Ethanperry/anomaly detection (#33)

Introduces the following new tools:
1. EWMA Anomaly Detection Server
2. HTTP/GRPC Shift Calculator Server
3. SQL Callout Server

---------

Co-authored-by: Ethan <ethan@ethansstress.guest.corp.microsoft.com>

lib/proto/message.proto

@@ -12,9 +12,6 @@ message Message {
     repeated Message array = 6;
 }
 
-message Empty {
-}
-
 service Sender {
-    rpc Send(Message) returns (Empty) {}
+    rpc Send(Message) returns (Message) {}
 }

samples/anomaly-detection/Dockerfile

@@ -0,0 +1,21 @@
+# syntax=docker/dockerfile:1
+
+FROM mcr.microsoft.com/oss/go/microsoft/golang:1.21-cbl-mariner2.0 AS build
+
+COPY ./lib /workdir/lib
+
+COPY ./samples/anomaly-detection /workdir/samples/anomaly-detection
+
+WORKDIR /workdir/samples/anomaly-detection
+
+RUN go mod download
+
+RUN go install github.com/magefile/mage@latest
+
+RUN mage ci
+
+RUN go build -o ./bin/anomaly-detection ./cmd/anomaly-detection
+
+EXPOSE 2112
+
+CMD [ "/bin/anomaly-detection" ]

samples/anomaly-detection/README.md

@@ -0,0 +1,133 @@
+# Anomaly Detection Server
+
+## Usage
+
+### Server as a Pod
+
+```sh
+# From the root of the anomaly-detection directory.
+docker build ../.. -f Dockerfile -t <yourcrhere>.azurecr.io/anomaly-detection:latest
+
+# Or if running from the root of the explore-iot-operations repository.
+# docker build . -f ./samples/anomaly-detection/Dockerfile -t <yourcrhere>.azurecr.io/anomaly-detection:latest
+
+# Push or load your newly built image into your cluster, depending on the k8s setup.
+# docker push <yourcrhere>.azurecr.io/anomaly-detection:latest # Using AKS + Connected ACR
+# minikube load <yourcrhere>.azurecr.io/anomaly-detection:latest # Using minikube
+# docker save <yourcrhere>.azurecr.io/anomaly-detection:latest | k3s ctr images import - # Using K3s
+
+kubectl run anomaly-detection --image=<yourcrhere>.azurecr.io/anomaly-detection:latest --stdin < config.yml
+```
+
+### Configuration
+
+```yaml
+logger: # Logger settings
+  level: 0
+server: # Server settings
+  route: /anomaly
+  port: 3333
+algorithm: # Algorithm settings
+  temperature: # Temperature related algorithm settings
+    lambda: 0.25 # Lambda value for temperature anomaly detection
+    lFactor: 3 # L factor for temperature anomaly detection
+    controlT: 90 # Control limit T constant
+    controlS: 20 # Control limit S constant
+    controlN: 10 # Control limit N constant
+  vibration: # Vibration related algorithm settings
+    lambda: 0.25 
+    lFactor: 3
+    type: dynamic # Use dynamic algorithm, no requirement for control limit T, S, or N values
+  humidity: # Humidity related algorithm settings
+    lambda: 0.25
+    lFactor: 3
+    controlT: 80
+    controlS: 20
+    controlN: 10
+```
+
+## Algorithms
+
+Use the `type: dynamic` property in the configuration to choose an algorithm from the following.
+
+### Algorithm #1 - Control Limit Formula with Estimated Control Limits
+
+$$B = L\frac{S}{\sqrt{n}}\sqrt{\frac{\lambda}{2 - \lambda}[1 - (1 - \lambda)^{2i}]}$$
+
+$$B_u = T + B$$
+
+$$B_l = T - B$$
+
+<center>
+
+| Symbol | Meaning  |
+|---|---|
+| $B$  | Control Limit Value  |
+| $B_u$  | Upper Control Limit  |
+| $B_l$  | Lower Control Limit  |
+| $L$  | L Factor  |
+| $\lambda$  | Lambda Factor $(0 \leq \lambda \leq 1)$ |
+| $i$  | Iteration or Count |
+| $T$  | Constant Estimation of Sample Mean |
+| $S$  | Constant Estimation of Sample Std Dev |
+| $n$  | Constant Estimation of Rational Subgroup Size |
+
+</center>
+
+### Algorithm #2 - Control Limit Formula with Dynamically Evaluated Control Limits (`type: dynamic`)
+
+
+$$B = L\sigma_i\sqrt{\frac{\lambda}{2 - \lambda}[1 - (1 - \lambda)^{2i}]}$$ 
+
+$$B_u = \mu_i + B$$
+
+
+$$B_l = \mu_i - B$$
+
+<center>
+
+| Symbol | Meaning  |
+|---|---|
+| $B$  | Control Limit Value  |
+| $B_u$  | Upper Control Limit  |
+| $B_l$  | Lower Control Limit  |
+| $L$  | L Factor  |
+| $\lambda$  | Lambda Factor $(0 \leq \lambda \leq 1)$ |
+| $i$  | Iteration or Count |
+| $\mu_i$  | Sample Mean from Iteration $i$ |
+| $\sigma_i$  | Sample Std Dev from Iteration $i$ |
+
+</center>
+
+### EWMA Value and Anomaly Detection Conditions
+
+The following formulas are shared between the two algorithms.
+
+__EWMA Value Formula__
+
+$$z_{i} = \lambda x_i + (1 - \lambda)z_{i-1}$$
+
+__Anomaly Condition__
+
+$$
+a=
+\begin{cases}
+0, & \text{if } B_l \leq z_i \leq B_u \\
+1, & \text{otherwise}
+\end{cases}
+$$
+
+
+<center>
+
+| Symbol | Meaning  |
+|---|---|
+| $B_u$  | Upper Control Limit  |
+| $B_l$  | Lower Control Limit  |
+| $\lambda$  | Lambda Factor $(0 \leq \lambda \leq 1)$ |
+| $i$  | Iteration or Count |
+| $x_i$  | Observed Value from Iteration $i$ |
+| $z_i$  | EWMA Value for Iteration $i$ |
+| $a$  | Anomaly Occurance, $1$ Implies an Anomaly |
+
+</center>

samples/anomaly-detection/cmd/config.yml

@@ -0,0 +1,24 @@
+logger:
+  level: 0
+server:
+  route: /anomaly
+  port: 3333
+algorithm:
+  temperature:
+    lambda: 0.25
+    lFactor: 3
+    controlT: 90
+    controlS: 20
+    controlN: 10
+  vibration:
+    lambda: 0.25
+    lFactor: 3
+    controlT: 50
+    controlS: 20
+    controlN: 10
+  humidity:
+    lambda: 0.25
+    lFactor: 3
+    controlT: 80
+    controlS: 20
+    controlN: 10