Edge-Cloud Synergy: Unleashing the Potential of Parallel Processing for Big Data Analytics

Abstract

If an edge-node orchestrator can partition Big Data tasks of variable computational complexity between the edge and cloud resources, major reductions in total task completion times can be achieved even at low Wide Area Network (WAN) speeds. The percentage time savings are greater with increasing task computational complexity and higher WAN speeds are required for low-complexity tasks. We demonstrate from numerical simulations that low-complexity tasks can benefit either by task partitioning between an edge node and multiple cloud servers. The orchestrator can also achieve greater time benefits by rerouting Big Data tasks directly to a single cloud resource if the balance of parameters (WAN speed and the ratio between edge and cloud processing speeds) is favourable.

Edge AI: A survey

“…The evolving discussion on Edge Computing has identified four distinct paradigms differentiated on the basis of intended markets and business models [59]. FC may turn out to be the preferred solution for IoT applications to minimize the time required for time-critical processing, for example, facial recognition by surveillance cameras in public spaces, related machine-to-machine operations and Big Data analytics [203]. MEC, in contrast, is centred on extending the applications available to and intended for genuinely mobile devices.…”