Abstract
The delivery of latency sensitive packets is a crucial issue in real-time applications of communication networks. Such packets often have a firm deadline and a packet becomes useless if it arrives after its deadline. The deadline, however, applies only to the packet’s journey through the entire network; individual routers along the packet’s route face a more flexible deadline. We study policies for admitting latency sensitive packets at a router. Each packet is tagged with a value. A packet waiting at a router loses value over time as its probability of arriving at its destination on time decreases. The router is modeled as a non-preemptive queue, and its objective is to maximize the total value of the forwarded packets. When a router receives a packet, it must either accept it (and delay future packets), or reject it immediately. The best policy depends on the set of values that a packet can take. We consider three natural sets: an unrestricted model, a real-valued model, where any value over 1 is allowed, and an integral-valued model. For the unrestricted model, we prove that there is no constant competitive ratio algorithm. For the real-valued model, we give a randomized 4-competitive algorithm and a matching lower bound (up to low order terms). We also provide a deterministic lower bound of ϕ3- ε≈ 4.236 , almost matching the previously known 4.24-competitive algorithm. For the integral-valued model, we describe a deterministic 4-competitive algorithm, and prove that this is tight even for randomized algorithms (up to low order terms).
Original language | English |
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Pages (from-to) | 337-353 |
Number of pages | 17 |
Journal | Journal of Scheduling |
Volume | 20 |
Issue number | 4 |
DOIs | |
State | Published - 1 Aug 2017 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2016, Springer Science+Business Media New York.
Keywords
- Dual fitting
- Latency sensitive packets
- Non-preemptive buffering problems
- Online algorithms
ASJC Scopus subject areas
- Software
- General Engineering
- Management Science and Operations Research
- Artificial Intelligence