Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Network Slicing. <s> Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environments. The goals of the proposed VirtualPower approach to online power management are (i) to support the isolated and independent operation assumed by guest virtual machines (VMs) running on virtualized platforms and (ii) to make it possible to control and globally coordinate the effects of the diverse power management policies applied by these VMs to virtualized resources. To attain these goals, VirtualPower extends to guest VMs `soft' versions of the hardware power states for which their policies are designed. The resulting technical challenge is to appropriately map VM-level updates made to soft power states to actual changes in the states or in the allocation of underlying virtualized hardware. An implementation of VirtualPower Management (VPM) for the Xen hypervisor addresses this challenge by provision of multiple system-level abstractions including VPM states, channels, mechanisms, and rules. Experimental evaluations on modern multicore platforms highlight resulting improvements in online power management capabilities, including minimization of power consumption with little or no performance penalties and the ability to throttle power consumption while still meeting application requirements. Finally, coordination of online methods for server consolidation with VPM management techniques in heterogeneous server systems is shown to provide up to 34% improvements in power consumption. <s> BIB001 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Network Slicing. <s> Recent progress on virtualization and deeply programmable network technologies brings opportunities for network operators to flexibly deploy a diverse set of network functions as software-based middle boxes. We present a new deployment and configuration management framework for network functions, called toy block networking. In our framework, an operator can describe its network-wide deployment plan of various network functions by using a unified abstraction representing essential features of composition of network functions, without including the detail of the functions and specific configuration interfaces. This abstraction is based on a network-wide configuration graph that captures the relationships between various network functions, and thus network-wide consistency of their configurations is systematically validated by the framework. Our framework enables the network operator, like building toy blocks into a structure, to compose various network functions from different vendors into a unified network service plan, and deploy them in an operational network. <s> BIB002 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Network Slicing. <s> 5G networks are expected to be able to satisfy users' different QoS requirements. Network slicing is a promising technology for 5G networks to provide services tailored for users' specific QoS demands. Driven by the increased massive wireless data traffic from different application scenarios, efficient resource allocation schemes should be exploited to improve the flexibility of network resource allocation and capacity of 5G networks based on network slicing. Due to the diversity of 5G application scenarios, new mobility management schemes are greatly needed to guarantee seamless handover in network-slicing-based 5G systems. In this article, we introduce a logical architecture for network-slicing-based 5G systems, and present a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum-sharing two-tier systems based on network slicing, where both the co-tier interference and cross-tier interference are taken into account. Simulation results demonstrate that the proposed resource allocation scheme can flexibly allocate network resources between different slices in 5G systems. Finally, several open issues and challenges in network-slicing-based 5G networks are discussed, including network reconstruction, network slicing management, and cooperation with other 5G technologies. <s> BIB003
Supporting the separation of the control and user plane functions is one of the most significant principles of the 5G EPC architecture. With the advent of virtualization (NFV, SDN, and cloud technology), it is now possible to build networks in a more scalable, flexible, and dynamic way. The concept of flexibility applies not only to the hardware and software parts of the network, but also to its management. For example, setting up a network instance that uses different network functions optimized to deliver a specific service needs to be automated. With virtualization technology, resources can be isolated resulting in a so-called network slice, which refers to an isolated set of (programmable) resources to enable network functions and services. With network slicing, one physical network is sliced into multiple virtual ones, each architected and optimized for a specific service or application. The dockerized EPC architecture using the FLARE node (an open deeply programmable network node architecture) is introduced as a network slicing architecture example in    . The EPC is decomposed into network slices, each implementing a network service as illustrated in Figure 3 . In this figure, there are a number of FLARE control slices consisting of the virtualized MME, linked with the HSS and SP-GW (integrating SGW and PGW) hosted in a docker platform (a software container platform). In the data plane, only the SP-GW is present for data management for each FLARE slice. This architecture provides the EPC NFs in each network slice. In  , the FLARE architecture was used to resolve technical challenges that include ease of programming, reasonable and predictable performance, and the isolation among multiple concurrent logics for a faster and modular programming of the SDN data