Get Auto Scaling Group Recommendations

computeoptimizer_get_auto_scaling_group_recommendations

R Documentation

Returns Auto Scaling group recommendations

Description

Returns Auto Scaling group recommendations.

Compute Optimizer generates recommendations for Amazon EC2 Auto Scaling groups that meet a specific set of requirements. For more information, see the Supported resources and requirements in the Compute Optimizer User Guide.

Usage

computeoptimizer_get_auto_scaling_group_recommendations(accountIds,
  autoScalingGroupArns, nextToken, maxResults, filters,
  recommendationPreferences)

Arguments

accountIds

The ID of the Amazon Web Services account for which to return Auto Scaling group recommendations.

If your account is the management account of an organization, use this parameter to specify the member account for which you want to return Auto Scaling group recommendations.

Only one account ID can be specified per request.

autoScalingGroupArns

The Amazon Resource Name (ARN) of the Auto Scaling groups for which to return recommendations.

nextToken

The token to advance to the next page of Auto Scaling group recommendations.

maxResults

The maximum number of Auto Scaling group recommendations to return with a single request.

To retrieve the remaining results, make another request with the returned nextToken value.

filters

An array of objects to specify a filter that returns a more specific list of Auto Scaling group recommendations.

recommendationPreferences

An object to specify the preferences for the Auto Scaling group recommendations to return in the response.

Value

A list with the following syntax:

list(
  nextToken = "string",
  autoScalingGroupRecommendations = list(
    list(
      accountId = "string",
      autoScalingGroupArn = "string",
      autoScalingGroupName = "string",
      finding = "Underprovisioned"|"Overprovisioned"|"Optimized"|"NotOptimized",
      utilizationMetrics = list(
        list(
          name = "Cpu"|"Memory"|"EBS_READ_OPS_PER_SECOND"|"EBS_WRITE_OPS_PER_SECOND"|"EBS_READ_BYTES_PER_SECOND"|"EBS_WRITE_BYTES_PER_SECOND"|"DISK_READ_OPS_PER_SECOND"|"DISK_WRITE_OPS_PER_SECOND"|"DISK_READ_BYTES_PER_SECOND"|"DISK_WRITE_BYTES_PER_SECOND"|"NETWORK_IN_BYTES_PER_SECOND"|"NETWORK_OUT_BYTES_PER_SECOND"|"NETWORK_PACKETS_IN_PER_SECOND"|"NETWORK_PACKETS_OUT_PER_SECOND"|"GPU_PERCENTAGE"|"GPU_MEMORY_PERCENTAGE",
          statistic = "Maximum"|"Average",
          value = 123.0
        )
      ),
      lookBackPeriodInDays = 123.0,
      currentConfiguration = list(
        desiredCapacity = 123,
        minSize = 123,
        maxSize = 123,
        instanceType = "string"
      ),
      currentInstanceGpuInfo = list(
        gpus = list(
          list(
            gpuCount = 123,
            gpuMemorySizeInMiB = 123
          )
        )
      ),
      recommendationOptions = list(
        list(
          configuration = list(
            desiredCapacity = 123,
            minSize = 123,
            maxSize = 123,
            instanceType = "string"
          ),
          instanceGpuInfo = list(
            gpus = list(
              list(
                gpuCount = 123,
                gpuMemorySizeInMiB = 123
              )
            )
          ),
          projectedUtilizationMetrics = list(
            list(
              name = "Cpu"|"Memory"|"EBS_READ_OPS_PER_SECOND"|"EBS_WRITE_OPS_PER_SECOND"|"EBS_READ_BYTES_PER_SECOND"|"EBS_WRITE_BYTES_PER_SECOND"|"DISK_READ_OPS_PER_SECOND"|"DISK_WRITE_OPS_PER_SECOND"|"DISK_READ_BYTES_PER_SECOND"|"DISK_WRITE_BYTES_PER_SECOND"|"NETWORK_IN_BYTES_PER_SECOND"|"NETWORK_OUT_BYTES_PER_SECOND"|"NETWORK_PACKETS_IN_PER_SECOND"|"NETWORK_PACKETS_OUT_PER_SECOND"|"GPU_PERCENTAGE"|"GPU_MEMORY_PERCENTAGE",
              statistic = "Maximum"|"Average",
              value = 123.0
            )
          ),
          performanceRisk = 123.0,
          rank = 123,
          savingsOpportunity = list(
            savingsOpportunityPercentage = 123.0,
            estimatedMonthlySavings = list(
              currency = "USD"|"CNY",
              value = 123.0
            )
          ),
          savingsOpportunityAfterDiscounts = list(
            savingsOpportunityPercentage = 123.0,
            estimatedMonthlySavings = list(
              currency = "USD"|"CNY",
              value = 123.0
            )
          ),
          migrationEffort = "VeryLow"|"Low"|"Medium"|"High"
        )
      ),
      lastRefreshTimestamp = as.POSIXct(
        "2015-01-01"
      ),
      currentPerformanceRisk = "VeryLow"|"Low"|"Medium"|"High",
      effectiveRecommendationPreferences = list(
        cpuVendorArchitectures = list(
          "AWS_ARM64"|"CURRENT"
        ),
        enhancedInfrastructureMetrics = "Active"|"Inactive",
        inferredWorkloadTypes = "Active"|"Inactive",
        externalMetricsPreference = list(
          source = "Datadog"|"Dynatrace"|"NewRelic"|"Instana"
        ),
        lookBackPeriod = "DAYS_14"|"DAYS_32"|"DAYS_93",
        utilizationPreferences = list(
          list(
            metricName = "CpuUtilization"|"MemoryUtilization",
            metricParameters = list(
              threshold = "P90"|"P95"|"P99_5",
              headroom = "PERCENT_30"|"PERCENT_20"|"PERCENT_10"|"PERCENT_0"
            )
          )
        ),
        preferredResources = list(
          list(
            name = "Ec2InstanceTypes",
            includeList = list(
              "string"
            ),
            effectiveIncludeList = list(
              "string"
            ),
            excludeList = list(
              "string"
            )
          )
        ),
        savingsEstimationMode = list(
          source = "PublicPricing"|"CostExplorerRightsizing"|"CostOptimizationHub"
        )
      ),
      inferredWorkloadTypes = list(
        "AmazonEmr"|"ApacheCassandra"|"ApacheHadoop"|"Memcached"|"Nginx"|"PostgreSql"|"Redis"|"Kafka"|"SQLServer"
      )
    )
  ),
  errors = list(
    list(
      identifier = "string",
      code = "string",
      message = "string"
    )
  )
)

Request syntax

svc$get_auto_scaling_group_recommendations(
  accountIds = list(
    "string"
  ),
  autoScalingGroupArns = list(
    "string"
  ),
  nextToken = "string",
  maxResults = 123,
  filters = list(
    list(
      name = "Finding"|"FindingReasonCodes"|"RecommendationSourceType"|"InferredWorkloadTypes",
      values = list(
        "string"
      )
    )
  ),
  recommendationPreferences = list(
    cpuVendorArchitectures = list(
      "AWS_ARM64"|"CURRENT"
    )
  )
)