On-line scheduling of jobs with fixed start and end times
Theoretical Computer Science - Special issue on dynamic and on-line algorithms
Bounding the Power of Preemption in Randomized Scheduling
SIAM Journal on Computing
Mechanism design for online real-time scheduling
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Adaptive limited-supply online auctions
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Online auctions with re-usable goods
Proceedings of the 6th ACM conference on Electronic commerce
Online ascending auctions for gradually expiring items
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Approximation Algorithms for Orienteering and Discounted-Reward TSP
SIAM Journal on Computing
Optimizing web traffic via the media scheduling problem
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
New online algorithms for story scheduling in web advertising
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
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We study an online job scheduling problem motivated by storyboarding in web advertising, where an advertiser derives value from uninterrupted sequential access to a user surfing the web. The user ceases to browse with probability 1 -- β at each step, independently. Stories (jobs) arrive online; job s has length ls and per-unit value vs. A value vs is obtained for every unit of the job that is scheduled consecutively without interruption, discounted for the time at which it is scheduled. Jobs can be preempted, but no further value can be derived from the residual unscheduled units of the job. We seek an online algorithm whose total reward is competitive against that of the offline scheduler that knows all jobs in advance. We consider two models based on the maximum delay that can be allowed between the arrival and scheduling of a job. In the first, a job can be scheduled anytime after its arrival; in the second a job is lost unless scheduled immediately upon arrival, preempting a currently running job if needed. The two settings correspond to two natural models of how long an advertiser retains interest in a relevant user. We show that there is, in fact, a sharp separation between what an online scheduler can achieve in these two settings. In the first setting with no deadlines, we give a natural deterministic algorithm with a constant competitive ratio against the offline scheduler. In contrast, we show that in the sharp deadline setting, no (deterministic or randomized) online algorithm can achieve better than a polylogarithmic ratio.