Edge Computing (EC) is a promising concept to overcome some obstacles of traditional cloud data centers to support Internet of Things (IoT) applications, especially time-sensitive applications. However, EC faces some challenges, including the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. A relevant issue that needs to be addressed by a resource manager is the service placement problem, which is the decision-making process of determining where to place different services (or applications). A related issue of service placement is how to distribute workloads of an application placed on multiple locations. Hence, we jointly investigate the load distribution and placement of IoT applications to minimize Service Level Agreement (SLA) violations due to the limitations of EC resources and other conflicting objectives. In order to handle the computational complexity of the formulated problem, we propose a multi-objective genetic algorithm with the initial population based on random and heuristic solutions to obtain near-optimal solutions. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other conflicting objectives, such as operational cost and service availability.

边缘计算（EC）是一个有前途的概念，可以克服传统云数据中心在支持物联网（IoT）应用程序（尤其是对时间敏感的应用程序）中遇到的一些障碍。但是，EC面临一些挑战，包括在由分布式节点和资源受限节点组成的网络边缘为异构应用程序分配资源。资源管理器需要解决的一个相关问题是服务放置问题，它是确定在何处放置不同服务（或应用程序）的决策过程。服务放置的一个相关问题是如何分配放置在多个位置的应用程序的工作负载。因此，由于EC资源的限制和其他相互冲突的目标，我们联合调查了IoT应用程序的负载分配和放置，以最大程度地减少违反服务水平协议（SLA）的情况。为了解决所提问题的计算复杂性，我们提出了一种基于随机和启发式解的具有初始种群的多目标遗传算法，以获得近似最优解。评估结果表明，我们的建议在响应截止日期违规以及其他冲突目标（例如运营成本和服务可用性）方面均优于其他基准算法。我们提出了一种基于随机和启发式解的初始种群的多目标遗传算法，以获得接近最优的解。评估结果表明，我们的建议在响应截止日期违规以及其他冲突目标（例如运营成本和服务可用性）方面均优于其他基准算法。我们提出了一种基于随机和启发式解的初始种群的多目标遗传算法，以获得接近最优的解。评估结果表明，我们的建议在响应截止日期违规以及其他冲突目标（例如运营成本和服务可用性）方面均优于其他基准算法。