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Based on a novel algorithmic approach, GradientGraph from Reservoir Labs provides a new platform to detect, characterize, predict and resolve network bottlenecks in high-speed data networks. Enterprises who depend on the reliability and predictability of their network communication performance can rely on this cost-effective approach to system optimization.
GradientGraph from Reservoir Labs provides a new paradigm to qualitatively and quantitatively understand network bottlenecks and flows, enabling real-time traffic engineering, high-performance baselining, and a framework for high-precision network capacity planning. GradientGraph is based on a new mathematical model that captures the fundamental bottleneck structure of a network, revealing key operational insights such as the regions of influence of bottlenecks and the ripple effects of small perturbations in the network. Through the dualism between bottlenecks and flows, this also allows the identification of flows that, while not identifiable as heavy hitters in the traditional sense, lead to severe system-wide performance degradation. GradientGraph Analytics provides a cost-effective way for enterprises to optimize their network for reliable and predictable service.
GradientGraph enables network optimization for capacity, throughput, cost and Quality of Service (QoS) by modeling traffic engineering policies and predicting their effect on specific network segments as well as holistic infrastructures.
GradientGraph allows organizations to analyze and identify links in a network that are critical to the overall stability of the network, enabling a new approach to managing and ensuring network resilience.
Many organizations depend on networks that are provided by a number of owners. GradientGraph allows for unified visibility, planning and management in a heterogeneous environment, even under conditions where there is only partial network detail available.
In support of network design teams building a new network or upgrading an existing system, GradientGraph delivers detailed, application and network-specific analysis of bandwidth requirements for end-to-end system modeling of network capacity. Traditionally, capacity planning is performed by scaling based on projecting future demand for resources. GradientGraph brings a new perspective to the task of network upgrades: by allowing replay and accurate measure of the historical bottleneck structure, operators can design upgrade paths that are not only adequate for future demand, but drive toward optimized bottleneck structures, leading to more cost-effective operation.
As a consumer or a provider, an increasing number of firms require a guaranteed level of network-enabled service, typically based on service level agreements (SLA). GradientGraph ensures those business obligations can be met. Additionally, for organizations that have deadline-bound service needs with payloads that must complete within specific time parameters, GradientGraph ensures requirements are met for these flows, within the constraints of the network.
For organizations that require optimal path routing for high-priority flows, GradientGraph considers the unique network topology and identifies optimal routing for maximum throughput. Specifically for organizations with fat-tree or similar topologies, GradientGraph gives improved, cost-effective operation by reducing the number of links and providing optimal cost/benefit routing.
Noah Amsel, Research Engieer at Reservoir Labs, demonstrates the existence of bottleneck structures in communication networks that mathematically reveal the influences bottlenecks and flows exert on each other. This structure provides key insights to help identify optimal network designs as well as optimize traffic engineering.