IEEE Transactions on Networking (February)

College of Computing......Page 1
II. P REVIOUS W ORK......Page 2
A. Previous Work in Efficient Representation of Sparse Sets......Page 3
A. Bit Vector Linear Search......Page 4
B. Reducing Accesses by Aggregation......Page 5
C. Why Rearrangement of Rules can Help......Page 6
14 return BestRule;......Page 7
Proof: Consider a case in which we are intersecting two 64-bit v......Page 8
A. ABV Preprocessing......Page 9
C. Performance Evaluation on Industrial Firewall Databases......Page 10
Injecting Subprefixes: A second feature which directly affects t......Page 11
E. Performance Evaluation Using Synthetic Five-Dimensional Datab......Page 12
VIII. C ONCLUSIONS......Page 13
J. Xu, M. Singhal, and J. Degroat, A novel cache architecture to......Page 14
I. I NTRODUCTION......Page 15
II. A SSUMPTIONS AND C OMPUTATIONAL M ODELS......Page 16
Remark: It is clear from (a) that the calculation of GPS virtual......Page 17
C. Remarks on the Decision Tree Model......Page 18
Proof: (adapted from [ 18 ] ) Consider a decision tree algorithm......Page 19
Proof: To reduce scheduling to ${L}$ -membership, we construct a......Page 20
Fig. 5. Algorithm I for ${L}$ -membership test.......Page 21
Proof: [Sketch] The proof of this theorem is very similar to tha......Page 22
IV. C OMPLEXITY D ELAY T RADEOFFS W HEN A LLOWING L INEAR T ESTS......Page 23
Proof: We prove by contradiction. Let $\Gamma =\{T_{j}: 1\leq j\......Page 24
B. Our Complexity Results......Page 25
Proof: This proof is similar to that of Lemma 4. In the followin......Page 26
VII. C ONCLUSIONS......Page 27
S. Keshav, On the efficient implementation of fair queueing, Int......Page 28
I. I NTRODUCTION......Page 29
II. D EPLOYMENT I SSUES......Page 30
A. Baseline Algorithm......Page 31
B. Fair Throttle Algorithm......Page 32
Theorem 1: Assume that the server $S$ is overloaded (i.e., the a......Page 33
Experiment 3: Effect of $\delta $ on the convergence rate. Fig.€......Page 34
B. Packet Network Results......Page 35
Fig.€8. (a) Protection for good users under 20% evenly distribut......Page 36
Fig.€10. (a) Protection for good users, under four different att......Page 37
VII. S YSTEM I MPLEMENTATION......Page 38
VIII. R ELATED W ORK......Page 39
Fig.€16. Throughput performance of router throttling, as a funct......Page 40
D. K. Y. Yau and X. Chen, Resource management in software-progra......Page 41
I. I NTRODUCTION......Page 43
B. Equilibrium Objectives and Utility-Based Interpretation......Page 44
Fig.€1. General congestion control structure.......Page 45
Fig.€2. Overall feedback loop.......Page 46
Remark: The RTT used in (18) could be the real-time measurement,......Page 47
A. Local Stability Result......Page 48
Remark: Source laws (24) (25) are not the only ones that satisfy......Page 49
A. Marking and Estimation......Page 50
B. Simulation Results......Page 51
A. Packet Implementation and Simulation Results......Page 52
VII. C ONCLUSION......Page 53
Proof of Theorem 3: As discussed in Section€IV-A, we parallel th......Page 54
L. Massoulie, Stability of distributed congestion control with h......Page 55
Z. Wang and F. Paganini, Global stability with time delay in net......Page 56
I. I NTRODUCTION......Page 57
Proposition 2: If $ {\bf H}(1)=I$, there exists a constant $C_{q......Page 58
Proof: Since $\mu (n)$ is stationary, we can easily see that $ {......Page 59
Proof: We first have $$\eqalignno{{\rm P}\left \{Q_{l}^{c} > x\r......Page 60
A. Example of a Linearized Feedback Flow Control System......Page 61
B. Application......Page 62
C. Distributed Algorithm......Page 63
V. N UMERICAL R ESULTS......Page 64
Fig.€4. Tail probability at link 2.......Page 65
Fig.€9. Tail probability at link 2.......Page 66
D. Qiu and N. B. Shroff . (2001) Study of Predictive Flow Contro......Page 67
I. I NTRODUCTION......Page 69
B. Control Algorithms......Page 70
IV. ACC S CHEMES......Page 71
1) Congestion Estimation Protocol: Let us look at the definition......Page 72
C. Comparisons Between Vegas and Monaco......Page 73
D. Adaptive Virtual Delay Queueing......Page 74
B. Multiple Bottlenecks......Page 75
Fig.€8. Monaco with the same buffer as the above case (55 packet......Page 76
Fig.€10. Monaco with a large amount of background web traffic un......Page 77
VI. S UMMARY......Page 78
Proposition 2: The nonlinear programming problem $$\eqalignno{\h......Page 79
A. Venkatesan, An Implementation of Accumulation-Based Congestio......Page 80
I. I NTRODUCTION......Page 81
A. Congestion Control Schemes......Page 82
A. PFC and REM With Real Queue Marking......Page 83
Proof: From Lemma 1, for stability we require $$\tau < {{ 1}\ove......Page 84
Proof: The equilibrium marking probability when the disturbance......Page 85
Lemma 5: The linearized form of the system described by (2), (3)......Page 86
C. TCP Congestion Control and REM......Page 87
E. Multiple Users With Identical RTT......Page 88
Fig.€2. Evolution of the Queueing delay with PFC at source, VQ-b......Page 89
Fig.€7. Evolution of the Queueing delay with TCP at the source,......Page 90
Fig.€11. Comparison between RQ and VQ RED with TCP at the source......Page 91
W. Rudin, Real and Complex Analysis, 3rd ed. New York: McGraw-Hi......Page 92
I. I NTRODUCTION......Page 94
B. Proposed Integrated Dynamic Congestion Control Approach......Page 96
D. Dynamic Network Models......Page 97
Fig.€3. Time evolution of network system queue state obtained us......Page 98
B. Ordinary Traffic Control Strategy......Page 99
1) Simulation Model: Our ATM network model is shown in Fig.€4 .......Page 100
1) Steady State and Transient Behavior: Using the simulation mod......Page 101
Fig.€7. Switch 2 (last switch) time evolution of the Ordinary Tr......Page 102
Fig.€10. Network test configuration for demonstrating dynamic be......Page 103
V. C ONCLUSIONS......Page 104
Proof: The closed system is described by the (6) (11) . From (7)......Page 105
R. Satyavolu, K. Duvedi, and S. Kalyanaraman, Explicit Rate Cont......Page 106
B. Maglaris, D. Anastassiou, P. Sen, G. Karlsson, and J. Robbins......Page 107
I. I NTRODUCTION......Page 108
B. Window Adaptation......Page 109
A. Model for the Rate Modulating Process $\{M_{k}\}$......Page 110
C. Evolution of $\{Z_{k}\}$, and a Process $\{X_{k}\}$......Page 111
A. RATCP OldTahoe and TCP OldTahoe: Analysis and Simulation......Page 112
Fig.€5. Throughput variation of RATCP and TCP with the ephemeral......Page 113
C. Fairness......Page 114
D. Finite-Size File Transfers (HTTP-Like TCP Transfers)......Page 115
Random Loss: Fig.€14 shows the performance of web-like transfers......Page 116
VII. C ONCLUSIONS......Page 117
Transition Probability Calculations: New Arrivals, No Loss: $ \R......Page 118
S. Abraham and A. Kumar, A new approach for asynchronous distrib......Page 119
T. V. Lakshman and U. Madhow, The performance of TCP/IP for netw......Page 120
I. I NTRODUCTION......Page 121
II. N ETWORK M ODEL......Page 122
A. Basic Algorithm......Page 123
Example 3.1.1: Consider the network of Fig.€1 . The maximum rate......Page 124
Complexity: The distributed implementation terminates in $2DM$ u......Page 125
Fig.€2. We study the relative computation error in a dynamic net......Page 126
IV. D ISCUSSION......Page 127
Proof of lemma 4: We prove by induction. Let $k=1$ . The algorit......Page 129
Proof of lemma 7: We prove by induction. We first prove the lemm......Page 130
Proof of lemma 9: For the first part, it is sufficient to prove......Page 131
T. Bially, B. Gold, and S. Seneff, A technique for adaptive voic......Page 132
D. Taubman and A. Zakhor, Multirate 3-D subband coding of video,......Page 133
I. I NTRODUCTION......Page 134
II. G ROUP DH O VERVIEW......Page 135
Fig.€1. The radix-2 butterfly scheme for establishing a group ke......Page 136
A. Minimizing Total Cost......Page 137
Lemma 3: Suppose $b=(b_{1},b_{2},\cdots,b_{n})$, with $b_{j}\leq......Page 138
Algorithm 3. Improved algorithm for calculating the length vecto......Page 139
A. Comparison of Total Cost......Page 140
B. Feasibility Comparison......Page 141
VI. S YSTEM S ENSITIVITY TO F ALSE C OSTS......Page 142
B. Sensitivity to Costs From Untrustworthy Users......Page 143
VII. C ONCLUSION......Page 144
