A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

Abstract

With the rapid development of cloud computing and container technology, more and more applications are deployed to the cloud, and the scale of cloud platform is expanding. Due to the large number of container instances running in the platform, complex dependency relationship, fast version iteration and other characteristics, the update of business can often cause the change of the whole cloud resource environment, which triggers the repetitive scheduling problem of related tasks and affects stability of the business. In this paper, we propose a self-adapting task scheduling algorithm (ADATSA) using learning automata to solve these problems. Firstly, we design a learning automata model and objective function for the system on task scheduling problem. Then, we realize an effective reward-penalty mechanism for scheduling actions in combination with the idle state of resources and the running state of tasks in the current environment. Meanwhile, the environment is modeled by cluster, node and task, and the probability of action selected is optimized by scheduling execution, thus enhancing the adaptability to the cloud environment of the scheduling and accelerating convergence. Finally, we construct a framework of task load monitoring with buffer queue to achieve dynamic scheduling based on priority. The experimental part verifies the effectiveness of proposed algorithm with different angles such as resource imbalance degree, resource residual degree and QoS. Compared with other learning automata scheduling models such as LAEAS, non-automata technology based algorithms such as PSOS and K8S scheduling engine, ADATSA shows the better performance of environment adaptability, resource optimization efficiency and QoS in dynamic scheduling. The theoretical analysis was consistent with the experimental results.

DSTS: A hybrid optimal and deep reinforcement learning for dynamic scalable task scheduling on container cloud environment

“… To overcome the repeating scheduling issue, a self-accommodating task planning algorithm (ADATSA) is used [33]. The approach reduces the reliance of existing vibrant planning strategies on container cloud architecture and improves the connection between jobs and their runtime environments.…”

“… However, there are still certain obstacles to be solved in this project. Furthermore, the study literature [21]- [33] lacks methods and models that enable dynamic scalability, in which consumers get QoS and good performance while using the fewest amount of cloud resources possible, particularly for containerized services hosted on the cloud [30]- [32].…”

“…Problem statement Learning automata are used to suggest a self-accommodating duty scheduling algorithm (ADATSA)[33]. In conjunction through the futile formal of resources and the in succession stage of responsibilities in the present surroundings, the algorithm efficiently leveraged the re-inforcement educating capacity of learning mechanisms and achieves an operative remuneration-fine system for arranging activities.…”

DSTS: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment

“…• Learning automata are used to suggest a self-accommodating duty scheduling algorithm (ADATSA) [33]. In conjunction through the futile formal of resources and the in succession stage of responsibilities in the present surroundings, the algorithm efficiently leveraged the re-enforcement educating capacity of learning mechanisms and achieves an operative remuneration-fine system for arranging activities.…”

“…• However, there are still certain obstacles to be solved in this project. Furthermore, the study literature [21][22][23][24][25][26][27][28][29][30][31][32][33]35] lacks methods and models that enable dynamic scalability, in which consumers get QoS and good performance [36] while using the fewest amount of cloud resources possible, particularly for containerized services hosted on the cloud. • Cloud computing services benefit from dynamic scalability, which provides on-demand, timely, and dynamically changeable computing resources.…”