Horovod is a free and open-source distributed deep learning training framework for TensorFlow, Keras, PyTorch and Apache MXNet. It is designed to scale existing single-GPU training scripts to efficiently run on multiple GPUs and computer nodes with minimal code changes, using synchronous data-parallel training based on the ring-allreduce communication pattern. Horovod was initially developed at Uber and released as an open-source project in 2017, and is now hosted by the LF AI & Data Foundation, a project of the Linux Foundation. == History == Horovod was created at Uber as part of the company's internal machine learning platform Michelangelo to simplify scaling TensorFlow models across many GPUs. The first public release of the library, version 0.9.0, was tagged on GitHub in August 2017 under the Apache 2.0 licence. In October 2017, Uber Engineering publicly introduced Horovod as an open-source component of its deep learning toolkit. In February 2018 Alexander Sergeev and Mike Del Balso published a technical paper describing Horovod's design and benchmarking its performance on up to 512 GPUs, showing near-linear scaling for several image-classification models when compared with single-GPU baselines. In December 2018 Uber contributed Horovod to the LF Deep Learning Foundation (later LF AI & Data), making it a Linux Foundation project. Horovod entered incubation under LF AI & Data and graduated as a full foundation project in 2020. Since its initial release the project has expanded beyond TensorFlow to provide APIs for PyTorch, Keras and Apache MXNet, as well as integrations with frameworks such as Apache Spark and Ray, support for elastic training, and tooling for automated performance tuning and profiling. == Design and features == Horovod core principles are based on the MPI concepts size, rank, local rank, allreduce, allgather, broadcast, and alltoall. Horovod implements synchronous data-parallel training, in which each worker process maintains a replica of the model and computes gradients on different mini-batches of data. The gradients are aggregated across workers using the ring-allreduce communication pattern rather than a central parameter server, which reduces communication bottlenecks and can improve scaling on multi-GPU clusters. Communication is built on top of collective-communication libraries such as MPI, NCCL, Gloo and Intel oneCCL, and supports both GPU and CPU training. In the benchmark experiments reported in the original paper, Horovod achieved around 90% scaling efficiency on 512 GPUs for the ResNet-101 and Inception v3 convolutional neural networks, and around 68% scaling efficiency for the VGG-16 model. Horovod can be deployed on-premises or in cloud environments and is distributed as a Python package with optional GPU support via CUDA. The official documentation provides guides for running Horovod with Docker, Kubernetes (including via Kubeflow and the MPI Operator), commercial platforms such as Databricks, and cluster schedulers such as LSF. == Adoption and use cases == Within Uber, Horovod has been used for applications including autonomous driving research, fraud detection and trip forecasting. Major cloud providers have integrated Horovod into their managed machine learning offerings. Amazon Web Services supports distributed training with Horovod in services such as Amazon SageMaker and AWS Deep Learning Containers, while Microsoft Azure documents Horovod-based training workflows for Azure Synapse Analytics. Technical guides from academic and research computing centres, including Purdue University and the NASA Advanced Supercomputing programme, describe Horovod-based workflows for multi-GPU training on supercomputers and clusters. Horovod is also used in conjunction with Apache Spark and dedicated storage systems as part of end-to-end data processing and model-training pipelines. Industry blogs and technical tutorials describe deployments of Horovod on Kubernetes, on-premises clusters and cloud-managed Kubernetes services such as Amazon EKS.
