Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing

We are embracing an era of Internet of Things (IoTs). However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices seriously impacts the quality of service of user experienced. To address these shortcomings, the Mobile Edge Computing (MEC) platform provides a promising solution for the service provisioning of IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices, and the service response latency can be significantly reduced. Meanwhile, each IoT application usually imposes a service function chain enforcement for its data transmission, which consists of different service functions in a specified order, and each data packet transfer in the network from the gateways of IoT devices to the destination must pass through each of the service functions in order.In this paper, we study IoT-driven service provisioning in an MEC network for various IoT applications with service function chain requirements, where an IoT application consists of multiple data streams from different IoT sources that will be uploaded to the MEC network for aggregation, processing, and storage. We first formulate a novel cost minimization problem for IoT-driven service provisioning in MEC networks. We then show that the problem is NP-hard, and propose an IoT-driven service provisioning framework for IoT applications, which consists of streaming data uploading from multiple IoT sources to the MEC network, data stream aggregation and routing, and Virtual Network Function (VNF) instance placement and sharing in cloudlets in the MEC network. In addition, we devise an efficient algorithm for the problem, built upon the proposed service framework. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results demonstrate that the proposed algorithm is promising, compared with the lower bound on the optimal solution of the problem and another comparison heuristic.

The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one without the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.

The Mobile Edge Computing (MEC) paradigm emerges as a promising technology to provide services for various mobile applications at edges of core networks while meeting stringent service delay requirements of users. Orthogonal with the MEC, Network Function Virtualization (NFV) provides the network resource management with flexibility and scalability, where Virtual Network Functions (VNFs) are deployed over edge servers in a chained manner as Service Function Chains (SFCs) for enabling service applications. Provisioning reliable SFC-enabled services in MEC thus is fundamentally important. However, the VNF instances deployed usually are not reliable and affected by multiple factors, including the software implementation, the request execution duration, and so on. Empowered by digital twin techniques that can maintain the states of VNF instances by digital twins in a real-time manner and predict the reliability of VNF instances in edge servers, in this paper we study SFC-enabled reliable service provisioning in an MEC network by exploring the dynamics of VNF instance placement reliability. We first formulate a novel SFC-enabled reliable service problem in MEC networks - the online throughput maximization problem, and show its NP-hardness. We then propose an Integer Linear Programming (ILP) solution to the offline version of the problem, and develop an online algorithm with a provable competitive ratio for the problem. We finally evaluated the performance of the proposed algorithm through experimental simulations, and the results demonstrate that the proposed algorithm is promising.

Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We thirdly provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform their comparison benchmarks, and improve the performance of their comparison counterparts by no less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10.2 \%$</tex-math></inline-formula> .

The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent.

Resource allocation in RISs-assisted UAV-enabled MEC network with computation capacity improvement

Robust deadline-aware network function parallelization framework under demand uncertainty

UAVs-assisted QoS guarantee scheme of IoT applications for reliable mobile edge computing

Internet of Things (IoT) has been regarded as one of the most significant network paradigms in the future. For IoT, it is crucial to ensure the correctness of detection which includes the factors of accuracy and precision. On the other hand, Mobile Edge Computing (MEC) has emerged as a promising way to process big IoT data at the network edge so as to reduce the computation and transmission energy in the networks. In this paper, we explore the energy minimization problem in MEC networks by considering both the accuracy and precision requirements of IoT. Specifically, given 1) a set of IoT devices, 2) a set of observed targets, 3) an MEC network, 4) the energy consumption model, and 5) the accuracy and precision requirements, we formulate a new optimization problem, named Accuracy and Precision-Aware IoT Device Selection (APAIDS), to minimize the overall energy consumption in MEC networks. We prove the NP-hardness of APAIDS and then propose a new algorithm, named Energy Efficient Device and MEC Server Selection (EDMS), to minimize energy consumption by jointly selecting IoT devices, configuring MEC association, and selecting processing servers for dealing with the data of each target. Finally, we evaluate EDMS on two real networks. In comparison with the baseline schemes, the results manifest that the overall energy consumption can be reduced by more than 60%.

