Edge Network Accelerator for Low-Latency FaaS Offloading
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Solution Overview
Problem
Edge devices often face limitations in compute power and storage, leading to undesirable latencies in cloud-based computing architectures, as they rely on offloading workloads to cloud servers, which can be distant and resource-constrained, hindering efficient data processing and storage.
Innovation Solution
Implementing a device edge network with integrated accelerators like FPGAs and compute processors that expose low-latency function-as-a-service (FaaS) and accelerated FaaS (AFaaS) to endpoint devices, enabling direct access and offloading compute operations to device edge network computing devices for accelerated processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If computational workload is offloaded to cloud-based servers, then compute resources are preserved at edge devices, but transmission latency increases due to distance and network dependencies
Solution Approach 1:
The patent segments the centralized cloud computing architecture into distributed edge computing nodes (MEC servers) located at network edges. This segmentation allows computational tasks to be processed locally at edge devices rather than being offloaded to distant cloud servers, thereby reducing transmission latency while preserving compute resources at the edge.
Solution Approach 2:
The patent introduces a new spatial dimension by deploying MEC servers at the edge of cellular networks, creating a multi-layered computing architecture (cloud-edge-device). This dimensional change enables computation to occur closer to data sources without completely eliminating cloud connectivity, balancing resource preservation with latency reduction.
2Productivity
If more compute and storage resources are deployed at edge devices, then processing capability improves, but physical space restrictions prevent scaling
Solution Approach 1:
The patent introduces MEC servers as intermediary computing resources located at network edges (e.g., in base stations). These intermediaries provide additional compute and storage capacity to edge devices without requiring physical expansion of the devices themselves. The MEC servers act as a bridge between resource-constrained edge devices and the need for enhanced processing capability.
3Loss of time
If computational tasks are processed locally at edge devices, then latency is reduced, but compute resources are consumed at the devices
Solution Approach 1:
The patent merges the computing resources of edge devices with MEC servers at the network edge. This combination allows tasks to be processed locally (reducing latency) while leveraging the additional computational power of MEC servers (reducing individual device consumption). The merged architecture enables distributed processing that balances latency reduction with resource conservation.
Data Source
AI summary
Technologies for accelerating edge device workloads at a device edge network include a network computing device which includes a processor platform that includes at least one processor which supports a plurality of non-accelerated function-as-a-service (FaaS) operations and an accelerated platform that includes at least one accelerator which supports a plurality of accelerated FaaS (AFaaS) operation. The network computing device is configured to receive a request to perform a FaaS operation, determine whether the received request indicates that an AFaaS operation is to be performed on the received request, and identify compute requirements for the AFaaS operation to be performed. The network computing device is further configured to select an accelerator platform to perform the identified AFaaS operation and forward the received request to the selected accelerator platform to perform the identified AFaaS operation. Other embodiments are described and claimed.