TY - JOUR
T1 - Sparse basis selection
T2 - New results and application to adaptive prediction of video source traffic
AU - Atiya, Amir F.
AU - Aly, Mohamed A.
AU - Parlos, Alexander G.
N1 - Funding Information:
Manuscript received December 1, 2003; revised March 17, 2005. The work of A. G. Parlos was supported in part by the State of Texas Advanced Technology Program under Grants 999903–083, 999903–084, and 512-0225-2001, in part by the U.S. Department of Energy under Grant DE-FG07-98ID12641, and in part by the National Science Foundation under Grants CMS-0100238 and CMS-0097719.
PY - 2005/9
Y1 - 2005/9
N2 - Real-time prediction of video source traffic is an important step in many network management tasks such as dynamic bandwidth allocation and end-to-end quality-of-service (QoS) control strategies. In this paper, an adaptive prediction model for MPEG-coded traffic is developed. A novel technology is used, first developed in the signal processing community, called sparse basis selection. It is based on selecting a small subset of inputs (basis) from among a large dictionary of possible inputs. A new sparse basis selection algorithm is developed that is based on efficiently updating the input selection adaptively. When a new measurement is received, the proposed algorithm updates the selected inputs in a recursive manner. Thus, adaptability is not only in the weight adjustment, but also in the dynamic update of the inputs. The algorithm is applied to the problem of single-step-ahead prediction of MPEG-coded video source traffic, and the developed method achieves improved results, as compared to the published results in the literature. The present analysis indicates that the adaptive feature of the developed algorithm seems to add significant overall value.
AB - Real-time prediction of video source traffic is an important step in many network management tasks such as dynamic bandwidth allocation and end-to-end quality-of-service (QoS) control strategies. In this paper, an adaptive prediction model for MPEG-coded traffic is developed. A novel technology is used, first developed in the signal processing community, called sparse basis selection. It is based on selecting a small subset of inputs (basis) from among a large dictionary of possible inputs. A new sparse basis selection algorithm is developed that is based on efficiently updating the input selection adaptively. When a new measurement is received, the proposed algorithm updates the selected inputs in a recursive manner. Thus, adaptability is not only in the weight adjustment, but also in the dynamic update of the inputs. The algorithm is applied to the problem of single-step-ahead prediction of MPEG-coded video source traffic, and the developed method achieves improved results, as compared to the published results in the literature. The present analysis indicates that the adaptive feature of the developed algorithm seems to add significant overall value.
KW - Internet traffic
KW - MPEG
KW - Sparse basis
KW - Sparse representation
KW - Video traffic prediction
UR - http://www.scopus.com/inward/record.url?scp=26844553647&partnerID=8YFLogxK
U2 - 10.1109/TNN.2005.853426
DO - 10.1109/TNN.2005.853426
M3 - Article
C2 - 16252822
AN - SCOPUS:26844553647
SN - 1045-9227
VL - 16
SP - 1136
EP - 1146
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 5
ER -