What is NFS Shared Storage in the Cloud?

Gurbir Singh

Nov 08, 2022 / 4 min read

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Network File System (NFS) is a distributed file system protocol for shared storage. The NFS shared storage protocol defines the way files are stored and retrieved from storage devices across networks. It is one of several network-attached storage (NAS) distributed file system standards. With NFS shared storage in the cloud, you can share file systems on a networked server so remote computer users can access them.

Exploring the Benefits of NFS Shared Storage

In general, cloud storage benefits are similar to on-premises deployments. Data management is simpler, faster, more efficient, and more scalable. Additionally, some cloud storage solutions enable organizations to implement high availability and disaster recovery strategies.

For NFS shared storage, the benefits include:

  • Clients can access the same files on remote hosts as if they were local files.
  • Using shared applications reduces storage costs and eliminates the need for local disk space.
  • Data remains up-to-date and reliable since all users can access the same files.
  • All users have visibility into the file system.
  • Customers can run mixed technology from multiple vendors and use interoperable components in heterogeneous environments.
  • Data centralization reduces system administration overhead.

Cloud Providers’ NFS Shared Storage Services

Cloud providers’ products support NFS shared storage, such as:

  • Amazon EFS supports NFS shared storage for Linux applications that can run on AWS compute instances. There are two service levels available: standard and infrequent access, with automated tiering to place files at the best level for their usage. Access to files is parallelized to achieve “high levels” of throughput and input/output performance.
  • Microsoft Azure Files provides fully managed shared storage via NFS, server message block (SMB), or representational state transfer (REST) that can support cloud deployments of Windows, macOS, and Linux. Two service levels are offered in Azure File: standard and premium.
  • Google Cloud Filestore offers two performance tiers of NFS shared storage with up to 64 TB of capacity per share. Premium offers much higher throughput and input/output operations per second than standard.

Five Considerations for NFS Shared Storage in the Cloud

Here are five things to consider when migrating and running NFS shared storage in the cloud:


1. Maintain the Current NFS File Structures

You don't want to change NFS file constructs if your application already uses them. You should minimize file structure and application changes when transitioning to the cloud. The goal is to optimize performance by maximizing throughput, minimizing latency, and providing high availability simultaneously.


2. Ensure Business Continuity

During cloud migrations, it is essential to factor in downtime due to natural disasters and system failures. There are three components to ensuring business continuity:

  • Maintaining high availability while minimizing data loss and recovery time.
  • Deploying applications to be highly available and resilient to disasters or infrastructure failures.
  • Having complete access during migration to the cloud while you continue to sync and update your systems after the initial migration.


3. Protect File Data

Cloud security should be your top priority to ensure that access to the file system is always under your control. Data security must be addressed at all levels to prevent losses. Data security and privacy regulations must also be met in the new environment. 


4. Use Automation

You should automate the cloud migration process to save operational costs and time when deploying and managing multiple databases. Automating administrative tasks such as creating databases, managing backups, and monitoring is easy.


5. Cut Costs

When your NFS file shares are migrated correctly, you can reduce migration costs, lower ongoing cloud service costs, and minimize or eliminate developer costs. Data tiering enables you to run infrequently used, low-performance workloads on inexpensive storage and move high-performance workloads to high-performance drives.

The Role of NFS Shared Storage in EDA Workloads

For electronic design automation (EDA) workloads, high-performance NFS shared storage in the cloud works best. 

When selecting a cloud provider, ensure your company's data is safe, secure, and accessible. Data should be stored in multiple facilities and on multiple devices with backups. Natural disasters, operational errors, and other unexpected situations can damage your data if you do not spread out your storage geographically.

To fully benefit from shared storage in the cloud, you should choose a provider that can ensure high performance, uptime, accessibility, and speed while keeping costs low.

Synopsys, EDA, and the Cloud

Synopsys is the industry’s largest provider of electronic design automation (EDA) technology used in the design and verification of semiconductor devices, or chips. With Synopsys Cloud, we’re taking EDA to new heights, combining the availability of advanced compute and storage infrastructure with unlimited access to EDA software licenses on-demand so you can focus on what you do best – designing chips, faster. Delivering cloud-native EDA tools and pre-optimized hardware platforms, an extremely flexible business model, and a modern customer experience, Synopsys has reimagined the future of chip design on the cloud, without disrupting proven workflows.


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Synopsys technology drives innovations that change how people work and play using high-performance silicon chips. Let Synopsys power your innovation journey with cloud-based EDA tools. Sign up to try Synopsys Cloud for free!

About The Author

Gurbir Singh is group director, Cloud Engineering, at Synopsys. He has a demonstrated history of leadership in the software industry. In his current role, he leads the development of the Synopsys Cloud product, which enables customers to do chip design on the cloud using EDA-as-a-Service (SaaS) as well as flexible pay-per-use models. Gurbir has run organizations to develop cloud SaaS products, machine learning applications, AI/ML platforms, enterprise web applications, and high-end customer applications. He is experienced in building world- class technology teams. Gurbir has a master’s degree in computer science, along with patents and contributions to publications.

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