The growth, development, and commercialization of artificial intelligence-based technologies such as self-driving cars, augmented-reality viewers, chatbots, and virtual assistants are driving the need for increased computing power. Most of these applications rely on Deep Neural Networks (DNNs), which demand substantial computing capacity to meet user demands. However, this capacity cannot be fully provided by users’ local devices due to their limited processing power, nor by cloud data centers due to high transmission latency from long distances. Edge cloud computing addresses this issue by processing user requests through 5G, which reduces transmission latency from local devices to computing resources and allows the offloading of some computations to cloud back-ends. This paper introduces a model for a Mobile Edge Cloud system designed for an application based on a DNN. The interaction among multiple mobile users and the edge platform is formulated as a one-leader multi-follower Stackelberg game, resulting in a challenging non-convex mixed integer nonlinear programming (MINLP) problem. To tackle this, we propose a heuristic approach based on Karush-Kuhn-Tucker conditions, which solves the MINLP problem significantly faster than the commercial state-of-the-art solvers (up to 50,000 times). Furthermore, we present an algorithm to estimate optimal platform profit when sensitive user parameters are unknown. Comparing this with the full-knowledge scenario, we observe a profit loss of approximately 1%. Lastly, we analyze the advantages for an edge provider to engage in a Stackelberg game rather than setting a fixed price for its users, showing potential profit increases ranging from 16% to 66%.

The proliferation of the Internet of Things, artificial intelligence, and real-time data processing applications has driven the demand for distributed computing architectures that span cloud, fog, and edge layers in a computing continuum. These architectures must address critical challenges in component placement and resource optimization to ensure low latency, cost efficiency, and compliance with Quality of Service (QoS) constraints. This paper introduces a novel optimization framework for addressing the joint problem of component placement and resource optimization in computing continua. The framework employs a Mixed Integer Nonlinear Programming model, where application components are modeled as a Directed Acyclic Graph and their performance is predicted using analytical models. A method based on the Karush-Kuhn-Tucker conditions is employed to compute the optimal number of virtual machine instances for a given component placement. This optimization is embedded within a reinforcement learning loop that iteratively refines placement decisions in response to fluctuations in workload. This hybrid approach ensures cost effectiveness while adhering to QoS constraints. Extensive experimental evaluations demonstrate the superiority of our framework. It outperforms leading approaches, including BARON solver, SPACE4AI-D, PPO_DLX, and a minimum k-cut baseline, achieving average cost reductions of 19%, 60%, 11%, and 6%, respectively, under dynamic workload conditions. These results highlight the efficiency, scalability, and adaptability of our approach, making it a robust solution to the demands of modern distributed systems.

We evaluate the performance of a genetic algorithm to discover the best set of rules to implement an intrusion against an ICT network. The rules determine how the attacker selects and sequentializes its actions to implement an intrusion. The fitness of a set of rules is assigned after exploiting it in an intrusion. The evaluation of the distinct sets of rules in the populations the algorithm considers requires multiple intrusions. To avoid the resulting noise on the ICT network, the intrusions target a digital twin of the network. We present a preliminary experimental results that supports the feasibility of the proposed solution.

Abstract A global optimization approach for solving non-monotone equilibrium problems (EPs) is proposed. The class of (regularized) gap functions is used to reformulate any EP as a constrained global optimization program and some bounds on the Lipschitz constant of such functions are provided. The proposed global optimization approach is a combination of an improved version of the algorithm, which exploits local bounds of the Lipschitz constant of the objective function, with local minimizations. Unlike most existing solution methods for EPs, no monotonicity-type condition is assumed in this paper. Preliminary numerical results on several classes of EPs show the effectiveness of the approach.