Google Cloud Dataflow
Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines within the Google Cloud Platform ecosystem. Dataflow provides a fully managed service for executing Apache Beam pipelines, offering features like autoscaling, dynamic work rebalancing, and a managed execution environment. Dataflow is suitable for large-scale, continuous data processing jobs, and is one of the major components of Google's big data architecture on the Google Cloud Platform. At its core, Dataflow's architecture is designed to abstract away infrastructure management, allowing developers to focus purely on the logic of their data processing tasks. When a pipeline written using the Apache Beam SDK is submitted, Dataflow translates this high-level definition into an optimized job graph. The service then provisions and manages a fleet of Google Compute Engine workers to execute this graph in a highly parallelized and fault-tolerant manner. This serverless approach, combined with intelligent autoscaling of both the number of workers (horizontal) and the resources per worker (vertical), ensures that jobs have the precise amount of computational power needed at any given time, optimizing both performance and cost. The service's deep integration with the Google Cloud ecosystem makes it a powerful tool for a variety of use cases beyond simple data movement. For real-time analytics, Dataflow can ingest unbounded streams of data from Cloud Pub/Sub, perform complex transformations, and load results into BigQuery for immediate querying. In machine learning workflows, it is commonly used to preprocess and transform massive datasets stored in Cloud Storage, preparing them for training models in Vertex AI. This versatility makes it the central processing engine for modern ETL (Extract, Transform, Load) operations, streaming analytics, and large-scale data preparation within the cloud. == History == Google Cloud Dataflow was announced in June, 2014 and released to the general public as an open beta in April, 2015. In January, 2016 Google donated the underlying SDK, the implementation of a local runner, and a set of IOs (data connectors) to access Google Cloud Platform data services to the Apache Software Foundation. The donated code formed the original basis for Apache Beam. In August 2022, there was an incident where user timers were broken for certain Dataflow streaming pipelines in multiple regions, which was later resolved. Throughout 2023 and 2024, there have been various other updates and incidents affecting Google Cloud Dataflow, as documented in the release notes and service health history. The donation of the Dataflow SDK to the Apache Software Foundation was a pivotal moment, establishing Apache Beam as a unified, open-source programming model for defining both batch and streaming data pipelines. This strategic move decoupled the pipeline definition from the execution engine. As a result, developers could write portable data processing logic that was not locked into Google's ecosystem. A Beam pipeline can be executed on various runners, including Apache Flink, Apache Spark, and, of course, the highly optimized Google Cloud Dataflow service, providing flexibility and future-proofing data processing investments. == Features == Google Cloud Dataflow supports both batch and streaming data processing pipelines. It automatically handles resource provisioning, data sharding, and scaling according to workload, reducing manual configuration needed for large-scale data operations. == Use cases == Dataflow is used for ETL (Extract, Transform, Load) data pipelines, real-time analytics, and event stream processing for companies in industries such as finance, advertising, and IoT.
Group (online social networking)
A group (often termed as a community, e-group or club) is a feature in many social networking services which allows users to create, post, comment to and read from their own interest- and niche-specific forums, often within the realm of virtual communities. Groups, which may allow for open or closed access, invitation and/or joining by other users outside the group, are formed to provide mini-networks within the larger, more diverse social network service. Much like electronic mailing lists, they are also owned and maintained by owners, moderators, or managers, who can edit posts to discussion threads and regulate member behavior within the group. However, unlike traditional Internet forums and mailing lists, groups in social networking services allow owners and moderators alike to share account credentials between groups without having to log in to every group. == History == The rise of the World Wide Web resulted in an expansion of the varieties of methods for communication on the Internet, much of which was limited in the 1980s to discussion in newsgroups, BBS and chat rooms. While the initial rise of web-based mass communication took place in the form of early Internet forums in the mid-1990s, a few services such as MSN Groups, Yahoo! Groups and eGroups pioneered the combination of web-based mailing list archives with user profiles; by 2000, such services doubled as full-fledged mailing lists and Internet forums, allowing users to create an extremely large variety of discussion and networking mediums with comparatively sparse thresholds of complexity. Further features included chat rooms (often Java-based), image and video galleries, and group calendars. The second spurt of bullecalbel networking, one which was less dependent upon mailing list-related features and more upon Internet forum features, began in the early- to mid-2000s in the form of such services as LiveJournal, Friendster, MySpace and Facebook. These services continued the evolution of the web-based e-group as a discussion and organization medium. In the late 2000s, services such as Yammer and Micromobs further advanced e-group communication by taking advantage of microblog-style activity streams. == In virtual worlds == In Second Life, groups are centered less around discussion forums (as such, an asynchronous conferencing feature is not built into the Second Life network as of 2009) and common interest, and are more centered on maintenance of a particular geographic location inside the network. Such groups are often created by the owners of areas such as buildings, plots of land or whole islands in order to cater to the most frequent visitors and patrons of the regions. With the limited asynchronous messaging capability of Second Life, groups are also a means of mass-emailing announcements pertinent to the group, but are not completely capable of hosting discussion or deliberation of such announcement messages. == The importance of online social networking groups == Before people expanded their social life to the internet, they had small circles. These included the networks gained from rural areas or villages, such as family, friends and neighbors, and community groups such as churches. These networks represented a social safety net to support individuals. Since we have moved a huge part of our social life to the internet, online social networking groups have become a way to maintain a structure in social life. Online networking is made up by clusters of people, bounding themselves together on the World Wide Web. To be able to sort out the many different clusters we belong to we use online groups to helps us arrange and make sense of all our contacts. This sense-making is rooted within us, we sort and put people into compartments or sort by categories to make sense and try to understand our relationships to the people around us. Online social networking groups therefore enables us to do the same thing online. Online social networks have a huge impact on people’s lives. Since the social network revolution has offered people with more loose ties and diversity in their relationships, it creates both stress and opportunities. Furthermore, the Internet revolution has transformed the contact point from a household to the individual. In addition, people are in constant communication with each other due to the mobile revolution. All in all, the mentioned revolutions created a new social operating system: "networked individualism". The way that people currently connect, communicate and exchange information can be described as a form of operating system because of the similarities between the structure of computer systems and the networked individualism that has taken form in society. These structures consist of unwritten rules, norms, constraints and opportunities which are apparent for those who are part of a specific network. == Concerns == There is some research claiming that fake news is infiltrating online social networking. A recent study claimed that people exposed to fake news generally revert to their original opinion even after finding out the information they were given was false.
