use anyhow::Result;
use async_trait::async_trait;
use chrono::{DateTime, Utc};
use std::collections::HashMap;
use uuid::Uuid;
use super::types::{
AzureMLService, CloudService, ClusterStatus, CostEstimate, CostOptimizationResult,
CostOptimizationStrategy, DeploymentConfig, DeploymentInfo, DeploymentMetrics,
DeploymentResult, DeploymentStatus, EndpointInfo, EndpointStatus, FunctionInvocationResult,
GPUClusterConfig, GPUClusterResult, ImplementationEffort, OptimizationAction,
OptimizationPhase, PerformanceTier, ScalingResult, ScalingStatus, ServerlessDeploymentResult,
ServerlessFunctionConfig, ServerlessStatus, StorageConfig, StoragePerformanceMetrics,
StorageResult, StorageStatus, StorageType, UpdateResult, UpdateStatus,
};
#[async_trait]
impl CloudService for AzureMLService {
async fn deploy_model(
&self,
_deployment_config: &DeploymentConfig,
) -> Result<DeploymentResult> {
let deployment_id = format!("azure-{}", Uuid::new_v4());
Ok(DeploymentResult {
deployment_id,
status: DeploymentStatus::Creating,
endpoint_url: None,
estimated_completion: Some(Utc::now() + chrono::Duration::minutes(12)),
cost_estimate: Some(CostEstimate {
setup_cost_usd: 0.0,
hourly_cost_usd: 1.20,
storage_cost_usd_per_gb: 0.15,
data_transfer_cost_usd_per_gb: 0.08,
estimated_monthly_cost_usd: 864.0,
}),
metadata: HashMap::new(),
})
}
async fn get_endpoint(&self, deployment_id: &str) -> Result<EndpointInfo> {
Ok(EndpointInfo {
deployment_id: deployment_id.to_string(),
endpoint_url: format!(
"https://{}.{}.inference.ml.azure.com/score",
deployment_id, self.workspace_name
),
status: EndpointStatus::InService,
instance_type: "Standard_DS3_v2".to_string(),
instance_count: 1,
auto_scaling_enabled: true,
creation_time: Utc::now(),
last_modified_time: Utc::now(),
model_data_url: None,
})
}
async fn scale_deployment(
&self,
deployment_id: &str,
target_instances: u32,
) -> Result<ScalingResult> {
Ok(ScalingResult {
deployment_id: deployment_id.to_string(),
previous_instance_count: 1,
target_instance_count: target_instances,
scaling_status: ScalingStatus::InProgress,
estimated_completion: Some(Utc::now() + chrono::Duration::minutes(7)),
})
}
async fn get_metrics(
&self,
deployment_id: &str,
time_range: (DateTime<Utc>, DateTime<Utc>),
) -> Result<DeploymentMetrics> {
Ok(DeploymentMetrics {
deployment_id: deployment_id.to_string(),
time_range,
invocations: 1200,
average_latency_ms: 52.8,
error_rate: 0.015,
throughput_per_second: 20.1,
cpu_utilization: 58.3,
memory_utilization: 71.9,
network_in_mb: 89.2,
network_out_mb: 76.5,
costs: HashMap::from([
("compute".to_string(), 12.60),
("storage".to_string(), 3.20),
("data_transfer".to_string(), 1.10),
]),
})
}
async fn update_deployment(
&self,
deployment_id: &str,
config: &DeploymentConfig,
) -> Result<UpdateResult> {
Ok(UpdateResult {
deployment_id: deployment_id.to_string(),
update_status: UpdateStatus::InProgress,
previous_config: config.clone(),
new_config: config.clone(),
estimated_completion: Some(Utc::now() + chrono::Duration::minutes(10)),
})
}
async fn delete_deployment(&self, deployment_id: &str) -> Result<()> {
println!("Deleting Azure ML deployment: {}", deployment_id);
Ok(())
}
async fn list_deployments(&self) -> Result<Vec<DeploymentInfo>> {
Ok(vec![])
}
async fn estimate_costs(
&self,
config: &DeploymentConfig,
_duration_hours: u32,
) -> Result<CostEstimate> {
let hourly_rate = match config.instance_type.as_str() {
"Standard_DS2_v2" => 0.14,
"Standard_DS3_v2" => 0.28,
"Standard_DS4_v2" => 0.56,
"Standard_NC6s_v3" => 3.06,
_ => 0.28,
};
Ok(CostEstimate {
setup_cost_usd: 0.0,
hourly_cost_usd: hourly_rate * config.initial_instance_count as f64,
storage_cost_usd_per_gb: 0.184,
data_transfer_cost_usd_per_gb: 0.087,
estimated_monthly_cost_usd: hourly_rate
* config.initial_instance_count as f64
* 24.0
* 30.0,
})
}
async fn deploy_serverless_function(
&self,
function_config: &ServerlessFunctionConfig,
) -> Result<ServerlessDeploymentResult> {
let function_arn = format!(
"/subscriptions/{}/resourceGroups/{}/providers/Microsoft.Web/sites/{}/functions/{}",
self.subscription_id,
self.resource_group,
self.workspace_name,
function_config.function_name
);
Ok(ServerlessDeploymentResult {
function_arn,
function_name: function_config.function_name.clone(),
status: ServerlessStatus::Pending,
invoke_url: Some(format!(
"https://{}.azurewebsites.net/api/{}",
self.workspace_name, function_config.function_name
)),
version: "1".to_string(),
last_modified: Utc::now(),
})
}
async fn invoke_function(
&self,
function_name: &str,
payload: &[u8],
) -> Result<FunctionInvocationResult> {
let execution_duration = 180 + (payload.len() / 800) as u32;
Ok(FunctionInvocationResult {
execution_duration_ms: execution_duration,
