Heuristic Load Scheduling Algorithm for Stateful Cloud BPM Engine. With the increasing popularity of cloud computing technology, the traditional Business Process Management System (BPMS) begins to transform to the architecture that deployed in the cloud. Since the traditional BPMS is often implemented as a stateful singleinstance architecture, the cloud BPMS providers will encounter the stateful service load scheduling problem when they refactor the traditional BPMS to the microservice architecture that deployed on the cloud server. In order to help realize the transformation of traditional BPMS and improve the load capacity of cloud BPMS with limited computing resources, we propose a heuristic load scheduling algorithm for stateful service scheduling. The algorithm makes use of the busyness metrics of the single BPMS engine instance microservice in the cloud BPMS architecture. Because the resource scheduling problem is always defined as online bin packing problem, we improve the BestFit algorithm to solve this kind of problem. We come up with the BestFit Decreasing algorithm based on cloud BPMS engine busyness measuring and load prediction to schedule the computing resources to business process instances. Compared to some common load scheduling algorithms, our algorithm help cloud BPMS increase load level by at least 15%.

Characterizing and Balancing the Workloads of SemiContainerized 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 QualityofService requirement of online services that requires short response time while maximizing the resource utilization. While applications should be colocated to improve the resource utilization, the colocation results in performance interference between the colocated applications that hurts the QoS of latencysensitive services. To understand this problem in depth, we characterize and analyze the real businesslevel application colocation 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).