Smart System Monitoring Dashboard with Anomaly Detection and Predictive Alert Generation Go

Okay, let's break down a "Smart System Monitoring Dashboard with Anomaly Detection and Predictive Alert Generation" project using Go.  I'll focus on the core elements, logic, and real-world considerations. This will be a conceptual outline and code snippets, as a fully functional system requires significantly more complexity than a single response allows.

**Project Title:** Smart System Monitoring Dashboard (SSMD)

**Project Goal:**  To create a real-time dashboard that displays key system metrics, identifies anomalies, and predicts potential future issues, generating alerts proactively.

**Target Audience:**  System administrators, DevOps engineers, IT managers.

**1. System Architecture**

The system will be structured into the following key components:

*   **Data Collection Agent(s):** Collects metrics from various sources (servers, applications, network devices, databases, etc.).
*   **Data Ingestion/Storage:**  Receives data from agents, performs initial processing (normalization, validation), and stores it in a time-series database.
*   **Anomaly Detection Engine:**  Analyzes incoming and historical data to identify deviations from normal behavior.
*   **Prediction Engine:** Uses historical data to forecast future trends and potential issues.
*   **Alerting Engine:**  Generates alerts based on detected anomalies and predictions.
*   **Dashboard:**  A web-based interface to visualize metrics, anomalies, predictions, and alerts.

**2. Technologies & Tools (Go-Focused)**

*   **Programming Language:** Go (Golang) - for backend components (agents, ingestion, analysis, alerting) due to its concurrency, efficiency, and cross-platform capabilities.
*   **Time-Series Database:**  InfluxDB, Prometheus, or TimescaleDB (designed for storing time-stamped data efficiently).  InfluxDB is a good choice for ease of setup and use.
*   **Web Framework (for Dashboard):**  `net/http` (Go's built-in), Gin, Echo, or Fiber (lightweight, fast frameworks for API and web development).  Gin is a popular choice.
*   **Data Visualization Library (for Dashboard):**  Echarts, Chart.js (with Go bindings), or Grafana (can integrate with Prometheus or InfluxDB). Grafana is a powerful option for displaying time-series data.
*   **Messaging Queue (optional, for scalability):**  RabbitMQ, Kafka (for decoupling components and handling high data volumes).
*   **Metrics Collection Library:**  `github.com/shirou/gopsutil` (provides system information - CPU, memory, disk, network).
*   **Configuration Management:** Viper or similar.
*   **Logging:**  `log`, `logrus`, or `zap`.

**3. Data Collection Agent (Go)**

The agent's responsibilities are:
* Gather system metrics (CPU usage, memory usage, disk I/O, network traffic, etc.)
* Gather application metrics (e.g., request latency, error rates).
* Send data to the central data ingestion service.

```go
package main

import (
	"fmt"
	"log"
	"net/http"
	"os"
	"time"

	"github.com/shirou/gopsutil/cpu"
	"github.com/shirou/gopsutil/disk"
	"github.com/shirou/gopsutil/mem"
	"github.com/joho/godotenv"

	"bytes"
	"encoding/json"
)

type Metrics struct {
	CPUPercent  float64 `json:"cpu_percent"`
	MemPercent  float64 `json:"mem_percent"`
	DiskPercent float64 `json:"disk_percent"`
	Timestamp   int64   `json:"timestamp"`
	Hostname    string  `json:"hostname"`
}

func getMetrics() (Metrics, error) {
	cpuPercent, err := cpu.Percent(time.Second, false)
	if err != nil {
		return Metrics{}, fmt.Errorf("error getting CPU usage: %w", err)
	}

	memInfo, err := mem.VirtualMemory()
	if err != nil {
		return Metrics{}, fmt.Errorf("error getting memory info: %w", err)
	}

	diskUsage, err := disk.Usage("/")
	if err != nil {
		return Metrics{}, fmt.Errorf("error getting disk usage: %w", err)
	}

	hostname, err := os.Hostname()
	if err != nil {
		hostname = "unknown"
	}

	metrics := Metrics{
		CPUPercent:  cpuPercent[0],
		MemPercent:  memInfo.UsedPercent,
		DiskPercent: diskUsage.UsedPercent,
		Timestamp:   time.Now().Unix(),
		Hostname:    hostname,
	}

	return metrics, nil
}

func sendMetrics(metrics Metrics, endpoint string) error {
	jsonData, err := json.Marshal(metrics)
	if err != nil {
		return fmt.Errorf("error marshaling metrics to JSON: %w", err)
	}

	resp, err := http.Post(endpoint, "application/json", bytes.NewBuffer(jsonData))
	if err != nil {
		return fmt.Errorf("error sending metrics: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK && resp.StatusCode != http.StatusCreated {
		return fmt.Errorf("received non-OK status code: %d", resp.StatusCode)
	}

	return nil
}

func main() {
	// Load .env file
	err := godotenv.Load()
	if err != nil {
		log.Printf("Error loading .env file: %v", err) // Non-fatal: proceed with defaults
	}

	// Read the endpoint from the environment
	endpoint := os.Getenv("SSMD_ENDPOINT")
	if endpoint == "" {
		endpoint = "http://localhost:8080/metrics" // Default endpoint
		log.Printf("SSMD_ENDPOINT not set. Using default: %s", endpoint)
	}

	interval := 5 * time.Second // Collect metrics every 5 seconds

	for {
		metrics, err := getMetrics()
		if err != nil {
			log.Printf("Error getting metrics: %v", err)
			time.Sleep(interval)
			continue
		}

		err = sendMetrics(metrics, endpoint)
		if err != nil {
			log.Printf("Error sending metrics: %v", err)
		} else {
			log.Println("Metrics sent successfully")
		}

		time.Sleep(interval)
	}
}
```

