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.

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing.

Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.

Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization.

Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.

EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework.

Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO's potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique's performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization.

Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency.