Li Ming, Zhang Jianshan, Lin Jingfeng, Chen Zheyi, Zheng Xianghan
College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China.
Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou 350002, China.
Sensors (Basel). 2023 Sep 12;23(18):7829. doi: 10.3390/s23187829.
The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers' concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.
新兴的无服务器计算已成为部署云应用程序的一种引人注目的范式，通过配置诸如延迟和内存限制等必要参数，减轻了开发人员对基础设施资源管理的担忧。现有的基于云的无服务器应用程序的资源配置解决方案大致可分为基于历史数据建模或稀疏测量与插值/建模相结合的方式。为了追求服务响应并节省网络带宽，平台已逐渐从传统云扩展到边缘。与云平台相比，无服务器边缘平台由于资源有限，通常会导致更多的运行开销，这在开发人员使用现有解决方案时会产生不理想的财务成本。同时，处理边缘平台的异构性极具挑战性，其特点是由于资源偏好不同而具有不同的定价。为应对这些挑战，我们提出了一种名为FireFace的自适应高效方法，它由预测和决策模块组成。预测模块提取无服务器应用程序中所有函数的内部特征，并使用此信息预测特定配置方案下函数的执行时间。基于预测模块，决策模块分析环境信息，并使用自适应粒子群优化算法和遗传算法算子（APSO-GA）算法为每个函数选择最合适的配置方案，包括CPU、内存和边缘平台。通过这种方式，可以在满足服务水平目标（SLO）的同时有效最小化财务开销。大量实验结果表明，我们的预测模型在所有三个指标下均获得了最优结果，实际无服务器应用程序的预测错误率在4.25%至9.51%范围内。我们的方法可以为每个应用程序找到最优资源配置方案，与其他经典算法相比，平均节省7.2%至44.8%。此外，FireFace具有快速适应性，能够有效响应动态环境调整资源分配方案。
