Task scheduling in cloud computing poses a significant challenge due to the diverse range of tasks with varying lengths and runtime capacities. Precisely allocating these tasks to suitable virtual reso

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments Using A3C Learning and Residual Recurrent Neural Networks (2020) · doi

  Efficiently utilizing edge and cloud resources to provide a better QoS and response time in stochastic environ- ments with dynamic workloads is a complex problem. This problem is complicated further due to the heterogeneity of multi-layer resources and difference in response times of devices in Edge-Cloud datacenters. Integrated usage of cloud and edge is a non-trivial problem as resources and network have completely different characteristics when users or edge-nodes are mobile. Prior work not only fails to consider these differences in edge and cloud devices but also ignores the effect of stochastic workloads and dynamic environments. This work aims to provide an end- to-end real-time task scheduler for integrated edge and cloud computing environments. We propose a novel A3C- R2N2 based scheduler that can consider all important pa- rameters of tasks and hosts to make scheduling decisions to provide better performance. Furthermore, A3C allows the scheduler to quickly adapt to dynamically changing environments using asynchronous updates, and R2N2 is able to quickly learn network weights also exploiting the temporal task/workload behaviours. Extensive simulation experiments using iFogSim and CloudSim on real-world Bitbrain dataset show that our approach can reduce energy consumption by 14.4%, response time by 7.74%, SLA vio- lations by 31.9% and cost by 4.64%. Moreover, our model has a negligible scheduling overhead of 0.002% compared to the existing baseline which makes it a better alternative for dynamic task scheduling in stochastic environments. As part of future work, we plan to implement this model in real edge-cloud environments. Implementation in real environments would require constant profiling CPU, RAM and disk requirements of new tasks. This can be done using exponential averaging of requirement values in the current scheduling interval with the average computed in the pre- vious interval. Further, the CPU, RAM, disk and bandwidth usage would have to be collected and synchronized across all A3C agents in the edge-cloud setup. Further to the scalablity analysis, we also plan to conduct tests to check the scalability of the proposed framework with number of hosts and tasks. The current model can schedule for a fixed number of edge nodes and tasks. However, upcoming scalable reinforcement learning models like Impala [56] can be investigated in future. Moreover, we plan to investigate the data privacy and security aspects in future. SOFTWARE AVAILABILITY Our code, experiment scripts and raw result files are avail- able online under GPL-3.0 License at: https://github.com/ Cloudslab/DLSF. ACKNOWLEDGEMENTS This research work is supported by the Melbourne-Chindia Cloud Computing (MC3) Research Network and the Aus- tralian Research Council. REFERENCES [1] R. Mahmud, S. N. Srirama, K. Ramamohanarao, and R. Buyya, “Quality of Experience (QoE)-aware placement of applications in Fog computing environments,” Journal of Parallel and Distributed Co

  Keywords: edge cloud environments real tasks scheduling resources provide better response time stochastic dynamic problem further
• Federated multi-cloud task scheduling with load balancing using multi-objective NSGA-II and reinforcement learning (2026) · doi

  This research introduced the hybrid scheduling framework MO-NSGAQ to address the challenges of task scheduling in federated cloud settings. It combines NSGA-II with Q-learning. By combining multi-objective optimization with RL, MO-NSGAQ effectively balances trying new things and learning, unlike traditional schedulers that rely on fixed rules or only evolutionary techniques. The proposed framework dynamically adapts to changing workloads, resource restrictions, and inter-cloud heterogeneity ARTICLE IN PRESSARTICLE IN PRESS ACCEPTED MANUSCRIPT owing to this hybrid combination. The proposed framework provided better results when compared with existing approaches, including MOPSO, MOABCQ, Max-Min, and FCFS. Comprehensive simulation runs are conducted on three different datasets, such as synthetic workloads, Google Cloud traces, and real-time IoT workloads. With high throughput, MO-NSGAQ improved significantly in other metrics such as makespan reduction, cost efficiency, load balancing, and resource utilization. Adaptability and intelligent feedback are important in complicated, multi- provider environments, where the performance increases are most considerable. A wide variety of distributed computing systems benefit from MO-NSGAQ's design, not just federated cloud scheduling. It can be used in edge-cloud hybrid systems, smart grid task management, fog computing, and managing sustainable data centres, where there are often conflicting goals and situations. It is a flexible option for intelligent infrastructure of the future because of its learning-driven flexibility and Pareto-optimal reasoning. In the future there are several ways this study may have a greater impact, including deep reinforcement learning integration, SLA-aware scheduling, energy and carbon-aware optimisation, real-time deployment in federated testbeds, and multi-agent Q-learning extensions. Future work will also include comparisons with existing DRL-based schedulers, as well as obvious evaluation of inter-cloud communication overhead, SLA violation rates, and scheduling decision latency to assess real-world deployability.

  Keywords: cloud scheduling learning nsgaq hybrid framework federated multi workloads real future task schedulers proposed resource
• An efficient deep reinforcement learning based task scheduler in cloud-fog environment (2024) · doi

  Task scheduling in cloud computing poses a significant challenge due to the diverse range of tasks with varying lengths and runtime capacities. Precisely allocating these tasks to suitable virtual resources is particularly difficult, especially when dealing with computationally intensive and time-sensitive tasks. Thus, we choose for Fog computing as an expansion to schedule heterogeneous jobs that are both time-sensitive and need significant computa- tional resources, depending on the priorities determined at the task manager level. This study introduces a multi-ob- jective task scheduler that prioritizes workloads and virtual machines based on energy efficiency and fault tolerance. The scheduler is coupled with a DQN model, which utilizes proposed reinforcement techniques. learning

  Keywords: task tasks computing signi cant virtual resources time sensitive scheduler scheduling cloud poses challenge diverse