Many real-world services can be provided through multiple VNF graphs, corresponding, e.g., to high- and low-quality variants of the service itself. Based on this observation, we extend the concept of service scaling in network orchestration to service shifting, i.e., switching between the VNF graphs implementing the same service. Service shifting can serve multiple goals, from reducing operational costs to reacting to infrastructure problems. Furthermore, it enhances the flexibility of service-level agreements between network operators and third party content providers ("verticals"). In this paper, we introduce and describe the service shifting concept, its benefits, and the associated challenges, with special reference to how service shifting can be integrated within real-world 5G architectures and implementations. We conclude that existing network orchestration frameworks can be easily extended to support service shifting, and its adoption has the potential to make 5G network slices easier for the operators to manage under high-load conditions, while still meeting the verticals' requirements.
A substantial body of works is dedicated to the decisions required in network slicing scenarios, i.e., the network orchestration problem. The work in @cite_10 identifies the unique algorithmic challenges associated with network slicing, including the need to account for different types of constraints -- from end-to-end delays to multi-tenancy and isolation issues. Furthermore, it presents a low-complexity solution concept for real-time network slicing, based on monitoring and forecasting the state of the network, and on an efficient, two-phase, online optimization. The study in @cite_8 focuses on a core network as a service (CNaaS) scenario, where multiple verticals share their virtual EPC (vEPC) instances. The high-level objective is to satisfy all the verticals' requirements with the smallest possible number of vEPC instances, hence, the lowest cost for the operator. To this end, the authors resort to cooperative game theory, and study how to build coalitions of verticals sharing the same vEPC instance.
@cite_10 @cite_8
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Many resource allocation problems in the cloud can be described as a basic Virtual Network Embedding Problem (VNEP): finding mappings of request graphs (describing the workloads) onto a substrate graph (describing the physical infrastructure). In the offline setting, the two natural objectives are profit maximization, i.e., embedding a maximal number of request graphs subject to the resource constraints, and cost minimization, i.e., embedding all requests at minimal overall cost. The VNEP can be seen as a generalization of classic routing and call admission problems, in which requests are arbitrary graphs whose communication endpoints are not fixed. Due to its applications, the problem has been studied intensively in the networking community. However, the underlying algorithmic problem is hardly understood. This paper presents the first fixed-parameter tractable approximation algorithms for the VNEP. Our algorithms are based on randomized rounding. Due to the flexible mapping options and the arbitrary request graph topologies, we show that a novel linear program formulation is required. Only using this novel formulation the computation of convex combinations of valid mappings is enabled, as the formulation needs to account for the structure of the request graphs. Accordingly, to capture the structure of request graphs, we introduce the graph-theoretic notion of extraction orders and extraction width and show that our algorithms have exponential runtime in the request graphs' maximal width. Hence, for request graphs of fixed extraction width, we obtain the first polynomial-time approximations. Studying the new notion of extraction orders we show that (i) computing extraction orders of minimal width is NP-hard and (ii) that computing decomposable LP solutions is in general NP-hard, even when restricting request graphs to planar ones. In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilistic queuing delays. In particular, a reliability measure is defined to characterize extreme events (i.e., when vehicles' queue lengths exceed a predefined threshold with non-negligible probability) using extreme value theory (EVT). Leveraging principles from federated learning (FL), the distribution of these extreme events corresponding to the tail distribution of queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov optimization is used to find the joint transmit power and resource allocation policies for each VUE in a distributed manner. The proposed solution is validated via extensive simulations using a Manhattan mobility model. It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79 reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60 reductions of VUEs with large queue lengths, without an additional power consumption, compared to an average queue-based baseline. Compared to systems with fixed power consumption and focusing on queue stability while minimizing average power consumption, the reductions in extreme events of the proposed method is about two orders of magnitude. The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommodate different business models. In particular, infrastructure providers need to implement radically new admission control policies to decide on network slices requests depending on their Service Level Agreements (SLA). When implementing such admission control policies, infrastructure providers may apply forecasting techniques in order to adjust the allocated slice resources so as to optimize the network utilization while meeting network slices' SLAs. This paper focuses on the design of three key network slicing building blocks responsible for (i) traffic analysis and prediction per network slice, (ii) admission control decisions for network slice requests, and (iii) adaptive correction of the forecasted load based on measured deviations. Our results show very substantial potential gains in terms of system utilization as well as a trade-off between conservative forecasting configurations versus more aggressive ones (higher gains, SLA risk). The next generation mobile transport networks shall transform into flexible and agile SDN NFV-based transport and computing platforms, capable of simultaneously supporting the needs of different vertical industries, e.g., automotive, e-health and media, by meeting a diverse range of networking and computing requirements. Network slicing, has emerged as the most promising approach to address this challenge by enabling per-slice management of virtualized resources and provisioning and managing slices tailored to the needs of different vertical industries. Service orchestration is the key enabler for slicing that allows efficient placement of virtual network functions over the infrastructure as well as optimal allocation of virtual resources among all network slices to deliver guaranteed, reliable and scalable services of different verticals. Besides, due to the limited footprint of infrastructure operators, it is also required to enable the interconnection and federation of multiple administrative domains, to effectively allow services to span across several providers. This paper presents the design of Service Orchestrator (SO) in the 5G-TRANSFORMER system, which deals with service orchestration and federation across multiple domains.