2007
DOI: 10.1109/tnet.2006.890089
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Finding a Path Subject to Many Additive QoS Constraints

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Cited by 106 publications
(82 citation statements)
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“…We used the pseudocut problems to formulate a vulnerability assessment TCVA with respect to an arbitrary additive QoS metric on a communications network. Future work would include extending this assessment to incorporate more than one QoS metric; however, this is likely to be difficult as the problem of finding a routing path satisfying two or more QoS constraints is NP-hard; however, approximation algorithms do exist for this problem [29]. In addition, the computational complexity of the uniform edge length version of our simplest problem, T-PCUT, is left open; our NPhardness proof required nonuniform edge lengths and we provided polynomial-time algorithms only for special cases.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…We used the pseudocut problems to formulate a vulnerability assessment TCVA with respect to an arbitrary additive QoS metric on a communications network. Future work would include extending this assessment to incorporate more than one QoS metric; however, this is likely to be difficult as the problem of finding a routing path satisfying two or more QoS constraints is NP-hard; however, approximation algorithms do exist for this problem [29]. In addition, the computational complexity of the uniform edge length version of our simplest problem, T-PCUT, is left open; our NPhardness proof required nonuniform edge lengths and we provided polynomial-time algorithms only for special cases.…”
Section: Discussionmentioning
confidence: 99%
“…We also used topologies generated according to Erdos-Renyi (ER) random graphs. To simulate a QoS metric, edges were weighted uniformly in the interval [1,10], following [29], [14]. The dataset statistics are as follows: ER1, an ER graph with n = 1000, m = 49995; RL1, router-level graph with n = 5000, m = 250000, generated by BRITE with default parameters and Waxman model; RL2, same as RL1 except n = 1000, m = 2000; RL3, same as RL1 except n = 100, m = 200; AS1, an AS-level graph generated by BRITE with default parameters and n = 10000, m = 498725; and finally, H1, a hierarchical BRITE top-down graph with 200 autonomous systems and 100 routers per AS, with n = 20000, m = 660604.…”
Section: A Datasets and Methodologymentioning
confidence: 99%
“…This was published in 1973 by Liu and Layland [5]. The algorithm was based on static task priorities.…”
Section: Rate Monotonic Scheduling Algorithm (Rms)mentioning
confidence: 99%
“…In order to prove this, one has to calculate for each task the time when its execution end. If the execution end time of each task is shorter than its time deadline, it means that the set of tasks is schedulable [5].…”
Section: Basic Properties Of Rate Monotonic Schedulingmentioning
confidence: 99%
“…For other applications, variants of the problem are needed. QoS requirements may set one or more constraints to be satisfied along the path [1]. In general, QoS constraints can be classified into additive, multiplicative, and concave.…”
Section: Introductionmentioning
confidence: 99%