Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> MEC Use Cases Scenarios. <s> Internet hosting centers serve multiple service sites from a common hardware base. This paper presents the design and implementation of an architecture for resource management in a hosting center operating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provision server resources for co-hosted services in a way that automatically adapts to offered load, improve the energy efficiency of server clusters by dynamically resizing the active server set, and respond to power supply disruptions or thermal events by degrading service in accordance with negotiated Service Level Agreements (SLAs).Our system is based on an economic approach to managing shared server resources, in which services "bid" for resources as a function of delivered performance. The system continuously monitors load and plans resource allotments by estimating the value of their effects on service performance. A greedy resource allocation algorithm adjusts resource prices to balance supply and demand, allocating resources to their most efficient use. A reconfigurable server switching infrastructure directs request traffic to the servers assigned to each service. Experimental results from a prototype confirm that the system adapts to offered load and resource availability, and can reduce server energy usage by 29% or more for a typical Web workload. <s> BIB001 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> MEC Use Cases Scenarios. <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> BIB002 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> MEC Use Cases Scenarios. <s> Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization. <s> BIB003 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> MEC Use Cases Scenarios. <s> Large events like the Super Bowl, where almost 75K attendees congegrate for several hours, poses a significant challenge in the planning, design and deployment of wireless networks. This was one of the first events where the LTE cellular network was available widely, in addition to almost 700 WiFi free hotspots. The Super Bowl in 2013 was also unprecedented because of a stadium-wide power outage for over half an hour. This study is the first to look in-depth at the user behaviours and traffic demand of a large ISP's celluar network at such an unique event. The findings of this study can be used to guide the design of the communication networks of large venues in the future. There are several key insights from our study of the data collected. First, LTE speeds enable subscribers at venues to stream high-quality video and this can be a signficant source of traffic. Second, the configuration of the uplink for such events is key, and a thoughtful approach to the design of applications that use the cloud for storing user data can substantially mitigate the congestion on the resource constrained uplink. Further, while it is tempting to take advantage of multicast on the cellular network (e.g., deploying technologies such as eMBMS in a venue), our results indicate that there is a need to combine multicast with caching to remove the strict requirement of overlap of requests from users to derive that benefit. <s> BIB004 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> MEC Use Cases Scenarios. <s> Due to the enormous energy consumption and associated environmental concerns, data centers have been increasingly pressured to reduce long-term net carbon footprint to zero, i.e., carbon neutrality. In this paper, we propose an online algorithm, called COCA (optimizing for COst minimization and CArbon neutrality), for minimizing data center operational cost while satisfying carbon neutrality without long-term future information. Unlike the existing research, COCA enables distributed server-level resource management: each server autonomously adjusts its processing speed and optimally decides the amount of workloads to process. We prove that COCA achieves a close-to-minimum operational cost (incorporating both electricity and delay costs) compared to the optimal algorithm with future information, while bounding the potential violation of carbon neutrality. We also perform trace-based simulation studies to complement the analysis, and the results show that COCA reduces cost by more than 25% (compared to state of the art) while resulting in a smaller carbon footprint. <s> BIB005 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> MEC Use Cases Scenarios. <s> The virtual machine (VM) is the most basic unit for virtualization and resource allocation. The study of VM power metering is the key to reducing the power consumption of data centers. In this paper, we make a comprehensive investigation in issues regarding VM power metering, including server models, sampling, VM power metering methods, and the accuracy of the methods. We will review many up-to-date power metering methods in this paper, and analyze their efficiencies, as well as evaluate their performance. Open research issues, such as VM service billing, power budgeting, and energy saving scheduling, are discussed, with an objective to spark new research interests in this field. <s> BIB006 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> MEC Use Cases Scenarios. <s> Merging Mobile Edge Computing (MEC), which is an emerging paradigm to meet the increasing computation demands from mobile devices, with the dense deployment of Base Stations (BSs), is foreseen as a key step towards the next generation mobile networks. However, new challenges arise for designing energy efficient networks since radio access resources and computing resources of BSs have to be jointly managed, and yet they are complexly coupled with traffic in both spatial and temporal domains. In this paper, we address the challenge of incorporating MEC into dense cellular networks, and propose an efficient online algorithm, called ENGINE (ENerGy constrained offloadINg and slEeping) which makes joint computation offloading and BS sleeping decisions in order to maximize the quality of service while keeping the energy consumption low. Our algorithm leverages Lyapunov optimization technique, works online and achieves a close-to-optimal performance without using future information. Our simulation results show that our algorithm can effectively reduce energy consumption while guaranteeing quality of service for users. <s> BIB007
