Staying Proactive with Forecasting Solutions

Today’s IT is increasingly complex, and the ever-growing need for computing resources demands proactive measures in planning for future consumption patterns. Failure to accurately forecast future needs could lead to adverse outcomes, including downtimes and outages, with potential reputation and cost implications.

What is required is a forecasting solution capable of sifting through complex webs of information, providing reliable estimates about future resource consumption, identifying systems requiring attention, and planning the appropriate course of action when systems reach a pre-determined threshold.

Evolution of Forecasting Solutions

Conventional forecasting often assumes significant headroom while taking a more react-at-the-11th-hour approach to storage and other resource needs, which is not ideal for today’s complex systems and requires solutions designed to reliably predict future resource consumption by considering all the influencing factors.

Modern-day Artificial Intelligence for IT Operations (AIOps) solutions provide tools and frameworks to handle such problems at scale, utilizing Machine Learning (ML) to analyze complex system behaviours for more reliable resource usage forecasting.

ML Stack for Forecasting

A typical ML process involves multiple stages: data ingestion, data transformation, prediction/inference, and post-processing.

Stages of Machine Learning (ML) Forecasting Process

The forecasting service ingests time-series data—historical trends of consumption and utilization metrics—and feeds it into pre-processing, where data cleansing and transformation occur.

Forecastability checks are then run on the data to determine if the input series can be forecasted into the future—this step ensures the forecasting service is providing reliable forecasts.

Next, the core forecast generation process leverages an ensemble of ML algorithms (combined with careful data choices, transformation, and post-processing steps) to calculate future values of time-series data. The ML strategy is tested and validated against time-series behaviours and easily handles forecasting requirements across products within the SolarWinds ecosystem.

SolarWinds products are integrated with the forecasting service to consume the generated forecasts and serve insights to users through different interfaces, including alerts and ITSM tickets.

Key Challenges

While ML-powered solutions are the way forward for handling forecasting problems, future uncertainty and black swan events pose challenges to the efficacy of forecasting solutions. In response, ML algorithms learn and mature over time, reducing the impact of such events while continuously learning from the data.

SolarWinds Forecasting Solutions

Most product offerings require a robust, proactive forecasting mechanism for responding to future resource demands, including ML algorithms to forecast metrics (like disk usage at scale) for proactive IT planning and monitoring. SolarWinds® offers a suite of applications designed to address today’s complex IT management issues involving networks, systems, IT security, IT Service Management (ITSM), and database and application management.

Looking Ahead

ML-based forecasting services are the way for IT to stay on top of resource management processes. SolarWinds offers forecasting solutions across its product suite to assist IT teams in proactive resource planning.

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