Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
In this paper, we deal with the problem of scheduling streaming applications on unreliable heterogeneous platforms. We use the realistic one-port model with full computation/commu...
Temporal networks play a crucial role in modeling temporal relations in planning and scheduling applications. Temporal Networks with Alternatives (TNAs) were proposed to model alte...
— In this paper, we use the Markov Decision Process (MDP) technique to find the optimal code allocation policy in High-Speed Downlink Packet Access (HSDPA) networks. A discrete ...
Hussein Al-Zubaidy, Jerome Talim, Ioannis Lambadar...
In this paper, we present algorithms for Grid resource provisioning that employ agreement-based resource management. These algorithms allow userlevel resource allocation and sched...