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    Function Compute:Instance types and usage modes

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    Function Compute:Instance types and usage modes

    Last Updated:Oct 31, 2024

    Function Compute provides elastic instances and GPU-accelerated instances. Both types of instances can be used in on-demand and provisioned modes. On-demand instances are billed based on actual execution durations. You can use on-demand instances together with the instance concurrency feature to improve resource utilization. Billing of a provisioned instance starts when Function Compute starts the provisioned instance and ends when you release the instance. Provisioned instances can effectively mitigate cold starts. This topic describes the types, usage modes, billing methods, and specifications of function instances in Function Compute.

    Instance type

    • Elastic instance: the basic instance type of Function Compute. Elastic instances are suitable for scenarios with bursty traffic or compute-intensive workloads.

    • GPU-accelerated instance: Instances that use the Ampere and Turing architectures for GPU acceleration. GPU-accelerated instances are mainly used in audio and video processing, Artificial Intelligence (AI), and image processing scenarios. Instances of this type accelerate business by offloading workloads to GPU hardware.

      The following topics describe best practices for GPU-accelerated instances in different scenarios:

      Important

      • GPU-accelerated instances can be deployed only by using container images.

      • When you use GPU-accelerated instances, join the DingTalk user group 64970014484 and provide the following information so that technical support can be provided in a timely manner:

        • Your organization name, such as your company name.

        • The ID of your Alibaba Cloud account.

        • The region in which you want to use GPU-accelerated instances. Example: China (Shenzhen).

        • Your contact information, such as your mobile number, email address, or DingTalk account.

    Usage modes

    GPU-accelerated instances and elastic instances support on-demand and provisioned mode. This section describes the two usage modes.

    On-demand mode

    Introduction

    On-demand instances are allocated and released by Function Compute. Function Compute automatically scales instances based on the number of function invocations. Function Compute creates instances when the number of function invocations increases, and destroys excess instances when the number of function invocations decreases. On-demand instances are automatically created upon requests. On-demand instances are destroyed if no requests are submitted for a period of time (usually 3 to 5 minutes). The first time you invoke an on-demand instance, a cold start occurs.

    By default, a maximum of 300 instances can be created in on-demand mode for an Alibaba Cloud account in each region. If you need to increase this limit, join the DingTalk group 64970014484 for technical support.

    Billing method

    Billing of an on-demand instance starts when requests are sent to the instance for processing and ends when the requests are processed. Each on-demand instance can process one or more requests at a time. For more information, see Configure instance concurrency.

    No instance is allocated if no request is submitted for processing, and therefore no fees are generated. In on-demand mode, you are charged only when your function is invoked. For more information about pricing and billing, see Billing overview.

    Note

    You can use the instance concurrency feature based on your business requirements to improve resource utilization. In this case, multiple tasks preemptively share CPU and memory resources on your instance to improve resource utilization.

    Instance concurrency = 1

    Measurement of execution duration starts when a request arrives at an instance and ends when the request is completely executed.

    image

    Instance concurrency > 1

    Measurement of the execution duration of an on-demand instance starts when the first request is received and ends when the last request is completely executed. You can reuse resources to concurrently process multiple requests. This way, resource costs can be reduced.

    image

    Provisioned mode

    Introduction

    In provisioned mode, you can manage the allocation and release of function instances. Provisioned instances are retained unless you release them. Invocation requests are preferentially distributed to provisioned instances. If provisioned instances are not enough to process the requests, Function Compute allocates on-demand instances to process excess requests. For more information about how to delete a provisioned instance, see Modify or delete a provisioned instance policy.

    Note

    If cold starts are an issue for you, we recommend that you use provisioned instances. You can specify a fixed number of provisioned instances or configure a scheduled auto scaling policy or water-level based auto scaling policy based on factors such as your resource budget, traffic fluctuations of your business, and resource usage thresholds. The average cold start latency of instances is significantly reduced when provisioned instances are used.

    Idle mode

    Elastic instances

    States of elastic instances are classified into the active state and the idle state based on whether vCPU resources are allocated. By default, the idle mode is enabled.

    • Active state

      Instances are considered active if they are processing requests or if the idle mode feature is disabled for them. If you disable Idle Mode, vCPUs are always allocated to provisioned instances regardless of whether the instances are processing requests or not. Running of background tasks is not affected.

    • Idle state

      If you enable Idle Mode, Function Compute freezes vCPUs of provisioned instances when the instances are not processing requests and the provisioned instances enter the idle state. You are not charged for vCPU resources when the instances are in the idle state, which helps you save costs. For more information about instance states, see Configure instance lifecycles.

