Faculty Publications

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Publications by NITK Faculty

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    Optimal Resource Allocation for Public Safety Device to Device Communication Using PSO
    (Springer Science and Business Media Deutschland GmbH, 2023) Dhruvik, N.; Pavan, R.; Neeraj; Manjappa, M.
    The Device to Device (D2D) communication allows two different devices in close proximity to communicate directly among themselves without relaying through the base stations (eNodeB or eNB). The D2D communication offloads the traffic from eNB and thus, has many advantages, including higher throughput and less end-to-end delay. Though the PSC was basically invented for Public Safety Communication (PSC) and to help the first responders, its distinct advantages have attracted other commercial applications as well. The eNB treats all the D2D applications equally during resource allocation and does a uniform resource allocation where one application is engaged in commercial activities. At the same time, the other saves one’s life. Thus, in this work authors proposed a novel optimized resource allocation algorithm for D2D applications which prioritizes PSC over commercial applications. In order to achieve the objective, Particle Swarm Optimization (PSO) technique was employed in the proposed work. Furthermore, a new weighted average fitness function was designed for PSO to suit the requirements. The proposed algorithm was simulated in NS-3, and the results were taken for different iterations. It was observed that the PSO algorithm for the designed fitness function achieved the local and global optimum values in a considerable amount of time. It was apparent from the results that PSC D2D pairs produced convincing results when compared to D2D pairs with commercial applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Dynamic resource allocation for multi-tier applications in cloud
    (Springer Verlag service@springer.de, 2016) Achar, R.; Santhi Thilagam, P.; Meghana; Niha Fathima Haris, B.; Bhat, H.; Ekta, K.
    Increasing demand for computing resources and widespread adaption of service-oriented architecture has made cloud as a new IT delivery mechanism. Number of cloud providers offer computing resources in the form of virtual machines to the cloud customers based on business requirements. Load experienced by the present business applications hosted in cloud are dynamic in nature. This creates a need for a mechanism which allocates resources dynamically to the applications in order to minimize performance degradations. This paper presents a mechanism which dynamically allocates the resources based on load of the application using vertical and horizontal scaling. Cloud environment is set up using Xen cloud platform and multi-tier web application is deployed on virtual machines. Experimental study conducted for various loads show that proposed mechanism ensures the response time is within the acceptable range. © Springer Science+Business Media Singapore 2016.
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    Interoperability based resource management in cloud computing by adaptive dimensional search
    (Institute of Electrical and Electronics Engineers Inc., 2017) Anithakumari, S.; Chandrasekaran, K.
    The concept of cloud computing is introduced as a new computing technology based on different computing techniques such as virtualization, which implements applications on virtual machines procreated on physical machines. The deployment of cloud computing can be of different types based on the implementation of service model and the availability of cloud services to the end users. One of the challenges need to be faced by cloud computing is related to the data interoperability and portability. Here we have established a mechanism for flexible resource allocation between the cloud service providers based on SLA mapping and clustering techniques to propose an interoperable cloud computing environment. This interoperabilityis made possible with the use of multiple techniques such as Adaptive Dimensional Search Algorithm (ADS), Clustering and SLA mapping. © 2017 IEEE.
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    Adaptive resource allocation in interoperable cloud services
    (Springer Verlag, 2019) Anithakumari, S.; Chandrasekaran, K.
    Interoperable cloud computing is the one in which the services or resources of one cloud can be accessed by another cloud. The implementation of interoperable cloud architecture is a challenging one because various characteristics of the cloud computing environment need to be considered for its achievement. The aim of this work is to implement interoperable cloud computing with the awareness of service-level agreements and to provide adequate resources when shortage of resources occurs at one cloud while providing the agreed services to the user. To achieve this, we proposed a methodology of interoperability-based flexible resource management. Initially, the SLA templates of private and public cloud are mapped using the Soft TF-IDF metric with case-based reasoning (CBR) approach. Then, based on the mapped SLAs, different clusters of cloud providers are formed with the help of K-means clustering technique. And finally, if one of the cloud in a cluster faces the problem of resource shortage, the flexible resource allocation is provided through the adaptive dimensional search algorithm. © Springer Nature Singapore Pte Ltd. 2019.
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    Allocation of cloud resources in a dynamic way using an sla-driven approach
    (Springer Verlag service@springer.de, 2019) Anithakumari, S.; Chandrasekaran, K.
    Cloud computing provides a wide access to complex applications running on virtualized hardware with its support for elastic resources that are available in an on-demand manner. In cloud environment, multiple users can request resources simultaneously and so it has to be made available to them in an efficient manner. For the efficient utilization, these computing resources can be dynamically configured according to varying workload. Here in this paper, we proposed an efficient resource management system to allocate elastic resources dynamically according to dynamic workload. © 2019, Springer Nature Singapore Pte Ltd.
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    Design and evaluation of load balanced termite: A novel load aware bio inspired routing protocol for mobile Ad Hoc network
    (Kluwer Academic Publishers, 2014) Manjappa, M.; Guddeti, G.
    Bio inspired computing based on Swarm Intelligence is successful in dealing with the networking problems such as routing, congestion and load balancing by finding an optimal path to the destination. Most of the existing bio inspired protocols for MANETs focused only on the routing problem. In this paper, a novel heuristic bio inspired routing with load balancing algorithm referred to as Load Balanced Termite (LB-Termite) is proposed for MANETs by exploiting the salient features of social insect, "Termites". The primary objective of the LB-Termite algorithm is to find the stable nodes and thereby giving preferences for these stable nodes during the path setup; thus finding the reliable route to the destination. The secondary objective of the proposed LB-Termite algorithm is to mitigate the stagnation problem by using pheromone heuristic control method. The simulation results of LB-Termite are compared with other state-of-the-art bio inspired routing algorithms (ACO based Simple Ant Routing Algorithm and the Termite algorithm) and non bio inspired (Ad Hoc on Demand Distance Vector Routing Algorithm) routing protocols for its performance evaluation and the results are found to be encouraging. © 2013 Springer Science+Business Media New York.
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    Stochastic dynamic programming model for optimal resource allocation in vehicular ad hoc networks
    (Springer India, 2018) Bhuvaneswari, M.; Paramasivan, B.; Kandasamy, A.
    Vehicular ad hoc network (VANET) is an emerging trend where vehicles communicate with each other and possibly with a roadside unit to assist various applications like monitoring, managing and optimizing the transportation system. Collaboration among vehicles is significant in VANET. Resource constraint is one of the great challenges of VANETs. Because of the absence of centralized management, there is pitfall in optimal resource allocation, which leads to ineffective routing. Effective reliable routing is quite essential to achieve intelligent transportation. Stochastic dynamic programming is currently employed as a tool to analyse, develop and solve network resource constraint and allocation issues of resources in VANET. We have considered this work as a geographical-angular-zone-based two-phase dynamic resource allocation problem with a homogeneous resource class. This work uses a stochastic dynamic programming algorithm based on relaxed approximation to generate optimal resource allocation strategies over time in response to past task completion status history. The second phase resource allocation uses the observed outcome of the first phase task completion to provide optimal viability in resulting decisions. The proposed work will be further extended for the scenario that deals with heterogeneous resource class. Simulation results show that the proposed scheme works significantly well for the problems with identical resources. © 2018, Indian Academy of Sciences.
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    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.
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    Multi-Objective Energy Efficient Virtual Machines Allocation at the Cloud Data Center
    (Institute of Electrical and Electronics Engineers, 2019) Sharma, N.K.; Guddeti, R.M.R.
    Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation. © 2019 IEEE.
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    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.