Latency Access Controller Clusters Compute Instances
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Solution Overview
Problem
Current edge computing systems face challenges in efficiently managing latency across distributed compute instances, leading to suboptimal performance in latency-sensitive applications like online gaming and IoT, where network congestion and high latency can hinder seamless execution of functions.
Innovation Solution
The implementation of a latency access controller that clusters compute instances based on round trip time (RTT) and enables remote latency calls to migrate functions to compute instances with the required latency performance, ensuring efficient execution by identifying and assigning RTT classes to each cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If functions are executed on distributed compute instances in edge computing systems, then application delivery performance is improved and network congestion is reduced, but latency variability increases leading to suboptimal performance in latency-sensitive applications
Solution Approach 1:
The system segments compute instances into distinct latency clusters based on their round trip time characteristics. Each cluster represents a group of compute instances with similar latency profiles, allowing the system to match function execution requirements with appropriate latency characteristics. This segmentation resolves the contradiction by enabling selective execution on low-latency clusters for time-sensitive applications while maintaining the benefits of distributed execution.
Solution Approach 2:
The system changes the parameter of latency characterization by introducing latency clusters with defined upper bound round trip times. Instead of treating all distributed compute instances uniformly, the system categorizes them by latency parameters, allowing optimization of function execution based on specific latency requirements. This parameter-based classification resolves the latency variability issue while preserving distributed execution benefits.
2Productivity
If compute instances are distributed across multiple locations for edge computing, then network congestion is reduced and application delivery is improved, but managing latency across distributed instances becomes complex
Solution Approach 1:
The system introduces a function broker as an intermediary component that manages the complexity of latency coordination across distributed compute instances. The broker receives function execution requests, determines the appropriate latency cluster based on function requirements, and routes execution to suitable compute instances. This intermediary abstraction resolves the contradiction by centralizing latency management logic while maintaining distributed execution benefits.
Solution Approach 2:
The system creates equipotential latency zones by organizing compute instances into clusters with comparable latency characteristics. Within each latency cluster, compute instances have similar round trip time profiles, making them interchangeable for functions with matching latency requirements. This equipotential organization simplifies latency management across distributed locations while maintaining optimal application delivery performance.
3Reliability
If functions are migrated to compute instances with specific latency characteristics, then latency-sensitive application performance is improved, but system complexity increases due to clustering and migration management
Solution Approach 1:
The system creates a universal latency cluster architecture where compute instances can be dynamically assigned to different latency clusters based on current conditions and function requirements. The same compute instance may serve multiple latency clusters at different times, and the function broker can route to any appropriate cluster. This multi-functionality approach resolves the contradiction by providing reliable latency performance without permanent complex cluster assignments.
Data Source
AI summary
A device receives, from a first compute instance, a Remote Latency Call, that includes a target latency measure, a function, and one or more arguments for the function. The device clusters a plurality of compute instances into multiple clusters of compute instances based on a respective latency measure associated with each compute instance of the plurality of compute instances, and determines a first cluster of compute instances from the multiple clusters of compute instances that satisfies the target latency measure. The device causes a second compute instance within the first cluster of compute instances to execute the function, using the one or more arguments, and return one or more results.