Network Interface Hardware Resource Selection for Edge Computing
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Solution Overview
Problem
Edge computing devices face challenges in efficiently processing data streams due to power and space constraints, particularly in multi-tenancy environments where tenant-specific service level agreements (SLAs) and quality of service (QoS) requirements must be met, and existing solutions do not effectively utilize hardware devices in low power or sleep states for data processing.
Innovation Solution
The system selects hardware devices for data processing based on data processing measurements, including time or number of clock cycles, priority levels, and power state, allowing devices in low power or sleep states to be considered for processing by calculating the time to wake up and power up, thereby optimizing resource allocation and meeting SLAs or QoS requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware devices are kept in active state to meet processing speed requirements, then data processing latency is reduced, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the power state of hardware devices based on workload demands and SLA requirements. Devices can transition between active, low-power, and sleep states, with the selection determined by real-time evaluation of data processing measurements and wake-up time requirements. This dynamic state management resolves the contradiction by adapting power consumption to actual processing needs while maintaining service level agreements.
Solution Approach 2:
The system changes the operational parameters of hardware devices by selecting different power states (active, low-power, sleep) based on calculated data processing measurements. The selection criterion includes wake-up time from low-power states, allowing the system to optimize between speed and power consumption by adjusting the power state parameter according to current workload and SLA constraints.
2Use of energy by moving object
If hardware devices in low power states are utilized for data processing, then power consumption is reduced, but data processing latency increases due to wake-up time
Solution Approach 1:
The system performs preliminary evaluation of wake-up times and data processing measurements for hardware devices in low-power states before assigning workloads. By pre-calculating the total time required (wake-up time + processing time) and comparing it against SLA thresholds, the system can proactively determine whether a low-power device is suitable for a given task, thus avoiding latency violations while maximizing power savings.
Solution Approach 2:
The system implements feedback mechanisms that monitor actual data processing performance and power consumption of devices in different states. This feedback informs future selection decisions, allowing the system to learn from past experiences and optimize the balance between utilizing low-power devices and maintaining acceptable latency performance.
3Reliability
If more hardware devices are maintained in active state to meet tenant-specific SLAs, then service level agreement compliance is improved, but resource utilization efficiency decreases
Solution Approach 1:
The system creates a universal resource pool where hardware devices can serve multiple tenants and workloads dynamically. By evaluating data processing measurements and wake-up times against SLA requirements, the same pool of devices can be allocated to different tenants based on current demands, allowing low-power devices to serve less time-critical workloads while active devices handle SLA-sensitive tasks, thus improving overall resource utilization efficiency while maintaining SLA compliance.
Solution Approach 2:
The system dynamically allocates hardware devices to different tenants and workloads based on real-time SLA requirements and device performance measurements. This dynamic allocation allows the system to optimize resource utilization by matching device power states with appropriate workloads, rather than maintaining fixed active states for all devices, thereby improving both SLA compliance and resource efficiency.
4Productivity
If hardware devices in low power states are considered for data processing, then device complexity increases due to state management, but overall system efficiency improves
Solution Approach 1:
The system introduces an intermediary resource selection mechanism that manages the complexity of handling hardware devices in different power states. This intermediary layer evaluates data processing measurements, wake-up times, and SLA requirements to make intelligent selection decisions, shielding the rest of the system from the complexity of low-power state management while enabling efficient utilization of these devices.
Data Source
AI summary
Examples described herein relate to a network interface device. In some examples, the network interface device includes circuitry to: based on a request to process data by a particular operation: determine available hardware resources, where the available hardware resources include a hardware resource in a reduced power state, and select a hardware resource among the available hardware resources based on a data processing measurement for the particular operation.