The integration of unmanned aerial vehicles (UAVs) and satellite technologies into modern border surveillance offers significant potential, eliminating the deployment limitations of terrestrial networks in remote and dynamic environments. These technologies ensure comprehensive coverage, and provide continuous connectivity in all areas with dynamic deployment. However, the integration presents optimization challenges in terms of resource management, communication, and energy efficiency. In this work, we propose a novel hierarchical optimization framework to address these challenges, modeling a terrestrial-non-terrestrial border surveillance (TNTBS) system in which telecom functions and surveillance services (e.g., synthetic aperture radar) are containerized and dynamically deployed into UAVs and satellites. We deal with battery, backhaul, coverage and surveillance service constraints to optimize the operational cost. To overcome the computational intractability of the large-scale problem of TNTBS, which is modeled as a mixed-integer linear programming (MILP) problem, we introduce a Stackelberg game-based approach that enables scalable and distributed decision making with multiple agents and each agent can leverage a local solver or learning model to optimize its local objective. Our extensive simulations demonstrate that our solution achieves near-optimal performance, while ensuring real-time operation and computational efficiency.

Recently, the exponential growth of the Internet-of-Things (IoT) services with heterogeneous requirements becomes a burden to the traditional cloud/data center platform. Edge computing is an emerging solution to gain business value of IoT services where real-time demands become satisfied by moving computing resources close to data sources. Nevertheless, edge resources are still limited to be able to fulfill all demands at the same time. Among new approaches, resource sharing between edge/cloud service providers has been considered as a promising mechanism to address resource scarcity and pursue cost reduction. In this article, we propose an allocation and sharing model in the edge cloud network where providers team up to efficiently utilize resources, named the share-to-run IoT services (SRIS). In particular, we formulate a resource allocation and sharing optimization model to implement IoT services of multiple edge/cloud providers that can maximize the providers’ utility while satisfying service constraints. We relax SRIS into a tractable form that can be solved efficiently using well-known distributed convex frameworks, such as the dual decomposition and alternating direction method of multipliers. Finally, we evaluate our methods by providing several simulation cases, in which our proposed mechanisms show outstanding outcomes by obtaining a faster convergence, increasing by 6.9% of utilization, and 16% of acceptance rate compared to the nonoptimal approach.

With the capability of decoupling the control plane and the data plane of networks, Software-Defined Network (SDN) enables flexible and efficient implementations in networks. In addition, Network Function Virtualization (NFV) with Virtual Network Function (VNF) service chain capabilities provides high-performance networks with greater scalability, elasticity, and adaptability. Such an elastic deployment of service chains results in different Service Level Agreements (SLA) and resource requirements on the control plane. In this work, we illustrate the impact of service chains on the control plane and formulate the dynamic controller/switch mapping (DCSM) problem in NFV networks in order to reduce the operational cost. We address the combinatorial optimization problem, DCSM, by designing a novel mechanism to relax DCSM into a tractable problem based on the Penalty Successive Upper Bound Minimization (PSUM) method. In doing so, we conduct several simulation scenarios to evaluate the performance. The experimental results show that our proposed algorithms can achieve a near-optimal result and reduce the operational cost up to 31.7% and 28.3% compared to K-Mean and the matching game-based approaches, respectively.

Internet of Things (IoT) services have been implemented for several network applications from smart cities to rural areas. However, there are many barriers to provide an efficient solution for the IoT service deployment underlying innovation SDN/NFV-based technologies. First, though an IoT service can flexibly deploy via virtual network functions (VNFs), a deployment scheme needs to solve the joint routing and resource allocation problem, which becomes more difficult than the traditional centralized cloud/datacenter solution due to distributed resources in the edge-cloud network. In addition, due to uncertain workloads in IoT services, static optimization solutions may not deal with uncompleted knowledge of the entire input, which is often given by assumptions, but unrealistic in current provisioning approaches. Aiming to address these issues, we model an online mechanism for the dynamic IoT service chain deployment to optimize the operational cost in a finite horizon. We propose a JOint Routing and Placement problem for IoT service chain (JORP) that can dynamically scale in/out the number of VNF instances. We then propose a learning method to efficiently solve JORP based on branch-and-bound (BnB). Our proposed learning mechanism can intelligently imitate the branching/pruning actions of BnB, and remove unlikely solutions in the search space based on the deep neural network model to improve the performance. In that respect, we take an intensive simulation that illustrates the promising result of our proposed deep learning method compared to BnB and the greedy baseline in terms of the performance of the algorithm and the operational cost reduction.

As an emerging solution to latency requirements of Internet of Things (IoT) services, edge computing can bring powerful processing capacity closer to data sources. However, with the limited resources at edge nodes, a major challenge is finding optimal resources in distributed edges to reduce the operational costs of service deployment. Prior works focus mainly on static optimization which may not work efficiently with the time-varying workloads and resource constraints. In this paper, we, therefore, consider a dynamic allocation framework in the edge-cloud network over the long run with uncertainty workloads. In such a system, we introduce a JOint Routing and Placement problem for IoT services, called JORP, that dynamically assigns resources according to workload demand in order to reduce the operational costs in long term. Inspired from the well-known algorithm, branch-and-bound (BnB), for solving the mixed-integer non linear problems (MINLPs) like JORP, we bring the learning concept to address the high complexity of BnB when the search space is huge. Particularly, we design a deep neural network (DNN) and train it under the imitation learning to mimic branching behaviors in BnB for searching the optimal solution. Finally, simulations show our solution outperforms baselines in terms of convergence and operational cost.

