Merge pull request #23 from kleis-technology/feature/trusted-lib-ademe

feature/trusted lib ademe

README.md

@@ -260,35 +260,30 @@ but nothing stops you from assessing as many virtual machines as you want.
 The ambition of Cloud Assess is to offer a library of *transparent*,
 *PCR-compliant* and *executable* LCA models in the sector of digital services.
 More precisely, this work builds on the
-existing [PCR](https://codde.fr/wp-content/uploads/2023/01/referentiel_rcp_datacenter_services_cloud.pdf)
-for data center and cloud services.
-The PCR defines 11 functional units, covering the hosting infrastructure (physical datacenter) up to
-more abstract FUs, e.g., Software Services.
-Cloud Assess aims at covering all functional units, but also FU that are not explicitly
-covered by the PCR
-
-| PCR No. | Functional Unit       | Status      |
-|---------|-----------------------|-------------|
-| 1       | Datacenter            | in progress |
-| 2       | Physical server       | in progress |
-| 3       | Storage               | in progress |
-| 4       | Network equipment     | in progress |
-| 5       | Computing resource    | in progress |
-| 6       | Virtual machine       | ✅           |
-| 7       | Database              | in progress |
-| 8       | Block storage         | in progress |
-| 9       | Platform as a Service | planned     |
-| 10      | Function as a Service | planned     |
-| 11      | Software as a Service | planned     |
+new PCR (to be published soon) for data center and cloud services.
+The PCR defines 7 functional units, covering the hosting infrastructure (physical datacenter) up to
+more abstract ones, e.g., virtual machines. 
+Cloud Assess aims at covering all functional units, but also FU that are not explicitly covered by the PCR
+
+| PCR No. | Functional Unit    | Model | API endpoint |
+|---------|--------------------|-------|--------------|
+| 1       | Datacenter         | n/a   | n/a          |
+| 2       | Physical server    | ✅     | planned      |
+| 3       | Storage            | ✅     | planned      |
+| 4       | Network equipment  | ✅     | planned      |
+| 5       | Computing resource | ✅     | planned      |
+| 6       | Virtual machine    | ✅     | ✅            |
+| 7       | Block storage      | ✅     | planned      |
 
 These models are specified under the folder `trusted_library`.
 
 The library also contains intermediate/generic models, e.g.,
 
-| Functional unit            | Status |
-|----------------------------|--------|
-| Pool of servers            | ✅      |
-| Pool of network equipments | ✅      |
+| Functional unit            | Model |
+|----------------------------|-------|
+| Pool of servers            | ✅     |
+| Pool of storage equipments | ✅     |
+| Pool of network equipments | ✅     |
 
 which can be used for specific purposes, e.g., a mail service running on a dedicated infrastructure.
 

build.gradle.kts

@@ -11,7 +11,7 @@ plugins {
 }
 
 group = "org.cloud_assess"
-version = "1.7.3"
+version = "1.7.4"
 
 java {
     sourceCompatibility = JavaVersion.VERSION_17
@@ -30,7 +30,7 @@ repositories {
 }
 
 dependencies {
-    val lcaacVersion = "1.7.3"
+    val lcaacVersion = "1.7.10"
     implementation("ch.kleis.lcaac:core:$lcaacVersion")
     implementation("ch.kleis.lcaac:grammar:$lcaacVersion")
 
@@ -62,7 +62,7 @@ tasks.withType<KotlinCompile> {
 }
 
 tasks.withType<Test> {
-    useJUnitPlatform() {
+    useJUnitPlatform  {
         excludeTags("Performance")
     }
 }

openapi/api.yaml

@@ -192,13 +192,10 @@ components:
       type: object
       required:
         - id
-        - service_level
         - meta
       properties:
         id:
           type: string
-        service_level:
-          $ref: '#/components/schemas/QuantityDimensionlessDto'
         meta:
           type: object
           default: { }
@@ -233,6 +230,7 @@ components:
       required:
         - id
         - pool_id
+        - quantity
         - ram
         - storage
         - vcpu
@@ -242,6 +240,8 @@ components:
           type: string
         pool_id:
           type: string
+        quantity:
+          $ref: '#/components/schemas/QuantityDimensionlessDto'
         ram:
           $ref: '#/components/schemas/QuantityMemoryDto'
         storage:

samples/virtual_machines.http

@@ -10,6 +10,10 @@ Content-Type: application/json
     {
       "id": "vm-01",
       "pool_id": "client_vm",
+      "quantity": {
+        "amount": 1.0,
+        "unit": "u"
+      },
       "ram": {
         "amount": 4.0,
         "unit": "GB"
@@ -30,6 +34,10 @@ Content-Type: application/json
     {
       "id": "vm-02",
       "pool_id": "client_vm",
+      "quantity": {
+        "amount": 1.0,
+        "unit": "u"
+      },
       "ram": {
         "amount": 16.0,
         "unit": "GB"
@@ -50,6 +58,10 @@ Content-Type: application/json
     {
       "id": "vm-03",
       "pool_id": "client_vm",
+      "quantity": {
+        "amount": 1.0,
+        "unit": "u"
+      },
       "ram": {
         "amount": 8.0,
         "unit": "GB"

