AWS Architecture Blog

Improving Performance and Reducing Cost Using Availability Zone Affinity

One of the best practices for building resilient systems in Amazon Virtual Private Cloud (VPC) networks is using multiple Availability Zones (AZ). An AZ is one or more discrete data centers with redundant power, networking, and connectivity. Using multiple AZs allows you to operate workloads that are more highly available, fault tolerant, and scalable than would be possible from a single data center. However, transferring data across AZs adds latency and cost.

This blog post demonstrates an architectural pattern called “Availability Zone Affinity” that improves performance and reduces costs while still maintaining the benefits of Multi-AZ architectures.

Cross Availability Zone effects

AZs are physically separated by a meaningful distance from other AZs in the same AWS Region, although they all are within 60 miles (100 kilometers) of each other. This generally produces single digit millisecond roundtrip latency between AZs in the same Region. Roundtrip latency between two instances in the same AZ is generally sub-millisecond when using enhanced networking.1 This can be even lower when the instances use cluster placement groups. Additionally, when data is transferred between two AZs, data transfer charges apply in both directions.

To better understand these effects, we’ll analyze a fictitious workload, the “foo service,” shown in Figure 1. The foo service provides a storage platform for other workloads in AWS to redundantly store data. Requests are first processed by an Application Load Balancer (ALB). ALBs always use cross-zone load balancing to evenly distribute requests to all targets. Next, the request is sent from the load balancer to a request router. The request router performs a few operations, like authorization checks and input validation, before sending it to the storage tier. The storage tier replicates the data sequentially from the lead node, to the middle node, and finally the tail node. Once the data has been written to all three nodes, it is considered committed. The response is sent from the tail node back to the request router, back through the load balancer, and finally returned to the client.

An example system that transfers data across AZs

Figure 1. An example system that transfers data across AZs

We can see in Figure 1 that, in the worst case, the request traversed an AZ boundary eight times. Let’s calculate the fastest possible, zeroth percentile (p0), latency. We’ll assume the best time for non-network processing of the request in the load balancer, request router, and storage tier is 4 ms. If we consider 1 ms as the minimum network latency added for each AZ traversal, in the worst-case scenario of eight AZ traversals, the total processing time can be no faster than 12 ms. At the 50th percentile (p50), meaning the median, let’s assume the cross-AZ latency is 1.5 ms and non-network processing is 8 ms, resulting in a total of 20 ms for overall processing. Additionally, if this system is processing millions of requests, the data transfer charges could become substantial over time. Now, let’s imagine that a workload using the foo service must operate with p50 latency under 20 ms. How can the foo service change their system design to meet this goal?

Availability Zone affinity

The AZ Affinity architectural pattern reduces the number of times an AZ boundary is crossed. In the example system we looked at in Figure 1, AZ Affinity can be implemented with two changes.

  1. First, the ALB is replaced with a Network Load Balancer (NLB). NLBs provide an elastic network interface per AZ that is configured with a static IP. NLBs also have cross-zone load balancing disabled by default. This ensures that requests are only sent to targets that are in the same AZ as the elastic network interface that receives the request.
  2. Second, DNS entries are created for each elastic network interface to provide an AZ-specific record using the AZ ID, which is consistent across accounts. Clients use that DNS record to communicate with a load balancer in the AZ they select. So instead of interacting with a Region-wide service using a DNS name like, they would instead use

Figure 2 shows the system with AZ Affinity. We can see that each request, in the worst case, only traverses an AZ boundary four times. Data transfer costs are reduced by approximately 40 percent compared to the previous implementation. If we use 300 μs as the p50 latency for intra-AZ communication, we now get (4×300μs)+(4×1.5ms)=7.2ms. Using the median 8 ms processing time, this brings the overall median latency to 15.2 ms. This represents a 40 percent reduction in median network latency. When thinking about p90, p99, or even p99.9 latencies, this reduction could be even more significant.

The system now implements AZ Affinity

Figure 2. The system now implements AZ Affinity

Figure 3 shows how you could take this approach one step farther using service discovery. Instead of requiring the client to remember AZ-specific DNS names for load balancers, we can use AWS Cloud Map for service discovery. AWS Cloud Map is a fully managed service that allows clients to look up IP address and port combinations of service instances using DNS and dynamically retrieve abstract endpoints, like URLs, over the HTTP-based service Discovery API. Service discovery can reduce the need for load balancers, removing their cost and added latency.

The client first retrieves details about the service instances in their AZ from the AWS Cloud Map registry. The results are filtered to the client’s AZ by specifying an optional parameter in the request. Then they use that information to send requests to the discovered request routers.

AZ Affinity implemented using AWS Cloud Map for service discovery

Figure 3. AZ Affinity implemented using AWS Cloud Map for service discovery

Workload resiliency

In the new architecture using AZ Affinity, the client has to select which AZ they communicate with. Since they are “pinned” to a single AZ and not load balanced across multiple AZs, they may see impact during an event affecting the AWS infrastructure or foo service in that AZ.

During this kind of event, clients can choose to use retries with exponential backoff or send requests to the other AZs that aren’t impacted. Alternatively, they could implement a circuit breaker to stop making requests from the client in the affected AZ and only use clients in the others. Both approaches allow them to use the resiliency of Multi-AZ systems while taking advantage of AZ Affinity during normal operation.

Client libraries

The easiest way to achieve the process of service discovery, retries with exponential backoff, circuit breakers, and failover is to provide a client library/SDK. The library handles all of this logic for users and makes the process transparent, like what the AWS SDK or CLI does. Users then get two options, the low-level API and the high-level library.


This blog demonstrated how the AZ Affinity pattern helps reduce latency and data transfer costs for Multi-AZ systems while providing high availability. If you want to investigate your data transfer costs, check out the Using AWS Cost Explorer to analyze data transfer costs blog for an approach using AWS Cost Explorer.

For investigating latency in your workload, consider using AWS X-Ray and Amazon CloudWatch for tracing and observability in your system. AZ Affinity isn’t the right solution for every workload, but if you need to reduce inter-AZ data transfer costs or improve latency, it’s definitely an approach to consider.

  1. This estimate was made using t4g.small instances sending ping requests across AZs. The tests were conducted in the us-east-1, us-west-2, and eu-west-1 Regions. These results represent the p0 (fastest) and p50 (median) intra-AZ latency in those Regions at the time they were gathered, but are not a guarantee of the latency between two instances in any location. You should perform your own tests to calculate the performance enhancements AZ Affinity offers.
Michael Haken

Michael Haken

Michael is a Principal Solutions Architect on the AWS Strategic Accounts team where he helps customers innovate, differentiate their business, and transform their customer experiences. He has 15 years’ experience supporting financial services, public sector, and digital native customers. Michael has his B.A. from UVA and M.S. in Computer Science from Johns Hopkins. Outside of work you’ll find him playing with his family and dogs on his farm.