http://hdl.handle.net/2080/20 2026-04-10T00:40:19Z http://hdl.handle.net/2080/3601 Title: Sustainable Service Allocation using Metaheuristic Technique in Fog Server for Industrial Applications Authors: Mishra, Sambit Kumar; Puthal, Deepak; Rodrigues, Joel J. P. C.; Sahoo, Bibhudatta; Dutkiewicz, Eryk Abstract: Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or health care industry required rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a non-deterministic polynomial-time hard (NP-hard) problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a trade-off is maintained between the energy consumption and makespan. Specifically, this paper propose a metaheuristic-based service allocation framework using three metaheuristic techniques, such as Particle Swarm Optimization (PSO), Binary PSO (BPSO) and Bat algorithm (BAT). These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic based service allocation algorithms by conducting a set of rigorous evaluations. Description: Copyright of this document belongs to journal publisher. 2018-01-01T00:00:00Z http://hdl.handle.net/2080/3600 Title: Hybrid neural network approach for predicting maintainability of object-oriented software Authors: Kumar, L; Rath, S K Abstract: Estimation of different parameters for object-oriented systems development such as effort, quality, and risk is of major concern in software development life cycle. Majority of the approaches available in literature for estimation are based on regression analysis and neural network techniques. Also it is observed that numerous software metrics are being used as input for estimation. In this study, object-oriented metrics have been considered to provide requisite input data to design the models for prediction of maintainability using three artificial intelligence (AI) techniques such as neural network, Neuro-Genetic (hybrid approach of neural network and genetic algorithm) and Neuro-PSO (hybrid approach of neural network and Particle Swarm Optimization). These three AI techniques are applied to predict maintainability on two case studies such as User Interface System (UIMS) and Quality Evaluation System (QUES). The performance of all three AI techniques were evaluated based on the various parameters available in literature such as mean absolute error (MAE) and mean Absolute Relative Error (MARE). Experimental results show that the hybrid technique utilizing Neuro-PSO technique achieved better result for prediction of maintainability when compared with the other two. 2014-01-01T00:00:00Z http://hdl.handle.net/2080/3599 Title: An Adaptive Task Allocation Technique for Green Cloud Computing Authors: Mishra, Sambit Kumar; Puthal, Deepak; Sahoo, Bibhudatta; Jena, Sajay Kumar Abstract: The rapid growth of todays IT demands reflects the increased use of cloud data centers. Reducing computational power consumption in cloud data center is one of the challenging research issues in the current era. Power consumption is directly proportional to a number of resources assigned to tasks. So, the power consumption can be reduced by a demotivating number of resources assigned to serve the task. In this paper, we have studied the energy consumption in cloud environment based on varieties of services and achieved the provisions to promote green cloud computing. This will help to preserve overall energy consumption of the system. Task allocation in the cloud computing environment is a well-known problem, and through this problem, we can facilitate green cloud computing. We have proposed an adaptive task allocation algorithm for the heterogeneous cloud environment. We applied the proposed technique to minimize the makespan of the Cloud system and reduce the energy consumption. We have evaluated the proposed algorithm in CloudSim simulation environment and simulation results show that our proposed algorithm is energy efficient in cloud environment compared to other existing techniques. Description: Copyright of this document belongs to journal publisher. 2018-01-01T00:00:00Z http://hdl.handle.net/2080/3429 Title: Wettability, Thermal and Sliding Behavior of Thermally Sprayed Fly Ash Premixed Red Mud Coatings on Mild Steel Authors: Sutar, Harekrushna; Mishra, Birupakshya; Murmu, Rabiranjan; Patra, Sangram; Patra, Sarat Chandra; Mishra, Subash Chandra; Roy, Debashis Abstract: The present experimental work reveals the surface characteristics like wettability, thermal and sliding wear behaviour of plasma-sprayed red mud (RM) coatings premixed with fly ash (FA). Varying weight % of FA (10, 20, 30 and 40)—RM composite powder is used as precursor for coating. Atmospheric plasma-sprayed coatings are developed at different operating power like 5 kW, 10 kW, 15 kW and 20 kW separately on mild steel substrate. Tribological behaviour viz. sliding wear properties are studied at distinct operating load (10N, 15N, 20N, 25N), speed (40 rpm, 50 rpm, 60 rpm, 70 rpm) and track diameter of 100 mm using a pin on disc tribometer for duration of 30 minutes with 3 minute gap period for each experiment. The DSC and TGA experiments of the coatings are performed to understand the high temperature application areas. The contact angle result signifies the wettability of the prepared coatings is principally a function of composition. The reaction of surface roughness and spraying power is insignificant on water contact angle (WCA). In conclusion, the sliding wear experiments are optimized by Taguchi method to ascertain the influencing parameter on wear. Description: Materials Sciences and Applications, 2020, 11, 12-26 https://doi.org/10.4236/msa.2020.111002 2019-12-01T00:00:00Z