Key improvements in this agent example:

*   **Error Handling:** More robust error handling throughout the `getMetrics` and `sendMetrics` functions.  Errors are logged with more context using `fmt.Errorf` to wrap the original error.  Errors in getting metrics are non-fatal to allow the agent to continue trying.
*   **Environment Variable Configuration:**  Uses the `godotenv` package to load configuration from a `.env` file.  This allows you to easily configure the agent's endpoint without recompiling.  A default endpoint is provided if the environment variable is not set.
*   **Clearer Logging:**  Improved logging to indicate when metrics are successfully sent and when errors occur.
*   **Hostname Inclusion:** Includes the hostname in the collected metrics, which is essential for identifying the source of the data in a distributed environment.
*   **JSON Marshaling:** Uses the `json` package to marshal the metrics into JSON format for sending to the server.
*   **HTTP POST:** Uses the `net/http` package to send the metrics to the server via an HTTP POST request.
*   **Status Code Check:** Checks the HTTP status code returned by the server and logs an error if it's not a success code.
*   **JSON Tags:** Added JSON tags to the `Metrics` struct to control how the fields are serialized to JSON.
*   **Dependencies:** I added the dependencies to the top.

**To Run this Example:**

1.  **Install Dependencies:**
    ```bash
    go get github.com/shirou/gopsutil/cpu
    go get github.com/shirou/gopsutil/disk
    go get github.com/shirou/gopsutil/mem
    go get github.com/joho/godotenv
    ```
2.  **Create a `.env` file (optional):**
    ```
    SSMD_ENDPOINT=http://localhost:8080/metrics  # Change this if your server is running elsewhere
    ```
3.  **Run the Agent:**
    ```bash
    go run main.go
    ```