    You can choose whether to enable the idle mode feature based on your business requirements.

    • Costs

      If you want to use provisioned instances to mitigate cold starts and hope to save costs, we recommend that you enable the idle mode feature. This feature allows you to pay only for memory and disk resources of provisioned instances if provisioned instances are in the idle state, and requests can be responded without cold starts.

    • Background tasks

      If your function needs to run background tasks, we recommend that you do not enable the idle mode feature. The following items provide example scenarios:

      • Some application frameworks rely on the built-in scheduler or background features. Some dependent middleware needs to regularly report heartbeats.

      • Some asynchronous operations are performed by using Goroutine lightweight threads that use Go, the async functions that use Node.js, or the asynchronous threads that use Java.

    GPU-accelerated instance

    GPU-accelerated instances can be classified into active and idle states based on whether they are allocated GPU resources. By default, the idle mode is turned on for GPU-accelerated instances.

    • Active state

      Instances are considered active if they are processing requests or if Idle Mode is disabled for them. After the idle mode is enabled, Function Compute freezes the GPU cards of the provisioned instances and the instances enter the idle state.

    • Idle state

      Provisioned instances for which the idle mode is enabled are idle instances when they are not processing requests .

    Billing method

    • Active state

      The billing of provisioned instances starts when the provisioned instances are created and ends when the provisioned instances are released. Provisioned instances are requested and released by yourself. Therefore, you are charged for the provisioned instances based on active mode prices even if they are not processing any requests if the instances are not released and the idle mode is not enabled.

      image

    • Idle state

      After the idle mode is enabled, the provisioned instances enter the idle state when they are not processing requests. The prices of idle instances are much lower than prices of active instances. For more information, see Conversion factors.

    Instance specifications

    • Elastic instances

      The following table describes specifications of elastic instances. You can configure instance specifications based on your business requirements.

      vCPU

      Memory size (MB)

      Maximum code package size (GB)

      Maximum function execution duration (seconds)

      Maximum disk size (GB)

      Maximum bandwidth (Gbit/s)

      0.05–16

      Note: The value must be a multiple of 0.05.

      128–32768

      Note: The value must be a multiple of 64.

      10

      86,400

      10

      Valid values:

      • 512 MB. This is the default value.

      • 10 GB.

      5

      Note

      The ratio of vCPU to memory capacity (in GB) is 1: N. N must be a value that ranges from 1 to 4.

    • GPU-accelerated instance

      The following table describes specifications of GPU-accelerated instances. You can configure instance specifications based on your business requirements.

      Note

      • fc.gpu.tesla.1 GPU instances provide essentially the same GPU performance as physical NVIDIA T4 cards.

      • fc.gpu.ampere.1 GPU instances provide essentially the same GPU performance as physical NVIDIA A10 cards.

      Instance specifications

      Full GPU size (GB)

      Computing power of full GPU card (TFLOPS)

      Available specifications

      On-demand mode

      Regular provisioned mode

      Idle provisioned mode

      FP16

      FP32

      vGPU memory (MB)

      vGPU computing power (card)

      vCPUs

      Memory size (MB)

      fc.gpu.tesla.1

      16

      65

      8

      Valid values: 1024 to 16384 (1 GB to 16 GB).

      Note: The value must be a multiple of 1,024.

      The value is calculated based on the following formula: vGPU computing power = vGPU memory (GB)/16. For example, if you set the vGPU memory to 5 GB, the maximum vGPU computing power is 5/16 memory cards.

      The computing power is automatically allocated by Function Compute and does not need to be manually allocated.

      Valid values: 0.05 to the value of [vGPU memory (GB)/2].

      Note: The value must be a multiple of 0.05. For more information, see GPU specifications.

      Valid values: 128 to the value of [vGPU memory (GB) x 2048].

      Note: The value must be a multiple of 64. For more information, see GPU specifications.

      Y

      Y

      Y

      fc.gpu.ampere.1

      24

      125

      30

      Valid values: 1024 to 24576 (1 GB to 24 GB).

      Note: The value must be a multiple of 1,024.

      The value is calculated based on the following formula: vGPU computing power = vGPU memory (GB)/24. For example, if you set the vGPU memory to 5 GB, the maximum vGPU computing power is 5/24 memory cards.

      The computing power is automatically allocated by Function Compute and does not need to be manually allocated.

      Valid values: 0.05 to the value of [vGPU memory (GB)/3].

      Note: The value must be a multiple of 0.05. For more information, see GPU specifications.

      Valid values: 128 to the value of [(vGPU memory (GB) x 4096)/3].

      Note: The value must be a multiple of 64. For more information, see GPU specifications.

