Evaluating the Role of Edge Computing in Reducing Latency and Enhancing Efficiency in Cloud-Based Data Storage and Access

Digital data is exploding, thanks to cloud services, IoT gadgets, real-time apps, and the whole big data wave. It’s put a lot of pressure on old-school cloud systems. Sure, centralized cloud setups are great for scaling and staying flexible, but they stumble when it comes to high-latency, network jams, and wasted resources—especially with stuff like self-driving cars, industrial robots, health monitors, or online games where every millisecond counts. So, this research dives into mixing edge computing with cloud storage, building a hybrid edge–cloud system that tackles these headaches head-on. We tested it out using both made-up and real data, looking at things like latency, throughput, bandwidth, CPU, and memory. The numbers were pretty striking: latency dropped by up to 39.3%, throughput jumped by 27.5%, bandwidth use shrank by over 30%, and CPU and memory use got way better. It all points to one thing—processing time-sensitive stuff at the edge really works. It takes the heat off the main cloud, cuts down delays, and makes everything run smoother. In the end, hybrid edge–cloud systems stand out as a solid fix for today’s data-heavy, speed-hungry applications, bringing faster responses, better reliability, and a smoother experience for users.