START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks

  title={START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks},
  author={Shreshth Tuli and Sukhpal Singh Gill and Peter Garraghan and Rajkumar Buyya and Giuliano Casale and Nicholas R. Jennings},
Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are slow running instances that increase the overall response time. Such tasks can significantly impact the system’s Quality of Service (QoS) and the Service Level Agreements (SLA). To combat this issue, there is a need for automatic… 

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  • Huanle XuW. Lau
  • Computer Science
    IEEE Transactions on Parallel and Distributed Systems
  • 2017
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