Performance interference of virtual machines: A survey
The rapid development of cloud computing with virtualization technology has benefited both
academia and industry. For any cloud data center at scale, one of the primary challenges is …
academia and industry. For any cloud data center at scale, one of the primary challenges is …
Task placement and resource allocation for edge machine learning: A gnn-based multi-agent reinforcement learning paradigm
Machine learning (ML) tasks are one of the major workloads in today's edge computing
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
Tapfinger: Task placement and fine-grained resource allocation for edge machine learning
Machine learning (ML) tasks are one of the major workloads in today's edge computing
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
A global DAG task scheduler using deep reinforcement learning and graph convolution network
Parallelization of tasks and efficient utilization of processors are considered important and
challenging in operating large-scale real-time systems. Recently, deep reinforcement …
challenging in operating large-scale real-time systems. Recently, deep reinforcement …
A reinforcement learning-based approach for online optimal control of self-adaptive real-time systems
B Haouari, R Mzid, O Mosbahi - Neural Computing and Applications, 2023 - Springer
This paper deals with self-adaptive real-time embedded systems (RTES). A self-adaptive
system can operate in different modes. Each mode encodes a set of real-time tasks. To be …
system can operate in different modes. Each mode encodes a set of real-time tasks. To be …
A novel algorithm for priority-based task scheduling on a multiprocessor heterogeneous system
RM Sahoo, SK Padhy - Microprocessors and Microsystems, 2022 - Elsevier
Task scheduling is a major challenging issue in parallel and distributed heterogeneous
computing systems. The task scheduling problem in a heterogeneous multiprocessor system …
computing systems. The task scheduling problem in a heterogeneous multiprocessor system …
Task scheduling based on adaptive priority experience replay on cloud platforms
C Li, W Gao, L Shi, Z Shang, S Zhang - Electronics, 2023 - mdpi.com
Task scheduling algorithms based on reinforce learning (RL) have been important methods
with which to improve the performance of cloud platforms; however, due to the dynamics and …
with which to improve the performance of cloud platforms; however, due to the dynamics and …
An efficient combinatorial optimization model using learning-to-rank distillation
Recently, deep reinforcement learning (RL) has proven its feasibility in solving combinatorial
optimization problems (COPs). The learning-to-rank techniques have been studied in the …
optimization problems (COPs). The learning-to-rank techniques have been studied in the …
Optimal priority assignment for real-time systems: a coevolution-based approach
In real-time systems, priorities assigned to real-time tasks determine the order of task
executions, by relying on an underlying task scheduling policy. Assigning optimal priority …
executions, by relying on an underlying task scheduling policy. Assigning optimal priority …
[PDF][PDF] PSRL: A New Method for Real-Time Task Placement and Scheduling Using Reinforcement Learning.
B Haouari, R Mzid, O Mosbahi - SEKE, 2023 - ksiresearch.org
Modern real-time system development methodologies describe a stage in which application
tasks are deployed onto an execution platform. The deployment process is divided into two …
tasks are deployed onto an execution platform. The deployment process is divided into two …