Enabling Age-Aware Big Data Analytics in Serverless Edge Clouds

With the fast development of artificial intelligence applications, large-volume big data generated in the edge of networks are waiting for real-time analysis, such that the valuable information is unveiled. Analytic developers for big data applications usually face the burden of managing the underlying cloud resources, which greatly drags the speed of analytic development. Serverless Computing is envisioned as an enabling technology to release the management burden of developers and to enable agile big data analytics. That is, big data analytics can be implemented in short-lived functions via the Function-as-a-Service (FaaS) programming paradigm. In this paper, we aim to fill the gap between serverless computing and mobile edge computing, via enabling query evaluations for big data analytics in short-lived functions of a serverless edge cloud (SEC). Specifically, we formulate novel age-aware big data query evaluation problems in an SEC so that the age of data is minimized, where the age of data is defined as the time difference between the current time and the generation time of the dataset. We propose approximation algorithms for the age-aware big data query evaluation problem with a single query, by proposing a novel parameterized virtualization technique that strives for a fine trade-off between short-lived functions and large resource demands of big data queries. We also devise an online learning algorithm with a bounded regret for the problem with multiple queries arriving dynamically and without prior knowledge of resource demands of the queries. We finally evaluate the performance of the proposed algorithms by extensive simulations. Simulation results show that the performance of our algorithms is promising.