Softwarization in Future Mobile Networks and Energy Efficient Networks

The data growth generated by pervasive mobile devices and the Internet of Things at the network edge (i.e., closer to mobile users), couple with the demand for ultra-low latency, requires high computation resources which are not available at the end-user device. This demands a new network design paradigm in order to handle user demands. As a remedy, a new MN network design paradigm has emerged, called Mobile Edge Computing (MEC), to enable low-latency and location-aware data processing at the network edge. MEC is based on network function virtualization (NFV) technology, where mobile network functions (NFs) that formerly existed in the evolved packet core (EPC) are moved to the access network [i.e., they are deployed on local cloud platforms in proximity to the base stations (BSs)]. In order to reap the full benefits of the virtualized infrastructure, the NFV technology shall be combined with intelligent mechanisms for handling network resources. Despite the potential benefits presented by MEC, energy consumption is a challenge due to the foreseen dense deployment of BSs empowered with computation capabilities. In the effort to build greener 5G mobile network (MN), we advocate the integration of energy harvesting (EH) into future edge systems

Level of education and HIV viral load suppression in a population under universal anti-retroviral therapy in eSwatini

Introduction: HIV/AIDS continues to be amongst the leading causes of morbidity and mortality in Sub-Saharan Africa. There is no approved cure for HIV, but the disease can be managed using Anti-Retroviral Therapy (ART). If drug adherence of about 95% is achieved, odds for a better clinical outcome increase. The level of education as a marker for many socio- economic status indicators has an impact on an individual ́s health. If a country is devasted by the virus, the nation faces both macro and micro economic effects. HIV is the leading cause of reduced disability adjusted life years in eSwatini, and the country has the highest HIV prevalence in the world. However, through donor agencies like United States President Emergency Plan for AIDS Relief (PEPFAR) and Global Fund, eSwatini can provide free ART. Purpose: To determine if the level of education lowers the risk for VLS failure in eSwatini to a population that has universal access to ART. Material and Methods: The present study included a representative sample of 2025 HIV positive participants with traces of ART in blood-samples who have used ART for at least 6 months aged 15 years or older from the SHIMS 2 study in eSwatini 2016-2017. This cross- sectional survey includes data from blood samples analyses Viral Load Suppression (VLS) and Viral Load Count VLC) and self-administered questionnaires. Multi-variate logistic regression models were used to estimate the un-biased association between level of education and VLS- Failure. Results: The study included 620 (30.6%) males and 1405 (69.4%) females. The mean age was 40.25 years (range 15 - 80 years). 92.9% of the participants had achieved VLS. In a multivariate regression analysis, increased level of education was associated with lower risk of VLS- failure. After adjusting for confounders (adherence, age, and wealth quintile) people with tertiary education had 76 % less risk of VLS failure compared to people with primary education (OR, 0.24; CI, 0.05-1.03; P, 0.05). For Secondary and High-school levels of education, the ORs were 0.65 (CI, 0.43-0.99; P, 0.05) and 0.55 (CI, 0.33-0.92; P, 0.02) compared to the reference group (Primary education). Conclusion: This population-based survey is, to the best of our knowledge, the first study to provide data on the association of level of education and VLS-Failure in eSwatini in a population with blood traces of ART. This study reveals that people who have completed tertiary education have lower risks of VLS-Failure

Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks

This thesis investigates resource management procedures, within the Multi-access Edge Computi ng (MEC) paradigm, to obtain energy savings and guarantee Quality of Service(QoS) in Mobile Networks (MNs). Here, we enable energy savings within green-aware network apparatuses (i.e., communication and computing facilities) through the application of learning and control techniques, together with energy management procedures (BS sleep mode, VM soft-scaling, tuning of transmission drivers). In this study, we consider the MEC deployment scenarios suggested by ETSI and mobile operators for our system models. Firstly, we investigate energy-saving strategies within a remote site fully powered by only green/renewable energy (solar and wind). Here, we consider a single Base Station (BS) co-located with the MEC server, i.e., the BS is empowered with computing capabilities. To address the energy consumption problem within the remote site, we propose an online algorithm for edge network management. The algorithm make use of a Long Short-Term Memory (LSTM) neural network for estimating the short-term future traffic load and harvested energy, and control theory, specifically the Limited Lookahead Control (LLC) principles, for foresighted optimization. It also make use of energy management procedures, i.e., BS sleep modes and Virtual Machine (VM) soft-scaling (the reduction of computing resources per time instance). To obtain the energy savings and guarantee QoS, per time instance, the algorithm considers the future BS loads, onsite green energy available and then provisions edge network resources based on the learned information. Secondly, we study the energy consumption problem within an environment where BSs are densely-deployed, i.e., similar to an urban or semi-urban scenario. This work extend the energy consumption problem from a single BS case to multiple BSs. Here, each BS is powered by hybrid energy supplies (solar and power grid) and also empowered with computation capabilities (each BS is co-located with a MEC server). Towards edge system management, we propose a controller-based network architecture for managing energy harvesting (EH) BSs empowered with computation capabilities where on/off switching strategies allow BSs and VMs to be dynamically switched on/off, depending on the traffic load and the harvested energy forecast, over a given look-ahead prediction horizon. To solve the energy consumption minimization problem in a distributed manner, the controller partitions the BSs into clusters based on their location; then, for each cluster, it minimizes a cost function capturing the individual communication site energy consumption and the users’ QoS. To manage the communication sites, the controller performs online supervisory control by forecasting the traffic load and the harvested energy using a LSTM neural network, which is utilized within a LLC policy to obtain the system control actions that yield the desired trade-off between energy consumption and QoS. Finally, we investigate the energy consumption problem within a virtualized MEC server placed in proximity to a group of BSs. To address this challenge, we consider a computing-plus-communication energy model, within the MEC paradigm, where we focus on the communication-related energy cost in addition to the energy drained due to computing processes. Towards server management, an online algorithm based on traffic engineering and MEC Location Service is proposed. To obtain the energy savings and QoS guarantee, we jointly launch an optimal number of VMs for computing and transmission drivers coupled with the location-aware traffic routing for real-time data transfers. In order to efficiently provisioned edge system resources, we forecast the server workloads and harvested energy by using a LSTM neural network and the output is then used within the LLC-based algorithm. Our numerical results, obtained through trace-driven simulations, show that the proposed optimization strategies (algorithms) leads to a considerable reduction in the energy consumed by the edge computing and communication facilities, promoting energy self-sustainability within the MN through the use of green energy

Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks

This thesis investigates resource management procedures, within the Multi-access Edge Computi ng (MEC) paradigm, to obtain energy savings and guarantee Quality of Service(QoS) in Mobile Networks (MNs). Here, we enable energy savings within green-aware network apparatuses (i.e., communication and computing facilities) through the application of learning and control techniques, together with energy management procedures (BS sleep mode, VM soft-scaling, tuning of transmission drivers). In this study, we consider the MEC deployment scenarios suggested by ETSI and mobile operators for our system models. Firstly, we investigate energy-saving strategies within a remote site fully powered by only green/renewable energy (solar and wind). Here, we consider a single Base Station (BS) co-located with the MEC server, i.e., the BS is empowered with computing capabilities. To address the energy consumption problem within the remote site, we propose an online algorithm for edge network management. The algorithm make use of a Long Short-Term Memory (LSTM) neural network for estimating the short-term future traffic load and harvested energy, and control theory, specifically the Limited Lookahead Control (LLC) principles, for foresighted optimization. It also make use of energy management procedures, i.e., BS sleep modes and Virtual Machine (VM) soft-scaling (the reduction of computing resources per time instance). To obtain the energy savings and guarantee QoS, per time instance, the algorithm considers the future BS loads, onsite green energy available and then provisions edge network resources based on the learned information. Secondly, we study the energy consumption problem within an environment where BSs are densely-deployed, i.e., similar to an urban or semi-urban scenario. This work extend the energy consumption problem from a single BS case to multiple BSs. Here, each BS is powered by hybrid energy supplies (solar and power grid) and also empowered with computation capabilities (each BS is co-located with a MEC server). Towards edge system management, we propose a controller-based network architecture for managing energy harvesting (EH) BSs empowered with computation capabilities where on/off switching strategies allow BSs and VMs to be dynamically switched on/off, depending on the traffic load and the harvested energy forecast, over a given look-ahead prediction horizon. To solve the energy consumption minimization problem in a distributed manner, the controller partitions the BSs into clusters based on their location; then, for each cluster, it minimizes a cost function capturing the individual communication site energy consumption and the users’ QoS. To manage the communication sites, the controller performs online supervisory control by forecasting the traffic load and the harvested energy using a LSTM neural network, which is utilized within a LLC policy to obtain the system control actions that yield the desired trade-off between energy consumption and QoS. Finally, we investigate the energy consumption problem within a virtualized MEC server placed in proximity to a group of BSs. To address this challenge, we consider a computing-plus-communication energy model, within the MEC paradigm, where we focus on the communication-related energy cost in addition to the energy drained due to computing processes. Towards server management, an online algorithm based on traffic engineering and MEC Location Service is proposed. To obtain the energy savings and QoS guarantee, we jointly launch an optimal number of VMs for computing and transmission drivers coupled with the location-aware traffic routing for real-time data transfers. In order to efficiently provisioned edge system resources, we forecast the server workloads and harvested energy by using a LSTM neural network and the output is then used within the LLC-based algorithm. Our numerical results, obtained through trace-driven simulations, show that the proposed optimization strategies (algorithms) leads to a considerable reduction in the energy consumed by the edge computing and communication facilities, promoting energy self-sustainability within the MN through the use of green energy

Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey

Future mobile networks (MNs) are required to be flexible with minimal infrastructure complexity, unlike current ones that rely on proprietary network elements to offer their services. Moreover, they are expected to make use of renewable energy to decrease their carbon footprint and of virtualization technologies for improved adaptability and flexibility, thus resulting in green and self-organized systems. In this article, we discuss the application of software defined networking (SDN) and network function virtualization (NFV) technologies towards softwarization of the mobile network functions, taking into account different architectural proposals. In addition, we elaborate on whether mobile edge computing (MEC), a new architectural concept that uses NFV techniques, can enhance communication in 5G cellular networks, reducing latency due to its proximity deployment. Besides discussing existing techniques, expounding their pros and cons and comparing state-of-the-art architectural proposals, we examine the role of machine learning and data mining tools, analyzing their use within fully SDN- and NFV-enabled mobile systems. Finally, we outline the challenges and the open issues related to evolved packet core (EPC) and MEC architectures

Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey

Future mobile networks (MNs) are required to be flexible with minimal infrastructure complexity, unlike current ones that rely on proprietary network elements to offer their services. Moreover, they are expected to make use of renewable energy to decrease their carbon footprint and of virtualization technologies for improved adaptability and flexibility, thus resulting in green and self-organized systems. In this article, we discuss the application of software defined networking (SDN) and network function virtualization (NFV) technologies towards softwarization of the mobile network functions, taking into account different architectural proposals. In addition, we elaborate on whether mobile edge computing (MEC), a new architectural concept that uses NFV techniques, can enhance communication in 5G cellular networks, reducing latency due to its proximity deployment. Besides discussing existing techniques, expounding their pros and cons and comparing state-of-the-art architectural proposals, we examine the role of machine learning and data mining tools, analyzing their use within fully SDN- and NFV-enabled mobile systems. Finally, we outline the challenges and the open issues related to evolved packet core (EPC) and MEC architectures