Digital intermediate
Digital intermediate (DI) is a motion picture finishing process which classically involves digitizing a motion picture and manipulating the color and other image characteristics. == Definition and overview == A digital intermediate often replaces or augments the photochemical timing process and is usually the final creative adjustment to a movie before distribution in theaters. It is distinguished from the telecine process in which film is scanned and color is manipulated early in the process to facilitate editing. However the lines between telecine and DI are continually blurred and are often executed on the same hardware by colorists of the same background. These two steps are typically part of the overall color management process in a motion picture at different points in time. A digital intermediate is also customarily done at higher resolution and with greater color fidelity than telecine transfers. Although originally used to describe a process that started with film scanning and ended with film recording, digital intermediate is also used to describe color correction and color grading and even final mastering when a digital camera is used as the image source and/or when the final movie is not output to film. This is due to recent advances in digital cinematography and digital projection technologies that strive to match film origination and film projection. In traditional photochemical film finishing, an intermediate is produced by exposing film to the original camera negative. The intermediate is then used to mass-produce the films that get distributed to theaters. Color grading is done by varying the amount of red, green, and blue light used to expose the intermediate. The digital intermediate process uses digital tools to color grade, which allows for much finer control of individual colors and areas of the image, and allows for the adjustment of image structure (grain, sharpness, etc.). The intermediate for film reproduction can then be produced by means of a film recorder. The physical intermediate film that is a result of the recording process is sometimes also called a digital intermediate, and is usually recorded to internegative (IN) stock, which is inherently finer-grain than original camera negative (OCN). One of the key technical achievements that made the transition to DI possible was the use of 3D look-up tables, which could be used to mimic how the digital image would look once it was printed onto release print stock. This removed a large amount of guesswork from the film-making process, and allowed greater freedom in the colour grading process while reducing risk. The digital master is often used as a source for a DCI-compliant distribution of the motion picture for digital projection. For archival purposes, the digital master created during the digital intermediate process can be recorded to very stable high dynamic range yellow-cyan-magenta (YCM) separations on black-and-white film with an expected 100-year or longer life. While still subject to the natural degradation of any analog chemical master, this archival format, long used in the industry prior to the invention of DI, was considered valuable for providing an archival medium that is independent of changes in digital data recording technologies and file formats that might otherwise render digitally archived material unreadable in the long term. A "film intermediate" is an analog variation of a digital intermediate, where a project shot on digital video is printed onto film stock and transferred back to digital video to emulate film. The term was coined after it was used on the Oscar-winning 2012 short film "Curfew". The process was also used on the films Dune (2021) and The Batman (2022). == History == Telecine tools to electronically capture film images are nearly as old as broadcast television, but the resulting images were widely considered unsuitable for exposing back onto film for theatrical distribution. Film scanners and recorders with quality sufficient to produce images that could be inter-cut with regular film began appearing in the 1970s, with significant improvements in the late 1980s and early 1990s. During this time, digitally processing an entire feature-length film was impractical because the scanners and recorders were extremely slow and the image files were too large compared to computing power available. Instead, individual shots or short sequences were processed for visual effects. In 1992, Visual Effects Supervisor/Producer Chris F. Woods broke through several "techno-barriers" in creating a digital studio to produce the visual effects for the 1993 release Super Mario Bros. It was the first feature film project to digitally scan a large number of VFX plates (over 700) at 2K resolution. It was also the first film scanned and recorded at Kodak's just launched Cinesite facility in Hollywood. This project based studio was the first feature film to use Discreet