Mahantesh G. Kambalimath

Work place: Electronics and Instrumentation Engineering Department Basaveshwar Engineering College (Autonomous), Bagalkot - 587102, Karnataka, India



Research Interests: Computer systems and computational processes, Autonomic Computing, Computer Networks, Mathematics of Computing


Mr. Mahantesh G Kambalimath. received his B. E (Instrumentation Technology) from Karnataka University, and M. Tech (CSE) from Visvesvaraya Technological University (VTU), Belgaum. Presently working as Assistant Professor in the Department of Electronics and Instrumentation Engineering, Basaveshwar Engineering College, Bagalakot.  His research interests are in the area of Vehicular Ad hoc Networks and cloud computing.

Author Articles
Cost Optimization based Resource Allocation Scheme for Vehicular Cloud Networks

By Mahantesh G. Kambalimath Mahabaleshwar S. Kakkasageri

DOI:, Pub. Date: 8 Apr. 2020

Efficient Resource management in an Vehicular Cloud Networks (VCN) results in an increase resource utilization and reduction of the cost. Proper resource allocation schemes in VCN provides the better performance in terms the reduction of cost, reduction in the waiting time of vehicle (client) and also the waiting queue length. Resources are required to provide more efficiently by the cloud providers for the requested services by the vehicle. For this reason it is necessary to design proper resource allocation schemes in VCN. The aim of resource allocation scheme in VCN is to allocate the appropriate computing resources for the client vehicle application. Efficient resource allocation scheme in VCN plays a major role in the overall performance of the system. Members of VCN change dynamically due to the mobility in their movement. Vehicles may face high costs or issue related to the performance parameter when proper resource allocation schemes are not applied. In this work, we proposed the cost effective based resource allocation in VCN. The proposed cost model provides the resource to vehicle by considering the lesser expensive approach hence by achieving in the reduction of cost. We compare the results of the cost optimization with the generic algorithm that uses a combination of best fit and first fit techniques for resource allocation in VCN.

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Dynamic Resource Discovery Scheme for Vehicular Cloud Networks

By Mahantesh G. Kambalimath Mahabaleshwar S. Kakkasageri

DOI:, Pub. Date: 8 Dec. 2019

To discover computing resources available for any application before they are allocated to requests dynamically on demand, developing effective mechanism for resource discovery in Vehicular Cloud Networks (VCN) is very important. Providing the services to the requested vehicle in time is a major concern in the VCN environment. Dynamic and intelligent resource discovery schemes are essential in VCN environment so that services are provided to the vehicles in time. Resource discovery is key characteristic of VCN. VCN requires intelligent algorithms for resource discovery. Creating a mechanism for resource management and search resources is the largest challenge in VCN. There is a need to consider for dynamic way to discover the resources in the VCN. The lack of intelligence in resource handling, less flexible for dynamic simultaneous requests, and low scalability are issues to be addressed for the resource discovery in VCN. In this paper we proposed dynamic resource discovery scheme in VCN. Proposed resource discovery scheme uses Honey Bee Optimization (HBO) technique integrated with static and mobile agents. Mobile agent collects the vehicular cloud information and static agent intelligently identifies the required resources by the vehicle. Dynamic discovery model will take into account different parameters influencing the task execution time to optimize subsequent schedule. To test the performance effectiveness of the scheme, proposed dynamic resource discovery scheme is compared with fixed time scheduling algorithm. The objective of the proposed scheme is to search the resources in VCN with a minimum delay.  The simulation results of the proposed scheme is better than the existing scheme.

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