WiMi Developed Deep Reinforcement Learning-Based Task Scheduling Algorithm in Cloud Computing

Rhea-AI Summary

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) developed a deep reinforcement learning-based task scheduling algorithm in cloud computing to enhance performance and resource utilization. The algorithm uses deep reinforcement learning to transform the task scheduling problem into a reinforcement learning problem, achieving adaptivity, nonlinear modeling, end-to-end learning, and generalization ability. WiMi's algorithm has significantly improved task scheduling effectiveness and system performance.

BEIJING, Dec. 6, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed a deep reinforcement learning-based task scheduling algorithm in cloud computing to improve the performance and resource utilization of cloud computing systems. Deep reinforcement learning can solve complex decision-making problems by learning and optimizing strategies. By using deep reinforcement learning, the task scheduling problem can be transformed into a reinforcement learning problem by training a deep neural network to learn the optimal strategy for task scheduling. The advantage of reinforcement learning is that it can automatically adjust the policy according to the changes in the environment and can be adapted to complex task scheduling scenarios. Deep reinforcement learning has the advantages of adaptivity, nonlinear modeling, end-to-end learning, and generalization ability in task scheduling, and it can comprehensively consider factors such as the execution time of the task, the resource demand, the load situation of the virtual machine, and the network latency, so as to carry out the task scheduling more accurately, and to improve the performance of the system and the utilization rate of resources.

WiMi's deep reinforcement learning-based task scheduling algorithm in cloud computing includes state representation, action selection, reward function and training and optimization of the algorithm. State representation is an important link. By transforming various information in the cloud computing environment into a form that can be processed by the machine learning model, it can help the model to better understand the current task scheduling situation, so as to make more reasonable and accurate task scheduling decisions. Action selection is also a key step, where at each time step, the agent needs to select an action to execute to decide the task scheduling strategy at the current moment. Such an algorithm can select an optimal action based on the current system state to achieve efficient cloud computing task scheduling. The reward function, on the other hand, is used to evaluate the reward value obtained by the agent after executing an action, which in turn guides the decision-making process of the agent. The reward function can enable the agent to learn and optimize better during the task scheduling process.

In addition, the training and optimization of the deep reinforcement learning-based task scheduling algorithm in cloud computing are also very critical. First, a reinforcement learning environment applicable to the task scheduling problem needs to be constructed, including the definition of states, actions and reward functions. The state can include information such as the current system load situation, attributes and priority of the task; the action can choose to assign the task to a certain virtual machine or decide whether to delay the processing of the task; and the reward function can be defined based on the completion time of the task, resource utilization and other metrics. The algorithm is then trained using a deep reinforcement learning algorithm such as Deep Q-Network (DQN), a neural network-based reinforcement learning algorithm that can make decisions by learning a value function. During the training process, by interacting with the environment, the algorithm continuously updates the parameters of the neural network to optimize the decision-making strategy for task scheduling. In addition, some optimization techniques, such as experience playback and objective networks, can be used to further improve the performance and stability of the algorithm. Through continuous training and optimization, the algorithm will gradually learn the optimal strategy for task scheduling, thus improving the performance and efficiency of the system.

The deep reinforcement learning-based task scheduling algorithm in cloud computing has achieved significant improvements in both task scheduling effectiveness and system performance. There are still some research directions that can be further explored in this technology area. In the future, WiMi will improve the performance and adaptability of the deep reinforcement learning-based task scheduling algorithm in cloud computing through multi-objective optimization, adaptation in dynamic environments, model uncertainty handling, real-time decision making, and improving the algorithm to provide better support for practical applications.

About WIMI Hologram Cloud

WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

View original content:https://www.prnewswire.com/news-releases/wimi-developed-deep-reinforcement-learning-based-task-scheduling-algorithm-in-cloud-computing-302007268.html

SOURCE WiMi Hologram Cloud Inc.

FAQ

What did WiMi Hologram Cloud Inc. (NASDAQ: WIMI) announce?

WiMi announced the development of a deep reinforcement learning-based task scheduling algorithm in cloud computing to improve performance and resource utilization.

What is the advantage of using deep reinforcement learning in task scheduling?

Deep reinforcement learning provides adaptivity, nonlinear modeling, end-to-end learning, and generalization ability in task scheduling, allowing for more accurate and efficient task scheduling decisions.

How does WiMi's algorithm improve task scheduling effectiveness?

The algorithm comprehensively considers factors such as task execution time, resource demand, virtual machine load situation, and network latency to carry out more accurate task scheduling, thus improving system performance and resource utilization.

What are some critical components of WiMi's deep reinforcement learning-based task scheduling algorithm?

The critical components include state representation, action selection, reward function, and training and optimization of the algorithm.

What are the future research directions for WiMi's technology?

WiMi plans to improve the algorithm's performance and adaptability through multi-objective optimization, adaptation in dynamic environments, model uncertainty handling, real-time decision making, and better support for practical applications.