What Is Scalability in Software Engineering?
Scalability is the ability to handle increased demand on a system to meet changing user requirements. It’s all about how an application and its resources, such as processing power, memory, or storage space, will react when more users are added.
In software engineering scalability is the ability of an application or web service to add new users quickly when needed.
Scalability can be understood in terms of the number of concurrent users an application can handle, its network response time, or the amount of data it can process. It is also related to its ability to keep available resources up and running.
The scalability of applications is key to its success as a solution for a given business need. This scalability enables an organization to scale up as needed in order to meet increasing demand.
Key to any successful scalability is systems thinking. The effective scaling of an application involves analyzing and assessing the software architecture, the physical resources required, and network connectivity. Among the key factors that affect scalability include:
– Design patterns
– Development tooling
– Application deployment strategies
– Security practices and strategies.
The scalability of an application is closely related to the scalability of the underlying supporting infrastructure. The architecture of an application can be tuned to match the physical limitations of the underlying network and systems.
The different components within the architecture can be tuned and configured to allow for a higher throughput or it can be configured to handle more connections or data.
The scalability of a system also depends on the nature of its application, its design and structure.
These include layered architectures that are composed of components with varying degrees of autonomy, synchronization points where services are clustered together for better resource use, sticky data access patterns (which may involve caching for performance), etc.
In many cases, the scalability of a system is measured as the maximum concurrent users it can serve. For example, a web application that serves 1 million concurrent users is considered scalable if it can do so in under 50ms.
In addition to being measured with respect to the maximum amount of data that can be served, scalability also depends on how fast the consumer requests are served by a resource and how quickly responses are sent back from that resource.
These metrics are usually referred to as response time and transaction latency respectively.
In its simplest form, scalability can be measured as the percentage of a system that is able to serve additional users when more are requested. The following are common scalability measures:
In its most extreme form, a system is considered “not scalable”, and should be redesigned if it cannot support increased load.
For example, a website is deemed not scalable if it fails to handle 1 million requests per second.
Why Is Scalability Important?
The scalability of an application is important for a variety of reasons, including:
- Scalability enables a web service to provide the maximum number of users with the minimum possible resource cost. This is especially important for business applications that need to allow for many users accessing in parallel.
- A properly designed system will show increased performance as it grows, particularly at higher numbers of users.
- The most basic characteristic of scalable systems is their ability to respond quickly and reliably to load fluctuations while maintaining acceptable response times (usually paramount to the user’s experience).
- In the event that additional users are put on a system, it provides a monitoring tool to identify the resources required to service them. If a resource cannot be increased in size or number, the new users may be able to be serviced by scaling down other non-essential services.
- Scaling out (enabling more users through adding new application servers) means that if a single server crashes, only one user is impacted instead of many.
- It enables a system to be easily migrated to various platforms such as the cloud or PaaS.
- It allows for efficient unplanned upgrades and maintenance.
- It enables portability, an important factor for cloud computing, in which applications can be deployed and run on any number of virtualized servers (or even traditional physical servers) without any modification, allowing resources to be allocated dynamically.
- It allows for more effective use of the database and database resources by spreading across multiple servers.
- It allows scaling of services, such as application servers, which in turn allow them to run on any number of virtualized or physical servers, being able to take advantage of additional resources when they are available.
- It allows for easier management of the system.
What Is Scalability Testing?
In the development of an application, testing is crucial to a software’s success. For example, testing for scalability should consider the nature of the application and its operating system, such as number and type of hardware resources, setup and involved versions of software configurations, and network connectivity.
Testing may also include measuring the system under various load conditions. In assessing scalability with respect to load growth a successful design should demonstrate that the system will scale automatically even when it has more users.
The purpose of testing is to determine that a system meets all requirements and to ensure it performs well in the face of increasing load.
This is called scalability testing. In testing, a good design will allow the number of users to rise without problems, while providing acceptable performance and reliability.
