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Optimal scheduling for jobs with progressive deadlines

机译:具有截止期限的作业的最佳计划

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This paper considers the problem of server-side scheduling for jobs composed of multiple pieces with consecutive (progressive) deadlines. One example is server-side scheduling for video service, where clients request flows of content from a server with limited capacity, and any content not delivered by its deadline is lost. We consider the simultaneous goals of 1) minimizing overall loss, and 2) differentiating loss fractions across classes of flows in proportion to relative weights. State-of-the-art policies, like Discriminatory Processor Sharing and Weighted Fair Queueing, use a fixed static proportional allocation of service rate and fail to achieve both goals. The well-known Earliest Deadline First policy minimizes overall loss, but fails to provide proportional loss across flows, because it treats packets as independent jobs. This paper introduces the Earliest Progressive Deadline First (EPDF) class of policies. We prove that all policies in this broad class minimize overall loss. Furthermore, we demonstrate that many EPDF policies accurately differentiate loss fractions in proportion to class weights, satisfying the second goal.
机译:本文考虑了由多个具有连续(渐进)截止日期的片段组成的作业的服务器端调度问题。一个示例是视频服务的服务器端调度,其中客户端从容量有限的服务器请求内容流,而在截止日期之前未交付的任何内容都会丢失。我们考虑同时实现的目标:1)最小化整体损失,以及2)按相对权重按比例区分流量类别中的损失分数。最先进的策略,例如歧视性处理器共享和加权公平队列,使用固定的服务速率静态比例分配,无法实现这两个目标。众所周知的“最早截止日期优先”策略可最大程度地减少总体损失,但由于它会将数据包视为独立的作业,因此无法提供跨流的比例损失。本文介绍了最早的渐进截止日期优先(EPDF)类策略。我们证明,这一大类中的所有策略都将总体损失降到最低。此外,我们证明了许多EPDF策略可以根据类别权重准确区分损失分数,从而满足第二个目标。

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