The evolving landscape of edge computing envisions platforms operating as dynamic intermediaries between application providers and edge servers (ESs), where task offloading is coupled with payments for computational services. This paper investigates a multi - agent system where both the platform and ESs are self - interested entities, addressing the joint optimization of revenue maximization, resourc e allocation, and task offloading. We propose a novel Stackelberg game - based framework to model interactions between stakeholders and solve the optimization problem using a Bayesian Optimization - based centralized algorithm. Extensive numerical evaluations demonstrate the effectiveness of t he proposed mechanisms in achieving superior performance compared to existing baselines. Keywords -- Mobile edge computing, computation offloading, resource slicing, DRL - driven traffic prediction I. Introduction In recent years, a surge of novel applications, such as augmented reality, interactive gaming, and autonomous driving, has placed unprecedented demands on computational and network resources. These applications are both resource - intensive and delay - sensitive, necessitating robust and low - latency computi ng frameworks. Multi - access edge computing (MEC), previously referred to as mobile edge computing, has emerged as a promising paradigm to address these challenges.

Network slicing achieves cost-efficient slice customization to support heterogeneous applications and services. Configuring cross-domain resources to end-to-end slices based on service-level agreements, however, is challenging, due to the complicated underlying correlations and the simulation-to-reality discrepancy between simulators and real networks. In this paper, we propose Atlas, an online network slicing system, which automates the service configuration of slices via safe and sample-efficient learn-to-configure approaches in three interrelated stages. First, we design a learning-based simulator to reduce the sim-to-real discrepancy, which is accomplished by a new parameter searching method based on Bayesian optimization. Second, we offline train the policy in the augmented simulator via a novel offline algorithm with a Bayesian neural network and parallel Thompson sampling. Third, we online learn the policy in real networks with a novel online algorithm with safe exploration and Gaussian process regression. We implement Atlas on an end-to-end network prototype based on OpenAirInterface RAN, OpenDayLight SDN transport, OpenAir-CN core network, and Docker-based edge server. Experimental results show that, compared to state-of-the-art solutions, Atlas achieves 63.9% and 85.7% regret reduction on resource usage and slice quality of experience during the online learning stage, respectively.

These words conjure visions of decision-making computers replacing whole departments and divisions -- a future many companies believe is too far away to warrant investment. But the reality is that artificial intelligence is here, and here to stay. Particularly at the enterprise level, a growing number of companies are tuning in to the data science, productivity, and promise of machines that can think for themselves. Recent data from the National Venture Capital Association shows that 1,356 AI-related companies raised $18.5 billion in 2019 in the US, up from the $16.8 billion in 2018. Despite scaremongering projections that millions will need to switch occupations as robots and algorithms take over specific tasks once done by humans, most analyses project job gains as a result of AI, machine learning, and deep learning.

In this work, we study the guaranteed delivery model which is widely used in online advertising. In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers. A crucial problem for the advertising platform is how to fully utilize the valuable user traffic to generate as much as possible revenue. Different from previous works which usually minimize the penalty of unsatisfied contracts and some other cost (e.g. representativeness), we propose the novel consumption minimization model, in which the primary objective is to minimize the user traffic consumed to satisfy all contracts. Under this model, we develop a near optimal method to deliver ads for users. The main advantage of our method lies in that it consumes nearly as least as possible user traffic to satisfy all contracts, therefore more contracts can be accepted to produce more revenue. It also enables the publishers to estimate how much user traffic is redundant or short so that they can sell or buy this part of traffic in bulk in the exchange market. Furthermore, it is robust with regard to priori knowledge of user type distribution. Finally, the simulation shows that our method outperforms the traditional state-of-the-art methods.