Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user’s movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user’s mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user’s mobility traces, avoiding the random generation of users’ traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices’ batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider.

中文翻译：

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大规模网络-物理-社交系统的具有 QoS 保证的移动性和节能服务组合算法

由于网络物理社会系统（CPSS）中服务的移动性和随机性，开发确保高可用性、最小能耗和高质量服务（QoS）的服务组合方法仍然是一个复杂的挑战。在过去的二十年中，文献中提出了几种服务组合方法来应对这一挑战。然而，现有的方法具有一定的局限性，特别是在服务可能从一个位置移动到另一个位置、由于大量电池使用而变得不可用、遇到故障或质量下降的情况下。这些限制通常会出现，因为这些方法在以随机方式定义用户的移动时不能同时集成移动性、能量和 QoS 约束。本文提出基于学习的群体优化感知服务组合算法（LS-SCA）来克服上述缺点。该方法通过在服务组合过程中同时考虑用户的移动性、能量和 QoS 标准，超越了现有方法。首先，本研究采用运动小世界（SWIM）移动模型来确定用户的移动轨迹，避免用户轨迹的随机生成。其次，提出了一种能源消耗模型，通过避免设备电池的过度使用来提高能源效率，过度使用会降低服务的可用性并导致组合故障。第三，在组合过程中使用两阶段基于学习的群体优化器（TPLSO）方法来寻找满足全局QoS约束的次优组合，并且在移动性、能量和QoS方面具有最高效用。与大多数现有的基于元启发式的服务组合方法不同，本文利用 TPLSO 方法仅改进组合的子集，从而减少了组合时间并提高了 QoS 效用。的组成。使用两个真实数据集的模拟场景表明，LS-SCA 方法在能耗、QoS 实用性和组合可用性方面优于六个基线。这种显着的性能使得所提出的方法更适合实际应用，其中能源效率、服务质量和可用性是需要考虑的关键因素。