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The output received by the customer as a result of the service provided is the main focus of the service level agreement. Service level agreements are also defined at different levels: Customer-based SLA: An agreement with an individual customer group, covering all the services they use. For example, an SLA between a supplier (IT service ...
The SLO are formed by setting goals for metrics (commonly called service level indicators, SLIs). As an example, an availability SLO may be defined as the expected measured value of an availability SLI over a prescribed duration (e.g. four weeks). The availability SLI used will vary based on the nature and architecture of the service.
SLIs form the basis of service level objectives (SLOs), which in turn form the basis of service level agreements (SLAs); [1] an SLI can be called an SLA metric (also customer service metric, or simply service metric). Though every system is different in the services provided, often common SLIs are used.
For example, electricity that is delivered without interruptions (blackouts, brownouts or surges) 99.999% of the time would have 5 nines reliability, or class five. [10] In particular, the term is used in connection with mainframes [11] [12] or enterprise computing, often as part of a service-level agreement.
An operational-level agreement (OLA) defines interdependent relationships in support of a service-level agreement (SLA). [1] The agreement describes the responsibilities of each internal support group toward other support groups, including the process and timeframe for delivery of their services.
ITU-T Y.1564 is designed to serve as a network service level agreement (SLA) validation tool, ensuring that a service meets its guaranteed performance settings in a controlled test time, to ensure that all services carried by the network meet their SLA objectives at their maximum committed rate, and to perform medium- and long-term service testing, confirming that network elements can properly ...
For example, if there are 200 to 300 unique page definitions for a given application, group them into 8–12 high-level categories. This allows for meaningful SLA reports, and provides trending information on application performance from a business perspective: start with broad categories and refine them over time.
Per-port metrics are collected using flow-based monitoring and protocols such as NetFlow (now standardized as IPFIX) or RMON. End-user metrics are collected through web logs, synthetic monitoring, or real user monitoring. An example is ART (application response time) which provides end to end statistics that measure Quality of Experience.