time. © IEEE. 2019 IEEE.Item Pod Scheduling and Proactive Resource Management in an Edge Cluster using MCDM and Federated Learning(Springer Science and Business Media B.V., 2025) Kumar, N.K.; B, A.; J, H.; Srinivasan, S.; Sand, S.S.Edge computing, which locates computational resources closer to the data sources, has become crucial in meeting the demands of applications that need high bandwidth and low latency. To cater to edge computing scenarios, KubeEdge, an extension of Kubernetes(K8s), expands its capabilities to meet edge-specific requirements such as limited resources, irregular connections, and heterogeneous environments. Edge trace data cannot be shared between cloud providers because of privacy issues, which makes generic distributed training ineffective. However, even with edge computing’s potential advantages, the built-in scheduling algorithms have several drawbacks. A significant problem is the lack of efficient resource management and allocation mechanisms at the edge, which causes edge nodes to be underutilized or overloaded which leads to violation of Quality of Service(QoS) and inefficient utilization of resources leads to Service Level Agreement(SLA) violations. In this regard, VIKOR and ELECTRE III based pod scheduling strategy is proposed in this paper and evaluated using Wikipedia and NASA server workload. The experimental results shows that 50% reduction in standard deviation for ELECTRE III and 40% reduction in standard deviation for VIKOR against default scheduler of Kubernetes. The average response time of 30.6593ms and 31.8803ms is achieved for Electre III and VIKOR for Wikipedia dataset. A proactive resource management system is proposed for KubeEdge containerized services where it incorporates a federated learning framework to predict future workloads using the Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). The experimental comparison of federated learning shows 99.65%, 98.64% reduction in MSE for CPU utilization % and 89.72%, 76.57% reduction in MSE for Memory utilization % with respect to GRU and BI-LSTM models in contrast to centralized learning. The proposed approach effectiveness is evaluated through statistical techniques and found significant. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.Item DCRDA: deadline-constrained function scheduling in serverless-cloud platform(Springer, 2025) Birajdar, P.A.; Meena, D.; Satpathy, A.; Addya, S.K.The serverless computing model frees developers from operational and management tasks, allowing them to focus solely on business logic. This paper addresses the computationally challenging function-container-virtual machine (VM) scheduling problem, especially under stringent deadline constraints. We propose a two-stage holistic scheduling framework called DCRDA targeting deadline-constrained function scheduling. In the first stage, the function-to-container scheduling is modeled as a one-to-one matching game and solved using the classical Deferred Acceptance Algorithm (DAA). The second stage addresses the container-to-VM assignment, modeled as a many-to-one matching problem, and solved using a variant of the DAA, the Revised-Deferred Acceptance Algorithm (RDA), to account for heterogeneous resource demands. Since matching-based strategies require agent preferences, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ranking mechanism is employed to prioritize alternatives based on execution time, deadlines, and resource demands. The primary goal of DCRDA is to maximize the success ratio (SR), defined as the ratio of functions executed within the deadline to the total functions. Extensive test-bed validations over commercial providers such as Amazon EC2 show that the proposed framework significantly improves the success ratio compared to baseline approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.