## 1 Introduction
Latency of Voice (Vo) communication over Internet Protocol (IP) must be considered for analyzing the performance of VAs in the Cloud. In [31], the authors presented a theoretical review of the implementation of VoIP analyzing Session Initiation Protocol (SIP) proxies as well as CODECs and presented the usual values of latency and jitter. In [32], the authors presented a position paper that also reviewed the importance of CODECs using Digital Signal Processing (DSP) and proxies but did not present any experimental results. The authors of [33] presented the performance of video streaming in a Data Center by analyzing (a) Traffic received at the destination node, (b) Traffic sent from the destination node, and (c) Packet Point to Point Delay. All the above papers recognize that latency is an important issue in delivering voice in IP, but none of them presented experimental results in which they observed the influence of proxies (caches) in energy saving as we did. Moreover, we did not consider the influence of CODEC on energy saving in our experimental results. Another commonly used technique is Acoustic Echo Cancellation (AEC), which eliminates echo in the voice signal [34]. The Echo can be caused by transmitting the signal through different channels, such as a phone line or data network. In general, Noise Reduction (NR) is applied to eliminate such effects in the voice signal, resulting in lower energy consumption to process a clean signal. To implement this type of proxy, audio and video tools such as Squid, Varnish, or Nginx are used, which allow audio and video streams to be cached. However, it is essential to note that implementing these applications may require greater processing power in the server hosting the proxy. While the solution in this study shares similarities with the presented applications, it is carried out in the local memory of VAs, handling plain text data and adding more speed to the process. Alternatively, caching could be performed using a cloud service provider, such as AWS Storage Gateway Volume in cache mode with SQL Server [35]. This allows recently accessed data or a full-volume copy to be stored locally in cache memory so that applications can benefit from quick access to the data. This solution requires a cloud service for deployment, which could become complex and robust, making it less attractive to VA users described in this work. Regarding mathematical models for energy consumption, several metrics exist to measure energy consumption in different systems. In [36], a systematic literature review found 41 energy consumption metrics, which are reliable, although they are purely oriented toward software systems, not considering device hardware. Some metrics relate to standby energy consumption, energy consumption during active use, and usage time, which measures how long the device is used during a given period; another is \"energy efficiency\", which calculates how much energy is consumed per unit of performance or task completed. [PERSON] and [PERSON] compare three modeling techniques for electrical consumption: linear regression, neural networks, and decision trees [37]. These models are predictive, a characteristic not represented in this work. The model presented in this study does not correspond to predictive models like those mentioned in the previous paragraph, nor does it depend solely on the software running on the devices; the model and metrics presented in this work derive primarily from an experimental process supported by a series of functional tests, where measurements and calculations are performed based on temporal variables from each phase of the process, ultimately reflecting a value for VA energy consumption. It is important to note that the precision of these models may vary depending on training data, the complexity of the usage scenario, the device manufacturer, and the quality of the wireless network.