Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment.

Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.

Theoretical study on the physical properties of synthesized SrMO3 (M = Hf and Pt) oxide perovskites using DFT.

We investigated the physical behavior of SrMO3 (M = Hf and Pt) compounds, which are strontium-based oxide perovskites. We utilized the WIEN2k software to simulate and investigate their physical properties. The structural stability of SrHfO3 and SrPtO3 was verified using the Birch-Murnaghan equation of states for optimization. We also checked the elastic stability through the computation of elastic constants using the IRelast software. Our results indicate the stability of these compounds and showed their anisotropic, ductility, scratch-resistive, and plastic strain-resistant characteristics. Using the TB-mBJ potential approach, we determined that SrHfO3 is an insulator, whereas SrPtO3 is a metal in nature. The density of states computations was used to find the band structure as well as the contribution of different electronic states. Optical property research was conducted using the band gap energies of these substances. Our findings suggest that these crystals have low energy absorption and reflectivity of up to 65%, making them suitable for use in high-frequency UV devices. Specifically, SrHfO3 is more transparent before the energy point 2.80 eV, while the compound SrPtO3 after 6.50 eV to 12.0 eV and SrHfO3 from 12.0 and 14.0 eV. This study represents the first DFT-based investigation of these discussed crystals according to the best of our knowledge.