A Model-Driven Approach for Solving the Software Component Allocation Problem

Abstract

The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by component-based software engineering, finding the optimal allocation of software artifacts to the pool of available devices and computation units could bring many benefits, such as improved quality of service (QoS), reduced energy consumption, reduction of costs, and many others. Therefore, in this paper, we introduce a model-based framework that aims to solve the software component allocation problem (CAP). We formulate it as an optimization problem with either single or multiple objective functions and cover both cases in the proposed framework. Additionally, our framework also provides visualization and comparison of the optimal solutions in the case of multi-objective component allocation. The main contributions introduced in this paper are: (1) a novel methodology for tackling CAP-alike problems based on the usage of model-driven engineering (MDE) for both problem definition and solution representation; (2) a set of Python tools that enable the workflow starting from the CAP model interpretation, after that the generation of optimal allocations and, finally, result visualization. The proposed framework is compared to other similar works using either linear optimization, genetic algorithm (GA), and ant colony optimization (ACO) algorithm within the experiments based on notable papers on this topic, covering various usage scenarios—from Cloud and Fog computing infrastructure management to embedded systems, robotics, and telecommunications. According to the achieved results, our framework performs much faster than GA and ACO-based solutions. Apart from various benefits of adopting a multi-objective approach in many cases, it also shows significant speedup compared to frameworks leveraging single-objective linear optimization, especially in the case of larger problem models.

A software platform for constructing a digital twin of the integrated energy system

“…The MDE concept is a set of methodological approaches to the automated construction of complex software systems based on predeveloped models. This concept of software development is currently actively developed; approaches based on it make it possible to successfully develop complex software systems [16][17][18]. Approaches to automating the stages of software construction based on metaprogramming are described in [19,20].…”

Automated Design Space Exploration for Resource Allocation in Software-Defined Vehicles

“…Al-Azzoni et al [7] presented an adaptable framework to solve resource allocation problems, in which users can develop individual meta-and instance models to describe their systems. This framework differs from the above-mentioned works, whose meta-models were designed for particular systems; it provides a customized meta-model for software component allocation problems containing classes for target components and constraints.…”

Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

“…Both pymoo and PyEcore provide the necessary tools to develop a network planning pipeline within the so-called pyallocation framework [94] for optimal component allocation. Whereas PyEcore ensures the precise definition of input and output, pymoo provides the necessary tools and mechanisms responsible for the solution of the optimization problem itself.…”

“…Table 2 illustrates the mapping between source element(s) from the WNP metamodel and target elements(s) in the CAP metamodel, where each row corresponds to a single model-to-model transformation rule. More details about the underlying transformation mechanisms we rely on in the context of model-driven CAP are presented in our previous work [94].…”

“…e transformation maps the concepts in the network planning domain to their matching ones in the domain of component allocation. In a transformation, the generated component allocation model represents an optimization problem that is solved using the pyallocation framework [94]. e transformation is designed so that the solutions to the optimization problem corresponding to the CAP are the same as the ones for the network planning model.…”