Meta-Heuristics for Solving the Software Component Allocation Problem

Al-Azzoni, Issam; Iqbal, Saqib

doi:10.1109/access.2020.3015864

Cited by 6 publications

(13 citation statements)

References 27 publications

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“…In the first set of experiments, we apply our framework to solve the component allocation problems presented in [19]. These problems were collectively referred to as Systems 0 through 9.…”

Section: Resultsmentioning

confidence: 99%

“…In [19], the authors implement two meta-heuristics for solving the CAPs. The two metaheuristics are genetic algorithm (GA) and ant colony optimization (ACO).…”

Section: Related Workmentioning

confidence: 99%

“…Model transformation plays a central role in our framework. The Appendix shows a model transformation program that can be used to transform models conforming to the meta-model defined in [19] to models conforming to the CAP meta-model. This program is used in this paper as an example to demonstrate the role of model transformation in our framework.…”

Section: Model Transformationmentioning

confidence: 99%

“…The source meta-model in [19] accommodates the definition of software component allocation problems in embedded, heterogeneous systems. The meta-model assumes that there exist three resources provided by the computational units: CPU, memory, and power.…”

Section: Model Transformationmentioning

confidence: 99%

“…In the meta-model, there is a TradeOffVector element that is used to define the trade-off weights assigned to the three resources. These weights are used to define the singleobjective function used by the component allocation problems considered in [19]. The meta-model is defined in Ecore [9].…”

Section: Model Transformationmentioning

confidence: 99%

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

2021

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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.

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“…In the first set of experiments, we apply our framework to solve the component allocation problems presented in [19]. These problems were collectively referred to as Systems 0 through 9.…”

Section: Resultsmentioning

confidence: 99%

“…In [19], the authors implement two meta-heuristics for solving the CAPs. The two metaheuristics are genetic algorithm (GA) and ant colony optimization (ACO).…”

Section: Related Workmentioning

confidence: 99%

Section: Model Transformationmentioning

confidence: 99%

Section: Model Transformationmentioning

confidence: 99%

Section: Model Transformationmentioning

confidence: 99%

See 3 more Smart Citations

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

2021

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Problem Solving in Crowd Management Using Heuristic Approach

et al. 2022

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There are many problems that procedural algorithms can solve efficiently. However, these algorithms are sometimes too slow to abide by the time available for performing the solution; other times, it is impossible to get a solution using procedural algorithms. A heuristic method is a practical approach that can reach an approximation of an efficient solution where the optimum is not guaranteed. Heuristic techniques are applied in many real-world problems, including crowd management; using heuristic-based models helped to comprehend crowd behavior better and increase simulation reliability. This paper reviews many heuristic-related articles to gather the aspects of the topic in one place and clear the fuzziness to make it easy to comprehend. The paper covers some of the previous works with similar approaches and presents state-of-the-art heuristic solutions for real-world problems. These techniques are discussed under three classifications: simple heuristics, meta-heuristics, and hyper-heuristics. Most importantly, the paper explores the heuristic role in crowd field problems concluding that heuristics are primarily applied in modeling when it comes to the Crowd field. It investigates different heuristics for crowd management. The main intent of this review is to establish a comprehensive understanding of heuristics-related operations in the crowd management field. Moreover, it aims to support other researchers' future work and fill research gaps by highlighting the absence of crowd problems from heuristics literature and the limitations of each heuristics approach.

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