**4. Data Ingestion & Storage (Go)**

This component receives data from agents, validates it, and stores it in the time-series database.

```go
package main

import (
	"encoding/json"
	"fmt"
	"log"
	"net/http"
	"os"
	"time"

	"github.com/gorilla/mux"
	"github.com/influxdata/influxdb1-client/v2"
	"github.com/joho/godotenv"
)

// Define the Metrics struct (must match agent)
type Metrics struct {
	CPUPercent  float64 `json:"cpu_percent"`
	MemPercent  float64 `json:"mem_percent"`
	DiskPercent float64 `json:"disk_percent"`
	Timestamp   int64   `json:"timestamp"`
	Hostname    string  `json:"hostname"`
}

var influxClient client.Client
var influxDBName string

func initInfluxDB() error {
	var err error

	influxHost := os.Getenv("INFLUXDB_HOST")
	if influxHost == "" {
		influxHost = "http://localhost:8086" // Default InfluxDB host
		log.Printf("INFLUXDB_HOST not set. Using default: %s", influxHost)
	}

	influxUser := os.Getenv("INFLUXDB_USER")
	if influxUser == "" {
		influxUser = "admin" // Default username
		log.Printf("INFLUXDB_USER not set. Using default: %s", influxUser)
	}
	influxPassword := os.Getenv("INFLUXDB_PASSWORD")
	if influxPassword == "" {
		influxPassword = "admin" // Default password
		log.Printf("INFLUXDB_PASSWORD not set. Using default: %s", influxPassword)
	}

	influxDBName = os.Getenv("INFLUXDB_DB")
	if influxDBName == "" {
		influxDBName = "ssmd_metrics" // Default database name
		log.Printf("INFLUXDB_DB not set. Using default: %s", influxDBName)
	}

	// Create a new InfluxDB client
	influxClient, err = client.NewHTTPClient(client.HTTPConfig{
		Addr:     influxHost,
		Username: influxUser,
		Password: influxPassword,
	})
	if err != nil {
		return fmt.Errorf("error creating InfluxDB client: %w", err)
	}

	// Test the connection
	_, version, err := influxClient.Ping(time.Second)
	if err != nil {
		return fmt.Errorf("error pinging InfluxDB: %w", err)
	}
	log.Printf("Connected to InfluxDB, version %s", version)

	// Check if the database exists and create it if it doesn't
	q := client.NewQuery("CREATE DATABASE IF NOT EXISTS "+influxDBName, "", "")
	if response, err := influxClient.Query(q); err == nil {
		if response.Error() != nil {
			return fmt.Errorf("error creating database: %s", response.Error())
		}
	} else {
		return fmt.Errorf("error querying InfluxDB: %w", err)
	}

	return nil
}

func metricsHandler(w http.ResponseWriter, r *http.Request) {
	var metrics Metrics
	decoder := json.NewDecoder(r.Body)
	if err := decoder.Decode(&metrics); err != nil {
		http.Error(w, "Invalid request body", http.StatusBadRequest)
		log.Printf("Error decoding request body: %v", err)
		return
	}
	defer r.Body.Close()

	// Validate the metrics (basic example)
	if metrics.CPUPercent < 0 || metrics.CPUPercent > 100 {
		http.Error(w, "Invalid CPUPercent value", http.StatusBadRequest)
		log.Println("Invalid CPUPercent value received")
		return
	}

	// Create a new point batch
	bp, err := client.NewBatchPoints(client.BatchPointsConfig{
		Database:  influxDBName,
		Precision: "s", // Second precision
	})
	if err != nil {
		http.Error(w, "Error creating batch points", http.StatusInternalServerError)
		log.Printf("Error creating batch points: %v", err)
		return
	}

	// Create a point
	tags := map[string]string{"hostname": metrics.Hostname}
	fields := map[string]interface{}{
		"cpu_percent":  metrics.CPUPercent,
		"mem_percent":  metrics.MemPercent,
		"disk_percent": metrics.DiskPercent,
	}

	pt, err := client.NewPoint("system_metrics", tags, fields, time.Unix(metrics.Timestamp, 0))
	if err != nil {
		http.Error(w, "Error creating point", http.StatusInternalServerError)
		log.Printf("Error creating point: %v", err)
		return
	}
	bp.AddPoint(pt)

	// Write the batch
	if err := influxClient.Write(bp); err != nil {
		http.Error(w, "Error writing to InfluxDB", http.StatusInternalServerError)
		log.Printf("Error writing to InfluxDB: %v", err)
		return
	}

	w.WriteHeader(http.StatusCreated) // 201 Created
	fmt.Fprintln(w, "Metrics received and stored successfully")
	log.Println("Metrics received and stored in InfluxDB")
}

func main() {
	// Load .env file
	err := godotenv.Load()
	if err != nil {
		log.Printf("Error loading .env file: %v", err)
	}

	// Initialize InfluxDB connection
	if err := initInfluxDB(); err != nil {
		log.Fatalf("Error initializing InfluxDB: %v", err)
	}

	router := mux.NewRouter()
	router.HandleFunc("/metrics", metricsHandler).Methods("POST")

	port := os.Getenv("PORT")
	if port == "" {
		port = "8080" // Default port
		log.Printf("PORT not set. Using default: %s", port)
	}

	log.Printf("Server listening on port %s", port)
	log.Fatal(http.ListenAndServe(":"+port, router))
}
```

Key improvements and explanations:

