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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

A resource scheduling method for reliable and trusted distributed composite services in cloud environment based on

Lei Yu¹, Philip S Yu², Yucong Duan³

¹Department of Computer Science, Inner Mongolia University, Hohhot, China.

Frontiers in Genetics

|October 27, 2022

Summary

This study introduces an improved Deep Q-Network algorithm for scheduling composite biological cloud services. The new method enhances efficiency, reduces completion time, and improves quality of service and resource utilization in containerized cloud environments.

Keywords:

artificial intelligence composite services container cloud deep reinforcement learning service scheduling

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Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Area of Science:

Cloud Computing
Bioinformatics
Artificial Intelligence

Background:

Internet technology drives application migration to the cloud, essential for fields like biomedicine.
Biological cloud platforms are crucial for integrating and analyzing complex biomedical data, improving efficiency and enabling intelligent processing.
Increasingly complex user requirements necessitate advanced service scheduling strategies in cloud computing.

Purpose of the Study:

To enhance the process efficiency of biological cloud services.
To address the challenges of complex and diversified user business requirements in cloud service scheduling.
To leverage deep reinforcement learning for optimizing service scheduling and resource allocation in cloud environments.

Main Methods:

Design of a composite service scheduling model using a hybrid reservation and on-demand container instance mode.
Description of composite services using a three-level structure: composite service, service, and service instance (minimum scheduling unit).
Application of an improved Deep Q-Network (DQN) algorithm for composite service scheduling in a container cloud environment.

Main Results:

The improved DQN algorithm effectively reduces the completion time of composite services.
The proposed method significantly improves the Quality of Service (QoS) in the container cloud environment.
Enhanced resource utilization is achieved within the container cloud infrastructure.

Conclusions:

The developed composite service scheduling model and improved DQN algorithm offer an effective solution for biological cloud services.
The approach successfully balances reservation and on-demand instances to meet diverse user needs.
This research contributes to more efficient and reliable biological cloud service delivery.