openstack-manuals/doc/arch-design-draft/source/overview-customer-requirements.rst

Customer requirements

The following sections describe business, usage, and performance considerations for customers which will impact cloud design.

Cost

Financial factors are a primary concern for any organization. Cost considerations may influence the type of cloud that you build. For example, a general purpose cloud is unlikely to be the most cost-effective environment for specialized applications. Unless business needs dictate that cost is a critical factor, cost should not be the sole consideration when choosing or designing a cloud.

As a general guideline, increasing the complexity of a cloud architecture increases the cost of building and maintaining it. For example, a hybrid or multi-site cloud architecture involving multiple vendors and technical architectures may require higher setup and operational costs because of the need for more sophisticated orchestration and brokerage tools than in other architectures. However, overall operational costs might be lower by virtue of using a cloud brokerage tool to deploy the workloads to the most cost effective platform.

Consider the following costs categories when designing a cloud:

• Compute resources
• Networking resources
• Replication
• Storage
• Management
• Operational costs

It is also important to consider how costs will increase as your cloud scales. Choices that have a negligible impact in small systems may considerably increase costs in large systems. In these cases, it is important to minimize capital expenditure (CapEx) at all layers of the stack. Operators of massively scalable OpenStack clouds require the use of dependable commodity hardware and freely available open source software components to reduce deployment costs and operational expenses. Initiatives like OpenCompute (more information available at http://www.opencompute.org) provide additional information and pointers. Factors to consider include power, cooling, and the physical design of the chassis. Through customization, it is possible to optimize your hardware and systems for specific types of workloads when working at scale.

Time-to-market

The ability to deliver services or products within a flexible time frame is a common business factor when building a cloud. Allowing users to self-provision and gain access to compute, network, and storage resources on-demand may decrease time-to-market for new products and applications.

You must balance the time required to build a new cloud platform against the time saved by migrating users away from legacy platforms. In some cases, existing infrastructure may influence your architecture choices. For example, using multiple cloud platforms may be a good option when there is an existing investment in several applications, as it could be faster to tie the investments together rather than migrating the components and refactoring them to a single platform.

Revenue opportunity

Revenue opportunities vary based on the intent and use case of the cloud. The requirements of a commercial, customer-facing product are often very different from an internal, private cloud. You must consider what features make your design most attractive to your users.

Capacity planning and scalability

Capacity and the placement of workloads are key design considerations for clouds. One of the primary reasons many organizations use a hybrid cloud is to increase capacity without making large capital investments. The long-term capacity plan for these designs must incorporate growth over time to prevent permanent consumption of more expensive external clouds. To avoid this scenario, account for future applications' capacity requirements and plan growth appropriately.

It is difficult to predict the amount of load a particular application might incur if the number of users fluctuates, or the application experiences an unexpected increase in use. It is possible to define application requirements in terms of vCPU, RAM, bandwidth, or other resources and plan appropriately. However, other clouds might not use the same meter or even the same oversubscription rates.

Oversubscription is a method to emulate more capacity than may physically be present. For example, a physical hypervisor node with 32 GB RAM may host 24 instances, each provisioned with 2 GB RAM. As long as all 24 instances do not concurrently use 2 full gigabytes, this arrangement works well. However, some hosts take oversubscription to extremes and, as a result, performance can be inconsistent. If at all possible, determine what the oversubscription rates of each host are and plan capacity accordingly.

Performance

Performance is a critical consideration when designing any cloud, and becomes increasingly important as size and complexity grow. While single-site, private clouds can be closely controlled, multi-site and hybrid deployments require more careful planning to reduce problems such as network latency between sites.

For example, you should consider the time required to run a workload in different clouds and methods for reducing this time. This may require moving data closer to applications or applications closer to the data they process, and grouping functionality so that connections that require low latency take place over a single cloud rather than spanning clouds.

This may also require a CMP that can determine which cloud can most efficiently run which types of workloads.

Using native OpenStack tools can help improve performance. For example, you can use Telemetry to measure performance and the Orchestration service (heat) to react to changes in demand.

Note

Orchestration requires special client configurations to integrate with Amazon Web Services. For other types of clouds, use CMP features.

Cloud resource deployment

The cloud user expects repeatable, dependable, and deterministic processes for launching and deploying cloud resources. You could deliver this through a web-based interface or publicly available API endpoints. All appropriate options for requesting cloud resources must be available through some type of user interface, a command-line interface (CLI), or API endpoints.

Consumption model

Cloud users expect a fully self-service and on-demand consumption model. When an OpenStack cloud reaches the massively scalable size, expect consumption as a service in each and every way.