Abstract Braess' paradox is a classical result in the theory of congestion games. It motivates theoretically why adding a resource (e.g., an arc) to a network may sometimes worsen, rather than improve, the overall network performance. Differently from previous literature, which studies Braess' paradox in a non‐cooperative game‐theoretic setting, in this work, a framework is proposed to investigate its occurrence by exploiting cooperative games with transferable utility (TU games) on networks. In this way, instead of focusing on the marginal contribution to the network utility provided by the insertion of an arc when a single initial scenario is considered, the arc average marginal utility with respect to various initial scenarios, that is, its Shapley value in a suitably‐defined TU game, is evaluated. It is shown that, for choices of the utility function of the TU game modeling congestion, there are cases for which the Shapley value associated with an arc is negative, meaning that its average marginal contribution to the network utility is negative.

Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that service level agreements (SLAs) are met and avoiding wastes. In this paper we consider mathematical models for the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers.

The adoption of cloud technologies is steadily increasing. In such systems, applications can benefit from nearly infinite virtual resources on a pay-per-use basis. However, being the cloud massively multi-tenant and characterized by highly variable workloads the development of more and more effective provisioning policies assumes paramount importance. Boosted by the success of the cloud, the application of Game Theory models and methodologies has also become popular, since they have been demonstrated to suit perfectly to cloud social, economic, and strategic structures. This paper aims to study, model and efficiently solve the cost minimization problem associated with the service provisioning of SaaS virtual machines in multiple IaaSs. We propose a game-theoretic approach for the runtime management of resources from multiple IaaS providers to be allocated to multiple competing SaaSs, along with a cost model including revenues and penalties for requests execution failures. A distributed algorithm for identifying Generalized Nash Equilibria has been developed and analysed in detail. The effectiveness of our approach has been assessed by performing a wide set of analyses under multiple workload conditions. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (80 percent on average). Furthermore, increasing the number of IaaS providers SaaSs can achieve 9-15 percent cost savings from the workload distribution on multiple IaaSs.

The rapid evolution of mobile radio network technologies poses severe technical and economical challenges to mobile network operators (MNOs); on the economical side, the continuous roll-out of technology updates is highly expensive, which may lead to the extreme, where offering advanced mobile services becomes no longer affordable for MNOs which thus, are not incentivized to innovate. Mobile infrastructure sharing among MNOs becomes then an important building block to lower the required per-MNO investment cost involved in the technology roll-out and management phases. We focus on a radio access network (RAN) sharing situation where multiple MNOs with a consolidated network infrastructure coexist in a given set of geographical areas; the MNOs have then to decide if it is profitable to upgrade their RAN technology by deploying additional small-cell base stations and whether to share the investment (and the deployed infrastructure) of the new small-cells with other operators. We address such strategic problems by giving a mathematical framework for the RAN infrastructure sharing problem which returns the “best” infrastructure sharing strategies for operators (coalitions and network configuration) when varying techno-economic parameters such as the achievable throughput in different sharing configurations and the pricing models for the service offered to the users. The proposed formulation is then leveraged to analyze the impact of the aforementioned parameters/input in a realistic mobile network environment based on LTE technology.

Cloud computing is a new emerging paradigm that aims to streamline the on-demand provisioning of resources as services, providing end users with flexible and scalable services accessible through the Internet on a pay-per-use basis. Because modern cloud systems operate in an open and dynamic world characterized by continuous changes, the development of efficient resource provisioning policies for cloud-based services becomes increasingly challenging. This paper aims to study the hourly basis service provisioning problem through a generalized Nash game model. We take the perspective of Software as a Service (SaaS) providers that want to minimize the costs associated with the virtual machine instances allocated in a multiple Infrastructures as a Service (IaaS) scenario while avoiding incurring penalties for execution failures and providing quality of service guarantees. SaaS providers compete and bid for the use of infrastructural resources, whereas the IaaSs want to maximize their revenues obtained providing virtualized resources. We propose a solution algorithm based on the best-reply dynamics, which is suitable for a distributed implementation. We demonstrate the effectiveness of our approach by performing numerical tests, considering multiple workloads and system configurations. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (5% on average but up to 260% for individual SaaS providers). Furthermore, varying the number of IaaS providers means an 8%–15% cost savings can be achieved from the workload distribution on multiple IaaSs.