There are several use cases that will greatly benefit from pushing MEC towards the edges of the network, into small cells, Wi-Fi access points, media gateways, and even extending edge computing to user devices themselves. The benefits of edge storage or computation include low latency, traffic optimization, agility and adaptability, and context-awareness. In combination with visibility into prevailing radio conditions via the MEC's RNIS function, applications can adapt content delivery in real time to ensure an adequate QoE to the end users  . Some of the leading use cases are discussed next. (1) Radio Access Network-(RAN-) Aware Content Optimization. Video on demand is one of the services that constitutes half of the mobile network traffic and expected to be 82% of all consumer Internet traffic by 2021, up from 73% in 2016 [59] , while at the same time the available network capacity varies by an order of magnitude within seconds, as a result of fluctuations in the radio channel condition. In the case of rapidly varying channel conditions, the transmission control protocol (TCP) may not be able to adapt fast enough, leading to underutilization of radio resources. To overcome this problem, researchers in   proposed to use MEC technology to inform the video server of the optimal bit rate to use given the channel state conditions. The idea behind this strategy is to make use of analytics applications to estimate the throughput at the radio downlink interface for a user and then use packet headers to convey that information to the video server, so that it can adapt the stream quality accordingly, at runtime. (2) Edge Video Orchestration. From the traffic characteristics observed in BIB004 , it is observed that, during the time of a big event, some of the BSs are underutilized. Thus, large public venues are good candidates for MEC, especially where localized venue services are important in exchange for BS energy savings within the network. In this use case, the video from the big event, for example, a soccer or baseball match, can be served to on-site consumers from a MEC server running an appropriate application located, for example, within the stadium premises. This type of service can be linked with a dedicated RAN deployment at the venue such that MEC servers can be colocated with RAN controllers and the backhaul equipment. Edge orchestration requires that the recorded video be locally stored, processed, and directly delivered to the users at the event without backhaul connection to a centralized EPC. This enables the virtualization of the EPC functions in the edge servers for fast data delivery. (3) Geolocation. The availability of geolocation applications in mobile devices can enhance device tracking for service delivery as Geoanalytics application hosted in the MEC server can make use of the real time information provided by the network about the direction and location of the mobile devices. Such MEC applications will enable location based services for enterprises and consumers, for example, in venues, retail locations, and coverage areas where GPS coverage is not available. Moreover, geolocation can assist in monitoring the health of individuals while on transit and in case of emergency the device can send a report signal to the nearest MEC platform for emergency assistance. Energy Efficiency. To address power management challenges in the context of virtualized data centers, either large or micro, the authors in BIB002 proposed a new power management technique called "soft scaling," in addition to "hard scaling" where the processor frequency is either scaled down/up depending on the workload BIB001 . The idea behind soft scaling is to mimic hardware scaling by allocating reduced time periods for resource usage by each VM, using a VM scheduler. A new architecture is presented by the authors together with a two-tier policy approach, local and global policies, to be applied on the available resources within the virtualized data center. At global level, the system is responsible for coordinating VM migration (e.g., live migration), where VM migration refers to a mechanism for migrating one VM from its local server to another target server, within the data center. The process at local level involves performing actions corresponding to resource management procedures enforced by the controller. From the obtained results in BIB002 , it is shown that the combination of hard and soft scaling may yield better energy savings. Nonetheless, it has to be noted that the VMs are unaware of the power quantity they consume, due to lack of power metering capabilities in virtualized platforms. To provide visibility into VM power consumption, the authors in BIB003 propose a mechanism for VM power metering. They infer energy consumed from resource usage by each VM and then develop individual system component power models. From the obtained results, it is observed that hard scaling is not suitable for virtualized environments since it affects the performance of all running VMs in the server. The authors advocate for VMs power capping in order to eliminate undesirable "noisy neighbors effect," which is prevalent in cases where sufficient isolation is lacking. In BIB005 , the authors propose an online algorithm, based on Lyapunov optimization, for reducing the operational cost and satisfying carbon neutrality, within a data center, without future workload information. In this work, the server is allowed to autonomously tune its processing speed and to decide on the amount of workload it can compute per time slot. Despite the efforts made towards reducing power consumption in data centers, as presented in BIB002 BIB001 BIB003 , new management approaches are required for handling energy consumption in computing platforms as virtualization technology evolves, in addition to the advent of MEC and ultra dense networking (UDN). With MEC being part of 5G network plans, energy efficiency is also important in such computing environments, as the microplatforms host virtualized mobile functions, and the edge devices are empowered with computing and storage capabilities to serve user's requests locally. This requires edge devices tuning and soft scaling the VMs running on top of the hypervisors in order to improve energy efficiency in virtualized platforms. In addition, the dense deployment of MEC servers requires redesigning energy efficient procedures as with edge computing BSs will provide computing services apart from radio access services. In BIB007 , the authors try to address the issue of incorporating MEC into dense cellular networks, where each small cell BS is considered to have a computing platform colocated with it, and then propose an online algorithm for jointly managing offloading and BS sleep modes decision making, while keeping energy consumption low. Furthermore, they developed a decentralized algorithm for BS-BS cooperation in order to optimize sleep modes and offloading decisions. To optimize the average delay cost, the proposed online algorithm makes use of the Lyapunov optimization technique. However, to address 5G use cases in a more energy efficient way, visibility into power usage is required for developing power management policies in virtualized computing platforms. The computing platforms can either utilize on-site renewable energy, off-site renewable energy, grid power, or a combination of the sources depending on one's demands. Therefore, quantifying power usage can yield better power management policies in data centers, resulting in energy efficient 5G networks. The reader is referred to BIB006 for a comprehensive review regarding VM power metering, including server models, sampling, VM power metering methods, and the accuracy of the methods.