Edge Computing

Edge Computing is a transformative approach to network architecture that brings computation and data storage closer to the sources of customer data.

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Redefining the future of network architecture

Edge computing is a model that enhances performance, security, and efficiency by processing data at or near the source, rather than relying solely on centralised data centres.

By processing data closer to its source, this approach reduces latency, enhances security, improves operational efficiency, and offers scalability – ultimately delivering better user experiences.

CTC’s edge computing solutions

CTC partners with multiple IoT and edge computing vendors to bring the best value to our customers. Whether you are looking for edge video analytics, facial recognition, or smart building initiatives, we have the solutions to meet your needs.

Additionally, our IoT platforms provide a single management pane for different IoT products, while custom middleware development is used to seamlessly integrate various IoT and edge gateways.

Benefits

  • By keeping data processing closer to the source (where data is generated), edge computing reduces latency. This means applications can fetch data directly from endpoints, avoiding delays caused by centralised data centres.

  • Edge computing ensures that sensitive data remains within the local environment, reducing exposure to potential security risks. It also minimises the need for data to travel over the internet, enhancing privacy.

  • By optimising data processing and reducing the load on centralized servers, edge computing leads to substantial cost savings in infrastructure and maintenance.

Dive deeper with CTC

CTC’s edge computing solution leverages distributed computing resources to optimise your network’s performance. By strategically placing compute resources within the execution layer of your network, we ensure immediate data processing and reduced transmission times. This approach is ideal for applications requiring real-time analytics, such as IoT devices and AI-driven services.

Our phased methodology includes the following:

Assessment

  • Evaluating the current infrastructure and identifying optimal locations for edge computing nodes.

Design

  • Developing a solution that integrates seamlessly with existing systems and IoT devices.

Implementation

  • Deploying edge computing resources and ensuring minimal disruption to ongoing operations.

Optimisation

  • Continuously monitoring performance and adjusting resources to maintain efficiency and reliability.

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