Considerations in testing scalability
The following factors should be considered in testing scalability:
- The structural design and physical implementation of system services
- Physical resources, including CPU, memory and network bandwidth
- The operating system and its configuration (e.g., file system and caching)
- Network topology
- The load patterns of the services being tested; it is important to consider whether they require synchronous, asynchronous or semi-synchronous responses
- When a network service is under load testing, the latency of requests must be considered as well as their throughput, which determines the overall performance for users of the application
- Processing and data storage capacity of the server
- If a system relies on an external resource (e.g., a database) for service, it must be determined whether that resource can also scale, either as a separate system or by using sharding
- The response time of the service under test should be considered
- To measure performance, use existing industry benchmarks and be sure to define criteria and rules for measuring them
- At a minimum, the system should include performance counters for each resource. It is also advisable to measure the throughput and latency of the service, and consider the performance degradation between different load patterns.
- Test for scalability by simulating a large number of simultaneous users on an existing server. This can be done by placing random numbers in various locations in a database and seeing how many active users respond to each
- Consider testing with failure scenarios where making concurrent calls to a system may cause it to fail under load
- While scaling up or down, do not change more than one variable at a time (e.g., adding or removing servers, changing the database parameters, modifying the system architecture)
- While testing with increasing load, ensure that the system is able to respond quickly to increased requests
Analyzing scalability characteristics, and comparing multiple systems, is a key component of effective systems analysis.
It allows the analyst to choose a system based on the needs that are most critical to its success.
By identifying and analyzing scalability factors, an analyst can help determine whether or not a system is scalable, and what design modifications should be made to improve scalability if necessary.
What Is “Scalability Engineering”?
Scalability engineering is a specialized role in software development that focuses on optimizing software performance by improving functional design to scale better under high loads.
It is important to remember that scalability depends on good design, which requires careful analysis and planning of performance and reliability.
Software engineers who understand scalability engineering can improve software design, identify bottlenecks or unplanned changes, and ensure that software meets end-user requirements.
The following are the most important steps in developing a scalable system:
- Plan for scalability by analyzing the system’s design and determining which parts of the application can be abstracted or optimized, if at all
- Structure and architect a system that meets user needs while taking into account performance and reliability
- Optimize the architecture in such a way that it is close to optimal by setting up a framework with benchmarks to ensure that new systems will perform well under load
- Develop a test plan that includes load tests and synthetic load tests
- Analyze system performance using other tools such as simulations, profiling, and benchmarking
- Update standards or guidelines used to determine the best design for scalability
- Build a proper testing environment with appropriate hardware and servers able to meet the system’s requirements.
- Test the limits of the system
- Document the process for future use
- Use quality control methods to help reduce mistakes and determine whether or not the system is working properly
What Are “Software Quality Problems”?
Software quality problems are defects that reduce efficiency and reliability of a software application. These problems can be categorized in one of three ways:
- Type I – The system is “faulty”, or operating improperly, due to flaws in its design, implementation, or operation.
- Type II – The system fails to meet requirements and specifications. This can be caused by a variety of factors, such as lack of documentation, incomplete or incorrect specifications, or incorrect assumptions.
- Type III – The system is too complex for its intended purpose, and may result in increased operating costs when maintenance or development is required.
Causes of software quality problems
The main causes of software quality problems can be attributed to the following:
- Weak design and incorrect implementation causes the system to be unreliable
- Incorrect specifications or assumptions results in a system that is not meeting its intended purpose
- Poor management and team development leads to confusion or disagreements, causing delays in implementing the project’s goals.
- Lack of testing, documentation, and training lead to software defects
- Lack of standards makes it difficult for teams of programmers to follow certain guidelines when writing code that is to be integrated into other systems for them to work together
- Insufficient planning and documentation can result in unforeseen changes that may lead to confusion or disagreement in the development process
- Unsuitable hardware can result in maintenance problems, incomplete information, and increased risk of failure
- Insufficient testing time before implementation can cause defects due to rushed implementation with insufficient testing
- Ineffective testing methods and tools can lead to an incorrect understanding of the system’s behavior
- Undocumented knowledge transfer can put a strain on the receiving party to understand the system before writing code, resulting in more extensive testing periods
- Improper planning can result in an inability to meet requirements or deadlines
Most software quality problems are hard to identify in a given time period because of complexity, or are not obvious until later, when tested over time.
What is “scalability readiness” about?
Scalability readiness refers to a brief survey of an organization’s current IT resources and capabilities.
The survey typically contains a series of questions that, when answered, will indicate whether the organization is ready and able to implement a scalable system.
