A storage management scheme based on cross-layer packet size optimization in DTN

doi:10.7523/j.issn.2095-6134.2014.06.016

Abstract

Abstract:

Delivery of the application data in delay tolerant network relies on the custody transfer provided by the node storage. Fast release of the occupied node storage is significant for reducing congestion and enhancing network capacity. Studies have shown that the storage efficiency is affected by the size of packet at various network layers. We propose a storage management scheme based on cross-layer packet size optimization, OSUS. OSUS can dynamically adjust Bundle size and the transport layer frame size to maximize the node storage utilization. Simulation results show that, compared with traditional schemes where joint optimization algorithms are not adopted, OSUS can bring 15% improvement on average in the node storage utilization.

Key words: cross-layer optimization, node storage, delay tolerant network, protocol data unit

CLC Number:

TP393

JIANG Fukai, LU Hancheng. A storage management scheme based on cross-layer packet size optimization in DTN[J]. , 2014, 31(6): 836-842.

References

[1] Cerf V, Burleigh S, Hooke A, et al. Delay tolerant network architecture[S]. RFC 4838, 2007.

[2] Zhang X, Neglia G, Kurose J, et al. Performance modeling of epidemic routing[J]. Computer Networks, 2007, 51(10): 2 867-2 891.

[3] Rashid S, Abdullah A, Ayub Q, et al. Dynamic prediction based multi queue (DPMQ) drop policy for probabilistic routing protocols of delay tolerant network[J]. Journal of Network and Computer Applications, 2013, 36(5): 1 395-1 402.

[4] Lenas S A, Dimitriou S, Tsapeli F, et al. Queue management architecture for delay tolerant networking[C]//WWIC. 2011: 470-482.

[5] Elwhishi A, Ho P H, Naik K, et al. A novel message scheduling framework for delay tolerant networks routing[J]. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(5): 871-880.

[6] Rashid S, Ayub Q, Zahid M S M, et al. Message drop control buffer management policy for dtn routing protocols[J]. Wireless Personal Communications, 2013, 72(1): 653-669.

[7] Shin K, Kim K, Kim S. Traffic management strategy for delay-tolerant networks[J]. Journal of Network and Computer Applications, 2012, 35(6): 1 762-1 770.

[8] Samaras C V, Tsaoussidis V. Adjusting transport segmentation policy of DTN bundle protocol under synergy with lower layers[J]. Journal of Systems and Software, 2011, 84(2): 226-237.

[9] Khalili R, Salamatian K. A new analytic approach to evaluation of packet error rate in wireless networks[C]//Proceedings of the 3rd Annual Communication Networks and Services Research Conference. 2005: 333-338.

[10] Ramadas M, Burleigh S, Farrell S. Licklider transmission protocol-specification[S]. RFC 5326, 2008.

[11] Samaras C V, Tsaoussidis V. Design of delay tolerant transport protocol (DTTP) and its evaluation for Mars[J]. Acta Astronautica, 2010, 67(7/8): 863-880.

[12] Papastergiou G, Psaras I, Tsaoussidis V. Deep space transport protocol: a novel transport scheme for space DTNs[J]. Computer Communications, 2009, 32(16): 1 757-1 767.

[13] Jiang F K, Lu H C. Packet size optimization in delay tolerant networks[C]//Proceedings of the 11th Annual Consumer Communications and Networking Conference. 2014.

[14] Boyd S, Vandenberghe L. Convex optimization[M]. Cambridge University Press, 2004.

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