Mobile edge computing (MEC) is powerful for providing services with ultra-low latency and extremely high reliability to support newly emerging applications in 5G and beyond by leveraging network function virtualization (NFV) where each mobile user requests a service function chain (SFC). To guarantee the quality of experience (QoE), e.g., reduce the downtime of moving users, effective placement of virtual network functions (VNFs) in MEC networks is critical to cope with the user mobility. Placing multiple instances of SFC for each user is promising for reducing the downtime during moving. However, an extremely high resource cost is induced. Towards this challenge, this paper investigates the mobility-aware multi-instance (MAMI) VNF placement problem in MEC networks. According to the empirical probabilities that one user stays within the coverage of different edge nodes, both static and dynamic SFC instances are placed to balance the tradeoff between the downtime and resource cost. A MAMI VNF placement algorithm is proposed for resource cost minimization, where resource sharing among the SFC instances of each user is allowed to further reduce resource cost. Simulation results based on real-world-like moving users show the effectiveness of our proposed algorithm.

Conventional Internet of Things (IoT) applications involve data capture from various sensors in environments, and the captured data then is processed in remote clouds. However, some critical IoT applications (e.g., autonomous vehicles) require a much lower response latency and more secure guarantees than those offered by remote clouds today. Mobile edge clouds (MEC) supported by the network function virtualization (NFV) technique have been envisioned as an ideal platform for supporting such IoT applications. Specifically, MECs enable to handle IoT applications in edge networks to shorten network latency, and NFV enables agile and low-cost network functions to run in low-cost commodity servers as virtual machines (VMs). One fundamental problem for the provisioning of IoT applications in an NFV-enabled MEC is where to place virtualized network functions (VNFs) for IoT applications in the MEC, such that the operational cost of provisioning IoT applications is minimized. In this paper, we first address this fundamental problem, by considering a special case of the IoT application placement problem, where the IoT application and VNFs of each service request are consolidated into a single location (gateway or cloudlet), for which we propose an exact solution and an approximation algorithm with a provable approximation ratio. We then develop a heuristic algorithm that controls the resource violation ratios of edge clouds in the network. For the IoT application placement problem for IoT applications where their VNFs can be placed to multiple locations, we propose an efficient heuristic that jointly places the IoT application and its VNFs. We finally study the performance of the proposed algorithms by simulations and implementations in a real test-bed, Experimental results show that the performance of the proposed algorithms outperform their counterparts by at least 10 percent.

As an emerging network technology, Network Function Virtualization (NFV) enables network functions decoupling from dedicated hardware by replacing traditional middleboxes with software implemented Virtual Network Functions (VNFs). In NFV-enabled Internet of Things (IoT) networks, each IoT service can be represented as an ordered sequence of VNFs, referred to as Service Function Chain (SFC). Through NFV, operating expenditure and capital expenditure can be significantly reduced, thereby achieving flexible provisioning of IoT services. However, with the arriving of 6G era, the network scale of IoTs continuously expands, and service requirements of IoT users become more diversified. Particularly, 6G enabled IoT services have stringent delay requirements. How to efficiently place the SFCs in multi-domain IoT networks to satisfy the specific delay requirements while guaranteeing quality of service becomes a serious challenge. To this end, in this paper, we investigate the problem of delay guaranteed SFC placement in multi-domain IoT networks. Specifically, by taking in account QoS requirements and VNF dependency relationships, we formulate the problem of delay guaranteed SFC placement in multi-domain IoT networks as a multi-objective optimization model to maximize service acceptance ratio and minimize operational cost, while satisfying the delay requirements of SFC requests. To solve the problem, we further design a Delay Guaranteed heuristic SFC Placement (DGSP) algorithm with VNF parallelization. In the proposed DGSP algorithm, the VNFs without dependency relationships are placed in parallel in an adaptive and cost efficient manner, and virtual link mapping is performed based on the shortest path algorithm. Finally, we conduct simulation experiments for performance evaluation, and simulation results demonstrate the proposed DGSP algorithm can get higher service acceptance ratio and lower operational cost than comparison algorithms.

