Paper Info
Title
Decomposition of large-scale MDPs for wireless scheduling with load- and channel-awareness
Abstract
Scheduling delay-tolerant tasks based on both load-and channel-awareness can significantly reduce the peak demand in cellular networks. However, solving the optimal scheduling problem leads to a large-scale Markov Decision Process (MDP) with extremely high complexity. In this work, we propose a scalable and distributed approach to this problem, called Coordinated Scheduling (CoSchd). CoSchd decomposes the large-scale MDP problem into many individual MDP problems, each of which can be solved independently by each user under a limited amount of coordination signal from the BS. We show that CoSchd is close to optimal when the number of users becomes large. Further, we propose an online version of CoSchd that iteratively updates the scheduling policy based on online measurements. Simulation results demonstrate that exploiting load- and channel-awareness with CoSchd can effectively alleviate cellular network congestion.
Year
DOI
Venue
2014
10.1109/ITA.2014.6804222
ITA
Keywords
Field
DocType
coordinated scheduling,large-scale markov decision process,coschd,scheduling,load awareness,cellular radio,wireless scheduling,cellular network congestion,telecommunication congestion control,delay tolerant tasks,optimal scheduling problem,scheduling policy,channel awareness,mdp,cellular networks,markov processes,peak demand reduction,iterative methods
Fixed-priority pre-emptive scheduling,Mathematical optimization,Fair-share scheduling,Computer science,Deadline-monotonic scheduling,Two-level scheduling,Rate-monotonic scheduling,Earliest deadline first scheduling,Dynamic priority scheduling,Round-robin scheduling,Distributed computing
Conference
Citations 
PageRank 
References 
2
0.37
8
Authors
5
Name
Order
Citations
PageRank
Huasen Wu11238.50
Xiaojun Lin220.37
Xin Liu33919320.56
Kun Tan4135098.64
Yongguang Zhang53461248.65