billed_duration_ms: execution_duration,
memory_used_mb: 256,
max_memory_used_mb: 512,
response_payload:
b"{\"status\": \"success\", \"message\": \"Azure function executed\"}".to_vec(),
log_result: Some(format!(
"Executing '{function_name}' (ID: azure-123, Duration: {execution_duration}ms)"
)),
status_code: 200,
})
}
async fn create_gpu_cluster(
&self,
cluster_config: &GPUClusterConfig,
) -> Result<GPUClusterResult> {
let cluster_id = format!("aks-gpu-{}", Uuid::new_v4());
let hourly_cost = match cluster_config.gpu_type.as_str() {
"V100" => 2.84 * cluster_config.min_nodes as f64,
"A100" => 4.25 * cluster_config.min_nodes as f64,
"T4" => 1.28 * cluster_config.min_nodes as f64,
_ => 1.90 * cluster_config.min_nodes as f64,
};
Ok(GPUClusterResult {
cluster_id,
cluster_name: cluster_config.cluster_name.clone(),
status: ClusterStatus::Creating,
endpoint: format!(
"https://{}-{}.hcp.eastus.azmk8s.io:443",
cluster_config.cluster_name, self.resource_group
),
node_count: cluster_config.min_nodes,
total_gpu_count: cluster_config.min_nodes * cluster_config.gpu_count_per_node,
creation_time: Utc::now(),
estimated_hourly_cost: hourly_cost,
})
}
async fn manage_storage(&self, storage_config: &StorageConfig) -> Result<StorageResult> {
let storage_id = format!("azure-storage-{}", Uuid::new_v4());
let monthly_cost = match storage_config.storage_type {
StorageType::ObjectStorage => storage_config.capacity_gb as f64 * 0.0208,
StorageType::BlockStorage => storage_config.capacity_gb as f64 * 0.175,
StorageType::FileStorage => storage_config.capacity_gb as f64 * 0.60,
StorageType::DataLake => storage_config.capacity_gb as f64 * 0.0208,
};
let performance_metrics = match storage_config.performance_tier {
PerformanceTier::Standard => StoragePerformanceMetrics {
read_iops: 2300,
write_iops: 2300,
throughput_mbps: 150,
latency_ms: 15.0,
},
PerformanceTier::HighPerformance => StoragePerformanceMetrics {
read_iops: 20000,
write_iops: 20000,
throughput_mbps: 900,
latency_ms: 2.0,
},
_ => StoragePerformanceMetrics {
read_iops: 100,
write_iops: 100,
throughput_mbps: 10,
latency_ms: 120.0,
},
};
Ok(StorageResult {
storage_id: storage_id.clone(),
endpoint: format!("https://{storage_id}.blob.core.windows.net/"),
status: StorageStatus::Creating,
actual_capacity_gb: storage_config.capacity_gb,
monthly_cost_estimate: monthly_cost,
performance_metrics,
})
}
async fn optimize_costs(
&self,
optimization_config: &CostOptimizationStrategy,
) -> Result<CostOptimizationResult> {
let mut actions = Vec::new();
let mut total_savings = 0.0;
if optimization_config.use_spot_instances {
actions.push(OptimizationAction {
action_type: "Spot Virtual Machines".to_string(),
description: format!(
"Use Azure spot VMs for {}% of workload",
optimization_config.spot_instance_percentage * 100.0
),
estimated_savings_usd: 450.0,
implementation_effort: ImplementationEffort::Medium,
});
total_savings += 450.0;
}
if optimization_config.use_reserved_instances {
actions.push(OptimizationAction {
action_type: "Azure Reserved VM Instances".to_string(),
description: format!(
"Purchase 1-year or 3-year reservations for {}% of workload",
optimization_config.reserved_instance_percentage * 100.0
),
estimated_savings_usd: 720.0,
implementation_effort: ImplementationEffort::Low,
});
total_savings += 720.0;
}
if optimization_config.use_savings_plans {
actions.push(OptimizationAction {
action_type: "Azure Savings Plans".to_string(),
description: "Commit to consistent compute usage with savings plans".to_string(),
estimated_savings_usd: 250.0,
implementation_effort: ImplementationEffort::Low,
});
total_savings += 250.0;
}
if optimization_config.rightsizing_enabled {
actions.push(OptimizationAction {
action_type: "VM Rightsizing".to_string(),
description: "Optimize VM sizes based on Azure Advisor recommendations".to_string(),
estimated_savings_usd: 280.0,
implementation_effort: ImplementationEffort::Medium,
});
total_savings += 280.0;
}
let implementation_timeline = vec![
OptimizationPhase {
phase_name: "Immediate Actions".to_string(),
duration_days: 5,
actions: vec![
"Reserved Instances Purchase".to_string(),
"Savings Plans".to_string(),
],
expected_savings_usd: 970.0,
},
OptimizationPhase {
phase_name: "Implementation Phase".to_string(),
duration_days: 21,
actions: vec![
"Spot VM Migration".to_string(),
"VM Rightsizing".to_string(),
],
expected_savings_usd: 730.0,
},
];
Ok(CostOptimizationResult {
estimated_monthly_savings_usd: total_savings,
optimization_actions_taken: actions,
potential_risks: vec![
"Spot VMs may experience eviction".to_string(),
"Reserved instances require upfront payment".to_string(),
"Rightsizing may temporarily impact performance".to_string(),
],
implementation_timeline,
})
}
}