2017

Journal

Soft Computing

Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer on-demand various types of services (e.g., software, storage, network). One of the most popular service models is Software as a Service (SaaS). To allow flexibility and reusability, SaaS can be offered in a composite form, where a set of interacting application and data components cooperate to form a higher-level functional SaaS. However, this approach introduces new challenges to resource management in the cloud, especially finding the optimal placement for SaaS components to have the best possible SaaS performance. SaaS Placement Problem (SPP) refers to this challenge of determining which servers in the cloud’s data center can host which components without violating SaaS constraints. Most existing SPP approaches only addressed homogenous SaaS components placement and only considered one type of constraints (i.e., resource constraint). In addition, none of them has considered the objective of maintaining a good machine performance by minimizing the resource usage for the hosting machines. To allow finding the optimal placement of a composite SaaS, we adopt a new variation of PSO called ’Particle Swarm Optimization with Composite Particle (PSO-CP).’ In the proposed PSO-CP-based approach, each composite particle in the swarm represents a candidate SaaS placement scheme. Composite particles adopt a collective behavior to explore and evaluate the search space (i.e., data center) and adjust their structures by collaborating with other composite or independent particles (i.e., servers). The implementation and experimental results show the feasibility and efficiency of the proposed approach.

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