Deo Prakash Vidyarthi
Other affiliations: Banaras Hindu University
Bio: Deo Prakash Vidyarthi is an academic researcher from Jawaharlal Nehru University. The author has contributed to research in topics: Cloud computing & Scheduling (computing). The author has an hindex of 21, co-authored 137 publications receiving 1526 citations. Previous affiliations of Deo Prakash Vidyarthi include Banaras Hindu University.
Papers published on a yearly basis
TL;DR: This work has used a simple GA to optimize the reliability of DCS with task allocation and has shown that Genetic Algorithm has emerged as a successful tool for optimization purposes.
Abstract: Reliability is one of the very important characteristics of the distributed computing system (DCS), and articles on task allocation (an NP-Hard problem) to maximize the reliability of DCS have appeared in the past [S. Kartik, C.S. Ram Murthy, IEEE Trans. Comput. 46 (6) (1997) 719; S.M. Shatz, Wang, Goto, IEEE Trans. Comput. 41 (90) (1992) 1156]. Genetic Algorithm (GA) has emerged as a successful tool for optimization purposes. We, in this work, have used a simple GA to optimize the reliability of DCS with task allocation.
TL;DR: This work proposes a multi-attribute combinatorial double auction for the allocation of Cloud resources, which not only considers the price but other quality of service parameters also, which reflects the usefulness of the method.
Abstract: Recently, Cloud computing has emerged as a market where computing related resources are treated as a utility and are priced. There is a big competition among the Cloud service providers and therefore, the providers offer the services strategically. Auction, a market based resource allocation strategy, has received the attention among the Cloud researchers recently. The auction principal of resource allocation is based on demand and supply. This work proposes a multi-attribute combinatorial double auction for the allocation of Cloud resources, which not only considers the price but other quality of service parameters also. Auctioneer extends some of the parameters to the offered bids from the bidders in order to provide fairness and robustness. In case of not meeting the assured quality, a penalty is imposed on the provider and customer is compensated. The reputation of the provider also diminishes in the forthcoming rounds. Performance study of the proposed model is done by simulation which reflects the usefulness of the method.
TL;DR: A green routing algorithm using fork and join adaptive particle swarm optimization (FJAPSO) is proposed to maximize the lifetime of sensor network and outperforms other state of the art and significantly maximizes the life of the sensor network.
Abstract: Rapid growth in the domain of Internet of Things (IoT) leads to massive deployment of sensors and therefore the need to develop automatically reconfigurable complex wireless sensor network. Software defined networking (SDN) is a promising and widely adopted technique to automatic reconfigure the wireless sensor network. In SDN, control nodes are dynamically selected (to activate the functioning of the sensor network) in order to assign the tasks to other nodes and for routing data packets to control server. For residual energy of sensor nodes, and transmission distance among sensor nodes, the problem of control node selection has been formulated as an NP-hard problem. Usually, smart sensing devices of IoT suffers from low battery which are not frequently rechargeable. Therefore, an energy efficient routing mechanism is required to operate software defined wireless sensor network (SDWSN). In this paper, a green routing algorithm using fork and join adaptive particle swarm optimization (FJAPSO) is proposed to maximize the lifetime of sensor network. FJAPSO acts at two levels for auto optimization: optimal number of control nodes and optimal clustering of control nodes. Experimental results evidenced that FJAPSO outperforms other state of the art and significantly maximizes the lifetime of the sensor network.
TL;DR: The Genetic Algorithm, for this particular problem, is improved by introducing a new genetic operator, named pluck, that incorporates a problem-specific knowledge in population generation and leads to a better channel utilization by reducing the average blocked hosts.
Abstract: This paper exploits the potential of the Genetic Algorithm to solve the cellular resource allocation problem. When a blocked host is to be allocated to a borrowable channel, a crucial decision is which neighboring cell to choose to borrow a channel. It is an optimization problem and the genetic algorithm is efficiently applied to handle this. The Genetic Algorithm, for this particular problem, is improved by introducing a new genetic operator, named pluck, that incorporates a problem-specific knowledge in population generation and leads to a better channel utilization by reducing the average blocked hosts. The pluck operator makes the crucial decision of when and which cell to borrow with the future consideration that the borrowing should not lead the network to chaos. It makes a channel borrowing decision that minimizes the number of blocked hosts and improves the long-term performance of the network. Efficacy of the proposed method has been evaluated by experimentation.
TL;DR: This work presents a novel hybrid meta-heuristic that combines particle swarm optimization and genetic algorithm (PSO–GA) for the job/tasks in the form of directed acyclic graph (DAG) exhibiting inter-task communication.
Abstract: This work presents a novel hybrid meta-heuristic that combines particle swarm optimization and genetic algorithm (PSO---GA) for the job/tasks in the form of directed acyclic graph (DAG) exhibiting inter-task communication. The proposed meta-heuristic starts with PSO and enters into GA when local best result from PSO is obtained. Thus, the proposed PSO---GA meta-heuristic is different than other such hybrid meta-heuristics as it aims at improving the solution obtained by PSO using GA. In the proposed meta-heuristic, PSO is used to provide diversification while GA is used to provide intensification. The PSO---GA is tested for task scheduling on two standard well-known linear algebra problems: LU decomposition and Gauss---Jordan elimination. It is also compared with other states-of-the-art heuristics for known solutions. Furthermore, its effectiveness is evaluated on few large sizes of random task graphs. Comparative study of the proposed PSO-GA with other heuristics depicts that the PSO---GA performs quite effectively for multiprocessor DAG scheduling problem.
01 Jan 2003
13 Mar 2012
Abstract: System, method, and tangible computer-readable storage media are disclosed for providing a brokering service for compute resources The method includes, at a brokering service, polling a group of separately administered compute environments to identify resource capabilities and information, each compute resource environment including the group of managed nodes for processing workload, receiving a request for compute resources at the brokering service system, the request for compute resources being associated with a service level agreement (SLA) and based on the resource capabilities across the group of compute resource environments, selecting compute resources in one or more of the group of compute resource environments The brokering service system receives workload associated with the request and communicates the workload to the selected resources for processing The brokering services system can aggregate resources for multiple cloud service providers and act as an advocate for or a guarantor of the SLA associated with the workload
TL;DR: The proposed African Vultures Optimization Algorithm (AVOA) is named and simulates African vultures’ foraging and navigation behaviors and indicates the significant superiority of the AVOA algorithm at a 95% confidence interval.
Abstract: Metaheuristics play a crucial role in solving optimization problems. The majority of such algorithms are inspired by collective intelligence and foraging of creatures in nature. In this paper, a new metaheuristic is proposed inspired by African vultures' lifestyle. The algorithm is named African Vultures Optimization Algorithm (AVOA) and simulates African vultures' foraging and navigation behaviors. To evaluate the performance of AVOA, it is first tested on 36 standard benchmark functions. A comparative study is then conducted that demonstrates the superiority of the proposed algorithm compared to several existing algorithms. To showcase the applicability of AVOA and its black box nature, it is employed to find optimal solutions for eleven engineering design problems. As per the experimental results, AVOA is the best algorithm on 30 out of 36 benchmark functions and provides superior performance on the majority of engineering case studies. Wilcoxon rank-sum test is used for statistical evaluation and indicates the significant superiority of the AVOA algorithm at a 95% confidence interval.
TL;DR: The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.
Abstract: In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.