Ad-hoc Mobile Computing System for Long-Running Applications
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Solution Overview
Problem
Existing edge computing architectures are not configured to handle long-running application programs that require data-center-like capabilities, as they face challenges with transient and unpredictable availability of computing resources, high-speed all-to-all networking, daily variability of compute capabilities, locating idle clusters, pricing, and payment mechanisms in mobile networks like connected-car environments.
Innovation Solution
The formation of ad-hoc computation systems from clusters of idle mobile computing resources, where idle resources advertise their capabilities, negotiate and execute workloads, and are managed by stationary resources to optimize resource utilization and payment, enabling long-running applications in mobile networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If edge computing resources are used for long-running applications, then distributed computing capability is improved, but resource availability stability deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting future resource availability states and pre-forming ad-hoc computation systems before applications need to run. This allows the system to secure computing resources in advance, ensuring stability for long-running applications while maintaining the benefits of distributed edge computing.
Solution Approach 2:
The system dynamically adapts to changing resource availability by continuously monitoring mobile computing resources, predicting their future states, and reconfiguring ad-hoc computation systems accordingly. This dynamic approach allows the system to maintain reliable resource availability while utilizing the flexibility of distributed edge computing resources.
2Power
If mobile computing resources are aggregated for computation, then computing power is improved, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including a directory service that mediates between applications and mobile computing resources, and ad-hoc computation systems that act as intermediate layers coordinating resource aggregation. These intermediaries simplify the complexity of managing distributed mobile resources while enabling effective computing power aggregation.
Solution Approach 2:
The system segments the complex task of aggregating mobile computing resources into manageable components: resource discovery, availability prediction, ad-hoc system formation, and workload distribution. This segmentation reduces overall system complexity by breaking down the aggregation process into independent, modular functions.
3Productivity
If idle mobile resources are utilized for computation, then resource utilization efficiency is improved, but resource availability predictability deteriorates
Solution Approach 1:
The system performs preliminary prediction of resource availability states to identify mobile computing resources that will be idle during specific future time periods. This allows the system to secure these resources in advance for computation tasks, maintaining predictability while achieving high utilization efficiency by using resources that would otherwise be idle.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor actual resource availability and compare it with predictions. This feedback loop allows the system to refine its prediction accuracy over time, improving resource availability predictability while maintaining high utilization efficiency through iterative optimization.
Data Source
AI summary
An ad-hoc computation system is formed from one or more clusters of idle mobile computing resources to execute an application program within a given time period. The forming step further comprises: (i) determining at least a subset of idle mobile computing resources from the one or more clusters of idle mobile computing resources that are available, or likely to be available, to execute the application program within the given time period, and that collectively comprise computing resource capabilities sufficient to execute the application program within the given time period; and (ii) distributing a workload associated with the execution of the application program to the subset of idle mobile computing resources. The workload associated with the application program is executed via the subset of idle mobile computing resources forming the ad-hoc computation system.