src/main/kotlin/org/cloud_assess/service/PoolService.kt

@@ -46,16 +46,14 @@ class PoolService(
             TimeUnitsDto.hour -> "${pools.period.amount} hour"
         }
         val cases = pools.pools.associate {
-            val serviceLevel = when (it.serviceLevel.unit) {
-                DimensionlessUnitsDto.u -> "${it.serviceLevel.amount} u"
-            }
+            // TODO: swagger: remove service level
             val content = """
                 process __main__ {
                     products {
                         1 u __main__
                     }
                     inputs {
-                        $period service from service(pool_id = "${it.id}", service_level = $serviceLevel)
+                        $period service from service(id = "${it.id}")
                     }
                 }
             """.trimIndent()

src/main/kotlin/org/cloud_assess/service/VirtualMachineService.kt

@@ -13,9 +13,12 @@ import ch.kleis.lcaac.core.lang.expression.*
 import ch.kleis.lcaac.core.lang.register.DataKey
 import ch.kleis.lcaac.core.lang.register.DataSourceRegister
 import ch.kleis.lcaac.core.lang.value.DataValue
+import ch.kleis.lcaac.core.lang.value.QuantityValue
 import ch.kleis.lcaac.core.lang.value.RecordValue
+import ch.kleis.lcaac.core.lang.value.UnitValue
 import ch.kleis.lcaac.core.math.basic.BasicNumber
 import ch.kleis.lcaac.core.math.basic.BasicOperations
+import ch.kleis.lcaac.core.prelude.Prelude
 import org.cloud_assess.dto.*
 import org.cloud_assess.model.ResourceAnalysis
 import org.springframework.beans.factory.annotation.Value
@@ -112,6 +115,7 @@ class VirtualMachineService(
                         "ram_size" to localEval(vm.ram.toDataExpression()),
                         "storage_size" to localEval(vm.storage.toDataExpression()),
                         "vcpu_size" to localEval(vm.vcpu.toDataExpression()),
+                        "quantity" to localEval(vm.quantity.toDataExpression()),
                     )
                 )
             }
@@ -144,4 +148,10 @@ class VirtualMachineService(
             VCPUUnitsDto.vCPU -> EQuantityScale(BasicNumber(this.amount), EDataRef("vCPU"))
         }
     }
+
+    private fun QuantityDimensionlessDto.toDataExpression(): DataExpression<BasicNumber> {
+        return when(this.unit) {
+            DimensionlessUnitsDto.u -> EQuantityScale(BasicNumber(this.amount), EDataRef("u"))
+        }
+    }
 }

src/test/kotlin/org/cloud_assess/CloudAssessApplicationTest.kt

@@ -36,7 +36,7 @@ class CloudAssessApplicationTest(
         val pools = PoolListDto(
             period = QuantityTimeDto(1.0, TimeUnitsDto.hour),
             pools = listOf(
-                DtoFixture.poolDto("client_vm", 1.0)
+                DtoFixture.poolDto("client_vm")
             )
         )
 
@@ -48,7 +48,7 @@ class CloudAssessApplicationTest(
             val pool = actual[id]!!
             assertThat(pool.period).isEqualTo(QuantityTimeDto(1.0, TimeUnitsDto.hour))
             val gwp = pool.contribution(Indicator.GWP)
-            assertThat(gwp.amount.value).isCloseTo( 5.71403, Percentage.withPercentage(1e-3))
+            assertThat(gwp.amount.value).isCloseTo(7.5770786, Percentage.withPercentage(1e-3))
             assertThat(gwp.unit).isEqualTo(
                 UnitValue<BasicNumber>(
                     UnitSymbol.of("kg CO2-Eq"),
@@ -78,7 +78,7 @@ class CloudAssessApplicationTest(
             val vm = actual[id]!!
             assertThat(vm.period).isEqualTo(QuantityTimeDto(1.0, TimeUnitsDto.hour))
             val gwp = vm.contribution(Indicator.GWP)
-            assertThat(gwp.amount.value).isCloseTo(0.971400, Percentage.withPercentage(1e-3))
+            assertThat(gwp.amount.value).isCloseTo(2.458826278, Percentage.withPercentage(1e-3))
             assertThat(gwp.unit).isEqualTo(
                 UnitValue<BasicNumber>(
                     UnitSymbol.of("kg CO2-Eq"),

src/test/kotlin/org/cloud_assess/fixtures/DtoFixture.kt

@@ -93,21 +93,16 @@ class DtoFixture {
             return PoolListDto(
                 period = quantityHour(1.0),
                 pools = listOf(
-                    poolDto("client_vm", 1.0),
+                    poolDto("client_vm"),
                 )
             )
         }
 
         fun poolDto(
             id: String = "client_vm",
-            serviceLevel: Double = 1.0,
         ): PoolDto {
             return PoolDto(
                 id = id,
-                serviceLevel = QuantityDimensionlessDto(
-                    amount = serviceLevel,
-                    unit = DimensionlessUnitsDto.u,
-                ),
                 meta = mapOf(
                     "region" to "FR",
                 ),
@@ -133,6 +128,7 @@ class DtoFixture {
             return VirtualMachineDto(
                 id = id,
                 poolId = poolId,
+                quantity = quantityDimensionless(1.0),
                 ram = quantityMemory(ram),
                 storage = quantityMemory(storage),
                 vcpu = quantityVCPU(vcpu),
@@ -146,6 +142,13 @@ class DtoFixture {
             return QuantityVCPUDto(amount, VCPUUnitsDto.vCPU)
         }
 
