A tail-tolerant cloud storage scheduling based on precise periodicity detection

Abstract

Cloud storage is a fundamental component of the cloud computing system, which significantly affects the overall performance and quality of service of the cloud. Cloud storage servers face the challenge of imbalanced workloads. According to our observations on the time series generated by cloud storage, we found that the imbalance workloads will dramatically increase the tail latency of data access in the multi-tenant scenario. The intuitive solution is to periodicity detect the imbalance storage nodes and re-balance the loads. However, there are four challenges to accurately detect load of storage in the cloud with multiple tenants since the load may change frequently in cloud. This paper proposes PrecisePeriod, a precise periodicity detection algorithm customized for multi-tenant cloud storage. It removes outliers through data preprocessing, employs the discrete wavelet transform to remove high-frequency noise while keeping frequency domain information, computes the candidate periodicity queue using the autocorrelation function, and determines precise period through periodicity verification. Then, we design a cloud storage load balancing scheduling strategy based on PrecisePeriod, and the evaluation shows that the PrecisePeriod scheduling significantly reduces tail latency while only bringing $$1-2\%$$ 1 - 2 % overhead.