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Action-based Scheduling: Leveraging App Interactivity For Scheduler Efficiency

ACTION-BASED SCHEDULING: LEVERAGING APP INTERACTIVITY FOR SCHEDULER EFFICIENCY

  • Project Code :
  • TCREJA19_79
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ACTION-BASED SCHEDULING: LEVERAGING APP INTERACTIVITY FOR SCHEDULER EFFICIENCY

Action-Based Scheduling: Leveraging App Interactivity for Scheduler Efficiency

Abstract

When human users receive information from a server, they spend a few seconds of information processing before taking an action. The user processing time creates an idle communication period during the app session. Moreover, the generation of the future traffic depends on the service of the current query-response pair. In this paper, we aim at leveraging the properties of such interactions to reap quality-of-experience gains. Existing schedulers, both in practice and theory, are not designed in view of the aforementioned traffic characteristics. Theoretical works predominantly focus on scheduling of traffic that is either generated independently or directly controlled, but not governed by the specific dynamics caused by human interactions. Schedulers in practice, on the other hand, use round-robin and processor sharing methods to serve multiple ongoing sessions. We show that neither of these approaches is effective for serving apps that involve human interactivity. Instead, we show that optimal scheduling for interactive traffic is non-randomized over packets that we call action-based, as it avoids breaking ongoing service of actions in order to align human response times with the service of other actions. Since the design of optimal action-based policy is computationally prohibitive, we develop low-complexity suboptimal action-based policies that are optimal for two ongoing sessions. Our numerical studies based on a real-data trace reveal that our proposed action-based policies can reduce total delay by twenty second with respect to packet-based equal processor sharing.

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