Remote Access Session Initiation via Connection History Analysis
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Solution Overview
Problem
Conventional remote access systems require users to wait after logging in to initiate a remote access session, leading to increased energy consumption and delayed access times, as the server remains active even when the client is not connected.
Innovation Solution
A system that initiates a remote access session based on a history of connections and proximity of a mobile device, allowing the client to connect to the session before the user logs in by analyzing geographical location and usage patterns to determine optimal connection times, thereby reducing energy usage and access time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the server remains active continuously to provide remote access sessions, then the remote access service is always available, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by analyzing connection history and user patterns before the user actually needs remote access. It proactively determines optimal connection times and initiates server activation in advance, rather than keeping the server continuously active or waiting for user requests.
Solution Approach 2:
The system uses automated analysis of connection history and user behavior patterns to self-determine when remote access sessions should be initiated. The server activates and deactivates autonomously based on analyzed data, reducing the need for continuous monitoring and manual intervention.
2Use of energy by stationary object
If the server is activated only when needed, then energy consumption is reduced, but access time increases due to activation delay
Solution Approach 1:
The system performs preliminary analysis of connection history and user patterns to predict when remote access will be needed. It activates the server in advance of the actual user connection request, based on analyzed temporal patterns, thereby reducing the perceived activation delay while still maintaining energy efficiency.
Solution Approach 2:
The system dynamically adjusts server activation timing based on analyzed connection patterns and user behavior. Rather than using fixed activation schedules or reactive activation, the system adapts activation timing to match actual usage patterns discovered through historical data analysis.
3Loss of time
If the server activates in advance based on predicted usage, then access time is reduced, but energy consumption increases due to premature activation
Solution Approach 1:
The system uses feedback from analyzed connection history and user patterns to optimize server activation timing. It continuously learns from actual usage patterns and adjusts activation predictions accordingly, improving accuracy over time and reducing both premature and delayed activations.
Solution Approach 2:
The system changes key parameters such as activation timing, threshold values, and prediction windows based on analyzed usage patterns. It adapts these parameters dynamically to balance the trade-off between activation speed and energy consumption, optimizing performance for specific user behaviors and usage scenarios.
Data Source
AI summary
The subject matter of this specification can be implemented in, among other things, a method that includes comparing, by a processing device in a remote access system, one or more days and times of day corresponding to initiation of a remote access session at a server device to determine that the days and times of day are within a threshold range from a day and time of day. The method further includes storing, in a data storage at the remote access system, first session information identifying the day and time of day for the remote access session at the server device. The method further includes, in response to an occurrence of the day and time of day, causing the server device to initiate the remote access session.