HPE ASE Edge-to-Cloud key terms

A design approach that distributes workloads across devices at the network edge, on-premises data centers, and public cloud, connected by common management, identity, and data policies so resources act as one operating environment.

Processing data close to where it is generated, such as in a factory, store, or cell tower, to reduce latency and backhaul traffic. Edge sites are usually small, remote, and lightly staffed, so they need rugged hardware and remote management.

The maximum acceptable delay for a transaction or workload, measured end to end. Architects use it to decide which processing stays at the edge and which can safely run in a centralized data center or cloud region.

A data center network topology in which every leaf switch connects to every spine switch, giving predictable, low-hop paths between servers. Traffic between leaves crosses one spine, which supports equal-cost multipathing and scale-out growth.

A network virtualization design that carries Layer 2 segments over a Layer 3 IP fabric using VXLAN encapsulation, with Ethernet VPN control-plane signaling for MAC and IP learning. It enables large multi-tenant networks and stretched segments across sites.

A logical network built on top of physical infrastructure, using tunnels or encapsulation to connect endpoints regardless of their physical location. Overlays let operators define segments and policies in software rather than recabling.

The physical switches, links, and routing that carry encapsulated overlay traffic. It must provide stable, high-bandwidth IP connectivity, usually with Layer 3 routing to every node and consistent MTU settings across the fabric.

Maximum transmission unit is the largest packet size a link carries. Encapsulation adds headers, so fabrics often raise MTU to 9000 bytes (jumbo frames) to avoid fragmentation and keep tunneled traffic performing well end to end.

The ratio between the total capacity of downward-facing ports and the capacity of uplinks toward the spine. A 3:1 ratio means hosts can theoretically demand three times the available uplink bandwidth, which matters when sizing for bursty workloads.

A security model that grants access per request based on user and device identity and context, rather than trusting anything inside the network perimeter. Every session is authenticated and authorized, and least privilege is enforced continuously.

Dividing a network into fine-grained zones and writing allow-list rules between them, often enforced by the hypervisor or host firewall. It limits lateral movement after a compromise and is a core control in zero-trust designs.

A separate, dedicated path for administering servers and edge devices, typically through a baseboard management controller, independent of the production network and operating system. It allows recovery when the main network or OS is down.

Automatically configuring a new device by having it contact a controller on first boot, download its image and settings, and join the fleet without manual setup. It is essential for deploying hardware to sites without skilled staff.

The decision about which platform, site, or cluster runs a given application, based on latency, data gravity, compliance, cost, and available capacity. Placement is increasingly automated by schedulers and policy engines.

An environment combining private infrastructure with public cloud services, linked by networking, identity, and consistent management so applications and data can move between them. It is a operating model, not a single product or location.

The tendency for applications and new services to accumulate near large stores of data, because moving data is slow and costly. It explains why analytics often runs beside storage rather than being relocated to a distant cloud.

Running a workload on private infrastructure normally and extending it into public cloud when demand spikes, then returning to baseline. It requires portable images, shared identity, and fast, secure connectivity between environments.

Describing servers, networks, and services in version-controlled definition files that tools apply automatically. It makes environments repeatable and auditable and reduces configuration drift between edge sites, data centers, and cloud.

An operating model in which a Git repository holds the desired state of infrastructure and applications, and an automated agent continuously reconciles the live environment to match it. Changes flow through review and merge rather than manual steps.

The gradual divergence between a system's documented or intended settings and its actual state, caused by manual fixes and ad hoc changes. Drift causes inconsistent behavior and failed deployments, so it is detected and corrected by automation.

A service that authenticates users and issues assertions or tokens about them to other applications, often using SAML or OpenID Connect. Centralizing authentication lets one policy govern access across edge, data center, and cloud.

A trust arrangement in which one organization's identity provider vouches for a user to another organization's systems, so accounts need not be duplicated. Standards such as SAML and OIDC carry the assertions across domains.

A mechanism letting a user authenticate once and reach many applications without re-entering credentials. It improves security by centralizing authentication and simplifies access removal when someone leaves the organization.

A software-defined computer with virtual CPU, memory, disk, and network devices, run by a hypervisor on shared physical hardware. Each VM carries a full guest operating system and is isolated from its neighbors.

The software layer that creates and runs virtual machines, allocating physical CPU, memory, and I/O among them and enforcing isolation. Type 1 hypervisors run directly on hardware; Type 2 run as an application on a host OS.

A packaged application with its dependencies that shares the host operating system kernel, making it lighter and faster to start than a virtual machine. Containers are built from images and orchestrated in clusters.

An open-source orchestration platform that schedules containers across a cluster, restarts failed ones, and exposes services through declarative objects. It is the common runtime target for applications spanning edge, data center, and cloud.

A repository that stores and distributes versioned container images, with access control and vulnerability scanning. Clusters pull images from it, so reachability and trust of the registry matter at remote edge sites.

Moving a running virtual machine from one host to another with minimal downtime by copying memory pages while the VM continues to run. It supports maintenance, load balancing, and evacuation of failing hardware.

A group of hosts configured to detect failures and restart or relocate workloads automatically, reducing service interruption. Effective HA also requires redundant power, networking, and storage paths so no single fault stops the service.

The minimum number of cluster members that must agree before the cluster makes decisions, preventing two halves from acting independently during a network split. Losing quorum usually stops clustered services to protect data consistency.

A pool of compute, storage, and network resources that software assembles into servers on demand, rather than fixed cabling per server. It speeds redeployment and lets capacity follow workload needs.

A storage architecture that pools commodity disks across servers and delivers features such as replication, snapshots, and tiering in software. It scales by adding nodes and is managed through policy rather than array hardware.

A technique combining multiple physical drives into one logical unit for performance, capacity, or fault tolerance. Common levels mirror data (RAID 1), stripe with parity (RAID 5 or 6), or stripe without protection (RAID 0).

Storing data as fragments plus parity chunks so the original can be rebuilt from a subset of pieces, such as 8 data plus 2 parity. It uses less raw capacity than full replication but costs more CPU and rebuild time.

The number of independent copies of data the system keeps, commonly two or three. Higher factors improve durability and read availability but multiply raw capacity consumed and write amplification.

A point-in-time image of a volume or dataset that preserves its state for rollback or backup. Snapshots are usually space-efficient and fast, but they are not a substitute for copies stored on separate media or offsite.

Backup keeps historical copies to restore after corruption or deletion, while replication continuously copies current data to another location for availability. Replication alone cannot undo a logical error that propagates instantly.

The maximum acceptable amount of data loss measured in time, such as one hour of transactions. It drives backup frequency and replication design, and is set alongside the recovery time objective.

The maximum acceptable time to restore a service after an outage. It determines how much redundancy, automation, and standby capacity is justified, and is always agreed with the business rather than chosen by IT alone.

Placing data on media of different cost and speed, such as flash, fast disk, and archive, and moving it as access patterns change. Policies automate promotion of hot data and demotion of cold data.

A protocol extending the low-latency NVMe command set across a network such as Ethernet or Fibre Channel, giving remote storage near-local performance. It is used for shared all-flash arrays and disaggregated storage.

The ability to understand a system's internal state from its outputs: metrics, logs, and traces. Unlike basic monitoring, it lets teams ask new questions about unexpected behavior without shipping new code.

The collection, filtering, and forwarding path that moves metrics, logs, and events from devices and workloads to analysis tools. At the edge, buffering and store-and-forward behavior keep data intact through connectivity gaps.

A measurable target for a service, such as 99.9 percent availability over a month, derived from user expectations. Objectives drive alerting thresholds and error budgets rather than reacting to every individual symptom.