Logic's (now Autodesk) Flame and Inferno systems, which enjoyed early dominance as high resolution / high performance digital compositing systems. Digital film compositing for visual effects was immediately embraced, while optical printer use for VFX declined just as quickly. Chris Watts further revolutionized the process on the 1998 feature film Pleasantville, becoming the first visual effects supervisor for New Line Cinema to scan, process, and record the majority of a feature-length, live-action, Hollywood film digitally. The first Hollywood film to utilize a digital intermediate process from beginning to end was O Brother, Where Art Thou? in 2000 and in Europe it was Chicken Run released that same year. The process rapidly caught on in the mid-2000s. Around 50% of Hollywood films went through a digital intermediate in 2005, increasing to around 70% by mid-2007. This is due not only to the extra creative options the process affords film makers but also the need for high-quality scanning and color adjustments to produce movies for digital cinema. == Milestones == 1990: The Rescuers Down Under – First feature-length film to be entirely recorded to film from digital files; in this case animation assembled on computers using Walt Disney Feature Animation and Pixar's CAPS system. 1992: Visual effects supervisor and producer Chris F. Woods creates a VFX studio to produce the visual effects for the 1993 film Super Mario Bros. It was the first 35mm feature film to digitally scan a large number of VFX plates (over 700) at 2K resolution, as well as to output the finished VFX to 35mm negative at 2K. 1993: Snow White and the Seven Dwarfs – First film to be entirely scanned to digital files, manipulated, and recorded back to film at 4K resolution. The restoration project was done entirely at 4K resolution and 10-bit color depth using the Cineon system to digitally remove dirt and scratches and restore faded colors. 1998: Pleasantville – The first time the majority of a new feature film was scanned, processed, and recorded digitally. The black-and-white meets color world portrayed in the movie was filmed entirely in color and selectively desaturated and contrast adjusted digitally. The work was done in Los Angeles by Cinesite utilizing a Spirit DataCine for scanning at 2K resolution and a MegaDef color correction system from UK Company Pandora International 1998: Zingo - The first feature film to use digital color correction via digital intermediate in its entirety. The work was performed at the Digital Film Lab in Copenhagen, using a Spirit Datacine to transfer the entire film to digital files at 2K resolution. The digital intermediate process was also used to perform a digital blowup of the film's original Super 16 source format to a 35mm output. 1999: Pacific Ocean Post Film, a team led by John McCunn and Greg Kimble used Kodak film scanners & laser film printer, Cineon software as well as proprietary tools to rebuild and repair the first two reels of the 1968 Beatles' film Yellow Submarine for re-release. 1999: Star Wars: Episode I – The Phantom Menace - Industrial Light & Magic (ILM) scanned the entirety of the visual effects-laden film for the purposes of digital enhancement and the integration of thousands of separately filmed elements with computer generated characters and environments. Outside of the approximately 2000 effects shots that were digitally manipulated, the remaining 170 non-effects shots were also scanned for continuity. However, after the digital shots were manipulated at ILM, they were filmed out individually and sent to Deluxe Labs where they were processed and color timed photochemically. 2000: Sorted - The first feature-length, color 35mm motion picture to fully utilize the digital intermediate process in its entirety from inception to completion. The film was produced at Wave Pictures' digital intermediate film facility in London, England. It was scanned at 2K resolution with 8 bits color depth per color / per pixel using a pin registered, liquid gate Oxberry
Mixvoip
Mixvoip S.A. is a Luxembourg-based telecommunications service provider founded in 2008. The company offers IP telephony, high-speed Internet connectivity, and IT solutions to businesses and individuals. == Company history == In November 2017, Mixvoip expanded its operations to Belgium and Germany. At the beginning of 2019, the company acquired the telecommunications provider Voipgate. In December 2019, Mixvoip was named Telecom Company of the Year at the Luxembourg ICT Awards 2019 organized by Farvest and IT One. A 2024 article in Duke described the company's transition during the 2010s from traditional telephony services to cloud-based communication platforms. In the end of 2024, the ILR published the statistics about electronic communications in Luxembourg, including Mixvoip in the fix telephony section. In July 2025, Mixvoip acquired Crossing Telecom. In 2026, Mixvoip acquired Nomado's portfolio.