plane. To facilitate programming, the Toy-Block networking programming model BIB002 has been introduced and furthermore, the combination of computational resources was introduced to obtain a reasonable and predictable performance. To improve performance, a lightweight resource virtualization technique, called resource container for isolation of multiple logics, was proposed. The cores were partitioned into groups, each with a resource container. The isolation used virtualization techniques. Another architecture based on network slicing, called mobile-central office rearchitected as a datacenter (M-CORD), has been proposed  . M-CORD is expected to deliver the agility of a cloud provider, also featuring software   . The slice "slicer" works as a controller to distribute packets to an individual slice according to the input/output port the packets use or to the tag information that they may contain. PCI means "peripheral component interconnect" and acts as an interface between the data and the control slices. platforms that enable rapid creation of new services. Its objectives are to enhance resource utilization, especially in terms of radio spectrum, to provide customized services and better QoE to customers and to offer an agile and costefficient deployment through the virtualization of the EPC. The EPC consists of sliced SGW for the user plane, and the control functions of the EPC are hosted in an SDN controller. The data plane of the EPC is expected to be connectionless (no GTP), and the signaling side S11 (MME to SGW) will be extended to an SDN controller. However, the S1-MME interface (eNB to MME) will be largely intact. It is expected that this architecture will involve the integration of vendor solutions within the CORD service framework. Such include the use of ONOS (for "open network operating system") as an SDN controller, through the development of an SGW/PGW application in ONOS. The role of such application will be to map restful APIs from the MME and the application of Open Flow rules towards a Radisys SGW/PGW data plane component. Discussion. End-to-end (E2E) network quality is an important consideration when using SDN technology. E2E QoS depends on the radio access, the EPC, and the wired part of networks, and 5G systems should have the capability to tailor it by organizing functions and connectivity so as to satisfy the system requirements, for example, mission critical applications. Mobile users are supposed to be satisfied with the quality, when using any applications anytime, anywhere. Considering the wide variety of application domains to be supported by 5G, it is necessary to extend the concept of slicing to cover a wider range of use cases than those targeted by the current SDN/NFV technologies and to also address a number of issues on how to utilize slices created on top of programmable software defined infrastructures. There exists a gap between current SDN technology developments, as noted in current technology reports  , and the functionalities that are required by 5G networks for E2E quality. For example, current focus is towards coming up with robust SDN controllers for network control and rules enforcement in networks, that is, reducing bottlenecks, while the use of SDN controllers to manage network slices is overlooked, yet management of network slices, under latency constraints, is one of 5G requirements whereby each slice needs to be controlled in an efficient way for service provision, based on the quantity of data and quality requirements. Apart from slice(s) management, even the radio side of the network needs to be managed for resource reservation, especially in cases of disasters, thus guaranteeing end-to-end quality. In  , the slice "slicer" acts as a controller for packet distribution not for slice(s) management; thus SDN technology advancement must consider slice(s) management. The observed similarities in the proposed architectures   are that both of them reorganize the network into network slices, respectively, consisting of data and control planes. Also, they have controllers which perform different functions; the slice "slicer" works as a controller to distribute packets to an individual slice in  , whereas the SDN controller in  performs the control functions. The difference between them is that the MME and the integrated gateway, SP-GW, are implemented in the docker platform for control purposes  , whereas, in  , the controller manages the network as it performs control functionalities. Energy Efficiency. In future mobile networks, network slicing is considered as a key in realizing network flexibility  ; therefore it is imperative that the RAN and EPC network works jointly in executing the "extreme flexibility." Achieving efficient network flexibility will help to serve devices with different quality of service, through slice isolation and resources provision. However, network slicing still poses a challenge as there are many dimensions and technologies included in this paradigm BIB003 . The challenges include RAN and EPC reconstruction to support end-to-end network slicing, slice management, and cooperation with other 5G technologies BIB003 . In addition, focus towards energy efficiency in network slicing is still lacking, as observed in   , yet resources for the network slices can be set up based on various service characteristics, for example, bandwidth demand and latency demand, over the same or shared infrastructure. The slice manager, if present, can be able to allocate resources per slice and also trigger energy savings strategies in unused resources. Since each slice provides customized connectivity and also runs on the same, shared infrastructure, by employing "soft resource scaling" (allocating reduced time for each resource usage, by each VM) BIB001 resource usage can be minimized thus improving energy efficiency within the EPC.