Online Deadline-Aware Task Dispatching and Scheduling in Edge Computing
IEEE Transactions on Parallel and Distributed Systems(2020)
摘要
In this article, we study online deadline-aware task dispatching and scheduling in edge computing. We jointly consider the management of the networking and computing resources to meet the maximum number of deadlines. We propose an online algorithm, named
$\mathsf{ Dedas}$ Dedas
, which greedily schedules newly arriving tasks and considers whether to replace some existing tasks in order to make the new deadlines satisfied. We derive a non-trivial competitive ratio of
${\sf Dedas}$ Dedas
theoretically, and our analysis is asymptotically tight. Besides, we implement a distributed approximation
${\sf D-Dedas}$ D - Dedas
with a better scalability and less than 10 percent performance loss compared with the centralized algorithm
${\sf Dedas}$ Dedas
. We then build
${\sf DeEdge}$ DeEdge
, an edge computing testbed installed with typical latency-sensitive applications such as IoT sensor monitoring and face matching. We adopt a real-world data trace from the
${\sf Google\,cluster}$ Google cluster
for large-scale emulations. Extensive testbed experiments and simulations demonstrate that the deadline miss ratio of
${\sf Dedas}$ Dedas
is stable for online tasks, which is reduced by up to 60 percent compared with state-of-the-art methods. Moreover,
${\sf Dedas}$ Dedas
performs well in minimizing the average task completion time.
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关键词
Edge computing,task dispatching and scheduling,deadline-aware tasks,online algorithm
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