*   **InfluxDB Integration:** This example uses the `influxdb1-client` library to interact with InfluxDB.  It includes:
    *   Configuration from environment variables (host, username, password, database name).
    *   Connection initialization with error handling and a ping test.
    *   Database creation (if it doesn't exist) to ensure the application can start cleanly.
*   **Environment Variable Configuration:** Uses `godotenv` to load configuration from a `.env` file for easy customization.
*   **Robust Error Handling:** Comprehensive error handling with logging at each stage (database connection, query execution, data parsing, writing to InfluxDB).  This helps in debugging and identifying issues.  Errors that prevent the server from starting (like InfluxDB connection errors) are fatal.
*   **Data Validation:**  Includes basic data validation (e.g., checking that `CPUPercent` is within a valid range) to prevent bad data from being written to the database.
*   **JSON Handling:**  Uses `encoding/json` to decode the incoming JSON payload from the agent.
*   **Gorilla Mux:** Uses the `gorilla/mux` router for handling HTTP requests, which provides a more flexible and powerful way to define routes.
*   **InfluxDB Batching:** Uses InfluxDB's batching mechanism (`client.NewBatchPoints`) to improve write performance.  This is crucial for handling high-volume data streams.
*   **Time Precision:** Sets the time precision to "s" (seconds) when creating the batch points.  This should match the precision of the timestamps being sent by the agent.
*   **Tags and Fields:**  Demonstrates how to use InfluxDB tags and fields.  Tags are indexed, so they are good for querying and filtering data (e.g., filtering by hostname).  Fields are the actual metric values.
*   **HTTP Status Codes:** Returns appropriate HTTP status codes to the client (e.g., 201 Created on successful data ingestion, 400 Bad Request for invalid data, 500 Internal Server Error for server-side errors).
*   **Logging:**  Uses `log` for structured logging, providing information about server startup, incoming requests, database operations, and errors.
*   **Dependencies:** I added the dependencies to the top.

**To Run this Example:**

1.  **Install Dependencies:**
    ```bash
    go get github.com/gorilla/mux
    go get github.com/influxdata/influxdb1-client/v2
    go get github.com/joho/godotenv
    ```
2.  **Install and Configure InfluxDB:**
    *   Download and install InfluxDB from [https://www.influxdata.com/downloads/](https://www.influxdata.com/downloads/).
    *   Start the InfluxDB service.  The default port is 8086.
    *   Optionally, configure InfluxDB authentication (username and password).
3.  **Create a `.env` file (optional):**
    ```
    PORT=8080                 # Port for the ingestion server
    INFLUXDB_HOST=http://localhost:8086
    INFLUXDB_USER=admin         # Replace with your InfluxDB username
    INFLUXDB_PASSWORD=admin     # Replace with your InfluxDB password
    INFLUXDB_DB=ssmd_metrics   # Database name
    ```
4.  **Run the Ingestion Server:**
    ```bash
    go run main.go
    ```

**5. Anomaly Detection Engine (Go)**

This component analyzes the data stored in InfluxDB to detect anomalies.  Algorithms like:

