Understanding the behavior of distributed cloud service components in different load situations is important for efficient and automatic management and orchestration of these services. For this purpose and for practical research in distributed cloud computing in general, there is need for benchmarks and experimental data. In this paper, we describe our experiments for characterizing the relationship between resource demands of application components and the expected performance of applica- tions. We present initial results for predicting the interdependence between resource demands and performance characteristics using support vector regression and polynomial regression models. The data gathered from our experiments is publicly available.

Preemptive Routing and Wavelength Assignment (RWA) algorithms preempt established lightpaths in case not enough resources are available to setup a new lightpath in a Wavelength Division Multiplexing (WDM) network. The selection of lightpaths to be preempted relies on internal decisions of the RWA algorithm. Thus, if dedicated properties of the network topology are required by the applications running on the network, these requirements have to be known by the RWA algorithm. Otherwise it might happen that by preempting a particular lightpath these requirements are violated. If, however, these requirements include parameters only known at the nodes running the application, the RWA algorithm cannot evaluate the requirements. For this reason a RWA algorithm is needed which involves its users in the preemption decisions. We present a family of preemptive RWA algorithms for WDM networks. These algorithms have two distinguishing features: a) they can handle dynamic traffic by on-the-fly reconfiguration, and b) users can give feedback for reconfiguration decisions and thus influence the preemption decision of the RWA algorithm, leading to networks which adapt directly to application needs. This is different from traffic engineering where the network is (slowly) adapted to observed traffic patterns. Our algorithms handle various WDM network configurations including networks consisting of heterogeneous WDM hardware. To this end, we are using the layered graph approach together with a newly developed graph model that is used to determine conflicting lightpaths.