Glossary

Data Locality

Data locality is the practice of storing data physically or logically close to the applications, compute resources or users that need to access it. By reducing the distance data must travel between storage and processing resources, data locality can improve performance, lower latency and reduce network traffic.

The concept is especially important for distributed systems, cloud infrastructure, high-performance computing (HPC) and artificial intelligence (AI) workloads. These environments can involve large datasets that are expensive or time-consuming to move repeatedly across networks.

Data locality can refer to physical proximity, such as placing storage and compute resources in the same data center, as well as architectural proximity, such as ensuring applications process data on the same node, cluster, availability zone or region where it resides.

How does data locality work?

Data locality works by placing or accessing data at a location that minimizes unnecessary movement between storage and compute resources.

In a distributed environment, data may be spread across multiple servers, data centers, edge locations or cloud regions. When an application requests data, the infrastructure can attempt to execute the workload close to the relevant data rather than transferring the dataset to a distant compute resource.

Common approaches include:

• Co-locating storage and compute: Applications process data within the same infrastructure where it is stored.
• Data replication: Copies of frequently accessed data are placed closer to applications or users.
• Caching: Frequently requested data is temporarily stored near compute resources.
• Workload scheduling: Compute jobs are assigned to nodes or locations that already contain the required data.
• Regional data placement: Data is stored within the cloud region, data center or geographic area where workloads operate.

The appropriate approach depends on workload requirements, infrastructure architecture, data volumes and resilience objectives.

Why is data locality important?

Moving large amounts of data can consume substantial network bandwidth and introduce delays. As datasets grow, data movement can become a limiting factor for application performance and infrastructure efficiency.

Data locality helps organizations reduce unnecessary transfers by bringing compute to the data or positioning data closer to compute. This can provide several benefits.

Lower latency

Applications can retrieve data more quickly when storage is located close to processing resources. This is particularly relevant for workloads that perform frequent reads and writes or require rapid access to large datasets.

Higher application performance

Reducing network distance and data transfers can increase effective throughput and help applications spend more time processing data rather than waiting for it to arrive.

Reduced network traffic

Keeping data local can decrease traffic between servers, data centers, availability zones and cloud regions. This can preserve bandwidth for other workloads and reduce network congestion.

Lower data transfer costs

Public cloud platforms commonly charge for certain types of data transfer, particularly between regions or when data leaves the cloud environment. Data locality strategies can help limit unnecessary transfers and associated costs.

Improved data control

Organizations may need to keep certain datasets within specific infrastructure or geographic boundaries. Data locality can support these requirements by controlling where data is stored and processed.

Data locality in distributed storage

Data locality is an important consideration in distributed storage because data may be distributed across many nodes or locations.

Distributed storage systems typically use techniques such as replication or erasure coding to protect data and maintain availability. As a result, multiple data fragments or copies may exist across the infrastructure.

Applications and storage systems can take advantage of this distribution by directing requests toward appropriate data locations. However, data locality must be balanced with other requirements, including durability, availability, load balancing and failure-domain separation.

For example, concentrating all copies of data near one compute resource might improve access performance but weaken resilience if those copies share the same failure domain. Storage architecture therefore needs to consider locality alongside data protection requirements.

Data locality for AI and machine learning

Data locality has become increasingly relevant for AI infrastructure because training, inference and data preparation can involve very large datasets.

GPUs and other accelerators can process data at high speeds, but their utilization may decline if storage and networking cannot supply data quickly enough. Moving datasets repeatedly between storage systems, compute clusters and cloud environments can also increase operational complexity and cost.

A data locality strategy can help AI infrastructure by placing datasets within high-throughput reach of GPU clusters and other compute resources. This can support:

• Faster access to training datasets
• Higher GPU utilization
• Reduced data staging and copying
• More efficient AI data pipelines
• Lower network and data transfer overhead

Object storage can play an important role in these architectures by providing a shared data repository that multiple applications and compute environments can access without maintaining numerous independent copies.

Data locality vs. data gravity

Data locality and data gravity are related concepts, but they describe different aspects of data infrastructure.

Data locality describes the intentional placement of data near the resources that use it. It is an architectural strategy for improving access efficiency, performance or control.

Data gravity describes the tendency for applications, services and additional datasets to accumulate around large or difficult-to-move datasets.

Data gravity can influence infrastructure decisions, while data locality is one way organizations can design around those constraints.

Data locality vs. data sovereignty

Data locality primarily concerns where data is located relative to applications and compute resources.

Data sovereignty concerns the laws and regulatory requirements that apply to data based on the jurisdiction in which it is stored or processed.

The two concepts can overlap. An organization may choose a particular data location both to keep information close to users and to meet geographic or regulatory requirements. However, locating data within a particular country does not by itself establish compliance with all applicable data sovereignty requirements.

What are the challenges of data locality?

Data locality can improve infrastructure efficiency, but maintaining it becomes more complex as applications and data span multiple environments.

Common challenges include:

• Data duplication: Creating local copies can increase storage consumption and complicate synchronization.
• Changing workloads: The optimal data location can change as applications, users and compute resources move.
• Resilience requirements: Data must often be distributed across failure domains even when local placement would provide better performance.
• Hybrid and multi-cloud environments: Applications may access data across on-premises infrastructure, edge locations and multiple clouds.
• Data consistency: Replicated datasets must remain synchronized when applications modify data.
• Capacity management: Storage resources need sufficient capacity to maintain required data placement without creating hotspots.

Effective data locality therefore depends on coordinated storage, networking and compute architecture rather than data placement alone.

How object storage supports data locality

Object storage provides a scalable architecture for managing large volumes of unstructured data while making that data accessible to applications through standard interfaces such as the Amazon S3 API.

In data locality strategies, object storage can provide a shared data layer close to applications and compute infrastructure. Organizations can deploy object storage within their own data centers, private clouds or edge environments and position datasets according to workload requirements.

Distributed object storage can also support replication and geographic data placement across multiple sites. This allows organizations to determine where data resides while maintaining durability and availability across the storage environment.

For AI, analytics, backup and other data-intensive workloads, this approach can help reduce unnecessary movement while maintaining centralized data management and scalable capacity.

How does Scality support data locality?

Scality RING provides distributed object storage for large-scale unstructured data across on-premises, hybrid cloud and distributed environments.

Organizations can deploy storage close to applications, AI infrastructure and other compute resources while using the S3 API for application access. Scality RING can also support geographically distributed architectures where data placement, protection and availability requirements extend across multiple sites.

By maintaining scalable object storage within the infrastructure where workloads operate, organizations can reduce unnecessary data movement while retaining control over data placement, durability and access.

For data-intensive workloads such as AI, analytics, backup and archive, this architecture can provide a foundation for keeping large datasets accessible to the compute resources that use them.