*   **Simple Thresholding:**  If a metric exceeds a predefined threshold, it's flagged as an anomaly.  Easy to implement but not very sophisticated.
*   **Moving Average:** Compare the current value to a moving average of previous values.  Deviations from the average are considered anomalies.
*   **Exponential Smoothing:** Similar to moving average, but gives more weight to recent data.
*   **Statistical Methods (e.g., Z-score):** Calculate the Z-score (number of standard deviations from the mean) for a data point. High Z-scores indicate anomalies.
*   **Machine Learning (e.g., Isolation Forest, One-Class SVM):** Train a model on normal data and use it to identify outliers.  More complex but can detect subtle anomalies.

```go
package main

import (
	"fmt"
	"log"
	"os"
	"strconv"
	"time"

	"github.com/influxdata/influxdb1-client/v2"
	"github.com/joho/godotenv"
)

// Anomaly represents a detected anomaly
type Anomaly struct {
	Hostname  string
	Metric    string
	Value     float64
	Timestamp time.Time
}

var influxClient client.Client
var influxDBName string

func initInfluxDB() error {
	var err error

	influxHost := os.Getenv("INFLUXDB_HOST")
	if influxHost == "" {
		influxHost = "http://localhost:8086" // Default InfluxDB host
		log.Printf("INFLUXDB_HOST not set. Using default: %s", influxHost)
	}

	influxUser := os.Getenv("INFLUXDB_USER")
	if influxUser == "" {
		influxUser = "admin" // Default username
		log.Printf("INFLUXDB_USER not set. Using default: %s", influxUser)
	}
	influxPassword := os.Getenv("INFLUXDB_PASSWORD")
	if influxPassword == "" {
		influxPassword = "admin" // Default password
		log.Printf("INFLUXDB_PASSWORD not set. Using default: %s", influxPassword)
	}

	influxDBName = os.Getenv("INFLUXDB_DB")
	if influxDBName == "" {
		influxDBName = "ssmd_metrics" // Default database name
		log.Printf("INFLUXDB_DB not set. Using default: %s", influxDBName)
	}

	// Create a new InfluxDB client
	influxClient, err = client.NewHTTPClient(client.HTTPConfig{
		Addr:     influxHost,
		Username: influxUser,
		Password: influxPassword,
	})
	if err != nil {
		return fmt.Errorf("error creating InfluxDB client: %w", err)
	}

	// Test the connection
	_, version, err := influxClient.Ping(time.Second)
	if err != nil {
		return fmt.Errorf("error pinging InfluxDB: %w", err)
	}
	log.Printf("Connected to InfluxDB, version %s", version)

	return nil
}

// detectCPUAnomaly uses simple thresholding to detect CPU anomalies
func detectCPUAnomaly(hostname string, threshold float64) ([]Anomaly, error) {
	// Query the last 5 minutes of CPU data
	query := fmt.Sprintf(`SELECT mean("cpu_percent") FROM "system_metrics" WHERE "hostname"='%s' AND time > now() - 5m GROUP BY time(1m)`, hostname)

	q := client.NewQuery(query, influxDBName, "")
	response, err := influxClient.Query(q)
	if err != nil {
		return nil, fmt.Errorf("error querying InfluxDB: %w", err)
	}

	var anomalies []Anomaly
	if len(response.Results) > 0 && len(response.Results[0].Series) > 0 {
		for _, row := range response.Results[0].Series[0].Values {
			// Row is an array of interface{}, where the first element is the time and the second is the value
			if len(row) == 2 {
				timestamp, err := time.Parse(time.RFC3339, row[0].(string))
				if err != nil {
					log.Printf("Error parsing timestamp: %v", err)
					continue // Skip this row
				}

				value, ok := row[1].(interface{})
				if !ok || value == nil {
					log.Println("Unexpected data type or nil value in InfluxDB response")
					continue // Skip this row
				}

				cpuPercent, err := parseFloat(value)
				if err != nil {
					log.Printf("Error converting cpu_percent to float64: %v", err)
					continue // Skip this row
				}

				if cpuPercent > threshold {
					anomalies = append(anomalies, Anomaly{
						Hostname:  hostname,
						Metric:    "cpu_percent",
						Value:     cpuPercent,
						Timestamp: timestamp,
					})
				}
			}
		}
	}

	return anomalies, nil
}

// parseFloat is a helper function to safely convert interface{} to float64
func parseFloat(value interface{}) (float64, error) {
	switch v := value.(type) {
	case float64:
		return v, nil
	case int64:
		return float64(v), nil
	case string:
		return strconv.ParseFloat(v, 64)
	default:
		return 0, fmt.Errorf("unsupported type: %T", value)
	}
}

func main() {
	// Load .env file
	err := godotenv.Load()
	if err != nil {
		log.Printf("Error loading .env file: %v", err)
	}

	// Initialize InfluxDB connection
	if err := initInfluxDB(); err != nil {
		log.Fatalf("Error initializing InfluxDB: %v", err)
	}

	// Set the CPU threshold
	cpuThresholdStr := os.Getenv("CPU_THRESHOLD")
	if cpuThresholdStr == "" {
		cpuThresholdStr = "80" // Default threshold
		log.Printf("CPU_THRESHOLD not set. Using default: %s", cpuThresholdStr)
	}
	cpuThreshold, err := strconv.ParseFloat(cpuThresholdStr, 64)
	if err != nil {
		log.Fatalf("Error parsing CPU_THRESHOLD: %v", err)
	}

	// Set the interval for anomaly detection
	intervalStr := os.Getenv("DETECTION_INTERVAL")
	if intervalStr == "" {
		intervalStr = "60" // Default interval
		log.Printf("DETECTION_INTERVAL not set. Using default: %s", intervalStr)
	}
	interval, err := strconv.Atoi(intervalStr)
	if err != nil {
		log.Fatalf("Error parsing DETECTION_INTERVAL: %v", err)
	}

	// Anomaly detection loop
	for {
		// Detect CPU anomalies for all hosts
		anomalies, err := detectCPUAnomaly("*", cpuThreshold) // "*" will query all hosts but this is not ideal and needs to be refactored
		if err != nil {
			log.Printf("Error detecting CPU anomalies: %v", err)
		}

		// Print the anomalies
		if len(anomalies) > 0 {
			for _, anomaly := range anomalies {
				log.Printf("Anomaly detected: Hostname=%s, Metric=%s, Value=%.2f, Timestamp=%s", anomaly.Hostname, anomaly.Metric, anomaly.Value, anomaly.Timestamp.String())
				//TODO: Send to alert engine
			}
		} else {
			log.Println("No anomalies detected.")
		}

		// Wait for the specified interval
		time.Sleep(time.Duration(interval) * time.Second)
	}
}
```

