Chronos: A Unifying Optimization Framework for Speculative Execution of Deadline-critical MapReduce Jobs
Maotong Xu, Sultan Alamro, Tian Lan, Suresh Subramaniam

TL;DR
Chronos introduces a unified optimization framework for speculative execution in MapReduce, enhancing deadline guarantees and reducing costs by systematically analyzing and optimizing the probability of job completion before deadlines.
Contribution
It unifies various speculative scheduling strategies under a new optimization framework using the PoCD metric, providing an optimal algorithm for deadline and cost tradeoffs.
Findings
Achieves up to 80% PoCD in experiments.
Increases net utility by 50%.
Reduces execution costs by 88%.
Abstract
Meeting desired application deadlines in cloud processing systems such as MapReduce is crucial as the nature of cloud applications is becoming increasingly mission-critical and deadline-sensitive. It has been shown that the execution times of MapReduce jobs are often adversely impacted by a few slow tasks, known as stragglers, which result in high latency and deadline violations. While a number of strategies have been developed in existing work to mitigate stragglers by launching speculative or clone task attempts, none of them provides a quantitative framework that optimizes the speculative execution for offering guaranteed Service Level Agreements (SLAs) to meet application deadlines. In this paper, we bring several speculative scheduling strategies together under a unifying optimization framework, called Chronos, which defines a new metric, Probability of Completion before Deadlines…
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Taxonomy
TopicsCloud Computing and Resource Management · IoT and Edge/Fog Computing · Blockchain Technology Applications and Security
