Mobile edge computing (MEC) has emerged as a promising technology that offers resource-intensive yet delay-sensitive applications from the edge of mobile networks. With the emergence of complicated and resource-hungry mobile applications, offloading user tasks to cloudlets of nearby mobile edge-cloud networks is becoming an important approach to leverage the processing capability of mobile devices, reduce mobile device energy consumptions, and improve experiences of mobile users. In this article we first study the provisioning of virtualized network function (VNF) services for user requests in an MEC network, where each user request has a demanded data packet rate with a specified network function service requirement, and different user requests need different services that are represented by virtualized network functions instantiated in cloudlets. We aim to maximize the number of user request admissions while minimizing their admission cost, where the request admission cost consists of the computing cost on instantiations of requested VNF instances and the data packet traffic processing of requests in their VNF instances, and the communication cost of routing data packet traffic of requests between users and the cloudlets hosting their requested VNF instances. We study the joint VNF instance deployment and user requests assignment in MEC, by explicitly exploring a non-trivial usage tradeoff between different types of resources. To this end, we first formulate the cost minimization problem that admits all requests by assuming that there is sufficient computing resource in MEC to accommodate the requested VNF instances of all requests, for which we formulate an Integer Linear Programming solution and two efficient heuristic algorithms. We then deal with the problem under the computing resource constraint. We term this problem as the throughput maximization problem by admitting as many as requests, subject to computing resource capacity on each cloudlet, for which we formulate an ILP solution when the problem size is small; otherwise, we devise efficient algorithms for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising. To the best of our knowledge, we are the first to explicitly explore the usage tradeoff between computing and communication resources in the admissions of user requests in MEC through introducing a novel load factor concept to minimize the request admission cost and maximize the network throughput.

Provisioning reliable network services for mobile users in edge computing environments is the top priority of network service providers, as unreliable services will result in tremendous losses of revenues and customers. In this paper, we study a novel service reliability augmentation problem in a mobile edge computing (MEC) network, where mobile users request network services with service function chain (SFC) and reliability expectation requirements. To enhance the service reliability of user requests, it is a common practice to make use of redundant virtualized network function (VNF) instance placement in case the primary VNF instance fails. We aim to augment the service reliability of each admitted request to its specified reliability expectation, subject to computing capacity on each cloudlet. To this end, we first formulate a novel service reliability augmentation problem for each request with an SFC and a reliability expectation requirement, by augmenting its reliability through redundant VNF instance deployment. We then show that the problem is NP-hard, and provide an admission framework of user requests by placing primary VNF instances of network functions in the SFC to different cloudlets. We then deal with the service reliability augmentation problem of an admitted request under the assumption that all secondary VNF instances of each primary VNF instance must be placed into the cloudlets no more than$l$hops from the cloudlet of its primary VNF instance for a fixed$l$with$1\leq l \leq n-1$, where$n$is the number of cloudlets in the network, for which we formulate an integer linear program solution, and develop a randomized algorithm with a good approximation ratio and high probability, at the expense of moderate resource constraint violations. We also devise a deterministic heuristic for the problem without any resource violation. We third study the service reliability augmentation problem for a set of admitted requests by extending the proposed algorithm for the service reliability augmentation problem for a single request admission. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and their empirical results are superior to their analytical counterparts.

Mobile edge computing (MEC) has emerged as a promising technology to push the cloud frontier to the network edge, provisioning network services in proximity of mobile users. Serving users at edge clouds can reduce service latency, lower operational cost, and improve network resource availability. Along with the MEC technology, network function virtualization (NFV) is another promising technique that implements various network service functions as pieces of software in cloudlets (servers or clusters of servers). Providing virtualized network service for mobile users can improve user service experience, simplify network service deployment, and ease network resource management. However, mobile users move in networks arbitrarily, and different users usually request different services with different resource demands and delay requirements. It thus poses a great challenge to providing reliable and seamless virtualized network services for mobile users in an MEC network while meeting their individual delay requirements, subject to resource capacities on the network. In this paper, we focus on the provisioning of virtualized network function services for mobile users in MEC that takes into account user mobility and service delay requirements. We first formulate two novel optimization problems of user service request admissions with the aims to maximize the accumulative network utility and accumulative network throughput for a given time horizon, respectively. We then devise a constant approximation algorithm for the utility maximization problem. We also develop an online algorithm for the accumulative throughput maximization problem. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

