Two-Stage Network Simulation for SLA Compliance
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Solution Overview
Problem
Current capacity management processes in IP/MPLS networks face challenges in providing service level agreement (SLA) guarantees while optimizing network capacity and minimizing operational expenses, particularly due to imperfect predictions and the complexity of dynamic service environments, leading to risks of insufficient or excessive bandwidth provisioning.
Innovation Solution
A two-stage network simulation protocol is introduced, where a centralized controller periodically simulates network traffic and caches link utilizations in the background, allowing for rapid simulation of new demands at the time of request, thereby reducing simulation time without compromising fidelity and enabling real-time service activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network simulation is performed for each new demand request, then simulation fidelity is maintained, but simulation time and compute resources increase significantly with network size
Solution Approach 1:
The patent performs network simulations in advance and caches the results before actual demand requests arrive. The controller periodically simulates network traffic patterns and stores the simulation outcomes in a cache, so when a new demand request comes in, the system can quickly retrieve pre-computed results or make rapid adjustments based on cached data, avoiding time-consuming simulations at request time.
Solution Approach 2:
The patent divides the network simulation into baseline simulation (performed periodically in advance) and delta simulation (performed quickly at request time). The baseline simulation captures overall network behavior and is cached, while the delta simulation only computes the impact of new demands by comparing against the cached baseline, significantly reducing computation time for individual requests.
2Reliability
If comprehensive network simulations are performed to ensure SLA guarantees, then service reliability is improved, but operational expenses and device complexity increase
Solution Approach 1:
The controller proactively performs simulations and caches results before SLA violations can occur. By pre-computing network behavior under various demand scenarios and caching these results, the system can quickly assess whether new demands will violate SLAs without performing complex real-time simulations, thus ensuring reliability while reducing operational complexity.
Solution Approach 2:
The patent creates a virtual copy of the network state through simulation and caches this virtual representation. Instead of repeatedly simulating the actual complex network for each demand request, the system works with cached simulation results that replicate network behavior, reducing the computational complexity required for each decision while maintaining accurate SLA assessment.
3Reliability
If network capacity is over-provisioned to ensure SLA compliance, then service reliability is improved, but operational expenses increase due to excessive bandwidth provisioning
Solution Approach 1:
The system uses cached simulation results to provide feedback on how new demands will impact existing SLAs. By analyzing pre-computed network behavior data, the controller can accurately predict whether capacity is sufficient or if SLA violations would occur, enabling precise capacity provisioning decisions that avoid both over-provisioning and under-provisioning, thus optimizing operational expenses while maintaining compliance.
Solution Approach 2:
The patent dynamically adjusts network capacity allocation based on simulated demand patterns and cached performance data. Instead of static over-provisioning, the system uses simulation insights to optimize capacity parameters, allocating bandwidth precisely where and when it is needed to meet SLAs, thereby reducing unnecessary operational expenses while maintaining service reliability.
Data Source
AI summary
In an example, there is disclosed a computing apparatus, having: one or more logic elements, including at least a processor and a memory, providing a network simulation engine to: periodically perform a network traffic simulation; cache at least one network traffic simulation in a traffic state cache; receive a quest for additional network demand; and compute a network delta based at least in part on a difference between the request for additional network demand and the traffic state cache.