Section: New Results

Model-Driven Engineering and Video-surveillance

Participants : Sabine Moisan, Jean-Paul Rigault, Luis-Emiliano Sanchez.

keywords: Feature Model Optimization, Software Metrics, Requirement specification, Component-based system, Dynamic Adaptive Systems, Model-Driven Engineering, Heuristic Search, Constraint Satisfaction Problems

The domain of video surveillance (VS) offers an ideal training ground for Software Engineering studies, because of the huge variability in both the surveillance tasks and the video analysis algorithms [41] . The various VS tasks (counting, intrusion detection, tracking, scenario recognition) have different requirements. Observation conditions, objects of interest, device configuration... may vary from one application to another. On the implementation side, selecting the components themselves, assembling them, and tuning their parameters to comply with context may lead to a multitude of variants. Moreover, the context is not fixed, it evolves dynamically and requires run time adaptation of the component assembly.

Our work relies on Feature Models, a well-known formalism to represent variability in software systems. This year we have focused on an architecture for run time adaptation and on metrics to drive dynamic architecture changes.

Run Time Adaptation Architecture

Figure 35. Architecture of an adaptive system using models at run time. (In light blue the elements studied this year.)

The architecture of the run time system (also used for initialization at deployment time) is based on three collaborating modules as shown in Figure 35 . A Run Time Component Manager (RTCM) cooperates with the low levels (to manage the software components and capture events) and applies configuration changes. A Configuration Adapter (CA) receives events from the RTCM, and propagates them as features into the models to obtain a new configuration. The Model Manager (MM) embeds a specialized scripting language for Feature Models (Familiar [52] , [53] (Familiar has been developed at the I3S laboratory by the Modalis team.)) to manage the representation of the two specialized feature models and applies constraints and model transformations on them. The Model Manager produces new component configurations (a model specialization) that it sends to the CA. At its turn, the CA selects one single configuration (possibly using heuristics) and converts it into component operations to be applied by the RTCM.

This year we first finalized the interface between the Model Manager and the Configuration Adapter. On one hand, we transform the feature models obtained from Familiar into C++ representations enriched with software component information. On the other hand, we dynamically transform context change events into requests to Familiar .

Second, we searched for a suitable technology for handling components in the Run Time Component Manager. OSGi is an adequate de facto standard but it is mainly available in the Java world. However we could find a C++ implementation, complete enough for our needs (SOF, Service Oriented Framework [65] ). However, SOF has to be completed to adjust to the needs of our end users who are the video system developers. Thus, we are currently building a multi-threaded service layer on top of SOF, easy to use and hiding most of the nitty-gritty technical details of thread programming and SOF component manipulation. This layer provides end users with a set of simple patterns and allow them to concentrate only on the code of video services (such as acquisition, segmentation, tracking...).

As a matter of feasability study we are building an experimental video self adaptive system based on the afore mentionned architecture. Software components are implemented with the OpenCV library. In the final system, feature models and software components continuously interact in real time, modifying the whole system in response to changes in its environment.

Metrics on Feature Models to Optimize Configuration Adaptation at Run Time

As shown on figure 35 , the Configuration Adapter has to set up a suitable component configuration of the run time system. For this, each time the context changes, it receives a set of valid configurations (a feature sub-model) from the Model Manager. In most cases, this set contains more than one configuration. Of course, only one configuration can be applied at a given time and the problem is to select the “best” one. Here, “best” is a trade-off between several non-functional aspects: performance, quality of service, time cost for replacing the current configuration, etc.

It is thus necessary to rank the configurations. Our approach is to define metrics suitable for comparing configurations. Then the problem comes down to the widely studied problem of Feature model optimization  [55] . This problem is known to be an intractable combinatorial optimization problem in general.

We started with a study of the state of the art: metrics for general graphs as well specific to feature models, optimization and requirement specification on feature models... We obtained a structured catalog of quality and feature model metrics. Then we selected solutions based on heuristic search algorithms using quality and feature model metrics. We thus propose several strategies and heuristics offering different properties regarding optimality of results and execution efficiency.

These strategies and heuristics have been implemented, tested, and analyzed using random generated feature models. We got empirical measures about their properties, such as completeness, optimality, time and memory efficiency, scalability... This allows us to compare the performance of the different algorithms and heuristics, and to combine them in order to achieve a good trade-off between optimality and efficiency. Finally, the proposed algorithms have been introduced as part of the Configuration Adapter module.

This work is quite original from several aspects. First, we did not find any study using heuristic search algorithms for solving the feature optimization problem. Most studies apply Artificial Intelligence techniques such as CSP solvers, planning agents, genetic algorithms... Second, we do not restrict to the optimization of linear objective functions, but we also address non-linear ones allowing us to take into account a broader set of criteria. Among the possible criteria we consider quality of service of components, their performance, their set up delay, the cost of their replacement, etc. Finally, we apply our metrics at run time whereas most studies consider metrics only for static analysis of feature models.

Currently, we are still working on new variants of the search algorithms and new heuristics relying on techniques proposed in the domains of heuristic search and constraint satisfaction problems.