Abstract
Despite increased cloud service providers following advanced cloud infrastructure management, substantial execution time is lost due to minimal server usage. Given the importance of reducing total execution time (makespan) for cloud service providers (as a vital metric) during sustaining Quality-of-Service (QoS), this study established an enhanced scheduling algorithm for minimal cloudlet scheduling (CS) makespan with the deep Q-network (DQN) algorithm under MCS-DQN. A novel reward function was recommended to enhance the DQN model convergence. Additionally, an open-source simulator (CloudSim) was employed to assess the suggested work performance. Resultantly, the recommended MCS-DQN scheduler revealed optimal outcomes to minimise the makespan metric and other counterparts (task waiting period, resource usage of virtual machines, and the extent of incongruence against the algorithms).
Highlights
Cloud computing denotes an established shared-computing technology that dynamically conveys measurable on-demand services over the global network [1]
In [20], Che et al recommended a novel TS model with the deep Reinforcement learning (RL) (DRL) algorithm that incorporated TS into resource-utilisation (RU) optimisation. e recommended scheduling model that was evaluated against conventional TS algorithms in experiments demonstrated a higher model performance of the defined metrics. Another task scheduler under the DRL architecture was suggested by Dong et al [21] for minimal task execution time with a preceding dynamic link to cloud servers. e RLTS algorithm reflected that RLTS could efficiently resolve TS in a cloud manufacturing setting
The MCSDQN scheduler recommended TS problem enhancement and metric optimisation. e simulation outcomes revealed that the presented work attained optimal performance for minimal waiting time and makespan and maximum resource employment
Summary
Cloud computing denotes an established shared-computing technology that dynamically conveys measurable on-demand services over the global network [1]. E CS (task scheduling or TS) outlined independent task mapping processes on a set of obtainable resources within a cloud context (for workflow applications) for execution within users’ specified QoS restrictions (makespan and cost). Optimal resource identification for every workflow task (to fulfil user-defined QoS) was widely studied over the years, substantial intricacies required further research:. (1) e TS on a cloud computing platform is an acknowledged NP-hard problem (2) Multiple TS optimisation objectives are evident: completion time reduction and high resource usage for the entire task queue (3) Cloud resource dynamics, measurability, and heterogeneity resulted in high complexity. Scientific Programming outcomes by comparing TS measures (waiting time, makespan reduction, and enhanced resource usage). E remaining sections are arranged as follows: Section 2 outlines pertinent literary works, Section 3 presents the DQN algorithm, Section 4 highlights the recommended work, Section 5 explains the research experiment setup and simulation outcomes, and Section 6 offers the study conclusion
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