Coarse-Grain QoS-Aware Dynamic Instance Provisioning for Interactive Workload in the Cloud

Cloud computing paradigm renders the Internet service providers (ISPs) with a new approach to deliver their service with less cost. ISPs can rent virtual machines from the Infrastructure-as-a-Service (IaaS) provided by the cloud rather than purchasing them. In addition, commercial cloud providers (CPs) offer diverse VM instance rental services in various time granularities, which provide another opportunity for ISPs to reduce cost. We investigate a Coarse-grain QoS-aware Dynamic Instance Provisioning (CDIP) problem for interactive workload in the cloud from the perspective of ISPs. We formulate the CDIP problem as an optimization problem where the objective is to minimize the VM instance rental cost and the constraint is the percentile delay bound. Since the Internet traffic shows a strong self-similar property, it is hard to get an analytical form of the percentile delay constraint. To address this issue, we purpose a lookup table structure together with a learning algorithm to estimate the performance of the instance provisioning policy. This approach is further extended with two function approximations to enhance the scalability of the learning algorithm. We also present an efficient dynamic instance provisioning algorithm, which takes full advantage of the rental service diversity, to determine the instance rental policy. Extensive simulations are conducted to validate the effectiveness of the proposed algorithms.