1,338 research outputs found
Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing
Various mobile applications that comprise dependent tasks are gaining
widespread popularity and are increasingly complex. These applications often
have low-latency requirements, resulting in a significant surge in demand for
computing resources. With the emergence of mobile edge computing (MEC), it
becomes the most significant issue to offload the application tasks onto
small-scale devices deployed at the edge of the mobile network for obtaining a
high-quality user experience. However, since the environment of MEC is dynamic,
most existing works focusing on task graph offloading, which rely heavily on
expert knowledge or accurate analytical models, fail to fully adapt to such
environmental changes, resulting in the reduction of user experience. This
paper investigates the task graph offloading in MEC, considering the
time-varying computation capabilities of edge computing devices. To adapt to
environmental changes, we model the task graph scheduling for computation
offloading as a Markov Decision Process (MDP). Then, we design a deep
reinforcement learning algorithm (SATA-DRL) to learn the task scheduling
strategy from the interaction with the environment, to improve user experience.
Extensive simulations validate that SATA-DRL is superior to existing strategies
in terms of reducing average makespan and deadline violation.Comment: 13 figure
Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum
Workflow offloading in the edge-to-cloud continuum
copes with an extended calculation network among edge
devices and cloud platforms. With the growing significance of
edge and cloud technologies, workflow offloading among these
environments has been investigated in recent years. However,
the dynamics of offloading optimization objectives, i.e., latency,
resource utilization rate, and energy consumption among the
edge and cloud sides, have hardly been researched. Consequently,
the Quality of Service(QoS) and offloading performance also
experience uncertain deviation. In this work, we propose a
multi-objective robust offloading algorithm to address this issue,
dealing with dynamics and multi-objective optimization. The
workflow request model in this work is modeled as Directed
Acyclic Graph(DAG). An LSTM-based sequence-to-sequence
neural network learns the offloading policy. We then conduct
comprehensive implementations to validate the robustness of our
algorithm. As a result, our algorithm achieves better offloading
performance regarding each objective and faster adaptation
to newly changed environments than fine-tuned typical singleobjective
RL-based offloading methods
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