The implementation of Internet-of-Things (IoT) applications faces several challenges in practice, such as compliance with Quality-of-Service requirements, resource constraints, and energy consumption. In this context, the joint edge-cloud paradigm for IoT applications can resolve some of the issues arising in pure cloud computing scenarios, such as those related to latency, energy, or privacy. Therefore, an edge-cloud environment could be promising for resource and energy-efficient IoT applications that implement virtual network functions (VNFs) bound together into service function chains (SFCs). However, a resource and energy-efficient SFC placement requires smart SFC embedding mechanisms in the edge-cloud environment, as several challenges arise, such as IoT service chain modeling and evaluation, the tradeoff between resource allocation, energy efficiency and performance, and the resource dynamics. In this article, we address issues in modeling resource and energy utilization for IoT applications in edge-cloud environments. A smart traffic monitoring IP camera system is deployed as a use case for a realistic modeling of a service chain. The system is implemented in our testbed, which is designed and developed specifically to model and investigate the resource and energy utilization of SFC embedding strategies. A resource and energy-aware SFC strategy in the edge-cloud environment for IoT applications is then proposed. Our algorithm is able to cope with dynamic load and resource situations emerging from dynamic SFC requests. The strategy is evaluated systematically in terms of the acceptance ratio of SFC requests, resource efficiency and utilization, power consumption, and VNF migrations depending on the offered system load. Results show that our strategy outperforms some existing approaches in terms of resource and energy efficiency, thus it overcomes the relevant challenges from practice and meets the demands of IoT applications.

Mobile Edge Computing (MEC) has been envisioning as a promising technology to address limited computing and storage resources in mobile devices. The virtual services provided by the MEC platform are implemented as instances of Virtual Network Functions (VNFs). However, these VNF instances as pieces of software that run in virtual machines (VMs) are not always reliable. To provide reliable services for their users while meeting user service reliability requirements, the service providers of MEC usually adopt the replica policy that deploy a certain number of service replicas for each VNF instance. In this paper, we study reliable service provisioning in an MEC network through redundant placement of instances of VNFs. We assume that each service request consists of a Service Function Chain (SFC) requirement and a service reliability requirement. We formulate a novel reliability-aware service function chain provisioning problem with the aim to maximize the number of requests admitted, while meeting the specified reliability requirement of each admitted request. We first show that the problem is NP-hard, and formulate an ILP solution for the problem when the problem size is small. We then develop a randomized algorithm with a provable approximation ratio and high probability for the problem when the problem size is large, and the achieved approximation ratio is at the expense of moderate computing capacity and reliability constraint violations. We also devise an efficient heuristic for the problem without any resource and requirement constraint violations. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

Network Function Virtualization has been widely acknowledged as one of the fundamental technologies for 5G and beyond by consolidating network functions into general-purpose hardware. To support a specific type of service, a virtualized network topology, such as Service Function Chain, is constructed by logically connecting a set of virtual network functions (VNFs). Meanwhile, Mobile Edge Computing (MEC) provides cloud resources at the edge of networks, meeting the stringent service requirements of many emerging mobile applications. With the widespread of new compute-intensive and Internet of Things applications, the amount of service flows in edge networks is rapidly increasing, causing network congestion easily because of the sinking of the computing capabilities. Moreover, due to the limited coverage of edge servers and erratic user mobility, it is difficult to maintain satisfactory service performance. Therefore, in order to support diverse services with various Quality of Service requirements, an online dynamic VNF migration is imperative in mobile networks. In this paper, we investigate the NF migration in softwarization based mobile networks with MEC, with the aim to minimize the number of link congestion in the networks. We first formulate the link congestion problem as a multidimensional knapsack problem, which is proved NP-hard. Then we resort to deep reinforcement learning to solve the online migration problem. Numerical results show that the proposed migration strategy can significantly reduce the number of link congestion and end-to-end service delay in comparison with the state-of-the-art solutions.