Manash Kumar Mondal
@klyuniv.ac.in
Computer science and engineering
University of Kalyani
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Science Applications, Computer Science, Computer Networks and Communications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, Utpal Biswas, Jerry Chun-Wei Lin, Osama Alfarraj, and Amr Tolba
MDPI AG
Elephants are one of the largest animals on earth and are found in forests, grasslands and savannahs in the tropical and subtropical regions of Asia and Africa. A country like India, especially the northeastern region, is covered by deep forests and is home to many elephants. Railroads are an effective and inexpensive means of transporting goods and passengers in this region. Due to poor visibility in the forests, collisions between trains and elephants are increasing day by day. In the last ten years, more than 190 elephants died due to train accidents. The most effective solution to this collision problem is to stop the train immediately. To address this sensitive issue, a solution is needed to detect and monitor elephants near railroad tracks and analyze data from the camera trap near the intersection of elephant corridors and railroad tracks. In this paper, we have developed a fog computing-based framework that not only detects and monitors the elephants but also improves the latency, network utilization and execution time. The fog-enabled elephant monitoring system informs the train control system of the existence of elephants in the corridor and a warning light LED flashes near the train tracks. This system is deployed and simulated in the iFogSim simulator and shows improvements in latency, network utilization, and execution time compared to cloud-based infrastructures.
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Gautam Srivastava, Waleed Alnumay, Uttam Ghosh, and Utpal Biswas
Springer Science and Business Media LLC
Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, U. Biswas, Pushpita Chatterjee and Waleed S. Alnumay
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Pushpita Chatterjee, Wathiq Mansoor, and Utpal Biswas
IEEE
Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Pushpita Chatterjee, Wathiq Mansoor, and Utpal Biswas
IEEE
The pay-per-use model of cloud computing is booming in every industry. Power-hungry large heterogeneous data centers are providing these cloud services for 24× 7. Green cloud computing offers an energy-efficient and environment-friendly mechanism to offer cloud services while maintaining the quality of service (QoS). QoS is defined in terms of service-level agreement (SLA). VM Consolidation is one of the widely used frameworks that involve Virtual Machine (VM) migration and VM Placement to reduce energy consumption and maintain better QoS. Using the VM Placement policy VMs are allocated to suitable physical machines (PM). Efficient VM placement with better resource management can conserve electricity and provide better QoS. A new SLA and energy-aware VM placement policy have been developed in this research. The VMs are placed in a PM having the least energy consumption and SLA violation (SLAV). Further, the proposed VM placement policy has been implemented using CloudSim and it has been shown that the proposed VM placement policy outperforms state-of-art VM placement policies like PEBFD, MBFD, MFPED, and PEFED.
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, and Utpal Biswas
Springer Science and Business Media LLC
With the rapid demand for service-oriented computing in association with the growth of cloud computing technologies, large-scale virtualized data centers have been established throughout the globe. These huge data centers consume power at a large scale that results in a high operational cost. The massive carbon footprint from the energy generators is another great issue to deal global warming. It is essential to lower the rate of carbon emission and energy consumption as much as possible. The live-migration-enabled dynamic virtual machine consolidation results in high energy saving. But it also incurs the violation of service level agreement (SLA). Excessive migration may lead to performance degradation and SLA violation. The process of VM selection for migration plays a vital role in the domain of energy-aware cloud computing. Using VM selection policies, VMs are selected for migration. A new power-aware VM selection policy has been proposed in this research that helps in VM selection for migration. The proposed power-aware VM selection policy has been further evaluated using trace-based simulation environment.
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Chinmay Chakraborty, and Utpal Biswas
Elsevier