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Modern large-scale computing systems distribute jobs into multiple smallertasks which execute in parallel to accelerate job completion rates and reduceenergy consumption. However, a common performance problem in such systems isdealing with straggler tasks that are slow running instances that increase theoverall response time. Such tasks can significantly impact the system's Qualityof Service (QoS) and the Service Level Agreements (SLA). To combat this issue,there is a need for automatic straggler detection and mitigation mechanismsthat execute jobs without violating the SLA. Prior work typically buildsreactive models that focus first on detection and then mitigation of stragglertasks, which leads to delays. Other works use prediction based proactivemechanisms, but ignore heterogeneous host or volatile task characteristics. Inthis paper, we propose a Straggler Prediction and Mitigation Technique (START)that is able to predict which tasks might be stragglers and dynamically adaptscheduling to achieve lower response times. Our technique analyzes all tasksand hosts based on compute and network resource consumption using an EncoderLong-Short-Term-Memory (LSTM) network. The output of this network is then usedto predict and mitigate expected straggler tasks. This reduces the SLAviolation rate and execution time without compromising QoS. Specifically, weuse the CloudSim toolkit to simulate START in a cloud environment and compareit with state-of-the-art techniques (IGRU-SD, SGC, Dolly, GRASS, NearestFit andWrangler) in terms of QoS parameters such as energy consumption, executiontime, resource contention, CPU utilization and SLA violation rate. Experimentsshow that START reduces execution time, resource contention, energy and SLAviolations by 13%, 11%, 16% and 19%, respectively, compared to thestate-of-the-art approaches.

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