Network Function Virtualization (NFV) has attracted significant attention from both industry and academia as an important paradigm change in network service provisioning. Most existing studies on admissions of NFV-enabled requests focused on deploying dedicated Virtualized Network Function (VNF) instances to serve each individual request without exploring the sharing of VNF instances among multiple user requests. However, with ever-growing demands of user services, exclusive usages of VNF instances in most networks drastically degrade the network performance and largely under-utilize the VNF instance resources. In this paper, we jointly explore two different VNF instance scaling techniques, horizontal scaling and vertical scaling techniques, to improve the network throughput while minimizing the operational cost of the network, where horizontal scaling that migrates some existing VNF instances from their current locations to new locations to allow the VNF instances to be shared by multiple requests to reduce the resource consumption and operational cost of the network, and vertical scaling that instantiates new VNF instances to meet the demands of new request admissions if existing VNF instances sharing becomes more expensive or the end-to-end delay requirements of currently executing requests will be violated. We first propose a unified framework of maximizing the network throughput, by admitting as many as NFV-enabled requests while meeting the end-to-end delay requirements of the admitted requests. We then provide an Integer Linear Programming (ILP) solution for the problem when the problem size is small. Otherwise, we devise an efficient algorithm through a non-trivial reduction that reduces the problem to the minimum-weight feedback arc set problem and the generalized assignment problem (GAP). We finally conduct experiments to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm outperforms a baseline algorithm and achieves a performance on a par with its optimal ILP solution.

With the advent of Network Function Virtualization (NFV) technology, more and more mobile users make use of virtual network services in Mobile Edge Computing (MEC) networks. Each service request not only requests for a service but also a Service Function Chain (SFC) associated with the request. How to effectively allocate resources in MEC to meet the resource demands of user service requests to maximize the expected profit of the network service provider poses a great challenge. Most existing studies considered resource allocation and scheduling in MEC for user request admissions, under the assumption that the amounts of different resources demanded by each request are givena priorand do not change during the execution of the request. In practice, the resource demands during the implementation of a request are dynamically evolving. This uncertainty of resource demands at different execution stages of the request does impact the service quality and the profit of network service providers. Thus, providing robust services to users against their resource demand uncertainties is a critical issue. In this paper, we study the robust service function chain placement (RSFCP) problem under the uncertainty assumption of both computing resource and data rates demanded by request executions, through the placement of SFCs. We first formulate the RSFCP problem as a Quadratic Integer Programming (QIP) and show that the problem is NP-hard. We then develop a near-optimal approximation algorithm for it, by adopting the Markov approximation technique. We also analyze the proposed approximation algorithm with the optimality gap, and the bounds on the convergence time and perturbation caused by resource demand uncertainties. We finally evaluate the performance of the proposed algorithm through analytical and empirical analyses. Experimental results demonstrate that the proposed algorithm is promising, compared with existing baseline algorithms.

