Serverless computing are invented to free developers of analytic services from the management of cloud resources. Developers only need to submit their code to a serverless edge cloud (SEC) and the cloud platform matches the submitted code to itsserverless functions, enabled by the paradigm of Function as a Service (FaaS). However, serverless functions usually are short-lived with limited resources. In this paper, we aim to address the challenge of how to fully utilize the short-lived serverless functions to enable continuous analysis of the most freshly-generated big data. We first formulate an optimization problem of age-aware big data query evaluation in an SEC network so that theage of datais minimized, where the age of data is the duration between the generation time of the data and the current time. We then propose approximation algorithms for the age-aware big data query evaluation problem of a single query, by proposing a parameterized virtualization technique that smartly handles the large resource demands of big data queries in short-lived and resource-constraint serverless functions. We further consider the scenario where big data queries arrive into the system one-by-one and their resource demands are uncertain. To this end, we devise an online learning algorithm with bounded regret by leveraging a memory repacking mechanism to improve resource utilization, for the problem of online age-aware big data query evaluation. We evaluate the performance of the proposed algorithms through extensive simulations to testify their performances in large scales. We also validate the effectiveness of the proposed mechanism in a real test-bed built on Kubernetes and OpenWhisk with TPC-DS benchmarks. Experimental results show that the proposed algorithms outperform the state-of-the-arts, by reducing the age of data by$8.82\%$on average.

Federated continual learning (FCL) is emerging as a key technology for time-sensitive applications in highly adaptive environments including autonomous driving and industrial digital twin. Each FCL trains machine learning models using newly-generated datasets as soon as possible, to obtain a highly accurate machine learning model for new event predictions. Theage of data, defined as the time difference between the generation time of a dataset and the current time, is widely adopted as a key criterion to evaluate both timeline and quality of training. In this paper, we study the problem of age-aware FCL in a mobile edge computing (MEC) network. We not only investigate optimization techniques that optimize the data selection and aggregator placement for FCL but also implement a real system as a prototype for age-aware FCL. Specifically, we first propose an approximation algorithm with a provable approximation ratio for the age-aware data selection and aggregator placement problem for FCL with a single request. In real application scenarios, there are usually multiple FCL requests that require to train models, and delays in the MEC network are usually uncertain. We then study the problem of age-aware data selection and aggregator placement problem for FCL with uncertain delays and multiple requests, by devising an online learning algorithm with a bounded regret based on contextual bandits. We finally implement a prototype for FCL in an MEC network, with various heterogeneous user equipments (UEs) and cloudlets with different computing capabilities in the network. Experiment results show that the performance of the proposed algorithms outperform existing studies, by achieving 47% lower age of data and 12% higher model accuracy.

Multi-access edge computing (MEC) enables extreme low-latency AI services, such as Augmented Reality (AR) and Virtual Reality (VR), by deploying cloudlets in locations close to users. Meanwhile, a 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing for both computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network in a hierarchical service market. For the service caching problem, we first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with a provable approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed Stackelberg congestion game to measure the efficiency loss of the game due to selfishness of network service providers. Considering that the request rate of each service may not be given in advance, we study the service caching problem with the uncertainlity of request rates, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.