      Y

      Y

      Y

      fc.gpu.ada.1

      48

      119

      60

      49152 (48 GB)

      Note: Only 48 GB GPU memory is supported.

      By default, computer power of full GPU cards is allocated.

      The computing power is automatically allocated by Function Compute and does not need to be manually allocated.

      Valid value: 8.

      Value description: Only 8 vCPUs are supported.

      Valid value: 65536.

      Value description: Only 64 GB memory is supported.

      N

      Y

      Y

      The GPU-accelerated instances of Function Compute also support the following resource specifications.

      Image size (GB)

      Maximum function execution duration (seconds)

      Maximum disk size (GB)

      Maximum bandwidth (Gbit/s)

      Container Registry Enterprise Edition (Standard Edition): 15

      Container Registry Enterprise Edition (Advanced Edition): 15

      Container Registry Enterprise Edition (Basic Edition): 15

      Container Registry Personal Edition (free): 15

      86,400

      10

      5

      Note

      • Specifying the instance type as g1 achieves the same effect as selecting the fc.gpu.tesla.1 instance specification.

      • GPU-accelerated instances of Tesla series GPU cards are supported in the following regions: China (Hangzhou), China (Shanghai), China (Beijing), China (Zhangjiakou), China (Shenzhen), Japan (Tokyo), US (Virginia), and Singapore.

      • GPU-accelerated instances of Ampere series GPU cards are supported in the following regions: China (Hangzhou), China (Shanghai), Japan (Tokyo), and Singapore.

      • GPU-accelerated instances of Ada series GPU cards are supported in the following regions: China (Beijing), China (Hangzhou), China (Shanghai), and China (Shenzhen).

    GPU specifications

    Expand to view details of fc.gpu.tesla.1.

    vGPU memory (MB)

    vCPUs

    Maximum memory size (GB)

    Memory size (MB)

    1024

    0.05–0.5

    2

    128–2048

    2048

    0.05–1

    4

    128–4096

    3072

    0.05–1.5

    6

    128–6144

    4096

    0.05–2

    8

    128–8192

    5120

    0.05–2.5

    10

    128–10240

    6144

    0.05–3

    12

    128–12288

    7168

    0.05–3.5

    14

    128–14336

    8192

    0.05–4

    16

    128–16384

    9216

    0.05–4.5

    18

    128–18432

    10240

    0.05–5

    20

    128–20480

    11264

    0.05–5.5

    22

    128–22528

    12288

    0.05–6

    24

    128–24576

    13312

    0.05–6.5

    26

    128–26624

    14336

    0.05–7

    28

    128–28672

    15360

    0.05–7.5

    30

    128–30720

    16384

    0.05–8

    32

    128–32768

    Expand to view details of fc.gpu.ampere.1.

    vGPU memory (MB)

    vCPUs

    Maximum memory size (GB)

    Memory size (MB)

    1024

    0.05–0.3

    1.3125

    128–1344

    2048

    0.05–0.65

    2.625

    128–2688

    3072

    0.05–1

    4

    128–4096

    4096

    0.05–1.3

    5.3125

    128–5440

    5120

    0.05–1.65

    6.625

    128–6784

    6144

    0.05–2

    8

    128–8192

    7168

    0.05–2.3

    9.3125

    128–9536

    8192

    0.05–2.65

    10.625

    128–10880

    9216

    0.05–3

    12

    128–12288

    10240

    0.05–3.3

    13.3125

    128–13632

    11264

    0.05–3.65

    14.625

    128–14976

    12288

    0.05–4

    16

    128–16384

    13312

    0.05–4.3

    17.3125

    128–17728

    14336

    0.05–4.65

    18.625

    128–19072

    15360

    0.05–5

    20

    128–20480

    16384

    0.05–5.3

    21.3125

    128–21824

    17408

    0.05–5.65

    22.625

    128–23168

    18432

    0.05–6

    24

    128–24576

    19456

    0.05–6.3

    25.3125

    128–25920

    20480

    0.05–6.65

    26.625

    128–27264

    21504

    0.05–7

    28

    128–28672

    22528

    0.05–7.3

    29.3125

    128–30016

    23552

    0.05–7.65

    30.625

    128–31360

    24576

    0.05–8

    32

    128–32768

    Additional information

    • You can enable the idle mode when you configure auto scaling rules. For more information, see Configure provisioned instances.

    • For more information about the billing methods and billable items of Function Compute, see Billing overview.

    • When you call an API operation to create a function, you can use the instanceType parameter to specify an instance type. For more information, see CreateFunction.

    • For more information about how to specify the instance type and instance specifications in the Function Compute console, see Create a web function.