Automated Server Retrieval and Analysis

This article demonstrates how to automate the retrieval and analysis of server data from Clore’s API, providing insights into server availability, resource metrics, and pricing for efficient marketplace management. This type of automation can be particularly useful for tasks like identifying underperforming servers, assessing pricing trends, and understanding demand.

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1. Fetching Server Data for Analysis

Retrieve all servers from the Clore marketplace API. Here, we filter only the active servers and extract key parameters like server name, specifications, and pricing.

import requests

api_key = "YOUR_API_KEY"
marketplace_url = "https://api.clore.ai/v1/marketplace"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

def fetch_active_servers():
    response = requests.get(marketplace_url, headers=headers)
    data = response.json()
    active_servers = [server for server in data.get("servers", []) if server["online"]]
    return active_servers

# Fetch and display active server data
servers = fetch_active_servers()
for server in servers:
    print(f"Server: {server['name']}, Specs: {server['specs']}, Price: {server['pricing']}")

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2. Analyzing Server Specifications and Resource Allocation

This code segment retrieves and categorizes servers based on GPU memory and CPU cores, helping prioritize high-performance servers for specific tasks.

def analyze_server_specs(servers):
    high_memory_servers = [server for server in servers if server["specs"]["gpuram"] > 16]
    high_cpu_servers = [server for server in servers if server["specs"]["cpus"] >= "8/16"]

    print("Servers with High GPU Memory:")
    for server in high_memory_servers:
        print(f"Name: {server['name']}, GPU Memory: {server['specs']['gpuram']} GB")

    print("\nServers with High CPU Capacity:")
    for server in high_cpu_servers:
        print(f"Name: {server['name']}, CPU: {server['specs']['cpus']}")

# Analyze specifications of fetched servers
analyze_server_specs(servers)

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3. Tracking and Predicting Server Pricing Trends

Use past data to predict upcoming pricing trends. This analysis helps to identify optimal times for leasing.

import numpy as np
from sklearn.linear_model import LinearRegression

def predict_pricing_trends(server_id, historical_prices):
    X = np.array(range(len(historical_prices))).reshape(-1, 1)
    y = np.array(historical_prices)

    model = LinearRegression()
    model.fit(X, y)
    predicted_price = model.predict([[len(historical_prices) + 1]])
    print(f"Predicted next price for server {server_id}: {predicted_price[0]}")

# Example data: historical prices for a given server
historical_prices = [0.0001, 0.00012, 0.00015, 0.00017, 0.0002]
predict_pricing_trends("server_123", historical_prices)

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4. Identifying Underutilized Servers

Servers that are consistently idle may need to be adjusted or repriced. This function flags such servers.

def identify_underutilized_servers(servers):
    underutilized = [server for server in servers if not server["rented"]]
    
    print("Underutilized Servers:")
    for server in underutilized:
        print(f"Server: {server['name']}, Last rented: {server.get('last_rented', 'N/A')}")
        
# Identify servers that are currently underutilized
identify_underutilized_servers(servers)

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5. Automated Server Analysis Report Generation

Generate reports that summarize server statistics, giving insights into resource allocation, utilization, and profitability.

import json

def generate_server_report(servers):
    report = {
        "total_servers": len(servers),
        "high_memory_servers": len([s for s in servers if s["specs"]["gpuram"] > 16]),
        "high_cpu_servers": len([s for s in servers if s["specs"]["cpus"] >= "8/16"]),
        "underutilized_servers": len([s for s in servers if not s["rented"]]),
    }

    print("Server Analysis Report:")
    print(json.dumps(report, indent=4))

# Generate and display a server report
generate_server_report(servers)

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6. Automated Spot Price Adjustment Based on Demand Trends

Automatically adjust spot pricing based on demand trends, ensuring competitive pricing.

spot_price_url = "https://api.clore.ai/v1/set_spot_price"

def adjust_price_dynamically(server_id, base_price, demand_factor):
    adjusted_price = base_price * demand_factor
    payload = {"order_id": server_id, "desired_price": adjusted_price}
    response = requests.post(spot_price_url, headers=headers, json=payload)
    print(f"Adjusted spot price for server {server_id} to {adjusted_price}")
    
# Example: Adjust price dynamically based on a demand factor of 1.2
for server in servers:
    adjust_price_dynamically(server["id"], server["pricing"]["usd"], demand_factor=1.2)

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7. Performance Analysis of Server Cloning for High-Demand

For servers in high demand, clone and assess the impact on performance and revenue.

def clone_high_demand_servers(servers, demand_threshold):
    for server in servers:
        if server["rental_status"] == 2 and server["pricing"]["usd"] >= demand_threshold:
            new_server_id = clone_server(server["id"])
            print(f"Cloned server {server['name']} for high demand with new ID: {new_server_id}")

def clone_server(server_id):
    clone_url = f"https://api.clore.ai/v1/clone_server/{server_id}"
    response = requests.post(clone_url, headers=headers)
    return response.json().get("new_server_id")

# Clone servers above a demand threshold
clone_high_demand_servers(servers, demand_threshold=0.00015)

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8. Resource Utilization Optimization Based on Server Specs

Identify the most cost-effective servers for resource-heavy operations based on specifications.

def optimize_for_heavy_tasks(servers):
    optimal_servers = [
        server for server in servers
        if server["specs"]["gpuram"] > 16 and server["specs"]["cpus"] >= "8/16"
    ]
    for server in optimal_servers:
        print(f"Optimal server for heavy tasks: {server['name']}, Specs: {server['specs']}")

# Find optimal servers for resource-intensive tasks
optimize_for_heavy_tasks(servers)

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These code snippets offer automation strategies for server retrieval and analysis, providing insights that help streamline resource usage and optimize operations on Clore’s marketplace. Through these techniques, developers can maximize performance and profitability while maintaining an efficient, cost-effective infrastructure.