Hybrid Cloud-Edge Computing With Blockchain Task Routing
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Solution Overview
Problem
Existing computing architectures struggle to provide sufficient computational resources for fast and secure computation of complex tasks such as video encoding, transcoding, and artificial intelligence, particularly due to the high cost and inefficiency of centralized systems, and the need for accelerated data output within a threshold period.
Innovation Solution
A hybrid cloud-edge computing platform utilizing a decentralized network with a coordinator cluster that selects and routes tasks to edge and cloud computing nodes based on efficiency scores, registers tasks on a blockchain, and rewards worker nodes for successful task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized computing systems are used to provide computational resources for complex tasks, then computational power can be concentrated and managed, but the cost increases and efficiency decreases
Solution Approach 1:
The patent segments the centralized computing system into a distributed network of edge computing nodes, cloud computing nodes, and coordinator clusters. Each node operates independently but contributes to the overall computational task, thereby reducing the burden on any single system and improving overall efficiency while distributing costs across multiple participants.
Solution Approach 2:
The patent introduces a new dimension of decentralization by adding blockchain technology to the computing architecture. This creates a multi-layered system where computational tasks are distributed across spatial dimensions (edge nodes, cloud nodes) and organizational dimensions (decentralized governance, token-based incentives), resolving the contradiction between centralized management and distributed efficiency.
2Speed
If more computational resources are allocated to handle complex tasks within a threshold period, then computation speed increases, but resource costs and system complexity increase
Solution Approach 1:
The patent implements dynamic resource allocation where the system continuously monitors task complexity, node availability, and performance metrics. Coordinator clusters dynamically adjust task distribution in real-time, allocating more resources to urgent or complex tasks and scaling back when not needed, thereby achieving high computation speed without permanent over-provisioning of resources.
Solution Approach 2:
The patent incorporates feedback mechanisms where edge computing nodes report task completion status, performance metrics, and resource utilization back to coordinator clusters. This feedback loop enables the system to optimize resource allocation continuously, adjusting the distribution of computational tasks to maintain high speed while avoiding unnecessary resource expenditure and system complexity.
3Productivity
If decentralized edge computing nodes are used, then resource distribution improves and costs reduce, but coordination and task management become more complex
Solution Approach 1:
The patent introduces coordinator clusters as intermediary components between the blockchain network and edge computing nodes. These coordinator clusters handle the complex tasks of task registration, node selection, task distribution, and result verification, thereby simplifying the coordination process and reducing the management burden on individual nodes while maintaining efficient resource utilization.
Solution Approach 2:
The patent designs coordinator clusters with multi-functional capabilities, allowing them to perform task management, resource allocation, performance monitoring, and incentive distribution functions. This universal design consolidates multiple coordination functions into single entities, reducing overall system complexity while improving resource utilization efficiency across the decentralized network.
4Reliability
If blockchain technology is integrated for task registration and reward distribution, then security and transparency improve, but system complexity and computational overhead increase
Solution Approach 1:
The patent extracts the most critical security functions (task registration, reward distribution, node verification) and places them on the blockchain, while keeping the bulk of computational tasks off-chain. This selective approach ensures security and transparency for essential operations without requiring the entire system to operate on the blockchain, thereby reducing overall complexity and computational overhead.
Solution Approach 2:
The patent implements partial blockchain integration where only specific critical operations are recorded on the blockchain rather than all system operations. This partial action approach provides sufficient security and transparency for task verification and reward distribution while avoiding the excessive computational overhead and complexity that would result from full blockchain integration of all system functions.
Data Source
AI summary
Disclosed are systems and methods for a hybrid cloud-edge computing platform using decentralized networks for solving resource-intensive computation tasks such as machine learning and video processing tasks. In some respects, the cloud-edge computing platform comprises a coordinator cluster and worker nodes partitioned into shards, which include cloud-based networks and edge networks, and which cater to a wide range of computation requirements. The coordinator cluster manages workloads by selecting the appropriate shard and worker nodes to execute tasks based on computed efficiency scores. Worker nodes may be incentivized with rewards such as cryptocurrency tokens for successful completion of tasks. The disclosed architecture also supports the pipelining of multiple tasks in a workload, where the output of one task is used as input for another task. In some respects, the computing architecture includes a blockchain-supported edge network, which enables verifying compute tasks and managing reward distribution for worker nodes using smart contracts.