To accommodate the ever-growing traffic load and bandwidth demand generated by mobile users, mobile network operators (MNOs) need to frequently invest in high spectral efficiency technologies and increase their hold of spectrum resources; MNOs have then to weigh between building individual networks or entering into network and spectrum sharing agreements. We address here the problem of radio access network and spectrum sharing in 4G mobile networks by focusing on a case when multiple MNOs plan to deploy small cell base stations in a geographical area in order to upgrade their existing network infrastructure. We propose two cooperative game models (with and without transferable utility) to address the proposed problem: for given network (user throughput, MNO market, and spectrum shares) and economic (coalition cost and mobile data pricing model) settings, the proposed models output a cost division policy that guarantees coalition (sharing agreement) stability.

In recent years, the evolution and the widespread adoption of virtualization, service-oriented architectures, autonomic, and utility computing have converged letting a new paradigm to emerge: cloud computing. Clouds allow the on-demand delivering of software, hardware, and data as services. Currently, the cloud offer is becoming wider day by day because all the major IT companies and service providers, like Microsoft, Google, Amazon, HP, IBM, and VMWare, have started providing solutions involving this new technological paradigm. As cloud-based services are more numerous and dynamic, the development of efficient service provisioning policies becomes increasingly challenging. In this paper, we take the perspective of Software as a Service (SaaS) providers that host their applications at an Infrastructure as a Service (IaaS) provider. Each SaaS needs to comply with quality-of-service requirements, specified in service-level agreement (SLA) contracts with the end users, which determine the revenues and penalties on the basis of the achieved performance level. SaaS providers want to maximize their revenues from SLAs, while minimizing the cost of use of resources supplied by the IaaS provider. Moreover, SaaS providers compete and bid for the use of infrastructural resources. On the other hand, the IaaS wants to maximize the revenues obtained providing virtualized resources. In this paper, we model the service provisioning problem as a generalized Nash game and we show the existence of equilibria for such game. Moreover, we propose two solution methods based on the best-reply dynamics, and we prove their convergence in a finite number of iterations to a generalized Nash equilibrium. In particular, we develop an efficient distributed algorithm for the runtime allocation of IaaS resources among competing SaaS providers. We demonstrate the effectiveness of our approach by simulation and performing tests on a real prototype environment deployed on Amazon EC2. Results show that, compared to other state-of-the-art solutions, our model can improve the efficiency of the cloud system evaluated in terms of Price of Anarchy by 50-70 percent.

The most advanced solutions that are currently adopted in ports and terminals use technologies based on radio frequency identification (RFID) and the Global Positioning System (GPS) to identify and localize shipping containers in the yard. Nevertheless, because of the limitations of these solutions, the position of containers is still affected by errors, and it cannot be determined in real time. In this paper, a nonconventional approach is presented: Each container is equipped with nodes that use wireless communication to detect neighbor containers and to send proximity information to a base station. At the base station, geometrical constraints and proximity data are combined to determine the positions of containers. Missing information due to faulty nodes is tolerated by modeling geometrical constraints as an integer linear programming problem. Numerical simulations show that most of the containers can be localized, even when the number of nodes that are affected by faults is on the order of 30%.

Cloud computing is an emerging paradigm which allows the on-demand delivering of software, hardware, and data as services. As cloud-based services are more numerous and dynamic, the development of efficient service provisioning policies become increasingly challenging. Game theoretic approaches have shown to gain a thorough analytical understanding of the service provisioning problem. In this paper we take the perspective of Software as a Service (SaaS) providers which host their applications at an Infrastructure as a Service (IaaS) provider. Each SaaS needs to comply with quality of service requirements, specified in Service Level Agreement (SLA) contracts with the end-users, which determine the revenues and penalties on the basis of the achieved performance level. SaaS providers want to maximize their revenues from SLAs, while minimizing the cost of use of resources supplied by the IaaS provider. Moreover, SaaS providers compete and bid for the use of infrastructural resources. On the other hand, the IaaS wants to maximize the revenues obtained providing virtualized resources. In this paper we model the service provisioning problem as a Generalized Nash game, and we propose an efficient algorithm for the run time management and allocation of IaaS resources to competing SaaSs.