• Everything must be capable of automation. For example, everything from compute hardware, storage hardware, networking hardware, to the installation and configuration of the supporting software. Manual processes are impractical in a massively scalable OpenStack design architecture.
• Massively scalable OpenStack clouds require extensive metering and monitoring functionality to maximize the operational efficiency by keeping the operator informed about the status and state of the infrastructure. This includes full scale metering of the hardware and software status. A corresponding framework of logging and alerting is also required to store and enable operations to act on the meters provided by the metering and monitoring solutions. The cloud operator also needs a solution that uses the data provided by the metering and monitoring solution to provide capacity planning and capacity trending analysis.

Location

For many use cases the proximity of the user to their workloads has a direct influence on the performance of the application and therefore should be taken into consideration in the design. Certain applications require zero to minimal latency that can only be achieved by deploying the cloud in multiple locations. These locations could be in different data centers, cities, countries or geographical regions, depending on the user requirement and location of the users.

Input-Output requirements

Input-Output performance requirements require researching and modeling before deciding on a final storage framework. Running benchmarks for Input-Output performance provides a baseline for expected performance levels. If these tests include details, then the resulting data can help model behavior and results during different workloads. Running scripted smaller benchmarks during the lifecycle of the architecture helps record the system health at different points in time. The data from these scripted benchmarks assist in future scoping and gaining a deeper understanding of an organization's needs.

Scale

Scaling storage solutions in a storage-focused OpenStack architecture design is driven by initial requirements, including IOPS <Input/output Operations Per Second (IOPS)>, capacity, bandwidth, and future needs. Planning capacity based on projected needs over the course of a budget cycle is important for a design. The architecture should balance cost and capacity, while also allowing flexibility to implement new technologies and methods as they become available.

Network

It is important to consider the functionality, security, scalability, availability, and testability of the network when choosing a CMP and cloud provider.

• Decide on a network framework and design minimum functionality tests. This ensures testing and functionality persists during and after upgrades.
• Scalability across multiple cloud providers may dictate which underlying network framework you choose in different cloud providers. It is important to present the network API functions and to verify that functionality persists across all cloud endpoints chosen.
• High availability implementations vary in functionality and design. Examples of some common methods are active-hot-standby, active-passive, and active-active. Development of high availability and test frameworks is necessary to insure understanding of functionality and limitations.
• Consider the security of data between the client and the endpoint, and of traffic that traverses the multiple clouds.

For example, degraded video streams and low quality VoIP sessions negatively impact user experience and may lead to productivity and economic loss.

Network misconfigurations

Configuring incorrect IP addresses, VLANs, and routers can cause outages to areas of the network or, in the worst-case scenario, the entire cloud infrastructure. Automate network configurations to minimize the opportunity for operator error as it can cause disruptive problems.

Capacity planning

Cloud networks require management for capacity and growth over time. Capacity planning includes the purchase of network circuits and hardware that can potentially have lead times measured in months or years.

Network tuning

Configure cloud networks to minimize link loss, packet loss, packet storms, broadcast storms, and loops.

Single Point Of Failure (SPOF)

Consider high availability at the physical and environmental layers. If there is a single point of failure due to only one upstream link, or only one power supply, an outage can become unavoidable.

Complexity

An overly complex network design can be difficult to maintain and troubleshoot. While device-level configuration can ease maintenance concerns and automated tools can handle overlay networks, avoid or document non-traditional interconnects between functions and specialized hardware to prevent outages.

Non-standard features

There are additional risks that arise from configuring the cloud network to take advantage of vendor specific features. One example is multi-link aggregation (MLAG) used to provide redundancy at the aggregator switch level of the network. MLAG is not a standard and, as a result, each vendor has their own proprietary implementation of the feature. MLAG architectures are not interoperable across switch vendors, which leads to vendor lock-in, and can cause delays or inability when upgrading components.

Dynamic resource expansion or bursting

An application that requires additional resources may suit a multiple cloud architecture. For example, a retailer needs additional resources during the holiday season, but does not want to add private cloud resources to meet the peak demand. The user can accommodate the increased load by bursting to a public cloud for these peak load periods. These bursts could be for long or short cycles ranging from hourly to yearly.

Compliance and geo-location

An organization may have certain legal obligations and regulatory compliance measures which could require certain workloads or data to not be located in certain regions.

