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.
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SOURCE WiMi Hologram Cloud Inc.