The process of scalability readiness can be used in conjunction with the SLE process as part of a scalability checkup to ensure that an organization has produced a stable, robust system that it can successfully scale up (or down) in order to meet future user needs.
Scalability readiness surveys generally look for the following:
– User expectation
– Capacity planning
– Performance planning
– Capacity management
– Performance management
– Capacity analysis
Scalability and Maintenance
The importance of scalability as a means to meet growing user demand is often overlooked. While many organizations are concerned with maintaining their applications, they often ignore their ability to scale.
Achieving scalability through design and system development can keep the cost down by lowering the resources required to operate the system.
More importantly, it ensures that resource needs are met in real time and that the system remains healthy, stable, safe and secure.
Scalability in the Real World
Sometimes, scalability is not a goal. Consider the case of a web server that serves 10 million users per day. This means that this server can handle 10 million requests per second (RPS).
If there are no other constraints on the system, such as hardware or network availability, then the server can send back responses to all 10 million requests in under 50ms.
With a single user stuck in front of this system, this could be a very good performance result.
However, on a larger scale, this system could not handle these requests if it had to respond to them all in under 50ms (the ideal).
In the meantime, it would be receiving new requests. As long as this system managed to meet the minimum response time standards (ideally between 79 to 84 milliseconds), most users would be satisfied.
However, a few users would be very unhappy if they experienced response times of 200ms or more. They would be prevented from doing their jobs. In addition, the system would become unstable and no longer capable of dealing with its load.
The responsiveness and scalability of a system are important factors in meeting user demand while maintaining acceptable response time standards.
If the system is not able to keep up with user demand, it will still function but not as well as it should in terms of average response time and number of users.
Recommendations for real-world scalability
There are many approaches and techniques for achieving scalable systems. The most successful is to look for high availability, security, and service level agreements that can be utilized to meet user demands.
A system will have high availability when it can perform well even if multiple services are unavailable, such as when a database service is down.
A system with strong security will have less room to grow if a threat penetrates the system. It is important to protect the system from external attacks and limit the damage that can be caused when a breach occurs.
Service Level Agreements:
A system must have service level agreements in place to address performance requirements. This makes it easier for users to know what kind of performance they should expect from the application.
Service level agreements will also help mitigate some of the workloads so that they are not as taxing on the server.
This translates to fewer requests being sent to the servers, which means that less processing power is required on these servers to respond.
Service level agreements will also provide a stable environment for the application. A stable environment allows for growth in the application without requiring it to be scaled.
It will not need more hardware or software. Services can be added or removed as needed.
The agreement may specify how quickly new services can be started and how quickly they can be stopped, which may change based on the workload and user demand during certain periods of time.
1. What is scalability in cloud computing?
The ability to increase or decrease the resources available to a system or service to meet changing demands is known as scalability.
In the context of cloud computing, scalability describes the ability to add or remove computing resources as needed in order to meet fluctuating demands.
For businesses, this means being able to quickly adapt to changes in workloads and traffic patterns.
In summary, Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services).
Scalability in cloud computing is the ability to increase or decrease the size of the computing resources in the cloud to meet the changing needs of the users.
2. What is horizontal scalability?
Horizontal scalability is the ability of a system to add more of the same type of resource, in order to handle increased demand. This can be done by adding more nodes to a cluster, or by adding more parallel instances of the system.
Horizontal scalability is often contrasted with vertical scalability, which is the ability to add more powerful resources to a system in order to handle increased demand.
3. What is vertical scalability?
Vertical scalability is the ability to add resources (CPU, memory, disk, and network) to a system to handle increased load without affecting performance.
Vertical scalability is often contrasted with horizontal scalability, which is the ability of a system to add more nodes/resources of the same type as it needs in order to handle increased demand.
4. What are scalability issues?
A scalability issue is a problem that arises when an information technology system or application is unable to handle the increasing number of transactions or requests as it grows.
This can cause a number of problems for businesses, including lost sales, unhappy customers, and even bankruptcy.
In order to avoid these consequences, it is important to identify and address scalability issues before they cause too much damage.
Issues may arise in three ways:
– Larger increases in load will cause a system to become overloaded, which can cause performance problems or data loss. If a system cannot handle the increased load, performance may degrade and users may have to wait longer for their requests to be processed.
