Toward Stream Processing Efficiency Leveraging Cloud Burstable Instances
Vers une efficacité accrue du traitement des flux grâce aux instances Cloud Burstable
Résumé
Burstable instances offer a cost-efficient alternative to standard cloud instances by enabling short bursts of high CPU performance via a credit-based system. However, their limited ability to sustain long-term computational loads introduces challenges for continuous processing tasks. Stream Processing Systems (SPS), which handle real-time, unbounded data streams, often originating from highly dynamic and bursty sources, require flexible and responsive resource allocation. Given their workload variability, SPS applications present a promising opportunity for cost optimization through the strategic use of burstable instances. In this work, we examine the capabilities and limitations of deploying SPS tasks on burstable instances, assessing their effectiveness in maintaining performance while reducing costs. Our findings demonstrate that the limited resources of burstable instances can significantly impact processing latency. However, when correctly balanced in instance deployment and load distribution, they can achieve notable cost savings with low impact on service quality.
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