Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
6 results
Search Results
Item Evaluation of semiconducting p-type tin sulfide thin films for photodetector applications(Academic Press, 2019) Barman, B.; Bangera, K.V.; Shivakumar, G.K.Tin sulfide (SnS) is an important semiconductor as it is one of the less common p-type materials with a bandgap of 1.53 eV which makes it an attractive material for photo detector application. In the thin film form, it is a sensitive photo conductor with attractive opto-electronic characteristics. In the current report, tin sulfide thin films have been deposited by thermal evaporation in vacuum and the influence of substrate temperature on its compositional, morphological, structural, and opto-electrical properties was studied. X-ray diffraction (XRD) study shows that all the thermally deposited films are having an orthorhombic crystal structure along (111) plane as pre-dominant orientation and are polycrystalline in nature. Raman analysis verify the occurrence of SnS and Sn2S3 phases in the films. Surface morphology along with the elemental composition of the films was determined by scanning electron microscopy (SEM) in combination with energy dispersive spectroscopy (EDS). All the films were found to be homogeneous, uniform, pin-hole free and have high optical transmittance in the UV–Vis wavelength region. The optical bandgap energy of the films was calculated using Tauc's relation and it was found to be decreasing (1.576 eV–1.429 eV) with increasing substrate temperature. The activation energy of the SnS thin films was calculated from Arrhenius plot and it was also found to be decreasing with increasing substrate temperature. The opto-electrical parameters such as photo conductivity (?L), dark conductivity (?D), response time (?r), recovery time (?d), photoresponsivity (R), and photosensitivity (S) were calculated and was found best for the films grown at 323 K. © 2019 Elsevier LtdItem IntMA: Dynamic Interaction-aware resource allocation for containerized microservices in cloud environments(Elsevier B.V., 2020) Joseph, C.T.; Chandrasekaran, K.The Information Technology sector has undergone tremendous changes arising due to the emergence and prevalence of Cloud Computing. Microservice Architectures have also been attracting attention from several industries and researchers. Due to the suitability of microservices for the Cloud environments, an increasing number of Cloud applications are now provided as microservices. However, this transition to microservices brings a wide range of infrastructural orchestration challenges. Though several research works have discussed the engineering of microservice-based applications, there is an inevitable need for research on handling the operational phases of the microservice components. Microservice application deployment in containerized datacenters must be optimized to enhance the overall system performance. In this research work, the deployment of microservice application modules on the Cloud infrastructure is first modelled as a Binary Quadratic Programming Problem. In order to reduce the adverse impact of communication latencies on the response time, the interaction pattern between the microservice components is modelled as an undirected doubly weighted complete Interaction Graph. A novel, robust heuristic approach IntMA is also proposed for deploying the microservices in an interaction-aware manner with the aid of the interaction information obtained from the Interaction Graph. The proposed allocation policies are implemented in Kubernetes. The effectiveness of the proposed approach is evaluated on the Google Cloud Platform, using different microservice reference applications. Experimental results indicate that the proposed approach improves the response time and throughput of the microservice-based systems. © 2020 Elsevier B.V.Item Nature-inspired resource management and dynamic rescheduling of microservices in Cloud datacenters(John Wiley and Sons Ltd, 2021) Joseph, C.T.; Chandrasekaran, K.Distributed Cloud environments are now resorting to Cloud applications composed of heterogeneous microservices. Cloud service providers strive to provide high quality of service (QoS) and response time is one of the key QoS attributes for microservices. The dynamism of microservice ecosystems necessitates runtime adaptations and microservices rescheduling to avoid performance degradation. Existing works target rescheduling in hypervisor-based systems, while ignoring the influence of configuration parameters of container-based microservices. In an effort to address these challenges, this article describes a novel microservice rescheduling framework, throttling and interaction-aware anticorrelated rescheduling for microservices, to proactively perform rescheduling activities whilst ensuring timely service responses. Based on periodic monitoring of the performance attributes, the framework schedules container migrations. Considering the exponentially large solution space, a metaheuristic approach based on multiverse optimization is developed to generate the near-optimal mapping of microservices to the datacenter resources. Experimental results indicate that our framework provides superior performance with a reduction of up to 13.97% in the average response time, when compared with systems with no support for rescheduling. © 2021 John Wiley & Sons 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 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.Item Delay-aware partial task offloading using multicriteria decision model in IoT–fog–cloud networks(Academic Press, 2025) S.a, S.; E, M.; Addya, S.K.; Rahman, S.; Pal, S.; Karmakar, C.Fog computing plays a prominent role in offloading computational tasks in heterogeneous environments since it provides less service delay than traditional cloud computing. The Internet of Things (IoT) devices cannot handle complex tasks due to less battery power, storage and computational capability. Full offloading has issues in providing efficient computation delay due to more response time and transmission cost. A suitable solution to overcome this problem is to partition the tasks into splittable subtasks. Considering multi-criteria decision parameters like processing efficiency and deadline helps to achieve efficient resource allocation and task assignment. The matching theory is applied to map task nodes to heterogeneous fog nodes and VMs for stability. Compared to baseline algorithms, proposed algorithms like Resource Allocation based on Processing Efficiency (RABP) and Task Assignment Based on Completion Time (TAC) are efficient enough to provide reasonable service delay and discard the non-beneficial tasks, i.e., tasks that do not execute within the deadline. © 2025 The Authors