Self-scalable services in service oriented software for cost-effective data farming

• We introduce self-scalable services as an extension of Service Oriented Architecture.
• We define scaling rules to express scaling policy for the service.
• Evaluation of the concepts is based on a massively scalable platform for data farming.
• Cost-effectiveness is increased in comparison with management based on fulfilling peak load.

Software maintenance is one of the major concerns in service oriented ecosystem with an ever-increasing importance. In many cases, the cost of software maintenance is higher than the cost of software development. In particular, long-lasting services, which operate in a dynamically changing environment, require continuous management and administration. One of the important administration actions is scaling management. The problem lies in responding to workload changes of the hosted services as fast as possible. This is especially important in regard to (but not limited to) cloud environments where unnecessary resource usage leads to unnecessary costs. In this paper, we are introducing the self-scalable services and scaling rules, which are intended to support development of self-scalable systems based on Service Oriented Architecture. We propose a design of a self-scalable service based on some of the well-known software development practices along with a definition of scaling rules, which express scaling policy for the service. Both concepts were evaluated in the context of a massively scalable platform for data farming. The evaluation demonstrates advantages of utilizing the proposed concepts to manage the platform in comparison with traditional platform management strategies based on fulfilling peak load.