Failure management and cost-aware traffic engineering are two important tasks done in Network Operation Centers (NOC). These are performed by expert technicians who must carefully analyze the network state and the flow of incoming alarms to decide how, where and when to take actions on the network. While based on implicit guiding principles, these network actions are very hard to automate with explicit rules due to the high complexity of the system; hence NOC action is essentially a manual process today. To automate part of that process, in this paper we introduce an Action Recommendation Engine (ARE) that can learn implicit NOC action rules with supervised machine learning from historical data. As a result, ARE can recommend suitable action(s) to remedy network faults and engineer the traffic to minimize costs, all while maximizing the users' Quality of Experience. To quantify the effectiveness of different NOC action scenarios, we introduce the QoE-OPEX metric which balances between users' quality of Experience and ISP's operational costs. After proper model training on 56,000 data points with 66 features, we demonstrate that ARE can effectively reproduce implicit action-taking logic of NOC technicians, thus moving us one step closer to reliable autonomous networks and fully-automated NOCs.
SummarySwitched-capacitor DC-DC converters (SC DC-DC) are analyzed for loss sources, voltage regulation integrity, start-up latency, and ripple size, while the trade-offs between these metrics are derived. These analyses are used to design a SC DC-DC that achieves high efficiency in a wide load current range. Four-way interleaving was employed to reduce the output ripple and efficiency loss due to this ripple. The design can be reconfigured to achieve gains of 1/3 and 2/5 for inputs ranging between 1.4 and 3.6 V to generate output voltage range of 0.4 to 1.27 V and can supply peak load current of 22 mA. It uses thin-oxide MOS capacitors for their high density and achieves 75.4% peak efficiency with an input frequency of 100 MHz and a load capacitor of 10 nF. An augmenting LDO that only regulates during sudden load transients helps the converter respond fast to these transients. The chip was implemented using a 65-nm standard CMOS process.
| INTRODUCTIONEnergy efficient systems have been the focus of both the research and development community in the last decade with the increasing interest in IoT applications, medical sensor technologies (personal/body area networks) and sensor networks for smart homes/cities. These applications require extremely energy-efficient and low-cost solutions for maximum durations of autonomous operation without the need to charge or change the battery. 1 CMOS is the preferred technology for building such systems due to its wide availability, low cost, and suitability for high integration. Low supply voltages of recent CMOS technologies necessitate efficient ways to regulate the higher battery or harvested voltages to suitable levels for the technology that is used. 2 Traditionally, LDOs and switching (buck) regulators are used for regulating voltages, but they are inefficient or are large in size and costly. 3,4 Recent efforts have focused on using on-chip inductors to implement buck regulators with limited success due to the inherent small value and low-Q of these inductors. 3This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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