Device Availability Estimation via Local History Analysis
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Solution Overview
Problem
In pervasive peer-to-peer computing environments, devices lack effective methods to assess the availability of other devices without relying on central servers, leading to inefficient resource utilization and increased network traffic, which consumes resources and reduces battery life.
Innovation Solution
Devices maintain historic availability data and use algorithms to estimate the availability of other devices by observing communication attempts, recording availability indications, and computing probability curves to predict availability without a central server, employing either a simple linear model or bin-based segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If devices regularly probe other devices to assess availability, then availability information can be obtained, but network traffic increases and system performance decreases
Solution Approach 1:
The system performs preliminary actions by observing communication attempts and proactively building availability profiles before actual resource sharing is needed. Devices monitor and record availability indications from communication attempts, establishing predictive models in advance so that when resource sharing is required, the system can query pre-computed availability probabilities rather than probing in real-time.
2Reliability
If devices probe target devices before communication, then availability can be assessed, but time and resources are wasted
Solution Approach 1:
The system performs preliminary observation and recording of availability indications during normal communication attempts. By the time actual resource sharing is needed, the availability profile is already constructed, eliminating the need for time-consuming probing at the moment of resource request.
Solution Approach 2:
Instead of directly probing target devices to assess availability, the system creates and maintains copies of availability information through observation of communication attempts. These availability profiles are stored locally and can be queried without contacting the target device, thus avoiding time-wasting direct probes.
3Reliability
If a central server maintains availability information, then devices can access availability data, but bandwidth consumption increases and information may be outdated
Solution Approach 1:
The patent extracts the availability information maintenance function from a central server and distributes it to individual devices. Each device maintains its own availability profile by observing communication attempts locally, eliminating the need for continuous server-client communication and reducing bandwidth consumption while keeping information current.
Solution Approach 2:
Each device serves itself by maintaining its own availability information through local observation of communication attempts. Devices independently build and update their availability profiles without requiring external server intervention, thus eliminating bandwidth consumption associated with centralized information maintenance.
4Reliability
If devices communicate frequently to maintain availability information, then up-to-date information is available, but battery life decreases
Solution Approach 1:
Instead of continuous communication, the system uses periodic observation of natural communication attempts to update availability information. By leveraging periodically occurring communication events rather than initiating continuous probes, the system maintains information freshness while minimizing additional energy consumption.
Solution Approach 2:
Devices update their availability profiles passively by observing communication attempts that would occur anyway during normal operation. This self-service approach extracts availability information from existing communication traffic without requiring additional active communication, thus maintaining information freshness while conserving battery life.
Data Source
AI summary
A method and apparatus for estimating device availability in a pervasive peer to peer environment is presented. The method and apparatus include a linear estimation approach and a bin estimation approach, where the bin estimation approach may include enhanced bin estimation or adaptive bin estimation. A central node queries available contacted devices and builds a history of times and availability indications for the contacted device, and subsequently computes an availability probability curve based on assumptions regarding the availability indications and the time since the last received availability indication.