+        private fun quantityDimensionless(amount: Double = 1.0): QuantityDimensionlessDto {
+            return QuantityDimensionlessDto(
+                amount,
+                DimensionlessUnitsDto.u,
+            )
+        }
+
         private fun quantityMemory(amount: Double = 10.0): QuantityMemoryDto {
             return QuantityMemoryDto(
                 amount,

src/test/kotlin/org/cloud_assess/fixtures/QuantityFixture.kt

@@ -12,5 +12,9 @@ class QuantityFixture {
             BasicNumber(1.0),
             UnitValue(UnitSymbol.of("kg"), 1.0, Dimension.of("mass"))
         )
+        val oneUnit = QuantityValue(
+            BasicNumber(1.0),
+            UnitValue(UnitSymbol.of("u"), 1.0, Dimension.None)
+        )
     }
 }

trusted_library/00-globals/globals.lca

@@ -0,0 +1,4 @@
+variables {
+    timewindow = 1 hour
+    maintenance_intensity = 0 man * working_day / month
+}

trusted_library/units.lca → trusted_library/00-globals/units.lca

@@ -1,3 +1,23 @@
+unit week {
+    symbol = "week"
+    alias_for = 7 day
+}
+
+unit working_day {
+    symbol = "working_day"
+    alias_for = 7 hour
+}
+
+unit man {
+    symbol = "man"
+    dimension = "man"
+}
+
+unit month {
+    symbol = "month"
+    alias_for = 30 day
+}
+
 unit kW {
     symbol = "kW"
     alias_for = 1000 W
@@ -27,12 +47,8 @@ unit KB {
     alias_for = 1e-3 GB
 }
 
-unit core {
-    symbol = "core"
-    dimension = "core"
-}
-
 unit vCPU {
     symbol = "vCPU"
     dimension = "vCPU"
 }
+

trusted_library/01-hardware/01-datacenter-mutualized/mutualized.lca

@@ -0,0 +1,54 @@
+process mutualized_pool_fn {
+    labels {
+        phase = "embodied"
+    }
+    params {
+        dc_id = "dc-01"
+        lc_step = "manufacturing"
+    }
+
+    variables {
+        dt = 1 hour
+        mutualized_pool_id = "mutualized"
+        dc = lookup dc_inventory match ( id = dc_id )
+        internal_power = sum( server_inventory match ( pool_id = mutualized_pool_id, dc_id = dc_id ), quantity * power )
+                         + sum( network_inventory match ( pool_id = mutualized_pool_id, dc_id = dc_id ), quantity * power )
+                         + sum( storage_inventory match ( pool_id = mutualized_pool_id, dc_id = dc_id ), quantity * power )
+        available_power = ( dc.reserved_power / dc.power_usage_effectiveness ) - internal_power
+    }
+    products {
+        available_power * dt mutualized_pool
+    }
+    inputs {
+        dt mutualized_server_pool from mutualized_server_pool( dc_id = dc_id, lc_step = lc_step ) match ( phase = "embodied" )
+        dt mutualized_network_pool from mutualized_network_pool( dc_id = dc_id, lc_step = lc_step ) match ( phase = "embodied" )
+        dt mutualized_storage_pool from mutualized_storage_pool( dc_id = dc_id, lc_step = lc_step ) match ( phase = "embodied" )
+    }
+}
+
+process mutualized_pool_fn {
+    labels {
+        phase = "use"
+    }
+    params {
+        dc_id = "dc-01"
+    }
+
+    variables {
+        dt = 1 hour
+        internal_pool_id = "mutualized"
+        dc = lookup dc_inventory match ( id = dc_id )
+        internal_power = sum( server_inventory match ( pool_id = internal_pool_id, dc_id = dc_id ), quantity * power )
+                         + sum( network_inventory match ( pool_id = internal_pool_id, dc_id = dc_id ), quantity * power )
+                         + sum( storage_inventory match ( pool_id = internal_pool_id, dc_id = dc_id ), quantity * power )
+        available_power = ( dc.reserved_power / dc.power_usage_effectiveness ) - internal_power
+    }
+    products {
+        available_power * dt mutualized_pool
+    }
+    inputs {
+        dt mutualized_server_pool from mutualized_server_pool( dc_id = dc_id ) match ( phase = "use" )
+        dt mutualized_network_pool from mutualized_network_pool( dc_id = dc_id ) match ( phase = "use" )
+        dt mutualized_storage_pool from mutualized_storage_pool( dc_id = dc_id ) match ( phase = "use" )
+    }
+}