Multi-level Hierarchical Controller Assisted Task Scheduling and Resource Allocation in Large Cloud Infrastructures

The high-pace emergence in Cloud Computing technologies demands and alarmed academia-industries to attain Quality-of-Service (QoS) oriented solutions to ensure optimal network performance in terms of Service Level Agreement (SLA) provision as well as Energy-Efficiency. Majority of the at-hand solutions employ Virtual Machine Migration to perform dynamic resource allocation which fails in addressing the key problem of SLA-sensitive scheduling where it demands timely and reliable task-migration solution. Undeniably, VM consolidation may help achieve energy-efficiency along with dynamic resource allocation where the classical heuristic methods which are often criticized for its local minima and premature convergence doesn’t guarantee the optimality of the solution, especially over large cloud infrastructures. Considering these key problems as motivation, in this paper a highly robust and improved meta-heuristic model based on Ant Colony System is developed to achieve Task Scheduling and Resource Allocation. CloudSim based simulation over different PlanetLab cloud traces exhibited superior performance by the proposed task-scheduling model in terms of negligible SLA violence, minimum downtime, minimum energy-consumption and higher number of migrations over other heuristic variants, which make it suitable towards realistic Cloud Computing purposes.

@article{S2021,
  title     = {Multi-level Hierarchical Controller Assisted Task Scheduling and Resource Allocation in Large Cloud Infrastructures},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {11},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Jyothi S and B S Shylaja},
  doi       = {10.14569/IJACSA.2021.0121144},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121144}
}