Outline of deep learning
The following outline is provided as an overview of, and topical guide to, deep learning: Deep learning is a subfield of machine learning and artificial intelligence based on artificial neural networks with multiple processing layers. It emphasizes representation learning and is widely used in areas such as computer vision, natural language processing, speech recognition, recommender systems, robotics, and generative artificial intelligence. == Ways to categorize deep learning == A field of study A branch of artificial intelligence A subfield of machine learning A subfield of computer science A form of representation learning A class of methods based on artificial neural networks An approach used in computational statistics == History == === Precursors === Cybernetics Perceptron Connectionism Neocognitron Backpropagation === Milestones === LeNet Long short-term memory Deep belief network AlexNet Sequence to sequence learning Generative adversarial network Residual neural network Transformer BERT Generative pre-trained transformer Diffusion model === Related histories === History of artificial intelligence History of machine learning Timeline of machine learning == Core concepts == == Learning settings == Supervised learning Unsupervised learning Self-supervised learning Semi-supervised learning Reinforcement learning Transfer learning Multitask learning Multimodal learning Online machine learning Continual learning == Common tasks == Image classification Object detection Image segmentation Automatic speech recognition Neural machine translation Question answering Automatic summarization Text-to-image model Protein structure prediction == Architectures == === Feedforward and convolutional architectures === Feedforward neural network Multilayer perceptron Convolutional neural network Radial basis function network Residual neural network U-Net === Recurrent and sequence architectures === Recurrent neural network Long short-term memory Gated recurrent unit Sequence to sequence learning Recursive neural network === Representation-learning architectures === Autoencoder Denoising autoencoder Sparse autoencoder Variational autoencoder Restricted Boltzmann machine Deep belief network === Attention and transformer architectures === Attention (machine learning) Transformer BERT Generative pre-trained transformer Vision transformer === Generative and probabilistic architectures === Autoregressive model Diffusion model Energy-based model Generative adversarial network Mixture of experts === Graph and memory architectures === Graph neural network Graph convolutional network Siamese network Neural Turing machine Memory network Echo state network Capsule neural network == Neural network components and techniques == Artificial neuron Activation function Rectified linear unit Sigmoid function Softmax function Embedding Convolution Pooling layer Attention Batch normalization Layer normalization Residual connections == Training and optimization == Backpropagation Gradient descent Stochastic gradient descent Adam optimization Learning rate Loss function Cross-entropy Mean squared error Regularization Dropout Early stopping Batch normalization Data augmentation Transfer learning Knowledge distillation Ensemble learning Curriculum learning == Datasets and benchmarks == CIFAR-10 ImageNet MNIST database Common Objects in Context (COCO) General Language Understanding Evaluation (GLUE) benchmark LibriSpeech SQuAD == Applications == === Computer vision === Computer vision Facial recognition system Image classification Image segmentation Medical imaging Object detection Optical character recognition === Natural language processing === Automatic summarization Chatbot Information retrieval Large language model Natural language processing Neural machine translation Question answering Sentiment analysis === Speech and audio === Automatic speech recognition Music information retrieval Speaker recognition Speech synthesis === Science and medicine === Bioinformatics Computational biology Drug discovery Medical diagnosis Protein structure prediction === Robotics and control === Autonomous car Computer game bot Control theory Robotics === Recommendation, search, and forecasting === Anomaly detection Forecasting Fraud detection Recommender system Search engine === Generative artificial intelligence === Deepfake Generative artificial intelligence Large language model Speech synthesis Text-to-image model === Computer graphics and video games === Deep Learning Anti-Aliasing (DLAA) Deep Learning Super Sampling (DLSS) == Hardware == AMD Instinct AMD XDNA Application-specific integrated circuit Deep learning processor, Neural processing unit (NPU), or Neural Engine Field-programmable gate array General-purpose computing on graphics processing units (GPGPU) Graphics processing unit NVIDIA Deep Learning Accelerator (NVDLA) Tensor processing unit Vision processing unit Wafer-scale integration === Supporting software platforms === CUDA Metal ROCm == Software == === Open-source frameworks and libraries === === Neural network software === EDLUT Emergent Encog JOONE Neuroph NeuroSolutions OpenNN