KIOXIA: Software that delivers NVMe Flash as a Service


KumoScale software (developed by KIOXIA – formerly Toshiba Memory) is a cloud-focused storage solution that delivers high-performance NVMe flash storage volumes via a virtual storage layer between clients and flash devices.

The software implements a fast, networked block storage service based on the NVMe over Fabrics (NVMe-oF) standard.

Focused on speed and scale, KumoScale delivers the flexibility of a shared, clustered storage pool at the similar performance of DAS (Direct Attached Storage) NVMe SSD.

The software is designed to make NVMe flash more efficient, more cost-effective, and easier to deploy at scale.

How KumoScale benefits your business

Key Features

  • Born in the cloud – Integrated with Kubernetes, Ansible, Graphite and other cloud-native infrastructure software.
  • Any platform – Runs on standard x86-based (including AMD-based) storage platforms.
  • Any network – Supports RDMA for highest network performance and TCP/IP for broadest network compatibility.
  • Open – Works with standards-compliant NVMe-oF initiator and NVMe drives.
  • Resilient – Cross-domain volume replication for legacy applications; simple volumes for cloud-native applications.
  • Analytics-driven provisioning – Based on storage classes, QoS and telemetry data.

ApplicationsKumoScale software is designed for deployment at scale in medium to large scale on-premise clouds:

  • Cloud services
  • Consumer SaaS services
  • Marketplaces, clearinghouses (travel, tickets, stock trades)
  • Massively multi-player gaming

Benefits

  • Save storage CapEx with better flash capacity utilization.
  • Increase revenue agility by rapidly adapting stateful workloads during demand peaks.
  • Customize storage performance for each application based on its specific requirements.
  • Significantly improves query performance for OLTP and analytics databases.
  • Simplify procurement and maintenance by consolidating server SKUs.
  • Enable stateful applications to be efficiently load-balanced and elastically scaled.
  • Perform the same work with fewer compute nodes by blending workloads and eliminating stranded resources.
  • Accelerate decision making through faster ingest and analysis.

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