## 4 Algorithm  
### Demand Enqueue Rules  
To more realistically simulate the temporal correlation and load fluctuation of in situ data, the system caches the amount of access demand \(E\) generated in the most recent \(w\) rounds and dynamically updates it as the rounds progress. To better simulate the demand generation process, the expected access demand \(\theta(t)\) in round \(t\) is computed using an Exponentially Weighted Moving Average (EMA) over the historical demand data from the previous \(w\) rounds. The calculation is given by the following:  
\[\theta(t)=\sum_{k=1}^{w}\beta_{k}\cdot E(t-k),\quad\sum_{k=1}^{w}\beta_{k}=1, \quad\beta_{1}>\beta_{2}>\cdots>\beta_{w} \tag{14}\]  
where \(\beta_{k}\) denotes the weight assigned to the \(k\)-th historical value, which decays exponentially, and the total weight sums to 1. The EMA model assigns higher weights to more recent data, thereby effectively capturing short-term fluctuations in the system, making it well-suited for the rapidly changing access demand characteristics in in situ (InS) scenarios. To more realistically simulate the fluctuating generation process of access demand, this study adopts a Poisson distribution to model the number of access demands generated by the system. The number of demands enqueued at episode \(t\), denoted as \(E(t)\), can be expressed as follows:  
\[E(t)\sim\text{Poisson}(\lambda(t)),\quad\lambda(t)=\zeta\cdot\theta(t),\quad i \in\{low,medium,high\} \tag{15}\]  
where \(\lambda(t)\) denotes the mean of the Poisson distribution. The adjustment factor \(\zeta\) is used to control the scaling of the Poisson distribution, with a value range of \(\zeta\in[1.0,1.5]\). This range effectively captures the load fluctuations typical of in situ (InS) systems under both normal and moderately peaked conditions. Once the total access demand for each round is determined, a classification mechanism is applied to assign the specific number of access demands entering each queue \(Que_{i}\) in round \(t\): \(E_{low}(t)\), \(E_{medium}(t)\), and \(E_{high}(t)\). In practice, the required capacity of access demands is unevenly distributed, with a small number of large-capacity and a majority of small-capacity demands, which aligns with the characteristics of a log-normal distribution. \(E_{i}(k)\) denotes the number of access demands enqueued in queue \(Que_{i}\) at round \(k\). \(C_{i}^{(k,j)}\) represents the required capacity of the \(j\)-th access demand in queue \(Que_{i}\) at round \(k\), where \(j=1,2,\ldots,E_{i}(k)\). \(\mathcal{H}_{i}(t)\) is a set that records the required capacities of all access demands in queue \(Que_{i}\) during the \(w\) rounds preceding round \(t\). It is formally defined as follows:  
\[\mathcal{C}_{i}^{(k)}=\{C_{i}^{(k,1)},C_{i}^{(k,2)},\ldots,C_{i}^{(k,E_{i}(k)) }\},\quad i\in\{low,medium,high\},\quad k\in[t-w,t-1] \tag{16}\]  
\[\mathcal{H}_{i}(t)=\bigcup_{k=t-w}^{t-1}\mathcal{C}_{i}^{(k)},\quad i\in\{low,medium,high\} \tag{17}\]  
Subsequently, based on maximum likelihood estimation, two key parameters of the log-normal distribution followed by each queue in round \(t\) are estimated: the mean \(\mu_{i}(t)\) and the variance \(\sigma_{i}^{2}(t)\). Sampling is then performed to generate the required capacity \(C\) of each access demand in the current round \(t\). \[\mu_{i}(t)=\frac{1}{|\mathcal{H}_{i}(t)|}\sum_{C_{j}\in\mathcal{H}_{i}(t)} \log C_{j},\quad i\in\{low,medium,high\} \tag{18}\]  
\[\sigma_{i}^{2}(t)=\frac{1}{|\mathcal{H}_{i}(t)|}\sum_{C_{j}\in\mathcal{H}_{i}( t)}\bigl{(}\log C_{j}-\mu_{i}(t)\bigr{)}^{2},\quad i\in\{low,medium,high\} \tag{19}\]  
\[C\sim\text{LogNormal}(\mu_{i}(t),\sigma_{i}^{2}(t)) \tag{20}\]  
The Poisson distribution is not only a typical model for describing data generation in industrial scenarios, but also more accurately reflects the actual generation efficiency of in situ data access demand.