Software reliability and availability also have direct and indirect impacts on the business value, reputation, and profitability of a software system. For example, software failures and unavailability can cause user frustration, dissatisfaction, and loss of productivity, as well as damage to data, security, and compliance. On the other hand, software reliability and availability can enhance user experience, retention, and engagement, as well as reduce costs, risks, and liabilities.
In this e-book, we’ll look at four areas where metrics are vital to enterprise IT. Availability is measured as the percentage of time your service or configuration item is available. It tells you how well a service performed over the measurement period. Together they describe the level at which a user can expect a computer component or software to perform. The ability to conduct high availability testing and the capacity to take corrective action each time one of the stack’s components becomes unavailable are also essential. Having standard processes in place for handling common failure scenarios will decrease the amount of time your system is unavailable.
High Availability: Smart vs. Legacy Load Balancers
Software availability is often measured by metrics such as uptime, downtime, availability ratio, and mean time to repair (MTTR). While vendors work to promise and deliver upon SLA commitments, certain real-world circumstances may prevent them from doing so. In that case, vendors typically don’t compensate for the business losses, but only reimburses credits for the extra downtime incurred to the customer. Additionally, vendors only promise “commercially reasonable” efforts to meet certain SLA objectives. Software reliability and availability are important because they affect the user satisfaction, trust, and loyalty towards a software system.
Mean time between failures (MTBF) is one metric used to measure reliability. For most computer components, the MTBF is thousands or tens of thousands of hours between failures. The longer the uptime is between system outages, the more reliable the system is. MTBF is dividing the total uptime hours by the number of outages during the observation period.
It can then restart the problem application that tripped up the crashed server. On the other hand, implementing high availability strategies nearly always involves software. These are typically multiple web application firewalls placed strategically throughout networks and systems to help eliminate any single point of failure and enable ongoing failover processing. High-availability clusters are computers that support critical applications.
Vertical scaling (or “scaling up”) refers to upgrading a single resource. For example, installing more memory or storage capacity to a server. In a physical, on-premises setup, you would need to shut down the server to install the updates.
This term is used to describe “building out” a system with additional components. For example, you can add processing power or more memory to a server by linking it with other servers. Horizontal scaling is a good practice for cloud https://www.globalcloudteam.com/ computing because additional hardware resources can be added to the linked servers with minimal impact. These additional resources can be used to provide redundancy and ensure that your services remain reliable and available.
- Typically, availability as a whole is expressed as a percentage of uptime defined by service level agreements (SLAs).
- Two meaningful metrics used in this evaluation are Reliability and Availability.
- For critical infrastructure, such as hospital emergency rooms or power supply to nuclear power cooling plants, even the six-nines could potentially risk human lives.
- Was it one time of 30 minutes when a technician accidentally downed a router, or was it 10 times of three minutes each where no one knows what happened?
- System availability and asset reliability are often used interchangeably but they actually refer to different things.
In the real world of enterprise IT however, ideal service levels are virtually impossible to guarantee. For this reason, organizations evaluate the IT service levels necessary to run business operations smoothly, to ensure minimal disruptions in event of IT service outages. For either metric, organizations need to make decisions on how much time loss and frequency of failures they can bear without disrupting the overall system performance for end-users.
As demand on your resources decreases, you want to be able to quickly and efficiently downscale your system so you don’t continue to pay for resources you don’t need. Do not be content to just report on availability, duration, and frequency. Use availability information for your continuous improvement cycle. Furthermore, these methods are capable to identify the most critical items and failure modes or events that impact availability.
Similarly, they need to decide how much they can afford to spend on the service, infrastructure and support to meet certain standards of availability and reliability of the system. Another factor that impacts system availability is maintainability, which refers to how quickly technicians detect, locate, and restore asset functionality after downtime. Just like with asset reliability, the higher the maintainability, the higher the availability. This characteristic is commonly measured using a KPI called mean-time-to-repair (MTTR).
The system or component in question will be available 99.999% of the time. Such systems could only be down five minutes a year, so five nines is a high level of reliability. Organizations relying on high-availability systems often require a minimum of four nines or less than an hour of downtime per year.
Reliability refers to the probability that the system will meet certain performance standards in yielding correct output for a desired time duration. System availability is calculated by dividing uptime by the total sum of uptime and downtime. Proper planning and cloud visualization can help you address faults quickly so that they don’t become huge problems that keep people from accessing your cloud offerings.
Two meaningful metrics used in this evaluation are Reliability and Availability. Often mistakenly used interchangeably, both terms have different meanings, serve different purposes, and can incur different cost to maintain desired standards of service levels. Monitoring systems aren’t much use if action isn’t taken to fix the issues identified. To be most effective in maintaining system availability, establish processes and procedures that your team can follow to help diagnose issues and easily fix common failure scenarios.
Availability is the assurance that an enterprise’s IT infrastructure has suitable recoverability and protection from system failures, natural disasters or malicious attacks. For critical infrastructure, such as hospital emergency rooms or power supply to nuclear power cooling plants, even the six-nines could potentially risk human lives. For such specific use cases, several redundant layers of IT system and utility power infrastructure are deployed to reach High Availability figures close to 100%, such as nine-nines or perhaps, even better. Similarly, it is important to mention the difference between high availability and disaster recovery here.
Effective preventive maintenance is planned and scheduled based on real-time data insights, often using software like a CMMS. For example, an asset that never experiences unplanned downtime is 100 percent reliable but if it is shut down every 10 hours for routine maintenance, it would only be 90 percent available. System availability and asset reliability go hand-in-hand because if an asset is more reliable, it’s also going to be more available.