| dc.description.abstract | With the rapid development of the Internet of Things (IoT), the number of computation-
sensitive end devices has significantly increased. By offloading the computational tasks
of these devices to edge servers, edge computing has demonstrated its benefits in reduc-
ing task latency and alleviating the computational burden on cloud servers. However,
indiscriminately offloading computational tasks may lead to inefficient use of edge server
resources, resulting in increased latency and higher computational costs. Therefore, de-
signing effective task offloading and resource allocation strategies to optimize latency and
energy consumption is currently a key research focus and challenge. Reducing the
task latency and computational burden on cloud servers has become an important is-
sue. Without appropriate incentive mechanisms, edge servers may be unwilling to share
resources, making the provision of suitable rewards crucial. Traditional incentive mech-
anisms, such as auction theory and Stackelberg games, rely on frequent information ex-
change, leading to high signaling costs. Considering the risk of privacy leaks, mobile
users may be reluctant to disclose private information, resulting in information asymme-
try between cloud platforms and edge servers. Previous research often assumed that cloud
platforms have complete information about edge servers, which is not the case in prac-
tice. This paper proposes a contract incentive mechanism based on deep reinforcement
learning (DRL). Unlike traditional methods, DRL can operate without prior knowledge
of the environment’s details. DRL learns and adapts to design incentive mechanisms,
effectively motivating participants to complete tasks in dynamic and uncertain environ-
ments, achieving the maximum utility of the cloud platform. The contributions of this
paper include proposing the joint resource allocation and computation offloading incen-
tive problem under information asymmetry, systematically analyzing the necessary and
sufficient conditions for optimal contracts, formulating the contract incentive problem
as a Markov decision process under incomplete information, and designing a deep deter-
ministic policy gradient (DDPG) method to obtain computation resource and incentive
reward strategies in high-dimensional action and state spaces. | en_US |