Distributed Application Hosting for LLN Heterogeneity
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Solution Overview
Problem
Low Power and Lossy Networks (LLNs) face challenges such as heterogeneity, lossy links, low bandwidth, resource constraints, and limited interoperability, which complicate application development and increase WAN strain, leading to unreliable network connections and inefficient resource utilization.
Innovation Solution
A distributed application hosting environment is implemented using a platform as a service (PaaS) architecture that masks heterogeneity across LLNs, enabling applications to be deployed directly on LLN devices with a common data model and containerized applications, decoupling them from specific hardware and protocols, and providing lifecycle management to manage resource constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If applications are deployed in the cloud on remote servers, then heterogeneity of LLN devices is masked, but WAN bandwidth consumption increases and connection reliability decreases
Solution Approach 1:
The patent segments the application hosting function from centralized cloud servers and distributes it across multiple LLN devices. Each device can independently host applications, creating a distributed execution environment that reduces dependency on single WAN connections while maintaining heterogeneity masking through standardized interfaces.
Solution Approach 2:
The patent introduces a new architectural dimension by implementing a runtime environment layer between the heterogeneous LLN devices and the applications. This abstraction layer enables applications to run locally on constrained devices without requiring centralized cloud processing, thereby improving connection reliability while maintaining adaptability.
2Adaptability or versatility
If applications are deployed in the cloud on remote servers, then heterogeneity of LLN devices is masked, but WAN bandwidth consumption increases
Solution Approach 1:
The patent segments the data processing workload from centralized cloud operations and distributes it across local LLN devices. By executing applications locally rather than remotely, the system eliminates the need for continuous WAN communication for application execution, thereby masking device heterogeneity through standardized interfaces while minimizing WAN bandwidth consumption.
Solution Approach 2:
The patent enables LLN devices to self-host applications and perform local data processing without requiring constant WAN connectivity. Each device with a runtime environment can independently execute applications and process data locally, reducing WAN bandwidth consumption while maintaining heterogeneity masking through the standardized runtime interface.
3Power
If resource constraints of LLN devices are addressed by cloud deployment, then processing capability requirements are met, but device complexity increases
Solution Approach 1:
The patent implements a universal runtime environment that can execute multiple different applications on resource-constrained LLN devices. This single runtime infrastructure provides multi-functionality, enabling diverse applications to run on the same device without increasing overall system complexity, as the runtime handles device-specific variations uniformly.
Solution Approach 2:
The patent introduces a runtime environment as an intermediary layer between the heterogeneous LLN devices and the applications. This mediator abstracts device-specific complexities from applications, allowing resource-constrained devices to execute applications with full processing capability requirements met while the runtime manages the complexity of device variations.
4Productivity
If containerized applications are used, then deployment efficiency is improved, but memory requirements increase
Solution Approach 1:
The patent uses containerization technology to create isolated execution environments for applications on LLN devices. Each container packages the application with its dependencies, enabling efficient deployment without requiring system-wide installations. The containerization approach copies only the necessary runtime components into each container, improving deployment efficiency while minimizing additional memory requirements on resource-constrained devices.
Data Source
AI summary
In one embodiment, a device in a network receives data from one or more other devices in the network via one or more protocol adaptors. The device transforms the received data into a common data model. The device executes a containerized application. The device exposes the transformed data to the application.