Proof: We will show that there is an optimal solution in which o......Page 145
F. Fabris, A. Sgarro, and R. Pauletti, Tunstall adaptive coding......Page 146
I. I NTRODUCTION......Page 147
II. T HE B ASIC C ONE -B ASED T OPOLOGY C ONTROL A LGORITHM......Page 148
Example II.1: Suppose that $V=\{u_{0},u_{1},u_{2},u_{3},v\}$ . (......Page 149
Fig.€4. Illustration for the proof of Lemma II.1.......Page 150
A. The Shrink-Back Operation......Page 151
C. Pairwise Edge Removal......Page 152
IV. D EALING W ITH R ECONFIGURATION, A SYNCHRONY, AND F AILURES......Page 153
A. Simulation Environment......Page 154
B. Network Topology Characteristics......Page 155
C. Network Performance Analysis......Page 156
VI. C ONCLUSION......Page 157
G. J. Pottie and W. J. Kaiser, Wireless integrated network senso......Page 158
E. W. Zegura, K. Calvert, and S. Bhattacharjee, How to model an......Page 159
I. I NTRODUCTION......Page 160
II. R ELATED W ORK......Page 161
Definition 1: The probability of error $P_e$ is defined as the p......Page 162
C. Bayes Error and Blocking Probability......Page 163
C. Numerical Analysis......Page 164
B. Bayes Error......Page 165
D. Numerical Analysis......Page 166
1) Gaussian Approximation: An important step to obtain a close f......Page 167
A. Simulation Setup......Page 168
VIII. C ONCLUSION......Page 169
D ERIVATION OF THE C ORRELATION C OEFFICIENT $\rho_{g}$......Page 170
J. Yates, Wavelength converters in dynamically-reconfigurable WD......Page 171
H. Cramer, Mathematical Methods of Statistics . Princeton, NJ: P......Page 172
I. I NTRODUCTION......Page 173
Proof: Consider the case where $N$ is even, and envision a cut w......Page 174
Proof: We will conduct a proof by contradiction. Suppose there d......Page 175
Lemma 2: Given an adjacent pair of calls, it is possible to fit......Page 176
Proof: We will provide a proof by construction. Consider the fir......Page 177
Proof: The proof is by construction using the following algorith......Page 178
1) Symmetric Multi-Port Networks: We first consider the case of......Page 179
Proof: First, if the traffic set is unconnected, we use an appro......Page 180
Lemma 6: If for a given RWA there does not exist any converter a......Page 181
2) Symmetric Node Architecture: In other cases, we may prefer to......Page 182
Proof: Index the nodes $n_{1}, \ldots, n_{N}$ such that $n_{1},......Page 183
V. C ONCLUSIONS......Page 184
Theorem 8: For $k \in \{1,\ldots,N/2\}$ and $N/2$ integer, $$f\l......Page 185
A. F. Elrafaie, Multiwavelength survivable ring network architec......Page 186
Fig.€1. Example of the DIR method.......Page 187
A. Framework of the Analysis......Page 188
B. Blocking Due to Insufficient Network Capacity......Page 189
Calculating $f_{i,j}$ and $f_{i,j\vert i,j^{\prime}}(t_{j})$: Va......Page 190
E. Computational Complexity......Page 191
Fig.€2. Example of the specific SIR method.......Page 192
Calculating $v_{R,j}(n)$: $v_{R,j}(n)$ can be calculated iterati......Page 193
Fig.€4. Traffic blocking of the centralized method in the PacNet......Page 194
Fig.€8. Blocking analysis of the DIR method in the PacNet where......Page 195
V. C ONCLUSIONS......Page 196
L. Li and A. K. Somani, A new analytical model for multifiber WD......Page 197
I. I NTRODUCTION......Page 198
Example 1 Spare Capacity Sharing: In the five-node network in Fi......Page 199
III. A S PARE P ROVISION M ATRIX B ASED SCA M ODEL......Page 201
Example 2 Matrix Method: In the five-node undirected network in......Page 202
Fig.€3. SCA structure for protecting arbitrary failures.......Page 203
Example 3 Find a Backup Path in SSR: The Example 2 in Fig.€1 is......Page 204
Fig.€5. Find a backup path of flow 11 using successive survivabl......Page 205
Fig.€13. Network 8 ( $N=50$, $L=82$ ).......Page 206
Fig.€15. Comparison of redundancy $\eta =S/W$ versus CPU time of......Page 207
VII. N ODE F AILURES......Page 208
TABLE V N UMERICAL R ESULTS FOR N ODE F AILURES......Page 209
W. D. Grover, R. R. Iraschko, and Y. Zheng, Comparative methods......Page 210
Y. Liu, D. Tipper, and P. Siripongwutikorn, Approximating optima......Page 211