The Mobile Edge-Cloud (MEC) network has emerged as a promising networking paradigm to address the conflict between increasing computing-intensive applications and resource-constrained mobile Internet-of-Thing (IoT) devices with portable size and storage. In MEC environments, Virtualized Network Functions (VNFs) are deployed for provisioning network services to users to reduce the service cost on top of dedicated hardware infrastructures. However, VNFs may suffer from failures and malfunctions while network service providers have to guarantee continuously reliable services to their consumers to meet the ever-growing service demands of users, thereby securing their revenues for the service. In this article, we focus on reliable VNF service provisioning in MECs, by placing primary and backup VNF instances to cloudlets in an MEC network to meet the service reliability requirements of users. We first formulate a novel VNF service reliability problem with the aim to maximize the revenue collected by admitting as many as user requests while meeting their different reliability requirements, assuming that requests arrive into the system one by one without the knowledge of future arrivals, and the admission or rejection decision must be made immediately. We then develop two efficient online algorithms for the problem under two different backup schemes: the on-site (local) and off-site (remote) schemes, by adopting the primal-dual updating technique. Both algorithms achieve provable competitive ratios with bounded moderate resource capacity violations. We finally evaluate the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, compared with existing baseline algorithms.

Reliability-Aware Network Service Provisioning in Mobile Edge-Cloud Networks

Network function virtualization is a promising technique to greatly improve the effectiveness and flexibility of network services through a process named service function chain (SFC) mapping, with which network functions are deployed over virtualized and shared platforms in data centers However, failures are quite common in data centers Therefore, a practical and yet theoretically challenging issue in SFC mapping in such an environment is to manage the availability of the requests In this paper, we present a framework to provision availability of SFC requests in a data center with multiple layers of connected devices, and the devices follow heterogeneous failure processes with the objective of minimizing resource usage To expedite the process, we further propose an optimization problem of request mapping and backup estimation and solve it efficiently with an approximation algorithm With simulations, we demonstrate the effectiveness of our proposed framework

A Framework for Provisioning Availability of NFV in Data Center Networks

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.

Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

Network Function Virtualization (NFV) turns a sequence of network functions on hardwares into a service chain of virtual network functions (VNFs) provisioned on virtual machines or containers. However, the chain of VNFs may suffer from interruption as long as one VNF fails due to software faults or hardware malfunctions. A common approach to ensuring high availability is to provide backup nodes for primary VNFs. However, existing work on allocating backup nodes have not considered the heterogeneous resource demands of different VNFs. In this paper, we formalize the resource-aware backup allocation problem, which aims to minimize the backup resource consumption while meeting the overall availability demand. To this end, we prove the NP-hardness of this problem and propose the RABA-CDDE algorithm based on differential evolution to solve it. Besides, to reduce the computation overhead of RABA-CDDE, a greedy algorithm is proposed. Our extensive evaluation shows that the proposed algorithms can reduce the resource consumption by about 15% and 35% respectively compared to the state-of-art solutions in dedicated and shared protection scenarios.

RABA: Resource-Aware Backup Allocation For A Chain of Virtual Network Functions

By means of network function virtualization (NFV), dedicated proprietary network devices can be implemented as software and instantiated flexibly on common-off-the-shelf servers, in the form of virtual network functions (VNF). NFV can bring great cost reduction as well as operation flexibility. However, it also brings new problems, one of which is how to meet the availability of network services in the VNF deployment process, because of the error prone nature of software. The availability aware VNF deployment problem has attracted attention by academics, and reserving redundancy has been treated as the de facto technology. Compared with traditional backup schemes for physical machines, resource orchestration in NFV is more flexible and the characteristics of software should be considered to improve resource utilization efficiency. Based on the above considerations, in this paper we further study the availability aware VNF deployment problem in datacenter networks. To improve the resource utilization efficiency, the sharing mechanism of redundancy and multi-tenancy technology are taken into account. Then we formulate the problem mathematically and propose a joint deployment and backup scheme (JDBS). Finally, we conduct a numerical simulation in detail and compare it with four contrasting schemes in the existing literature. The simulation results show that JDBS is obviously superior to the contrasting schemes and can save about 40% resources at most.

Availability Aware VNF Deployment in Datacenter Through Shared Redundancy and Multi-Tenancy

Network Function Virtualization (NFV) is a promising technique to greatly improve the effectiveness and flexibility of network services through a process named Service Function Chain (SFC) mapping, with which network functions (NFs) are deployed over virtualized and shared platforms in data centers. NFV typically requires a higher availability at the carrier grade than conventional cloud-based IT services, provided by native IaaS mechanism, for example. To achieve the high availability, each VNFs in an SFC can be provisioned with sufficient on-site backups. However, having too many backups may greatly decrease the resource utilization. Therefore, an open challenge is to find an effective method to allocate backup resource in order to maximize the number of SFC requests that can be served while meeting their heterogeneous availability requirements. To address this challenge, we first study how to allocate a minimum amount of backup resource for a single SFC request as an integer nonlinear program and provide an optimal solution. Based on the solution, we then propose an online heuristic algorithm for mapping multiple SFC requests with the objective of maximizing the number of SFC requests that can be served. Last but not least, we introduce a novel backup pooling mechanism to further improve the efficiency of backup resource usage. Through simulations, we show that our proposed algorithm can significantly reduce resource consumption due to backups and increase the number of co-existing SFC requests that can be served.

Carrier-grade availability-aware mapping of Service Function Chains with on-site backups

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Carrier-grade availability-aware mapping of Service Function Chains with on-site backups