Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

This paper introduces the novel concept of proactive resource allocation for wireless networks, through which the predictability of user behavior is exploited to balance the wireless traffic over time, and significantly reduces the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which smart wireless devices are assumed to predict the arrival of new requests and submit them to the network time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases with the prediction duration . Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed light on multicasting with predictable demands and show that proactive multicast networks can achieve a significantly higher diversity gain that scales superlinearly with . Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive wireless resource framework.

[1]  Ali Movaghar-Rahimabadi,et al.  Non-preemptive earliest-deadline-first scheduling policy: a performance study , 2005, 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.

[2]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[3]  Injong Rhee,et al.  Multicast Scheduling in Cellular Data Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[4]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[5]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[6]  Anthony Ephremides,et al.  Optimal scheduling with strict deadlines , 1989 .

[7]  Jong-Moon Chung,et al.  Statistical admission control for real-time services under earliest deadline first scheduling , 2005, Comput. Networks.

[8]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[9]  Tara Javidi,et al.  High-SNR Analysis of Outage-Limited Communications With Bursty and Delay-Limited Information , 2009, IEEE Transactions on Information Theory.

[10]  F. G. Foster On the Stochastic Matrices Associated with Certain Queuing Processes , 1953 .

[11]  Sangtae Ha,et al.  Pricing by timing: innovating broadband data plans , 2012, OPTO.

[12]  Robert G. Gallager,et al.  Discrete Stochastic Processes , 1995 .

[13]  P. Glynn Upper bounds on Poisson tail probabilities , 1987 .

[14]  Don Towsley,et al.  Optimal scheduling policies for a class of Queues with customer deadlines to the beginning of service , 1990, PERV.

[15]  Juyul Lee,et al.  Asymptotically optimal policies for hard-deadline scheduling over fading channels , 2009, 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[16]  Damon Wischik,et al.  Big queues , 2004, Lecture notes in mathematics.

[17]  Demosthenis Teneketzis,et al.  Stochastic Scheduling in Priority Queues with Strict Deadlines , 1993 .

[18]  Chaiwat Oottamakorn,et al.  Statistical service assurances for traffic scheduling algorithms , 2000, IEEE Journal on Selected Areas in Communications.

[19]  Atilla Eryilmaz,et al.  Pricing for demand shaping and proactive download in smart data networks , 2013, 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[20]  Vijay Sivaraman,et al.  Statistical Analysis of Delay Bound Violations at an Earliest Deadline First (EDF) Scheduler , 1999, Perform. Evaluation.

[21]  Atilla Eryilmaz,et al.  Proactive multicasting with predictable demands , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.