Multi-tenant Edge Resource Orchestration via Intermediary Mediation
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Solution Overview
Problem
Existing edge computing systems face challenges in efficiently managing and orchestrating resources across multiple stakeholders and services in a distributed environment, leading to complexities in latency, compliance, and cost management.
Innovation Solution
The implementation of a multi-entity edge computing system that dynamically allocates and manages compute, memory, and storage resources across multiple edge nodes and tenants, utilizing techniques such as resource pooling, service level agreement (SLA) management, and security enforcement points to ensure efficient and secure resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If edge computing resources are distributed across multiple locations to reduce latency and improve service capabilities, then the system complexity in managing and orchestrating resources across multiple stakeholders increases
Solution Approach 1:
An intermediary orchestration layer is introduced between the distributed edge resources and the end services. This intermediary manages resource allocation, coordination, and orchestration across multiple edge locations and stakeholders, thereby reducing the complexity burden while maintaining the low-latency benefits of distributed computing.
Solution Approach 2:
A universal resource management framework is implemented that can handle multiple types of edge resources (compute, storage, networking) and serve multiple stakeholders through a common interface and orchestration mechanism. This multi-functional approach simplifies management by providing a unified system rather than separate management systems for each resource type or stakeholder.
2Loss of energy
If compute resources are brought closer to data sources at the network edge, then network backhaul traffic and energy consumption are reduced, but resource management and orchestration across distributed edge nodes becomes more complex
Solution Approach 1:
Edge nodes are equipped with self-service capabilities including automated resource discovery, self-provisioning, and local decision-making algorithms. This allows edge resources to autonomously manage their own operations and coordinate with other edge nodes, reducing the need for complex centralized management while achieving energy-efficient resource utilization.
Solution Approach 2:
The resource management system implements dynamic adaptation where edge nodes can dynamically adjust their resource allocation, service deployment, and coordination strategies based on real-time conditions such as energy availability, network status, and service demands. This dynamic approach simplifies management by allowing the system to self-optimize rather than requiring complex static management configurations.
3Adaptability or versatility
If edge computing deployments support multiple tenants and services in a distributed environment, then service capabilities and compliance with data privacy requirements are improved, but security management and resource isolation become more challenging
Solution Approach 1:
The system implements segmentation of edge resources into isolated tenant-specific environments using virtualization and containerization technologies. Each tenant's services and data are segmented into separate execution contexts with controlled access, enabling multi-tenant support while maintaining security boundaries and simplifying security management through automated isolation policies.
Solution Approach 2:
An intermediary security orchestration layer is introduced that manages security policies, authentication, and authorization across multiple tenants and services. This intermediary handles the complex security management tasks centrally while allowing individual tenants to operate independently, thereby improving service capabilities without proportionally increasing security management complexity at the edge nodes.
Data Source
AI summary
Various aspects of methods, systems, and use cases for multi-entity (e.g., multi-tenant) edge computing deployments are disclosed. Among other examples, various configurations and features enable the management of resources (e.g., controlling and orchestrating hardware, acceleration, network, processing resource usage), security (e.g., secure execution and communication, isolation, conflicts), and service management (e.g., orchestration, connectivity, workload coordination), in edge computing deployments, such as by a plurality of edge nodes of an edge computing environment configured for executing workloads from among multiple tenants.