Dynamic Provisioning of Service Composition in a MultiTenant SaaS Environment Multitenant service composition has become a common delivery model for business processes in cloud computing. To dynamically support the workload tenant variation, elasticity holds the promise of ensuring the quality of service (QoS) of the business process by providing the involved service instances at a low cost. However, integrating both of multitenancy and elasticity during service composition is a key problem for serving multiple tenants from a single process instance. Nowadays, existing approaches in the field of cloud service composition, although numerous, still fall short since they cannot adequately address issues related to supporting the scalability of the composed service and adapting it to the workload fluctuation. In this paper, we propose a holistic approach which makes the dynamic multitenant services matching and manages their elasticity in distributed business processes. This approach is based on a generic service pattern that integrates multitenancy property and handles elasticity at the process and service levels. Furthermore, we present elastic composition algorithms to compose multitenant cloud services and perform their elasticity through the proposed service pattern. The evaluation of our approach, compared to the baseline approach, proves that the latency taken to provide an elastic multitenant service composition and detect its SLA (Service Level Agreements) violation are reasonably short. We also show that the CPU overhead of using our approach is negligible. Furthermore, experimental results demonstrate the merits of our approach in terms of minimizing the memory consumption through the deployed service instances.

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).