# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 3 Three Versions of Running Skewness and Efficient Calculation Methods
### Performance Comparison among the Three CSS Calculation Methods
#### 4.3.4 Performance Analysis  
In addition to the correctness, we analyse the root causes of the performance difference in the three methods by investigating their algorithms together with the experimental results. First, we discuss the reason why the GSL method has the fastest speed when dealing with one-by-one data. Considering that GSL employs the Fisher-Pearson coefficient of skewness whereas GoL and this research employ the adjusted Fisher-Pearson coefficient (cf. Appendix A.3), both the GoL method and our method have involved _adjustment_ overhead in the CSS calculations. However, as mentioned in Section 4.3.3, the de facto software packages/products generally include an adjustment for sample size [8], and thus the GSL method may not be suitable for modern applications.  
Then, we discuss the reason why our method has the fastest speed when coping with chunk-by-chunk data. Due to the benefit of the unified pseudo code, it is convenient to count that Algorithm 3 has fewer math operations outside the if-else block than the other two algorithms. Although the operation difference is as small as one multiplication only, the performance difference will be magnified due to large sample sizes, especially in the long-term data streaming scenario. Since there is no need to obtain skewness values  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Size** & **GSL Method** & **GoL Method** & **This Research** & **Excel Skew( )** \\ \hline
3 & \(-0.378753\) & \(-1.704387\) & \(-1.704387\) & \(-1.704387\) \\
4 & \(-0.749576\) & \(-1.998869\) & \(-1.998869\) & \(-1.998869\) \\
5 & \(-0.292042\) & \(-0.60842  
within a data chunk (i.e., the code inside the if-else block is not executed in this case), the aforementioned _adjustment_ overhead will be tangibly reduced even if the chunk size is two, which eventually makes our method faster than the GSL method.  
As for the GoL method, in addition to the overhead of _adjustment_ and extra multiplication, Algorithm 2 also suffers an avoidable performance loss inside its if-else block. It should be noted that the math operations here can further be simplified into the same form as our method. The current form of Algorithm 2 will be reasonable if the variance value and the skewness value are both needed. However, as shown in Algorithms 1 and 3, the explicit variance calculation is not required when monitoring skewness values only.  
Overall, the performance advantage of this research is particularly meaningful and valuable in the context of online edge learning, because practitioners \"strive for millisecond learning; everything else comes second\" [49]. Moreover, considering that IoT devices generally have limited computing and memory capabilities, minimizing operations will be more IoT-friendly and be able to bring significant marginal utility. Even in the broader context of edge cloud computing, any (even small) performance improvement will matter, as \"every drop of 20 ms...latency will result in a 7-15% decrease in page load times\" [50].