Interpreting what the internet means to the world -- the good and the bad, its enormous scale and its endless potential -- was a challenge that immediately inspired me,

It is expected that the next generation cellular networks provide a connected society with fully mobility to empower the socio-economic transformation. Several other technologies will benefits of this evolution, such as Internet of Things, smart cities, smart agriculture, vehicular networks, healthcare applications, and so on. Each of these scenarios presents specific requirements and demands different network configurations. To deal with this heterogeneity, virtualization technology is key technology. Indeed, the network function virtualization (NFV) paradigm provides flexibility for the network manager, allocating resources according to the demand, and reduces acquisition and operational costs. In addition, it is possible to specify an ordered set of network virtual functions (VNFs) for a given service, which is called as service function chain (SFC). However, besides the advantages from service virtualization, it is expected that network performance and availability do not be affected by its usage. In this paper, we propose the use of reinforcement learning to deploy a SFC of cellular network service and manage the VNFs operation. We consider that the SFC is deployed by the reinforcement learning agent considering a scenarios with distributed data centers, where the VNFs are deployed in virtual machines in commodity servers. The NFV management is related to create, delete, and restart the VNFs. The main purpose is to reduce the number of lost packets taking into account the energy consumption of the servers. We use the Proximal Policy Optimization (PPO) algorithm to implement the agent and preliminary results show that the agent is able to allocate the SFC and manage the VNFs, reducing the number of lost packets.

The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns.

Google may be set to address one of the Android operating system's biggest security flaws. Android users may be surprised to learn that the current OS allows apps to track your network activity by studying your TCP/UDP connections, which indicate a server you may have accessed. That's likely to change, however, as Google is expected to roll out greater restrictions in its upcoming Android P software at its annual I/O developer conference, which kicks off on Tuesday. Google may be set to address one of the Android operating system's biggest security flaws. Among the other announcements that are predicted include updates to the Google Home, a standalone Google News app and new features or integrations for Google Assistant.

From the above plot, it is evident that the clusters 1, 7, and 9 have activity for all 24 hours and are the more revenue generating clusters. The clusters 1, 5, 7, 9, and 10 have activity in night hours. The cluster 5 has activity from 11.5 to 17 hours.

Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. Most of the telecom companies use CDR information for fraud detection by clustering the user profiles, reducing customer churn by usage activity, and targeting the profitable customers by using RFM analysis. The actual dataset contains 8 numerical features about SMS in and out activity, call in and out activity, Internet traffic activity, square grid ID where the activity has happened, country code, and timestamp information about when the activity has been started. Here, K-means is applied among "total activity and activity hours" to find the usage pattern with respect to the activity hours.

In this blog, we will discuss about clustering of customer activities for 24 hours by using K-means clustering feature in Tableau 10. This type of clustering helps you create statistically-based segments that provide insights about similarities in different groups and performance of the groups when compared to each other. You can use clustering on any type of visualization ranging from scatter plots to text tables and even maps. In our previous blog post – "Call Detail Record Analysis – K-means Clustering with R", we have discussed about CDR analysis using unsupervised K-means clustering algorithm. A daily activity file from Dandelion API is used as a data source, where the file contains CDR records generated by the Telecom Italia cellular network over the city of Milano.

From the above plot, it is evident that the clusters 1, 7, and 9 have activity for all 24 hours and are the more revenue generating clusters. The clusters 1, 5, 7, 9, and 10 have activity in night hours. The cluster 5 has activity from 11.5 to 17 hours. By using this clustering mechanism, you can find the clusters making more traffic to the telecom network in the measure of total activity. Similarly, you can obtain more information like square grid and country code information to understand the square grid likely creating more revenue and more traffic to the telecom network and to target high customers based on their geo location. In the upcoming blog, we will discuss about how RFM will be used to analyze call detail records. Bio: Rathnadevi Manivannan is working as a Senior Technical Writer in Treselle Systems, experienced and passionate about writing on different technologies and domains such as Big Data, Cloud Computing, Virtualization, Storage, Data Analytics, Business Analytics.

From the above plot, it is evident that the clusters 1, 7, and 9 have activity for all 24 hours and are the more revenue generating clusters. The clusters 1, 5, 7, 9, and 10 have activity in night hours. The cluster 5 has activity from 11.5 to 17 hours.