Virtual Network Functions as a Service (VNFaaS) is currently under attentive study by telecommunications and cloud stakeholders as a promising business and technical direction consisting of providing network functions as a service on a cloud (NFV Infrastructure), instead of delivering standalone network appliances, in order to provide higher scalability and reduce maintenance costs. However, the functioning of such NFVI hosting the VNFs is fundamental for all the services and applications running on top of it, forcing to guarantee a high availability level. Indeed the availability of an VNFaaS relies on the failure rate of its single components, namely the servers, the virtualization software, and the communication network. The proper assignment of the virtual machines implementing network functions to NFVI servers and their protection is essential to guarantee high availability. We model the High Availability Virtual Network Function Placement (HA-VNFP) as the problem of finding the best assignment of virtual machines to servers guaranteeing protection by replication. We propose a probabilistic approach to measure the real availability of a system and design both efficient and effective algorithms that can be used by stakeholders for both online and offline planning.
Even if VM and VNF resource placement in cloud systems is a recent area of research (see @cite_29 for a high-level comprehensive study), however there already exists orchestrators that are driven by optimization algorithms for the placement, such as @cite_0 . We now present few works in literature studying the optimization problems that arises in this context.
@cite_0 @cite_29
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Network Functions Visualization is focused on migrating traditional hardware-based network functions to software-based appliances running on standard high volume severs. There are a variety of challenges facing early adopters of Network Function Virtualizations; key among them are resource and service mapping, to support virtual network function orchestration. Service providers need efficient and effective mapping capabilities to optimally deploy network services. This paper describes TeNOR, a micro-service based network function virtualisation orchestrator capable of effectively addressing resource and network service mapping. The functional architecture and data models of TeNOR are described, as well as two proposed approaches to address the resource mapping problem. Key evaluation results are discussed and an assessment of the mapping approaches is performed in terms of the service acceptance ratio and scalability of the proposed approaches. Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .