1use anyhow::Result;
12use async_trait::async_trait;
13use chrono::{DateTime, Utc};
14use std::collections::HashMap;
15use uuid::Uuid;
16
17use super::types::{
18 AWSSageMakerService, CloudService, ClusterStatus, CostEstimate, CostOptimizationResult,
19 CostOptimizationStrategy, DeploymentConfig, DeploymentInfo, DeploymentMetrics,
20 DeploymentResult, DeploymentStatus, EndpointInfo, EndpointStatus, FunctionInvocationResult,
21 GPUClusterConfig, GPUClusterResult, ImplementationEffort, OptimizationAction,
22 OptimizationPhase, PerformanceTier, ScalingResult, ScalingStatus, ServerlessDeploymentResult,
23 ServerlessFunctionConfig, ServerlessStatus, StorageConfig, StoragePerformanceMetrics,
24 StorageResult, StorageStatus, StorageType, UpdateResult, UpdateStatus,
25};
26
27#[async_trait]
28impl CloudService for AWSSageMakerService {
29 async fn deploy_model(
30 &self,
31 __deployment_config: &DeploymentConfig,
32 ) -> Result<DeploymentResult> {
33 let deployment_id = Uuid::new_v4().to_string();
34 Ok(DeploymentResult {
35 deployment_id,
36 status: DeploymentStatus::Creating,
37 endpoint_url: None,
38 estimated_completion: Some(Utc::now() + chrono::Duration::minutes(10)),
39 cost_estimate: Some(CostEstimate {
40 setup_cost_usd: 0.0,
41 hourly_cost_usd: 1.50,
42 storage_cost_usd_per_gb: 0.10,
43 data_transfer_cost_usd_per_gb: 0.05,
44 estimated_monthly_cost_usd: 1080.0,
45 }),
46 metadata: HashMap::new(),
47 })
48 }
49 async fn get_endpoint(&self, deployment_id: &str) -> Result<EndpointInfo> {
50 Ok(EndpointInfo {
51 deployment_id: deployment_id.to_string(),
52 endpoint_url: format!(
53 "https://runtime.sagemaker.{}.amazonaws.com/endpoints/{}/invocations",
54 self.region, deployment_id
55 ),
56 status: EndpointStatus::InService,
57 instance_type: "ml.m5.large".to_string(),
58 instance_count: 1,
59 auto_scaling_enabled: true,
60 creation_time: Utc::now(),
61 last_modified_time: Utc::now(),
62 model_data_url: None,
63 })
64 }
65 async fn scale_deployment(
66 &self,
67 deployment_id: &str,
68 target_instances: u32,
69 ) -> Result<ScalingResult> {
70 Ok(ScalingResult {
71 deployment_id: deployment_id.to_string(),
72 previous_instance_count: 1,
73 target_instance_count: target_instances,
74 scaling_status: ScalingStatus::InProgress,
75 estimated_completion: Some(Utc::now() + chrono::Duration::minutes(5)),
76 })
77 }
78 async fn get_metrics(
79 &self,
80 deployment_id: &str,
81 time_range: (DateTime<Utc>, DateTime<Utc>),
82 ) -> Result<DeploymentMetrics> {
83 Ok(DeploymentMetrics {
84 deployment_id: deployment_id.to_string(),
85 time_range,
86 invocations: 1500,
87 average_latency_ms: 45.2,
88 error_rate: 0.02,
89 throughput_per_second: 25.3,
90 cpu_utilization: 65.5,
91 memory_utilization: 78.2,
92 network_in_mb: 123.4,
93 network_out_mb: 98.7,
94 costs: HashMap::from([
95 ("compute".to_string(), 15.75),
96 ("storage".to_string(), 2.30),
97 ("data_transfer".to_string(), 0.85),
98 ]),
99 })
100 }
101 async fn update_deployment(
102 &self,
103 deployment_id: &str,
104 config: &DeploymentConfig,
105 ) -> Result<UpdateResult> {
106 Ok(UpdateResult {
107 deployment_id: deployment_id.to_string(),
108 update_status: UpdateStatus::InProgress,
109 previous_config: config.clone(),
110 new_config: config.clone(),
111 estimated_completion: Some(Utc::now() + chrono::Duration::minutes(8)),
112 })
113 }
114 async fn delete_deployment(&self, deployment_id: &str) -> Result<()> {
115 println!("Deleting AWS SageMaker deployment: {}", deployment_id);
116 Ok(())
117 }
118 async fn list_deployments(&self) -> Result<Vec<DeploymentInfo>> {
119 Ok(vec![DeploymentInfo {
120 deployment_id: "sagemaker-endpoint-1".to_string(),
121 name: "embedding-model-prod".to_string(),
122 status: DeploymentStatus::InService,
123 model_name: "TransE-v1.0".to_string(),
124 instance_type: "ml.m5.large".to_string(),
125 instance_count: 2,
126 creation_time: Utc::now() - chrono::Duration::hours(24),
127 last_modified_time: Utc::now() - chrono::Duration::hours(2),
128 }])
129 }
130 async fn estimate_costs(
131 &self,
132 config: &DeploymentConfig,
133 _duration_hours: u32,
134 ) -> Result<CostEstimate> {
135 let hourly_rate = match config.instance_type.as_str() {
136 "ml.t3.medium" => 0.0464,
137 "ml.m5.large" => 0.115,
138 "ml.m5.xlarge" => 0.23,
139 "ml.c5.2xlarge" => 0.408,
140 "ml.p3.2xlarge" => 3.825,
141 _ => 0.115,
142 };
143 Ok(CostEstimate {
144 setup_cost_usd: 0.0,
145 hourly_cost_usd: hourly_rate * config.initial_instance_count as f64,
146 storage_cost_usd_per_gb: 0.125,
147 data_transfer_cost_usd_per_gb: 0.09,
148 estimated_monthly_cost_usd: hourly_rate
149 * config.initial_instance_count as f64
150 * 24.0
151 * 30.0,
152 })
153 }
154 async fn deploy_serverless_function(
155 &self,
156 function_config: &ServerlessFunctionConfig,
157 ) -> Result<ServerlessDeploymentResult> {
158 let function_arn = format!(
159 "arn:aws:lambda:{}:123456789012:function:{}",
160 self.region, function_config.function_name
161 );
162 Ok(ServerlessDeploymentResult {
163 function_arn,
164 function_name: function_config.function_name.clone(),
165 status: ServerlessStatus::Pending,
166 invoke_url: Some(format!(
167 "https://{}.lambda-url.{}.on.aws/",
168 function_config.function_name, self.region
169 )),
170 version: "1".to_string(),
171 last_modified: Utc::now(),
172 })
173 }
174 async fn invoke_function(
175 &self,
176 _function_name: &str,
177 payload: &[u8],
178 ) -> Result<FunctionInvocationResult> {
179 let execution_duration = 150 + (payload.len() / 1000) as u32;
180 Ok(FunctionInvocationResult {
181 execution_duration_ms: execution_duration,
182 billed_duration_ms: ((execution_duration + 99) / 100) * 100,
183 memory_used_mb: 128,
184 max_memory_used_mb: 256,
185 response_payload:
186 b"{\"statusCode\": 200, \"body\": \"Function executed successfully\"}".to_vec(),
187 log_result: Some(
188 "START RequestId: 123\nEND RequestId: 123\nREPORT RequestId: 123\tDuration: 150ms"
189 .to_string(),
190 ),
191 status_code: 200,
192 })
193 }
194 async fn create_gpu_cluster(
195 &self,
196 cluster_config: &GPUClusterConfig,
197 ) -> Result<GPUClusterResult> {
198 let cluster_id = format!("eks-gpu-{}", Uuid::new_v4());
199 let hourly_cost = match cluster_config.gpu_type.as_str() {
200 "V100" => 3.06 * cluster_config.min_nodes as f64,
201 "A100" => 4.50 * cluster_config.min_nodes as f64,
202 "T4" => 1.35 * cluster_config.min_nodes as f64,
203 _ => 2.00 * cluster_config.min_nodes as f64,
204 };
205 Ok(GPUClusterResult {
206 cluster_id,
207 cluster_name: cluster_config.cluster_name.clone(),
208 status: ClusterStatus::Creating,
209 endpoint: format!(
210 "https://{}.eks.{}.amazonaws.com",
211 cluster_config.cluster_name, self.region
212 ),
213 node_count: cluster_config.min_nodes,
214 total_gpu_count: cluster_config.min_nodes * cluster_config.gpu_count_per_node,
215 creation_time: Utc::now(),
216 estimated_hourly_cost: hourly_cost,
217 })
218 }
219 async fn manage_storage(&self, storage_config: &StorageConfig) -> Result<StorageResult> {
220 let storage_id = format!("s3-{}", Uuid::new_v4());
221 let monthly_cost = match storage_config.storage_type {
222 StorageType::ObjectStorage => storage_config.capacity_gb as f64 * 0.023,
223 StorageType::BlockStorage => storage_config.capacity_gb as f64 * 0.10,
224 StorageType::FileStorage => storage_config.capacity_gb as f64 * 0.30,
225 StorageType::DataLake => storage_config.capacity_gb as f64 * 0.021,
226 };
227 let performance_metrics = match storage_config.performance_tier {
228 PerformanceTier::Standard => StoragePerformanceMetrics {
229 read_iops: 3000,
230 write_iops: 3000,
231 throughput_mbps: 125,
232 latency_ms: 10.0,
233 },
234 PerformanceTier::HighPerformance => StoragePerformanceMetrics {
235 read_iops: 16000,
236 write_iops: 16000,
237 throughput_mbps: 1000,
238 latency_ms: 1.0,
239 },
240 _ => StoragePerformanceMetrics {
241 read_iops: 100,
242 write_iops: 100,
243 throughput_mbps: 12,
244 latency_ms: 100.0,
245 },
246 };
247 Ok(StorageResult {
248 storage_id,
249 endpoint: format!(
250 "s3://{}-bucket-{}.s3.{}.amazonaws.com",
251 storage_config.storage_type.clone() as u8,
252 Uuid::new_v4(),
253 self.region
254 ),
255 status: StorageStatus::Creating,
256 actual_capacity_gb: storage_config.capacity_gb,
257 monthly_cost_estimate: monthly_cost,
258 performance_metrics,
259 })
260 }
261 async fn optimize_costs(
262 &self,
263 optimization_config: &CostOptimizationStrategy,
264 ) -> Result<CostOptimizationResult> {
265 let mut actions = Vec::new();
266 let mut total_savings = 0.0;
267 if optimization_config.use_spot_instances {
268 actions.push(OptimizationAction {
269 action_type: "Spot Instances".to_string(),
270 description: format!(
271 "Use spot instances for {}% of workload",
272 optimization_config.spot_instance_percentage * 100.0
273 ),
274 estimated_savings_usd: 500.0,
275 implementation_effort: ImplementationEffort::Medium,
276 });
277 total_savings += 500.0;
278 }
279 if optimization_config.use_reserved_instances {
280 actions.push(OptimizationAction {
281 action_type: "Reserved Instances".to_string(),
282 description: format!(
283 "Purchase reserved instances for {}% of workload",
284 optimization_config.reserved_instance_percentage * 100.0
285 ),
286 estimated_savings_usd: 800.0,
287 implementation_effort: ImplementationEffort::Low,
288 });
289 total_savings += 800.0;
290 }
291 if optimization_config.rightsizing_enabled {
292 actions.push(OptimizationAction {
293 action_type: "Rightsizing".to_string(),
294 description: "Optimize instance sizes based on usage patterns".to_string(),
295 estimated_savings_usd: 300.0,
296 implementation_effort: ImplementationEffort::Medium,
297 });
298 total_savings += 300.0;
299 }
300 let implementation_timeline = vec![
301 OptimizationPhase {
302 phase_name: "Quick Wins".to_string(),
303 duration_days: 7,
304 actions: vec!["Reserved Instance Purchase".to_string()],
305 expected_savings_usd: 800.0,
306 },
307 OptimizationPhase {
308 phase_name: "Medium Term".to_string(),
309 duration_days: 30,
310 actions: vec![
311 "Spot Instance Implementation".to_string(),
312 "Rightsizing".to_string(),
313 ],
314 expected_savings_usd: 800.0,
315 },
316 ];
317 Ok(CostOptimizationResult {
318 estimated_monthly_savings_usd: total_savings,
319 optimization_actions_taken: actions,
320 potential_risks: vec![
321 "Spot instances may be interrupted".to_string(),
322 "Reserved instances require upfront commitment".to_string(),
323 ],
324 implementation_timeline,
325 })
326 }
327}