– A system may be unable to handle growing loads, due to lack of scaling components. This can result in system instability if the number of requests increases but its resources don’t support that level of load.
– Adding more users or applications to an overloaded system will cause even further strain on the resources, which will likely result in degraded performance.
5. What is performance?
Performance is defined as the quality of delivery and user satisfaction. The performance capability of a system or service can be described in terms of availability, responsiveness, and scalability.
6. What are the key performance indicators of scalability?
The key performance indicators of scalability are a measure of how well applications scale, either automatically or manually depending on the situation.
– IO operations per second (IOPS): The quantity of data that can be read from and written to disk. IOPS is an important measure for solid state drives, but it’s also important for hard drives as well.
– The average latency measured in milliseconds (ms) since the last request was completed. This time can be related to the average queue length for a system.
– The average response time per request: This is the amount of time that it takes from when a client makes a request to the response being returned to the client.
– The average transaction per second: This is an indicator of how many transactions are completed in one minute.
7. When planning for scalability, what is the most important consideration?
Some of the most important considerations are:
– The type of applications and services in use.
– What resources are being used, how, and by whom?
– Projected growth in demand for the application/service.
8. What are some common scaling issues?
Some common scaling issues are:
– An application may not be able to scale, because of a lack of resources. This can cause poor performance, data loss, and even downtime.
– Applications running on a system may become unstable, due to a lack of scaling components. This can cause system failures and even downtime.
– Scaling too quickly without considering performance may result in degraded performance for the application/service. This can cause unhappy users and lost revenue for the business.
9. What are scalability metrics?
Scalability metrics are used to help determine the actual scale and performance of a system or application. Some of these metrics include:
– The I/O test: This is a test that analyzes the performance of a system by measuring the time it takes to perform certain tasks.
– The average response time: This is an average for the number of milliseconds it takes for a client to receive a response.
– Average queue length: This is measured in number of requests outstanding, and provides an estimate of how many outstanding requests can be supported by any given system’s resources.
– The transaction per second: This is represented as a total of work units processed, and gives an estimate of the level of throughput that can be supported by a system.
– The CPU utilization: This is the current rate at which tasks are being processed by the CPU on a system.
10. What are some rules of thumb for capacity planning?
Some rules of thumb for capacity planning include:
– On average, every 100ms in latency adds 4% to response time.
– The amount of traffic a system can support is limited by the number of resources and the speed of each resource.
– 100ms is an approximation for average request latency, but it does provide an estimate for how much overhead a client will see handling additional requests.
– Coherence techniques that attempt to keep data in memory can overwhelm, or starve, a system.
11. What are cloud computing scales?
Cloud computing scales refer to the different types or levels of services or resources that can be ordered from a cloud provider. The most popular scales are:
– Single-tenant scale: A web application that is provided as a service.
– Multi-tenant scale: A group of individual web applications that share the same hardware and software.
– Utility scale: Unlimited capacity for resources, with the tradeoff being cost per unit of computing capacity.
12. What are cloud computing deployment models?
The most popular cloud computing deployment models are:
– Private cloud: A type of infrastructure that is operated solely for a single organization. The organization’s data, applications, and tools are managed in-house. Typically there are no public interconnections between systems.
– Community cloud or public cloud: An infrastructure that is shared among multiple organizations. The infrastructure is managed by a third party, but it is accessed from the organization of its choice through a public web service.
13. What are some benefits of cloud computing?
Some benefits of cloud computing include:
– Reduced costs and operation expenses with elastic scalability on demand and self-service for IT staff.
– Automation of manual processes allows for rapid response times to changing environments.
– Pay only for what you use, and pay as you go rather than commit to large capital expenditures.
14. How are cloud computing challenges overcome?
The major challenges associated with cloud computing can be overcome by:
– Planning ahead and recognizing what level of service or resources will be required.
– Understanding all of the costs involved in using a cloud provider, including fixed costs and variable costs.
– Use automation to manage the change process, so as to reduce human error and improve stability.
Scalability is the ability of a system to increase in capacity and capability as a result of increasing demand placed on it.
Scalability may be considered an attribute of a system, but it is not an absolute term. Instead, it exists on a spectrum according to how well the designer and builders manage scalability issues in their work.