Effective service deployment and orchestration will be essential to accommodate user workloads with diverse requirements in cloud-native beyond 5G Radio Access Networks (RAN). Orchestration will have to take into account individual service quality requirements, latency constraints, and dependencies, while leveraging unique characteristics of dominant workloads, such as machine learning (ML) models. In this work, we address the orchestration of ML-based services, considering users that request application services that rely on network services, such as localization, positioning, etc. Each service is composed of functions, at potentially different quality levels. The objective is to maximize the network operator’s revenue by determining service deployment, quality selection and computational resource allocation. The resulting problem is a mixed-integer non-convex problem, which we show is NP-hard. We provide sufficient conditions for the problem to be submodular, and for the general case we propose JADES, which relies on linear relaxation and convexification to decompose the problem into two subproblems, which are solved iteratively until convergence, followed by dependent randomized rounding. Our evaluation based on synthetic workloads shows that JADES outperforms baselines in terms of operator revenue and computational efficiency.

Edge computing(EC) is emerging as a key enabler for latency-sensitive applications such asAugmented Reality(AR), autonomous driving, and industrial IoT, by bringing computational resources closer toWireless Devices(WDs). However, the limited computational capacity inherent to EC presents challenges in resource allocation and in designing pricing mechanisms that provide the right incentives and are aligned with the user-perceived service quality. This paper addresses these challenges by formulating a Stackelberg game that models WDs’ valuation of EC services based on their offloading rates and the service quality they receive. We prove the existence of Stackelberg equilibria and we propose a tractable approximation technique based on log-barrier functions for computing approximate equilibria. Furthermore, to overcome computational issues, we build on the concept of a Differential Stackelberg Equilibrium (DSE) and we propose Stackelberg Gradient Play (SGP), an implicit gradient-based algorithm that ensures convergence to DSE while maintaining efficiency. Extensive simulations show that our approach significantly outperforms existing methods, achieving up to 70% higher revenue for the edge operator while reducing computational overhead substantially. These results underscore the viability of our framework for use in EC systems that require fast, adaptive, and service-aware joint resource management and pricing.

The commercial adoption of Edge Computing (EC) will require pricing schemes that cater to the financial interests of the operators and of the users. Pricing in EC is particularly challenging as it has to take into account the limited amount of edge resources as well as the stochasticity of user workloads due to location-specific workload characteristics and differences in user activity. We formulate the problem of maximizing the revenue of a serverless edge operator through dynamically pricing compute and memory resources under time varying workloads as a sequential decision making problem under uncertainty. We provide analytical results for the optimal pricing strategy in a Markovian setting in steady state. For the general case, we propose a novel Generalized Hidden Parameter Markov Decision Process (GHP-MDP) formulation of the revenue maximization problem, and we propose a dual Bayesian neural network approximator as a solution. The key novelty of the proposed solution is that it can be pre-trained on synthetic traces and adapts fast to previously unseen workload characteristics. We use simulations based on synthetic and real traffic traces to show that the proposed solution is sample-efficient thanks to effective transfer learning, and it outperforms state-of-the-art learning approaches in terms of revenue and learning rate by up to 50% on real traces.

Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction for edge computing, we consider the problem of caching and pricing applications for edge computation offloading in a dynamic environment whereWirelesss Devices(WDs) can be active or inactive at any point in time. We model the problem as a single leader multiple-follower Stackelberg game, where the service operator is the leader and decides what applications to cache and how much to charge for their use, while the WDs are the followers and decide whether or not to offload their computations. We show that the WDs' interaction can be modeled as a player-specific congestion game and show the existence and computability of equilibria. We then show that under perfect and complete information the equilibrium price of the service operator can be computed in polynomial time for any cache placement. For the incomplete information case, we propose a Bayesian Gaussian Process Bandit algorithm for learning an optimal price for a cache placement and provide a bound on its asymptotic regret. We then propose a Gaussian process approximation-based greedy heuristic for computing the cache placement. We use extensive simulations to evaluate the proposed learning scheme, and show that it outperforms state of the art algorithms by up to 50% at little computational overhead.

We consider the problem of resource allocation, pricing and application caching for latency sensitive task of floading in serverless edge computing. We model the interaction between a profit-maximizing operator and cost-minimizing Wireless Devices (WDs) as a Stackelberg game where the operator is the leader and decides the price, resource allocation and set of applications to cache, while the WDs are the followers and decide whether to offload their tasks. We first show that the game has a Subgame Perfect Equilibrium (SPE), but computing it, is NP-hard. Importantly, we show that an SPE, which maximizes the operator's revenue, results in minimal energy consumption among the WDs. For computing an approximate SPE, we propose a linear time approximation algorithm with bounded approximation ratio for resource allocation and pricing, and we propose an efficient heuristic based on the utility density of individual applications for the joint optimization of caching, resource allocation and pricing. Our results show that the proposed algorithm outperforms state-of-the-art methods by up to an order of magnitude both in terms of revenue and total energy savings and has small computational overhead. An interesting feature of our results is that the utility of the operator is maximized by a solution that maximizes the WDs' energy savings through computation offloading, which makes it a promising candidate for energy efficient edge cloud deployments.

We consider the interplay between latency constrained applications and function-level resource management in a serverless edge computing environment. We develop a game theoretic model of the interaction between rate adaptive applications and a load balancing operator under a function-oriented pay-as-you-go pricing model. We show that under perfect information, the strategic interaction between the applications can be formulated as a generalized Nash equilibrium problem, and use variational inequality theory to prove that the game admits an equilibrium. For the case of imperfect information, we propose an online learning algorithm for applications to maximize their utility through rate adaptation and resource reservation. We show that the proposed algorithm can converge to equilibria and achieves zero regret asymptotically, and our simulation results show that the algorithm achieves good system performance at equilibrium, ensures fast convergence, and enables applications to meet their latency constraints.