arXiv:2606.00835v1 Announce Type: new Abstract: Network routers that enforce Quality-of-Service (QoS) guarantees must decide, at every clock cycle, which expiring packet of information to transmit, even when the value of the packet is unknown until it is processed. We frame this problem as the Online Packet Scheduling with Deadlines (OPSD) problem under Partial Feedback: packets arrive at every clock cycle, with different deadlines, but the weights are only observed after execution. Under a stochastic assumption on the unknown weights, we explore different variants of the OPSD problem with ban
The increasing complexity and real-time demands of AI-driven systems necessitate more efficient and intelligent resource allocation, such as packet scheduling.
This research directly addresses a fundamental challenge in maintaining Quality-of-Service (QoS) for network routers, critical for the scalability and reliability of AI and other data-intensive applications.
Improved algorithmic approaches for online packet scheduling will enhance network performance and reliability, especially in scenarios with unknown packet values and deadlines.
- · AI infrastructure providers
- · Telecommunications companies
- · Cloud computing platforms
- · High-frequency trading firms
- · Legacy network hardware manufacturers resistant to algorithm integration
- · Applications highly sensitive to latency due to inefficient scheduling
More efficient data transmission and reduced latency in critical AI applications.
Enables new classes of real-time AI services that previously suffered from network bottlenecks.
Potentially democratizes access to sophisticated AI by making underlying network infrastructure more robust and scalable.
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Read at arXiv cs.LG