optimizing power consumption of 5G systems and next generation technology deployments is a critical problem. It is essential that the solution for optimizing power consumption takes into account the tradeoffs with maintaining service level agreement (SLA). Mobile network operator (MNO) may have different priorities for the objectives of saving power and for maintaining SLA, which depends on factors, such as customer contract, location, time of day, type of traffic, etc. In this paper, we design an intelligent solution using switching cells on-off action to save power, using machine learning (ML)/ deep learning (DL) methods to forecast future traffic load. We firstly identify the problem of training imbalance in traffic load prediction due to data imbalance in real cellular networks, and MNO preferences for the competing objectives of saving power and SLA maintenance. We then propose a novel solution that incorporates Balancing Loss Function, which addresses the training imbalance problem. Compared with the performance of previous approaches such as Mean Square Error (MSE) minimization traffic forecast based methods, we demonstrate using network field data that our method is able to achieve upto 3X improvement in service quality outage, with fairly similar power savings.

As new iterations of more powerful and better connected smartphones emerge, their limited battery life remains a leading factor adversely affecting the mobile experience of millions of smartphone users. While it is well-known that many apps can drain battery even while running in background, there has not been any study that quantifies the extent and severity of such background energy drain for users in the wild. To extend battery life, various new features are being incorporated within the phone, one of them being preventing applications from running in background, i.e., when the screen is off, but their impact is largely unknown. This paper makes several contributions. First, we present a large-scale measurement study that performs an in-depth analysis of the activities of various apps running in background on thousands of phones in the wild. Second, we quantify the amount of battery drain by all such background activities and possible energy saving. Third, we develop a metric to measure the usefulness of background activities that is personalized to each user. Finally, we present a system called HUSH (screen-off optimizer) that monitors the metric online and automatically identifies and suppresses background activities during screen-off periods that are not useful to the user experience. In doing so, our proposed HUSH saves screen-off energy of smartphones by 15.7% on average while incurring minimal impact on the user experience with the apps.

The limited battery life of modern smartphones remains a leading factor adversely affecting the mobile experience of millions of smartphone users. In order to extend battery life, it is critical to understand where and how is energy drain happening on users' phones under normal usage, for example, in a one-day cycle.

Since mobile devices are equipped with limited battery power, it is essential to have efficient power management mechanisms in mobile broadband networks such as mobile WiMAX that enable always on connectivity and provide high throughputs. In recent times Voice over IP (VoIP) has emerged as an important and dominant application in broadband mobile networks such as WiMAX. VoIP traffic is uniquely characterized by small packets sent regularly at periodic intervals and are bound by strict latency requirements for satisfactory user experience. This paper presents novel mechanisms to achieve enhanced power saving during a VoIP session by exploiting unique characteristics of VoIP traffic in next generation mobile WiMAX networks based on IEEE 802.16m standard. The analytical model and simulation results indicate that proposed methods reduce the average power consumption during a VoIP call by up to 70% compared to the existing IEEE 802.16m draft specifications. Moreover, the simulation results closely match our proposed analytical model.

Recent studies of network traffic utilization on campus networks have shown that data networks remain heavily under-utilized. Yet currently there is little attempt to save energy on network interfaces by using the low power modes available in Ethernet transceivers during periods of inactivity or low utilization. In this paper we design and evaluate a dynamic ethernet link shutdown (DELS) algorithm that utilizes current technology leading to significant benefits in energy savings with little noticeable impact on packet loss or delay. The algorithm uses buffer occupancy, the behavior of previous packet arrival times and a configurable maximum bounded delay to make sleeping decisions. The scheme was evaluated using simulations with inputs generated using a synthetic traffic generator for smooth and bursty traffic. The results show that the percentage of total time that a link can be shut down can be anywhere from 80% to 60% for traffic loads up to 5%.

Most Ethernet interfaces available for deployment in switches and hosts today can operate in a variety of different low power modes. However, currently these modes have very limited usage models. They do not take advantage of periods of inactivity, when the links remain idle or under-utilized. In this study, we propose methods that allow for detection of such periods to obtain energy savings with little impact on loss or delay. We evaluate our methods on a wide range of real-time traffic traces collected at a high-speed backbone switch within our campus LAN. Our results show that Ethernet interfaces at both ends can be put in extremely low power modes anywhere from 40%-98% of the time observed. In addition, we found that approximately 37% of interfaces studied (on the same switch) can be put in low power modes simultaneously which opens the potential for further energy savings in the switching fabric within the switch.

We examine the feasibility of introducing power management schemes in network devices in the LAN. Specifically, we investigate the possibility of putting various components on LAN switches to sleep during periods of low traffic activity. Traffic collected in our LAN indicates that there are significant periods of inactivity on specific switch interfaces. Using an abstract sleep model devised for LAN switches, we examine the potential energy savings possible for different times of day and different interfaces (e.g., interfaces connecting to hosts to switches, or interfaces connecting switches, or interfaces connecting switches and routers). Algorithms developed for sleeping, based on periodic protocol behavior as well as traffic estimation are shown to be capable of conserving significant amounts of energy. Our results show that sleeping is indeed feasible in the LAN and in some cases, with very little impact on other protocols. However, we note that in order to maximize energy savings while minimizing sleep-related losses, we need hardware that supports sleeping.

In this paper we examine the somewhat controversial subject of energy consumption of networking devices in the Internet, motivated by data collected by the U.S. Department of Commerce. We discuss the impact on network protocols of saving energy by putting network interfaces and other router & switch components to sleep. Using sample packet traces, we first show that it is indeed reasonable to do this and then we discuss the changes that may need to be made to current Internet protocols to support a more aggressive strategy for sleeping. Since this is a position paper, we do not present results but rather suggest interesting directions for core networking research. The impact of saving energy is huge, particularly in the developing world where energy is a precious resource whose scarcity hinders widespread Internet deployment.