Optimization of Component Allocations in Middleware Platforms using Performance Models

Distributed enterprise applications are typically implemented as system-ofsystems composed of components and linked via middleware. These systems often utilize corresponding resources far below available capacity. In order to increase resource utilizations the consolidation of components demands several tests on environments comparable to the production system. Performance models can be used to landscape such system architectures and to simulate changes in the component topology or resource environment without harming production systems. Therefore, this work aims at extracting performance models from distributed middleware platforms. Based on these models, an architecture optimizer is built to test different allocation topologies. Subsequently, the optimized model is simulated and the prediction accuracy of architecture changes is evaluated in this work. This allows architects to evaluate component changes and topology variations without a replica of the production system. 1 Motivation and Purpose Middleware based distributed system-of-systems architectures are state of the art in large scale enterprise applications [BVD14]. These systems are composed of components that can be moved and duplicated from one instance to another using a deployment management software [Woo09]. The placement of components is a complicated task that today is merely assisted by logical topology recommendations [Woo09, KKR11]. These recommendations can work as guidelines but cannot answer the questions on how to size the target environment for a specific deployment unit and how to optimize the topology to a certain optimization goal. Logical topologies utilize the hardware below their possible capacity as the average load of data centers today is under 25% [PVR12]. Virtualized server environments already reduce this overprovisioning thus, increasing the hardware utilization [SB10]. However, virtualized server environments limit the optimization opportunities to the granularity level of single virtual machines. Middleware systems rely on more fine-grained deployment units, allowing operation engineers and architects to utilize unused capacity more efficiently. Planning and testing such changes in productive environments comprises risks for the stability. In addition, productive alike test environments and productive systems have comparable prices. Furthermore, such environments are usually used to capacity by