Online computation and competitive analysis
Online computation and competitive analysis
Flow and stretch metrics for scheduling continuous job streams
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Packet reordering is not pathological network behavior
IEEE/ACM Transactions on Networking (TON)
A theory of multi-channel schedulers for quality of service
Journal of High Speed Networks
On-line multi-threaded scheduling
Journal of Scheduling - Special issue: On-line algorithm part I
Three dozen papers on online algorithms
ACM SIGACT News
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
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We study an online job scheduling problem arising in networks with aggregated links. The goal is to schedule n jobs, divided into k disjoint chains, on m identical machines, without preemption, so that the jobs within each chain complete in the order of release times and the maximum flow time is minimized. We present a deterministic online algorithm Block with competitive ratio O$(\sqrt{n/m})$, and show a matching lower bound, even for randomized algorithms. The performance bound for Block we derive in the paper is, in fact, more subtle than a simple competitive analysis, and it shows that in overload conditions (when many jobs are released in a short amount of time), Block's performance is close to the optimum. We also show efficient offline algorithms to minimize maximum flow time and makespan in our model for k = 1, and prove that minimizing the maximum flow time and makespan for k,m ≥ 2 is ${\mathcal NP}$-hard.