Faculty Publications
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Publications by NITK Faculty
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Item Development of scheduler for real time and embedded system domain(2008) Rao, M.V.P.; Shet, K.C.; Balakrishna, R.; Roopa, K.We discuss scheduling techniques to be used for real-time, embedded systems. Though there are several scheduling policies, the preemptive scheduling policy holds promising results. In this research paper, the different approaches to design of a scheduler for real-time Linux kernel are discussed in detail. The comparison of different preemptive scheduling algorithms is performed. Hence, by extracting the positive characteristics of each of these preemptive scheduling policies, a new hierarchical scheduling policy is developed. The proposed hierarchical scheduling for real time and embedded system will be implemented for a prototype system, using C or C++ language. It is expected that the new scheduling algorithm will give better performance with respect to satisfy the needs, such as time, capturing and usage of resources of different applications. © 2008 IEEE.Item LEASE: Leveraging Energy-Awareness in Serverless Edge for Latency-Sensitive IoT Services(Institute of Electrical and Electronics Engineers Inc., 2024) Verma, A.; Satpathy, A.; Das, S.K.; Addya, S.K.Resource scheduling catering to real-time IoT services in a serverless-enabled edge network is particularly challenging owing to the workload variability, strict constraints on tolerable latency, and unpredictability in the energy sources powering the edge devices. This paper proposes a framework LEASE that dynamically schedules resources in serverless functions catering to different microservices and adhering to their deadline constraint. To assist the scheduler in making effective scheduling decisions, we introduce a priority-based approach that offloads functions from over-provisioned edge nodes to under-provisioned peer nodes, considering the expended energy in the process without compromising the completion time of microservices. For real-world implementations, we consider a testbed comprising a Raspberry Pi cluster serving as edge nodes, equipped with container orchestrator tools such as Kubernetes and powered by OpenFaaS, an open-source serverless platform. Experimental results demonstrate that compared to the benchmarking algorithm, LEASE achieves a 23.34% reduction in the overall completion time, with 97.64% of microservices meeting their deadline. LEASE also attains a 30.10% reduction in failure rates. © 2024 IEEE.Item Decentralised priority-based shortest job first queue model for IoT gateways in fog computing(Inderscience Publishers, 2022) Jayashree, N.; Babu, B.S.; Talawar, B.An increased growth in time-critical IoT applications, led to a rise in real-time resource requirements. The stringent deadlines on latency have made IoT applications move out from far away cloud servers to distributed fog computing devices infrastructure which is available locally. To meet the touchstones of deadlines and processing times, there is a need to prioritise the job scheduling through the IoT gateways to appropriate fog devices. Studies showed that the queuing models exhibit uncertainties in choosing suitable computing devices, applying priorities to the jobs, deadline achievements, and minimum latency constraints. In this paper, we propose a Decentralised Priority-based Shortest Job First (DPSJF) queuing model for the IoT gateways for a fog computing infrastructure, which uses the priority-based jobs sorting technique to achieve better performance and also overcome most of the uncertainties in queuing. © © 2022 Inderscience Enterprises Ltd.Item A Workflow Scheduling Approach With Modified Fuzzy Adaptive Genetic Algorithm in IaaS Clouds(Institute of Electrical and Electronics Engineers Inc., 2023) Rizvi, N.; Ramesh, D.; Wang, L.; Annappa, B.The emergence of the cloud platform with substantial resources to offer on-demand instigated the researchers to migrate the scientific workflows to the cloud environment. The scheduling of workflows with diverse QoS parameters is not a trivial task, but an NP-Complete problem. Several heuristics for QoS constrained workflows have been investigated. However, most of them focus only on time and cost and do not guarantee high resource utilization. The scheduling of the workflow tasks over the minimum cloud resources under the defined time limit is a grave concern. In this article, an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) has been formulated to minimize the makespan and improve resource utilization under both deadline and budget constraints. A fuzzy logic controller has also been devised to control the crossover and mutation rates that prevent MFGA from getting stuck in a local optimum. MFGA has a novel crossover technique that adds the fittest solutions in the population. Additionally, a new mutation technique has also been introduced, which minimizes the makespan and increases the reusability of the resources. The simulation experiments with the real workflows show that the proposed MFGA outperforms other state-of-the-art algorithms. © 2008-2012 IEEE.