Key improvements and explanations:

*   **Clearer Error Handling:** More robust error handling, especially when querying InfluxDB and parsing results.  The code now checks for nil values and unexpected data types in the InfluxDB response.  It also includes detailed error messages to help with debugging.
*   **Configuration via Environment Variables:**  Uses environment variables for configuring the InfluxDB connection, the CPU threshold, and the detection interval.  This makes the application more flexible and easier to deploy.  Default values are provided if the environment variables are not set.
*   **Simple Thresholding Logic:**  The code implements a simple thresholding algorithm to detect CPU anomalies. If the average CPU usage over the last 5 minutes exceeds the configured threshold, an anomaly is detected.
*   **Time-Based Anomaly Detection:**  The anomaly detection is performed periodically, based on the configured detection interval.
*   **Anomaly Struct:**  Defines an `Anomaly` struct to represent a detected anomaly.  This makes it easier to work with and report anomalies.
*   **`parseFloat` Helper Function:**  Includes a helper function `parseFloat` to safely convert values from the InfluxDB response (which can be of type `float64`, `int64`, or `string`) to `float64`. This avoids common type assertion errors.
*    **Improved Query:** Query to detect the anomalies with group by and filter using a where clause to detect with time and host.
*   **Dependencies:** I added the dependencies to the top.

**To Run this Example:**

1.  **Install Dependencies:**
    ```bash
    go get github.com/influxdata/influxdb1-client/v2
    go get github.com/joho/godotenv
    ```
2.  **Install and Configure InfluxDB:**
    *   Follow the instructions from the Data Ingestion example.
3.  **Create a `.env` file (optional):**
    ```
    INFLUXDB_HOST=http://localhost:8086
    INFLUXDB_USER=admin
    INFLUXDB_PASSWORD=admin
    INFLUXDB_DB=ssmd_metrics
    CPU_THRESHOLD=80     # CPU usage threshold (percentage)
    DETECTION_INTERVAL=60  # Anomaly detection interval in seconds
    ```
4.  **Run the Anomaly Detection Engine:**
    ```bash
    go run main.go
    ```

**6. Prediction Engine (Go)**

This component uses historical data to predict future trends and potential issues.  Techniques:

*   **Time Series Forecasting:**  ARIMA (Autoregressive Integrated Moving Average), Exponential Smoothing (Holt-Winters).  Libraries like `gonum.org/v1/gonum/stat` might be helpful.
*   **Machine Learning (Regression):** Train a regression model (e.g., Linear Regression, Random Forest) to predict future metric values based on past values.  Go doesn't have a robust machine learning ecosystem, so you might consider using Python (with a Go wrapper) or a pre-trained model.
*   **Simple Trend Analysis:**  Identify upward or downward trends and extrapolate them into the future.

**Example (Conceptual - Requires External Libraries or Services):**

```go
// This is a very simplified example and requires significant expansion
func predictCPUUsage(hostname string, history []float64) (float64, error) {
    // Very basic linear extrapolation (NOT production-ready!)
    if len(history) < 2 {
        return 0, fmt.Errorf("not enough historical data")
    }

    lastValue := history[len(history)-1]
    previousValue := history[len(history)-2]
    trend := lastValue - previousValue

    predictedValue := lastValue + trend  // Projecting the trend

    //Clamp to ensure not negative or > 100
    if predictedValue < 0 {
        predictedValue = 0
    } else if predictedValue > 100 {
        predictedValue = 100
    }

    return predictedValue, nil
}
```

**7. Alerting Engine (Go)**

This component receives anomalies and predictions and generates alerts.

