Metaverse can unleash the potentials of Internet of Sense (IoS) communication by intertwining objects and environment between physical world and parallel virtual world. In order to digitally experience smell or taste and navigate effortlessly in virtual reality, optimal resource allocation to strengthen sensing data based infrastructure system is a critical research challenge. The Metaverse Infrastructure Service Providers (MISPs) tap into data marketplace and subscribe to resources in advance for fulfilling the needs of data consumers and users. The demand of the data based services being uncertain, non-optimal subscription schemes may lead to unwanted resource wastage or shortage. Thus, we propose a Stochastic Integer Programming (SIP) model with two phase reservation and on-demand plans for optimal resource allocation in data marketplace. Further along this line, we strive to predict the demand by leveraging Quantum Neural Networks (QNN) that is able to learn with fewer historical data in comparison to classical machine/deep learning paradigms. Extensive simulation results justify that QNN as a supporting model can significantly reduce the computational complexities of SIP formulation. This research can contribute to reduce Metaverse resource fabrication costs, upgrade the profit margin for MISPs by increasing data based service sales revenue, provide real-time resource management decisions, and overall make real impacts in the virtual world.

Metaverse refers to the intersection of parallel virtual worlds with their physical counterparts by allowing users to interact with virtual people, objects, and environments. Resource allocation in various aspects of Metaverse domains, called as MetaSlices hereinafter, is a crucial optimization research problem. To serve this purpose, we consider a MetaSlice framework with the notion of sharing resources among common functions and enable placing time-sensitive services at the edge of multi-tier architecture in proximity to users. Unfortunately, the classical Integer Linear Programming is inappropriate for such heavily constrained optimization problem due to the extensive running time and memory. Hence, we model a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation to simultaneously optimize resources and secure Quality of Service for MetaSlices as a paradigm shift towards quantum computing. Furthermore, we propose to employ a hybrid classical-quantum WSQA to optimize resource under uncertainty, offer ultra-low running time, and increase service acceptance rate/scalability in resource-hungry and dynamic Metaverse system. Extensive simulation results demonstrate that WSQA outperforms other classical and standalone quantum annealing approaches, even with the limited availability of qubits (quantum resources). Thus, this research paves the way to decrease massive resource fabrication costs and upgrade profit margin for Metaverse Internet Service Providers, while simultaneously providing real-time services for Metaverse users.

Metaverse can unleash the potentials of Internet of Sense (IoS) communication by intertwining objects and environment between physical world and parallel virtual world. In order to digitally experience smell or taste and navigate effortlessly in virtual reality, optimal resource allocation to strengthen sensing data based infrastructure system is a critical research challenge. The Metaverse Infrastructure Service Providers (MISPs) tap into data marketplace and subscribe to resources in advance for fulfilling the needs of data consumers and users. The demand of the data based services being uncertain, non-optimal subscription schemes may lead to unwanted resource wastage or shortage. Thus, we propose a Stochastic Integer Programming (SIP) model with two phase reservation and on-demand plans for optimal resource allocation in data marketplace. Further along this line, we strive to predict the demand by leveraging Quantum Neural Networks (QNN) that is able to learn with fewer historical data in comparison to classical machine/deep learning paradigms. Extensive simulation results justify that QNN as a supporting model can significantly reduce the computational complexities of SIP formulation. This research can contribute to reduce Metaverse resource fabrication costs, upgrade the profit margin for MISPs by increasing data based service sales revenue, provide real-time resource management decisions, and overall make real impacts in the virtual world.

The prospective Internet of Things (IoT) vertical use cases demand latency perception, privacy preservation, and scalability intelligence equipped Virtual Network Function (VNF) orchestration in a dynamic context. With the massive growth of IoT connectivity, smart VNF orchestration with real-time deployment abilities is vital for the ubiquitous digital network environment. Hence, this paper collaboratively considers all the future service orchestration specifications. Moreover, we urge the necessity to go beyond the traditional service deployment framework and introduce VNF allocation at edge cloudlet small scale data-centers. Extensive simulation results manifest the applicability and potential of our proposed deep learning models with the twist of ensemble techniques for automated VNF orchestration. Additionally, our proposed ensemble deep learning aided approach inspires the employment of intelligent orchestrator to address 6G network era challenges for perpetual telecommunication research enigmas.

Among the massive pool of Internet of Things (IoT) devices in network function virtualization (NFV) context, the urgency for efficient service orchestration is constantly growing. The emerging challenges can be addressed as collaborative optimization of resource utilities and ensuring Quality-of-Service (QoS) with prompt orchestration in dynamic, congested, and resource-hungry IoT networks. Traditional mathematical programming models are NP-hard, hence inappropriate for time sensitive IoT scenarios. This paper promotes the need to go beyond the realms and propose an intelligent Deep Q-Network (DQN) driven service function chain (SFC) orchestration, named as DSO hereafter. We further equip this proposed DSO model with the notion of sharing the flow of already deployed network function rather than urging a new instantiation. The sharing conceptualization improves resource utilization, and DQN is employed for adaptive, robust, and swift orchestration. Our extensive simulation results demonstrate the remarkable capability and adaptability of the proposed DSO model for cutting back running time (≈ 10 hours) and ensuring near-optimal resource utilization across extremely dense IoT substrate network settings. Thus, this research can be regarded as a pioneering tread to scale down massive IoT resource fabrication costs, upgrade profit margin for providers, and sustain QoS.

In the sixth generation (6G) networks, due to the massive Internet of Things (IoT) connectivity and substantial growth of communication traffic, an effective Virtual Network Function (VNF) orchestration scheme is anticipated to function dynamically and intelligently. Moving beyond the traditional paradigm of the VNF orchestration and employing VNFs on the network edge located cloudlets based on the inspiration from multi-access edge computing can intensify the overall performance of delay-sensitive applications. In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions. We also address the challenges associated with state-of-the-art approaches to deal with dynamic network traffic and topology patterns. Our envisioned methods, based on Convolutional Neural Networks and Artificial Neural Networks named as E-ConvNets and E-ANN respectively, suggest two proactive VNF deployment strategies. These VNF placement strategies demonstrate (simulation results) encouraging performance (optimality gap nearly 7%) in terms of minimizing relocation and communication costs, and high scalability intelligence factor (around 0.93). Moreover, the presented results are further indications of integrating edge computing and deep learning-based strategies into similar research enigmas for future telecommunication networks.