Telecommunication Cloud Policy Agent for SLA Compliance
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Solution Overview
Problem
Telecommunication clouds face challenges in managing resources effectively due to high demands on reliability, performance, and security, especially in distributed environments with limited resources at the edge, where analytics tasks can interfere with service applications and violate service-level agreements (SLAs).
Innovation Solution
A network node with a node policy agent and manager that obtains and enforces policies based on service-level agreements (SLAs) to allocate resources for analytics tasks, ensuring that service applications maintain priority and SLA compliance by dynamically managing resource utilization and scheduling analytics tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If analytics tasks are executed on the same network node as service applications, then resource utilization is improved, but service reliability and SLA compliance deteriorate due to resource interference
Solution Approach 1:
The network node is segmented into distinct resource pools: one dedicated to service applications and another to analytics tasks. This segmentation allows both types of workloads to coexist on the same physical node while preventing resource interference, thus maintaining service reliability while improving overall resource utilization.
Solution Approach 2:
The system dynamically adjusts resource allocation between service applications and analytics tasks based on current workload conditions and SLA requirements. When service demands are high, analytics resource allocation is reduced; when service demands are low, analytics can utilize more resources, thereby maintaining reliability while maximizing productivity.
2Productivity
If more resources are allocated to analytics tasks, then analytics processing capability is improved, but service application performance deteriorates
Solution Approach 1:
The system continuously monitors service application performance metrics and SLA compliance status, using this feedback to dynamically adjust the resource allocation to analytics tasks. When service performance approaches SLA thresholds, analytics resource allocation is automatically reduced, preventing performance degradation while maximizing analytics capability when conditions permit.
3Device complexity
If analytics tasks share resources with service applications, then device complexity is reduced, but resource management difficulty increases
Solution Approach 1:
A resource manager intermediary component is introduced to handle the complexity of resource allocation between service applications and analytics tasks. This intermediary automatically enforces resource policies, monitors usage, and adjusts allocations based on SLA requirements, thereby simplifying operations while maintaining low device complexity through automated management.
Data Source
AI summary
In a communication system, a first network node is configured to execute at least one service application executing a first service and at least one analytics application executing at least part of a distributed analytics service. The first network node obtains information about a new telecommunication service and transmits, to a second network node in the communication system, a request for a policy for the new telecommunication service. The first network node receives, from the second network node, the policy for the new telecommunication service and updates a currently applied policy on the basis of the received policy. The updated policy rebalances resources allocated from a shared computing resource pool of the first network node between the new telecommunication service and the at least one analytics application such that the new telecommunication service maintains adherence to the one or more requirements of a service level agreement.