Interless: Interference-Aware Deep Resource Prediction for Serverless Computing

Serverless is an emerging cloud computing paradigm that allows functions to share resources. However, function resource sharing introduces interference, which results in performance degradation. Existing resource prediction approaches ignore the function instance placement and interference between functions. Thus, they cannot predict the resource finely. This paper proposes Interless, an interference-aware resource prediction system for serverless computing with a sequence-to-sequence neural network. The Interless's encoder directly learns function instance interference by the TPA-LSTM module. TPA-LSTM can also capture historical request queuing for better prediction. Interless's decoder contains a GRU module for long-time series prediction. Long-time prediction is essential for time reservation in function scheduling and warm-up. Moreover, long-time series prediction helps Interless identify system anomalies and cyber threats by comparing monitored and predicted resource consumption. We implement Interless on top of Docker Swarm as a serverless system for resource prediction. Experimental results demonstrate that Interless reduces the MAPE, RSE, and SMAPE of prediction by 64%, 58%, and 65%, respectively, compared to the state-of-the-arts.