*   **Alerting Channels:**  Email, Slack, PagerDuty, SMS, etc.
*   **Alert Escalation:**  If an issue persists, escalate the alert to a higher-level engineer.
*   **Alert Suppression:**  Prevent duplicate alerts for the same issue.
*   **Configuration:** Allow users to configure alert thresholds, notification channels, and escalation policies.

```go
package main

import (
	"fmt"
	"log"
	"os"
	"time"

	"github.com/joho/godotenv"
	"gopkg.in/gomail.v2"
)

// Alert represents an alert to be sent
type Alert struct {
	Hostname  string
	Metric    string
	Value     float64
	Timestamp time.Time
	Message   string
}

var (
	smtpHost     string
	smtpPort     int
	smtpUsername string
	smtpPassword string
	senderEmail  string
	receiverEmail string
)

func initConfig() error {
	smtpHost = os.Getenv("SMTP_HOST")
	if smtpHost == "" {
		return fmt.Errorf("SMTP_HOST not set")
	}

	portStr := os.Getenv("SMTP_PORT")
	if portStr == "" {
		return fmt.Errorf("SMTP_PORT not set")
	}
	var err error
	smtpPort, err = ParseInt(portStr)
	if err != nil {
		return fmt.Errorf("invalid SMTP_PORT: %w", err)
	}

	smtpUsername = os.Getenv("SMTP_USERNAME")
	if smtpUsername == "" {
		return fmt.Errorf("SMTP_USERNAME not set")
	}

	smtpPassword = os.Getenv("SMTP_PASSWORD")
	if smtpPassword == "" {
		return fmt.Errorf("SMTP_PASSWORD not set")
	}

	senderEmail = os.Getenv("SENDER_EMAIL")
	if senderEmail == "" {
		return fmt.Errorf("SENDER_EMAIL not set")
	}

	receiverEmail = os.Getenv("RECEIVER_EMAIL")
	if receiverEmail == "" {
		return fmt.Errorf("RECEIVER_EMAIL not set")
	}

	return nil
}

// sendEmail sends an email alert
func sendEmail(alert Alert) error {
	m := gomail.NewMessage()
	m.SetHeader("From", senderEmail)
	m.SetHeader("To", receiverEmail)
	m.SetHeader("Subject", fmt.Sprintf("SSMD Alert: High %s on %s", alert.Metric, alert.Hostname))
	m.SetBody("text/plain", alert.Message)

	d := gomail.NewDialer(smtpHost, smtpPort, smtpUsername, smtpPassword)

	if err := d.DialAndSend(m); err != nil {
		return fmt.Errorf("failed to send email: %w", err)
	}

	log.Printf("Email sent to %s", receiverEmail)
	return nil
}

// processAlert receives an alert and sends it via email
func processAlert(alert Alert) {
	log.Printf("Processing alert: Hostname=%s, Metric=%s, Value=%.2f, Timestamp=%s, Message=%s", alert.Hostname, alert.Metric, alert.Value, alert.Timestamp.String(), alert.Message)

	err := sendEmail(alert)
	if err != nil {
		log.Printf("Error sending alert: %v", err)
	}
}

// ParseInt helper function to Parse String to Int
func ParseInt(value string) (int, error) {
	result, err := strconv.ParseInt(value, 10, 64)
	return int(result), err
}

func main() {
	// Load .env file
	err := godotenv.Load()
	if err != nil {
		log.Printf("Error loading .env file: %v", err)
	}

	// Initialize the configuration
	err = initConfig()
	if err != nil {
		log.Fatalf("Error initializing configuration: %v", err)
	}

	// Example Usage:  (replace with actual anomaly detection integration)
	exampleAlert := Alert{
		Hostname:  "server1",
		Metric:    "CPU",
		Value:     95.0,
		Timestamp: time.Now(),
		Message:   "CPU usage is above 95%",
	}

	processAlert(exampleAlert)

	// Simulate receiving alerts from anomaly detection engine
	// In a real system, you'd have a mechanism (e.g., a channel, a queue) to receive alerts from the anomaly detection engine

	log.Println("Alerting engine started.  Waiting for alerts...")

	// Keep the program running
	for {
		time.Sleep(time