This paper investigates an efficient placement and chaining of Virtual Network Functions (VNFs) to provide cloud based IoT services with minimal resource usage cost. We take into account bandwidth capacity and link delay of network connection between clouds where VNFs are allocated and underlying IoT networks where sensors and IoT gateways are deployed. Regarding the constantly changing network dynamics, input traffic of service components is considered at the lower granularity level of messages based on the communication between each VNF and corresponding sensors via IoT gateways. From the algorithm perspective, the specific topology of multiple edge clouds is leveraged to improve the solution. In this paper, we present an NFV-based high-level architecture for a system that enables the deployment of IoT services across multiple edges and clouds. We formulate the VNF placement problem using a non-convex Integer Programming model. Taking into account different IoT topologies, we devise two algorithms for small- and large-scale networks to find the near optimal solution: i) a customized Markov approximation with two techniques, i.e., multi-start and batching, and a node ranking-based heuristic. Simulation and experimental results show that the proposed solution improves the cost up to 21% compared to state-of-the-art schemes.

The emerging paradigm of Software Defined Network (SDN) and virtualization technology promises an efficient solution for network providers to deploy services. Adopting them not only facilitates network management but also helps reduce the cost of maintaining network infrastructure. However, despite these advantages, there are still obstacles that must be overcome before SDN and virtualization can advance to reality in industrial deployments. In this paper, we focus on two well-researched issues, namely controller-switch assignment and Virtual Network Function (VNF) placement. Unlike prior works, our purpose is to jointly solve these two problems, accounting for the complex and counter-intuitive manner they are related to each other. We present a service chain aware framework (SACO) that enables the controller-switch association in a multi-controller network regarding the relationship of switches via their connected VNFs that implement service components of the chain. We also propose a model and formulate the joint optimization problem of dynamic controller-switch mapping and VNF allocation. We apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problem and employ the Markov approximation method to find a near-optimal solution. Simulation results show that our service chain aware approach improves the system cost up to 10 ~ 43% compared to the state-of-the-art solutions.

This paper investigates an efficient placing mechanism for placing Virtual Network Function (VNF) on the cloud networks to enable the construction of Internet of Things (IoT) service chains with minimal resource usage cost. In particular, we propose a model taking IoT network infrastructure into account. Such the network composed of numerous sensors with constrained resource, dynamic connectivity toward multi-homing IoT gateways makes the problem of optimally placing VNFs with expected Quality of Service (QoS) more challenging and has not been considered in prior works. From the model, we formulate a non-convex Integer Programming (IP) placement problem and devise a batching Markov approximation placement (BMAP) algorithm to find the optimal solution. Simulation results show that the proposed approach improves the cost compared to those that do not consider the IoT network.

Accelerated Software Defined Networking (SDN) adoption makes SDN paradigm emerging in the state of the art. Especially, the combination of SDN and network functions virtualization (NFV) becomes a promising trend in deploying virtual network services for network operators. Although SDN can decouple networks into the control plane and the data plane to obtain flexible operation and programmability, there are many open issues that need to be addressed for SDN deployments, such as i) where to place SDN controllers in a given network, ii) how to assign connections from controllers to switches in terms of satisfying multiple objectives (e.g., resource utilization, failure, quality of services, etc.). In this work, we focus on the efficient assignment between SDN controllers and switches to guarantee a low operational cost, quality of services, and fault tolerance, in which the complexity of virtual links in network services, an omitted factor in current works, is considered and addressed. We formulate an optimization problem for dynamic controller/switch mapping (DCSM) in the network virtualization. We then proposed approximation algorithms in terms of relaxing the binary variables to solve the NP-hard problem, DCSM, in both centralized and distributed mechanisms. We also create various simulation schemes to evaluate our methods where they outperform state-of-the-art methods.

The emerging paradigm of network function virtualization (NFV) technology promises an efficient solution for optimized service deployment in the cloud computing environment thanks to its ability to dynamically add or remove virtual resources when there is a change in workload. Nevertheless, telecom providers are still facing a challenging issue in efficiently adopting NFV to deploy Web real-time communication (WebRTC) service on top of IP multimedia subsystem (IMS). Providing WebRTC service increases the inherent complexity of the IMS system in terms of the number of service nodes as virtual network functions (VNFs) and the way they interact, both of which play significant roles in the problem of optimally allocating resources. This paper proposes a virtualized interworking system between IMS and WebRTC called NFV-based interworking architecture, and describes the mechanism for VNFs to exchange messages with each other. We present an analytic system model considering the constraints of resources, quality of service (QoS), and service costs. A real-time Markov approximation-based resource allocation algorithm (RIDRA) is then designed allowing a provisioned resource at service nodes to be reconfigured in time to meet performance requirements. The proposed solution is evaluated on the large scale by simulation and on the small scale by our developed testbed. Experimental results reveal that our algorithm effectively responds to fluctuating service demands with a service cost reduced by 19% via efficiently allocating virtual resources while maintaining QoS requirement.

Although Internet of Things (IoT) has been growing rapidly in recent years, the deployment of large-scale IoT solutions are facing several challenges, in particular resource optimization. This is due to high requirements of IoT applications which are usually composed of numerous services each of which could be provided by multiple providers with various specifications. Since an IoT service is often micro-services, dynamically opting appropriate micro-services to guarantee the quality of delivered services to end-users is challenging because conventional network paradigm was not designed to support removal or insertion of modular services on the fly. In this paper, we investigate a cloud-based IoT service orchestration architecture to facilitate the IoT service deployment and propose algorithms to optimize service chaining process. Experimental results show our algorithms effectively orchestrating microservices by reducing total service cost up to 36% while maintaining real-time requirement.