As mobile apps get more sophisticated and demanding, offloading work to nearby cloud and edge resources is becoming ever more crucial. Technologies like augmented reality, virtual reality and artificial intelligence will continuously push the limits of our networks and drain precious battery life.
An intelligent solution is collaborative cloud-edge computing, which splits apps between cloud processors in the distance and edge nodes located close by. For instance, latency-dependent tasks like facial recognition run locally while resource-intensive jobs utilize distant cloud power.
This balanced approach considers both end-to-end latency and limited battery life. My colleagues investigated optimizing how tasks split between endpoints to maximize gains. They found teaming cloud and edge together performs better than relying on one alone.
Their investigation focused on incorporating this cooperative model into C-RAN infrastructure commonly used for 5G. C-RAN’s centralized approach enables effective interference control through joint signaling handling.
Fine-tuning resource allocation grows complex with factors like fronthaul compression schemes and cooperative radio combining. To minimize roundtrip delays, they developed an algorithm using matrix optimization techniques to untangle this huge, intricate optimization problem.
Looking ahead, strategically leveraging cloud, edge and network resources together will be pivotal for powering future cutting-edge applications at scale. Continuous refinement of how workloads spread across domains can help unlock the potential of these emerging technologies industry-wide.
While other solutions exist, hyperscalers and solution providers may also derive value by applying learnings to their own network offerings. Progress in this area is sure to light the path forward for 5G and beyond.
