20240048451. CONTEXT-SENSITIVE DEFRAGMENTATION AND AGGREGATION OF CONTAINERIZED WORKLOADS RUNNING ON EDGE DEVICES simplified abstract (VMWARE, INC.)
CONTEXT-SENSITIVE DEFRAGMENTATION AND AGGREGATION OF CONTAINERIZED WORKLOADS RUNNING ON EDGE DEVICES
Organization Name
Inventor(s)
NILANJAN Daw of Bangalore (IN)
Sairam Veeraswamy of Coimbatore (IN)
Raunak Ravindra Singwi of Pune (IN)
CONTEXT-SENSITIVE DEFRAGMENTATION AND AGGREGATION OF CONTAINERIZED WORKLOADS RUNNING ON EDGE DEVICES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240048451 titled 'CONTEXT-SENSITIVE DEFRAGMENTATION AND AGGREGATION OF CONTAINERIZED WORKLOADS RUNNING ON EDGE DEVICES
Simplified Explanation
The patent application describes computer-implemented methods, media, and systems for context-sensitive defragmentation and aggregation of containerized workloads running on edge devices. The methods involve monitoring telemetry data from multiple software-defined wide area network (SD-WAN) edge devices that run multiple workloads. The telemetry data includes resource utilization, inter-workload trigger dependency, and inter-workload data dependency among the multiple workloads. Based on the telemetry data, it is determined that at least two of the workloads have inter-workload trigger dependency or inter-workload data dependency. In response to this determination, a process of migrating the at least two workloads to a first SD-WAN edge device is initiated.
- The patent application focuses on optimizing the performance and resource utilization of containerized workloads running on edge devices.
- It introduces a context-sensitive defragmentation and aggregation approach based on monitoring telemetry data.
- The method considers factors such as resource utilization, inter-workload trigger dependency, and inter-workload data dependency to make decisions on workload migration.
- By migrating workloads to specific SD-WAN edge devices, the method aims to improve overall performance and efficiency.
- The innovation enables dynamic workload management and allocation based on real-time telemetry data.
- The approach can be applied to various edge computing scenarios where containerized workloads are distributed across multiple devices.
- It can be used in industries such as IoT, edge AI, and edge analytics to optimize workload distribution and resource allocation.
- The technology solves the problem of suboptimal workload distribution and resource utilization in edge computing environments.
- It addresses the challenge of managing inter-workload dependencies and ensuring efficient execution of containerized workloads.
- The method provides a solution for dynamically migrating workloads to appropriate edge devices based on their dependencies and resource requirements.
- It offers benefits such as improved performance, reduced resource wastage, and enhanced scalability in edge computing environments.
Original Abstract Submitted
computer-implemented methods, media, and systems for context-sensitive defragmentation and aggregation of containerized workloads running on edge devices are disclosed. one example method includes monitoring telemetry data from multiple software defined wide area network (sd-wan) edge devices that run multiple workloads, where the telemetry data includes at least one of resource utilization at the multiple sd-wan edge devices, inter-workload trigger dependency, or inter-workload data dependency among the multiple workloads. it is determined, based on the telemetry data, that at least two of the multiple workloads running on at least two sd-wan edge devices have the inter-workload trigger dependency or the inter-workload data dependency. in response to the determination that the at least two of the multiple workloads have the inter-workload trigger dependency or the inter-workload data dependency, a first process of migrating the at least two of the multiple workloads to a first sd-wan edge device of is initiated.
