2014
DOI: 10.1007/978-3-642-54423-1_53
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Packet Forwarding Algorithms in a Line Network

Abstract: Abstract. We initiate a competitive analysis of packet forwarding policies for maximum and average flow in a line network. We show that the policies Earliest Arrival and Furthest-To-Go are scalable, but not constant competitive, for maximum flow. We show that there is no constant competitive algorithm for average flow.

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Cited by 8 publications
(14 citation statements)
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“…What can be shown for different objectives such as maximum flow time or the k -norms of flow time? As mentioned, the work by Antoniadis et al [5] has addressed this case when the graph is a line network in what corresponds to our identical setting when all jobs have unit size. They also showed that the objective of maximum flow time becomes hard in this setting if the network is a tree.…”
Section: Resultsmentioning
confidence: 96%
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“…What can be shown for different objectives such as maximum flow time or the k -norms of flow time? As mentioned, the work by Antoniadis et al [5] has addressed this case when the graph is a line network in what corresponds to our identical setting when all jobs have unit size. They also showed that the objective of maximum flow time becomes hard in this setting if the network is a tree.…”
Section: Resultsmentioning
confidence: 96%
“…These works differ from ours because we requires all job (packets) to be completely processed and we consider the flow time objective over all the packets. One recent work [5] has considered minimizing total flow time when routing in a line network. Here for total flow time, no positive results were shown, but it was shown that no algorithm can be O(1)-competitive, giving evidence that the problem is algorithmically challenging.…”
Section: Related Workmentioning
confidence: 99%
“…All these works, however, consider minimizing the makespan and assume that the orientation of the graph is fixed. Antoniadis et al [2] also consider average flow time on a directed line. They give lower bounds for competitive ratios in the online setting and O(1) competitive algorithms with resource augmentation for the maximum flow time.…”
Section: Related Workmentioning
confidence: 99%
“…When applying (multiple) timestretches we use the following observation to assess the additional empty space created between jobs: Lemma 6.3. Consider two distinct times T 1 < T 2 with T 1 ∈ I x(1) and T 2 ∈ I x (2) . Applying time-stretches yields shifted times T 1 < T 2 with…”
Section: B Proofs Of Sectionmentioning
confidence: 99%
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