Provisioning reliable network services for mobile users in a mobile edge computing environment is the top priority for most network service providers, as unreliable or severely failed services will result in tremendous loss on their revenues and consumers. In this paper, we study a novel service reliability augmentation problem in a Mobile Edge-Cloud (MEC) network, where mobile users request various network services through issuing requests with service function chain (SFC) requirements and reliability expectations, and an admitted request may not meet its reliability expectation initially. To enhance its service reliability to reach its expectation, it is a common practice to make use of redundant backups, that is to place redundant VNF instances of each Virtual Network Function (VNF) in its SFC in case its primary VNF instance fails. In this paper, we aim to augment the reliability of each admitted request as much as possible with the ultimate objective to reach its reliability expectation, subject to computing capacity on each cloudlet in the network. To this end, we first formulate a novel service reliability augmentation problem. We then deal with the problem for the admitted request under the assumption that all the secondary VNF instances of each primary VNF instance in its SFC must be placed into the cloudlets no more than l hops from the cloudlet of the primary VNF instance, where 1 ≤ l ≤ n − 1 and n is the number of cloudlets in the network, for which we propose an integer linear program (ILP) solution, and develop a randomized algorithm with a provable approximation ratio while a moderate resource constraint violation. We also devise an efficient heuristic algorithm for the problem without any resource constraint violation. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and their empirical results are superior to their analytical counterparts.

With advances in wireless communication technology, more and more people depend heavily on portable mobile devices for business, entertainments and social interactions. This poses a great challenge of building a seamless application experience across different computing platforms. A key issue is the resource limitations of mobile devices due to their portable size, however this can be overcome by offloading computation-intensive tasks from the mobile devices to clusters of nearby computers called cloudlets through wireless access points. As increasing numbers of people access the Internet via mobile devices, it is reasonable to envision in the near future that cloudlet services will be available for the public through easily accessible public wireless metropolitan area networks (WMANs). However, the outdated notion of treating cloudlets as isolated data-centers-in-boxes must be discarded as there are clear benefits to connecting multiple cloudlets together to form a network. In this paper we investigate how to balance the workload among cloudlets in an WMAN to optimize mobile application performance. We first introduce a novel system model to capture the response time delays of offloaded tasks and formulate an optimization problem with the aim to minimize the maximum response time of all offloaded tasks. We then propose two algorithms for the problem: one is a fast heuristic, and another is a distributed genetic algorithm that is capable of delivering a more accurate solution compared with the first algorithm, but at the expense of a much longer running time. We finally evaluate the performance of the proposed algorithms in realistic simulation environments. The experimental results demonstrate the significant potential of the proposed algorithms in reducing the user task response time, maximizing user experience.

Along with Network Function Virtualization (NFV), Mobile Edge Computing (MEC) is becoming a new computing paradigm that enables accommodating innovative applications and services with stringent response delay and resource requirements, including autonomous vehicles and augmented reality. Provisioning reliable network services for users is the top priority of most network service providers, as unreliable services or severe service failures can result in tremendous losses of users, particularly for their mission-critical applications. In this paper, we study reliability-aware VNF instances provisioning in an MEC, where different users request different network services with different reliability requirements through paying their requested services with the aim to maximize the network throughput. To this end, we first formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request. We then show that the problem is NP-hard and formulate an Integer Linear Programming (ILP) solution. Due to the NP-hardness of the problem, we instead devise an approximation algorithm with a logarithmic approximation ratio for the problem. Moreover, we also consider two special cases of the problem. For one special case where each request only requests one primary and one secondary VNF instances, the problem is still NP-hard, and we devise a constant approximation algorithm for it. For another special case where different VNFs have the same amounts of computing resource demands, we show that it is polynomial-time solvable by developing a dynamic programming solution for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and the empirical results of the algorithms outperform their analytical counterparts as theoretical estimations usually are very conservative.

Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of end users at the mobile network edge. This provides end users with swift and powerful computing and storage capacities, energy efficiency, and mobility- and context-awareness support. Furthermore, Network Function Virtualization (NFV) is another promising technique that implements various network functions for many applications as pieces of software in servers or cloudlets in MEC networks. The provisioning of virtualized network services in MEC can improve user service experiences, simplify network service deployment, and ease network resource management. However, user requests arrive dynamically and different users demand different amounts of resources, while the resources in MEC are dynamically occupied or released by different services. It thus poses a significant challenge to optimize the performance of MEC through efficient computing and communication resource allocations to meet ever-growing resource demands of users. In this paper, we study NFV-enabled multicasting that is a fundamental routing problem in an MEC network, subject to resource capacities on both its cloudlets and links. Specifically, we first devise an approximation algorithm for the cost minimization problem of admitting a single NFV-enabled multicast request. We then develop an efficient algorithm for the throughput maximization problem for the admissions of a given set of NFV-enabled multicast requests. We third devise an online algorithm with a provable competitive ratio for the online throughput maximization problem when NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of mobile users at the mobile network edge. This provides end-users with swift and powerful computing, energy efficiency, storage capacity, mobility-and context-awareness support. Furthermore, provisioning virtualized network services in MEC can improve user service experience, simplify network service deployments, and ease network resource management. However, user requests usually arrive into the system dynamically and different user requests may have different resource demands. How to optimize and guarantee the performance of MEC is of significant importance and challenging. In this paper, we study the problem of online NFV-enabled multicasting in an MEC network with resource capacity constraints on both cloudlets and links. We first devise an approximation algorithm for the cost minimization problem for a single NFV-enabled multicast request admission. We then propose an online algorithm with a provable competitive ratio for the online throughput maximization problem where NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We admit the requests through placing or sharing VNF instances of network functions in their service chains to meet their computing and bandwidth resource demands, and we introduce a novel cost model to capture the dynamic usages of different resources and perform network resource allocations based on the proposed cost model. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

Traditional networks employ expensive dedicated hardware devices as middleboxes to implement Service Function Chains of user requests by steering data traffic along middleboxes in the service function chains before reaching their destinations. Network Function Virtualization (NFV) is a promising virtualization technique that implements network functions as pieces of software in servers or data centers. The integration of NFV and Software Defined Networking (SDN) further simplifies service function chain provisioning, making its implementation simpler and cheaper. In this paper, we consider dynamic admissions of delay-aware requests with service function chain requirements in a distributed cloud with the objective to maximize the profit collected by the service provider, assuming that the distributed cloud is an SDN that consists of data centers located at different geographical locations and electricity prices at different data centers are different. We first formulate this novel optimization problem as a dynamic profit maximization problem. We then show that the offline version of the problem is NP-hard and formulate an integer linear programming solution to it. We third propose an online heuristic for the problem. We also devise an online algorithm with a provable competitive ratio for a special case of the problem where the end-to-end delay requirement of each request is negligible. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are promising.

Network Function Virtualization (NFV) and Software-Defined Networking (SDN) have been envisioned an essential milestone in the evolution of communication networks. Their integration provides a more flexible and easier manageable software-based network environment that induces high expectations for reducing capital expenditures (CAPEX) and operational costs (OPEX) of network service providers. They also introduce technical challenges. One such challenge is to manage the placement of VNFs and to steer the data traffic of each NFV-enabled request through its specified network functions. In this paper, we opportunistically adopt the flexibility and cost-efficiency of VNF and SDN for NFV-enabled request admissions in SDNs and formulate profit maximization problems for static and dynamic NFV-enabled request admissions. We first provide an integer linear programming (ILP) solution to the problem in the static version if the problem size is small; otherwise, we devise a fast approximation algorithm with a provable approximation ratio for the static request admissions. We then propose an efficient online algorithm for dynamic request admissions, by leveraging VNF instance migrations and idle VNF releases back to the system. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are very promising.

Network Function Virtualization (NFV) has attracted significant attentions from both industry and academia as an important paradigm change in network service provisioning. Most existing studies on NFV dealt with admissions of user requests through deploying Virtualized Network Function (VNF) instances for individual user requests, without considering sharing VNF instances among multiple user requests to provide better network services and improve network throughput. In this paper, we study the network throughput maximization problem by adopting two different VNF instance scalings: (i) horizontal scaling by migrating existing VNF instances from their current locations to new locations; and (ii) vertical scaling by instantiating more VNF instances if needed. Specifically, we first propose a unified framework that jointly considers both vertical and horizontal scalings to maximize the network throughput, by admitting as many requests as possible while meeting their resource demands and end-to-end transmission delay requirements. We then devise an efficient heuristic algorithm for the problem. We finally conduct experiments to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm outperforms a baseline algorithm.

