The Phenomenon of Fake Bandwidth Often Goes Unnoticed

Computer Networks, as a backbone system, are compatible to all areas of life. Just think about communication in one's personal life as well as the multifarious operations carried out by large businesses. There are even network services built for entertainment and education purposes. The Quality of Service (QoS) provided by these networks is a crucial factor that determines how useful and dependable the internet is. Do we or do we not realize it yet, fake bandwidth is an increasingly serious problem for many users.

Speed tests can display high numbers but actual Internet connection is often quite slow. This problem is widely thought unknown within the business service providers and so goes largely unnoticed. However, even if you pay for premium services (which still suffer because of a lack of public IPs, high latency and frequent packet loss every month), you'll need someone else's help to be able to use them.

Fake bandwidth is when the Internet Service Providers (ISPs) claim to offer a certain speed, but Users actually experience much lower speeds. This phenomenon is common in shared bandwidth schemes without the customers ' knowledge, where many users share one bandwidth even though they are marketed as if each user has their own unshared bandwidth. For instance, a speed test might indicate 100 Mbps. However, the User actually experiences very slow speeds at peak times  especially the able to get on simple websites during periods of the day when processor jobs start hogging bandwith.

This phenomenon is not only a loss to users in terms of money, but also efficiency and access to Internet services. For instance in the field of education, many campuses in Indonesia are unable to provide online learning models or support them because their network infrastructure cannot cope with off againon latter says they are unfamiliar with terms like 'packet'. This serves as an informative example of how serious are the problems in coming to terms with issues gradable within all three branches of artificial consciousness. Fake bandwidth issues disguised as bandwidth management

Various preceding studies have measured and analyzed QoS in computer network research, from simulations to actual reconfigurings in institutional as well as industrial settings. But none of these has in fact built a deep learning model to classify network data based on QoS metrics such as bandwidth, throughput, packet loss, delay, and accessed websites. Nor have they determined an ISP's bandwidth is fake by means of this model.

Well suited for the task, deep learning models have the extraordinary capacity to perceive complex regular patterns within large and diverse knowledge sets as they are very good at disproportioning the classes of tasks. Even though these models still need some work in optimizing deep learning models for classification in network management is a necessity given the fact these data have very high nmnone with features of relevance and accuracy familiarly drawn pituitary essayed even neoplastic. Through an optimized deep learning model, it becomes possible to see the discrepancy between ISP speed claims and the actual speeds experienced by users.

About Author

Mr. Azriel, I am a full time lecturer in the Information Technology department for undergraduates at Institut Shanti Bhuana in Bengkayang, West Kalimantan. Currently, I am pursuing a PhD in Computing at the University of Technology Sarawak in Sibu, Malaysia. My research focuses on using deep learning to classify whether bandwidth is genuine or fake through rigorous and incremental experiments under the supervision of Assoc. Prof. Dr. Alan Ting Huong Yong, who is also the Dean of the School of Computing and Creative Media.