TLDR: HRS is a new time series forecasting framework for Crowdsourced Cloud-Edge Platforms. It combines numerical and image-based data representations to better predict extreme network loads. It also uses a “Scheduling-Aware Loss” function that prioritizes avoiding costly underpredictions over minor overpredictions. Experiments show HRS significantly reduces service level agreement (SLA) violations and improves platform profitability compared to existing methods.
Crowdsourced Cloud-Edge Platforms (CCPs) are vital for delivering streaming services, but they face significant challenges in managing highly dynamic and unpredictable network loads. These platforms often use a “Predict-then-Schedule” approach, where accurate load forecasting is crucial for maintaining Quality of Service (QoS) and profitability. However, existing forecasting methods often fall short, leading to either underprovisioning (which causes service disruptions and penalties) or conservative overprovisioning (which wastes resources).
Addressing Key Challenges in Cloud-Edge Forecasting
The research identifies two primary hurdles for current forecasting models in CCPs. First, accurately predicting extreme load values, like sudden traffic surges or sharp drops, is difficult. Traditional models, which mainly use numerical data, struggle to capture these rapid fluctuations, leading to inaccurate scheduling decisions. Second, there’s a mismatch between what forecasting models typically optimize for (minimizing prediction errors) and what scheduling needs (ensuring high service quality and avoiding costly Service Level Agreement, or SLA, violations). Standard error metrics treat underprediction and overprediction equally, even though underprediction can lead to severe penalties and lost revenue, while overprediction primarily results in higher, but less critical, resource costs.
Introducing HRS: A Hybrid Approach for Smarter Forecasting
To tackle these issues, researchers propose HRS, a Hybrid Representation framework with Scheduling awareness. HRS introduces two major innovations. First, it combines both numerical and image-based representations of time series data. This unique hybrid approach allows the model to better capture both the overall trends and the specific “shapes” of extreme load dynamics, such as sharp peaks or deep valleys, which are hard to see in numerical data alone. Imagine looking at a stock chart; the numerical data gives you the numbers, but the visual representation helps you see patterns in how the price moves.
Second, HRS incorporates a novel component called Scheduling-Aware Loss (SAL). Unlike traditional loss functions that treat all prediction errors the same, SAL understands that underpredicting traffic or server load is far more costly than overpredicting it. Underprediction can lead to overloaded servers, degraded service quality, and significant financial penalties for violating SLAs. Overprediction, while increasing resource costs, ensures service quality and avoids these severe penalties. SAL guides the model to make predictions that prioritize avoiding these costly underpredictions, even if it means slightly overprovisioning resources, thereby better supporting scheduling decisions and improving overall platform profitability.
How HRS Delivers Improved Performance
The HRS framework consists of several key modules. It starts with a “Representation Transfer” step, converting numerical time series data into images to highlight localized patterns. Then, a “Hybrid Representation Learning” component processes both the original numerical data and the newly created image data through separate branches, extracting different types of features. These features are then combined and fed into a “Multi-Dependency Learning Module” that captures complex interactions within the data. Finally, the entire system is trained using the Scheduling-Aware Loss function, ensuring that the learning process is aligned with real-world scheduling objectives.
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Demonstrated Success in Real-World Scenarios
Extensive experiments were conducted using four real-world datasets from a commercial Crowdsourced Cloud-Edge Platform, covering streaming traffic and server workloads. HRS consistently outperformed ten other state-of-the-art forecasting models. When compared to baselines trained with traditional error metrics, HRS significantly reduced SLA violation rates by an impressive 63.1% and decreased total profit loss by 32.3%. Even when other models were also trained with the Scheduling-Aware Loss, HRS still showed superior performance, demonstrating the power of its hybrid representation. The study also confirmed that HRS is computationally efficient, scaling linearly with the input data length, making it suitable for latency-sensitive cloud-edge environments.
This innovative framework offers a robust solution for time series forecasting in dynamic cloud-edge environments, leading to more stable services and improved financial outcomes for platform operators. For more technical details, you can refer to the full research paper here.