Peltarion Synapse SNNS === Platforms, tools, and deployment === Amazon SageMaker Google Colab Hugging Face Kaggle Kubeflow MLflow ONNX OpenVINO TensorFlow Hub == Algorithms for deep learning and neural networks == Backpropagation Conjugate gradient method Generalized Hebbian algorithm Gradient descent Levenberg–Marquardt algorithm Perceptron Quasi-Newton method Wake-sleep algorithm == Methods and related topics == === Representation and metric learning === Contrastive learning Embedding Feature learning Manifold learning Metric learning === Generative modeling === Autoregressive model Diffusion model Generative adversarial network Generative model Variational inference === Efficient and scalable deep learning === Knowledge distillation Low-rank approximation Mixture of experts Quantization Sparsity === Reliability, safety, and interpretability === Adversarial machine learning AI alignment Algorithmic bias Catastrophic forgetting Differential privacy Explainable artificial intelligence Federated learning Hallucination (artificial intelligence) == Conferences and workshops == Annual Meeting of the Association for Computational Linguistics Conference on Computer Vision and Pattern Recognition Conference on Neural Information Processing Systems International Conference on Computer Vision International Conference on Learning Representations International Conference on Machine Learning == Organizations == === Research laboratories and institutions === Allen Institute for AI Alberta Machine Intelligence Institute European Laboratory for Learning and Intelligent Systems Google DeepMind Meta AI Mila Microsoft Research Vector Institute === Companies === Anthropic Cerebras Cohere DeepSeek Mistral AI OpenAI Stability AI xAI == Publications == === Books === Deep Learning – Ian Goodfellow and Yoshua Bengio Neural Networks and Deep Learning – Michael Nielsen Perceptrons – Marvin Minsky and Seymour Papert === Journals === IEEE Transactions on Neural Networks and Learning Systems Neural Networks Neural Computation == Influential persons ==
Data plan
A data plan is a subscription plan from a cellular or other mobile service provider to provide internet data and connectivity. == Formatting == Data plans are usually created by a contract between the telecommunications carrier and the user of their service. This contract outlines a maximum amount of usable data, usually highlighted in either megabytes or gigabytes, allotted per month for the user. In most cases companies will allow a user to surpass the amount of data allowed in the contract, however, will have to pay a per-gigabyte fee, ranging anywhere from five to fifteen U.S. dollars. === Popularization of unlimited plans === Unlimited data plans have seen a large increase in usage by consumers since their initial introduction by U.S. network T-Mobile. These plans, instead of setting an overall maximum for the user, have an amount set-up that, when surpassed, will slow the speed of the network for that user. Unlimited plans typically cost significantly more than the traditional shared data plans, which is a major reason that carriers have set large boundaries and fees. The limits imposed on unlimited plans are designed to fight against attempts to misuse the network, such as a DDoS attack, but are more commonly reasoned as a method to increase the number of people that can use one tower simultaneously. === Data speed changes === When a network is near reaching peak capacity data speeds may be slowed down by carriers as part of most major telecom contracts. This, as stated previously, allows for more people to be utilizing one tower, reducing needed capital for the company. Since speed changes are allowed at the company's will, the user has no official guarantee of speed on most major networks. === Costs brought upon by additional data === In many cases both the user and carrier have to incur additional costs when a user utilizes more of a given data package, which has helped in the proliferation of data caps and other forms of shared data plans. Most of the charges that the carrier has to incur for additional data usage is partially or fully given to the user of the network. ==== Users ==== Users are required to pay flat-rate additional fees that occur when they go above the amount of data given to them in their contract, utility, or prepaid plan. The cost per gigabyte of this fee is usually higher than what the contract itself offers, which discourages users from over-utilizing data and incurring a charge for the carrier. Certain contracts, which do not offer paying additional fees for an increase in data, may result in a shutdown of service, or in extremely rare cases, termination of the service as a whole. ==== Carriers ==== Carriers incur costs for additional data usage, as it limits the number of customers, and associated contracts, that they can handle on one network. Creating more cell phone towers in a given area would be costly, and largely useless until particular spikes in traffic. When the peak usable amount of one tower is reached, it may cause negative public relations towards the reliability of the corporation as a whole.