Traditional networks employ expensive dedicated hardware devices as middleboxes to implement Service Chains (SC) of user requests by steering data traffic along the middleboxes in the service chains. Network Function Virtualization (NFV) is a promising virtualization technique by implementing network functions as pieces of software in servers or data centers. By leveraging the technique of Software Defined Networking (SDN), NFV can be further enabled in a flexible and dynamic way. In this paper, we consider dynamic admissions of delay-aware NFV-enabled requests in SDNs by leveraging pre-installed NFV instances in data centers. We first formulate a novel revenue maximization problem, and show that the problem is NP-hard. We then propose an online heuristic for the problem. We also devise an online algorithm with a provable competitive ratio for a special case of the problem where the end-to-end delay requirement of requests can be neglected. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are promising.

Data transfer in contemporary networks usually is associated with strict policy enforcement for data transfer security and system performance purposes. Such a policy is represented by a service chain consisting of a sequence of network functions such as firewalls, intrusion detection systems, transcoders, etc. Due to the high cost and inflexibility of managing hardware-based network functions, network function virtualization (NFV) has emerged as a promising technology to meet the stringent requirement imposed on the service chain of each data transfer request in a low-cost and flexible way. In this paper, we study policy-aware unicast request admissions with and without end-to-end delay constraints in a software defined network. We aim to minimize the operational cost of admitting a single request in terms of both computing resource consumption for implementing the NFVs in the service chain and bandwidth resource consumption for routing its data traffic, or maximize the network throughput for a sequence of requests without the knowledge of future request arrivals. We first formulate four novel optimization problems and provide a generic optimization framework for the problems. We then develop efficient algorithms for the admission of a single NFV-enabled request with and without the end-to-end delay constraint, where NFV-enabled requests are defined as the requests with policy enforcement requirements. We also devise online algorithms with a guaranteed performance for dynamic admissions of requests without the knowledge of future arrivals. In particular, we provide the very first online algorithm with a provable competitive ratio for the problem without the end-to-end delay requirement. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising and outperform existing heuristics.

Data transfers in contemporary networks depend upon network functions for ensuring data security and system performance. These policies are represented by a service chain that consists of different network functions such as firewalls, Intrusion Detection Systems (IDSs), transcoders, etc. Network Function Virtualization (NFV) has emerged as a promising technology to meet the stringent requirement imposed on the service chain. In this paper, we study NFV-enabled unicasting in SDNs with and without end-to-end delay constraints. We aim to maximize the network throughput for a sequence of NFV-enabled unicast requests without the knowledge of future arrivals. We first formulate the problems as novel optimization problems in terms of both computing and bandwidth resource consumptions, and provide a generic optimization framework. We then develop an online algorithm with guaranteed performance without the delay requirement and a heuristic with the delay requirement. We finally evaluate the performance of the proposed algorithms through experimental simulations. The results of the experimental simulations show that the proposed algorithms are promising.

Network function virtualization (NFV) has been emerging as a new paradigm to enable elastic and inexpensive network services in modern computer networks, through deploying flexible virtualized network functions (VNFs) running in virtual computing platforms. Different VNFs can be chained together to form different service chains, to meet various user data routing demands for different network services. In this paper we consider provisioning network services in an NFV-enabled network that consists of data centers for implementing VNF instances of service chains and switches. We study the throughput maximization problem with the aim to admit as many user requests as possible while minimizing the implementation cost of the requests, assuming that limited numbers of instances of each service chain have been stored in data centers. We first propose an optimal algorithm for the problem if all requests have identical packet rates; otherwise, we devise two approximation algorithms with probable approximation ratios, depending on whether the packet traffic of each request is splittable. We finally conduct experiments to evaluate the performance of the proposed algorithms by simulations. Experimental results show that the proposed algorithms achieve at least 15% more throughput than that of a greedy algorithm.