Characterizing and Balancing the Workloads of Semi-Containerized Clouds

With the development of Cloud computing, many applications are deployed on the Clouds (either public Clouds or private Clouds) for the elastic resource management. A key challenge here is satisfying the Quality-of-Service requirement of online services that requires short response time while maximizing the resource utilization. While applications should be co-located to improve the resource utilization, the co-location results in performance interference between the co-located applications that hurts the QoS of latency-sensitive services. To understand this problem in depth, we characterize and analyze the real business-level application co-location scenario based on the trace of an actual Cloud product released by Alibaba. Our observation shows that the datacenter suffers from severe load imbalance that often results in QoS violation of the online services on the servers with heavy workload. To balance the workload, we propose a scheduling algorithm based on the resource reservation and utilization of containers. Based on the comparison between the experiment and the existing operation results, the proposed algorithm reduces the standard deviation of the load on servers from 5.27 to 1.43 (CPU) and 9.83 to 1.93 (Memory).