Compliance considerations are particularly important for multi-site clouds. Considerations include:

• federal legal requirements
• local jurisdictional legal and compliance requirements
• image consistency and availability
• storage replication and availability (both block and file/object storage)
• authentication, authorization, and auditing (AAA)

Geographical considerations may also impact the cost of building or leasing data centers. Considerations include:

• floor space
• floor weight
• rack height and type
• environmental considerations
• power usage and power usage efficiency (PUE)
• physical security

Auditing

A well-considered auditing plan is essential for quickly finding issues. Keeping track of changes made to security groups and tenant changes can be useful in rolling back the changes if they affect production. For example, if all security group rules for a tenant disappeared, the ability to quickly track down the issue would be important for operational and legal reasons. For more details on auditing, see the Compliance chapter in the OpenStack Security Guide.

Security

The importance of security varies based on the type of organization using a cloud. For example, government and financial institutions often have very high security requirements. Security should be implemented according to asset, threat, and vulnerability risk assessment matrices. See security-requirements.

Service level agreements

Service level agreements (SLA) must be developed in conjunction with business, technical, and legal input. Small, private clouds may operate under an informal SLA, but hybrid or public clouds generally require more formal agreements with their users.

For a user of a massively scalable OpenStack public cloud, there are no expectations for control over security, performance, or availability. Users expect only SLAs related to uptime of API services, and very basic SLAs for services offered. It is the user's responsibility to address these issues on their own. The exception to this expectation is the rare case of a massively scalable cloud infrastructure built for a private or government organization that has specific requirements.

High performance systems have SLA requirements for a minimum quality of service with regard to guaranteed uptime, latency, and bandwidth. The level of the SLA can have a significant impact on the network architecture and requirements for redundancy in the systems.

Hybrid cloud designs must accommodate differences in SLAs between providers, and consider their enforceability.

Application readiness

Some applications are tolerant of a lack of synchronized object storage, while others may need those objects to be replicated and available across regions. Understanding how the cloud implementation impacts new and existing applications is important for risk mitigation, and the overall success of a cloud project. Applications may have to be written or rewritten for an infrastructure with little to no redundancy, or with the cloud in mind.

Application momentum

Businesses with existing applications may find that it is more cost effective to integrate applications on multiple cloud platforms than migrating them to a single platform.

No predefined usage model

The lack of a pre-defined usage model enables the user to run a wide variety of applications without having to know the application requirements in advance. This provides a degree of independence and flexibility that no other cloud scenarios are able to provide.

On-demand and self-service application

By definition, a cloud provides end users with the ability to self-provision computing power, storage, networks, and software in a simple and flexible way. The user must be able to scale their resources up to a substantial level without disrupting the underlying host operations. One of the benefits of using a general purpose cloud architecture is the ability to start with limited resources and increase them over time as the user demand grows.

Authentication

It is recommended to have a single authentication domain rather than a separate implementation for each and every site. This requires an authentication mechanism that is highly available and distributed to ensure continuous operation. Authentication server locality might be required and should be planned for.

Migration, availability, site loss and recovery

Outages can cause partial or full loss of site functionality. Strategies should be implemented to understand and plan for recovery scenarios.

• The deployed applications need to continue to function and, more importantly, you must consider the impact on the performance and reliability of the application when a site is unavailable.
• It is important to understand what happens to the replication of objects and data between the sites when a site goes down. If this causes queues to start building up, consider how long these queues can safely exist until an error occurs.
• After an outage, ensure the method for resuming proper operations of a site is implemented when it comes back online. We recommend you architect the recovery to avoid race conditions.

Disaster recovery and business continuity

Cheaper storage makes the public cloud suitable for maintaining backup applications.

Migration scenarios

Hybrid cloud architecture enables the migration of applications between different clouds.

Provider availability or implementation details

Business changes can affect provider availability. Likewise, changes in a provider's service can disrupt a hybrid cloud environment or increase costs.

Provider API changes

Consumers of external clouds rarely have control over provider changes to APIs, and changes can break compatibility. Using only the most common and basic APIs can minimize potential conflicts.

Image portability

As of the Kilo release, there is no common image format that is usable by all clouds. Conversion or recreation of images is necessary if migrating between clouds. To simplify deployment, use the smallest and simplest images feasible, install only what is necessary, and use a deployment manager such as Chef or Puppet. Do not use golden images to speed up the process unless you repeatedly deploy the same images on the same cloud.

API differences

Avoid using a hybrid cloud deployment with more than just OpenStack (or with different versions of OpenStack) as API changes can cause compatibility issues.

Business or technical diversity

Organizations leveraging cloud-based services can embrace business diversity and utilize a hybrid cloud design to spread their workloads across multiple cloud providers. This ensures that no single cloud provider is the sole host for an application.