Modern systems often use automation for resource scaling. Current automatic scaling systems focus on systemgenerated metrics like CPU and memory utilization to determine scaling. Although these indicators are precise, they often lack insight into the operational performance of applications affected by scaling. Distributed traces model end-to-end requests or transactions as a hierarchical graph of spans, each representing a measured execution point. Trace data can reveal insights into latencies that are not captured by system-level metrics. To address this, we introduce SCALE, an automated resource scaler that uses distributed traces for scaling decisions. SCALE samples trace spans for duration anomalies and processes them through a heuristic-based modeling engine against defined latency thresholds. If thresholds are exceeded, SCALE triggers scaling commands for orchestration systems. SCALE is evaluated using trace data from Kubernetes microservice benchmarks.

Electromagnetic narrowband signals were transmitted through the Earth at 27 coal mines located throughout the United States. From these tests, apparent Earth conductivity values were derived based upon a homogeneous half-space model of the Earth. The derived conductivity values were found to be inversely proportional to the transmitted frequency and mine depth. This result is not consistent with the assumption of a homogeneous half-space Earth. A linear regression model relating the logarithm of the conductivity to the mine depth was formulated. The author found that the apparent Earth conductivity could be estimated from a linear combination of the transmitted frequency and mine depth.>