|Amber Burnett, Daniel Clark, and Edward Fry|
Abstract— In this paper, we present how to improve database efficiency and reduce costs by using elastic database pools in software-as-a-service (SaaS) applications. Integral to this is a full tutorial on how elastic pools can be implemented at scale for databases with varying usage patterns while maintaining security, isolation, performance, availability and scalability using the tools available in Microsoft Azure. Underlying this is the concept of a data management strategy, which involve both an understanding of each business case, user’s data, and also the impact of each one’s usage load on the others. Examples we explore include specifying a different database for each customer, co-locating all customers within a single or small number of databases, and setting up a database pool with discrete databases contained therein. Using Azure’s SaaS Wingtip Application, we demonstrated an elastic database pool reducing provisioning costs for a ticketing service provider without sacrificing performance. The methods used can scale to any number of tenants which mimic real world requirements, and which conclude that elastic databases can offer cost benefits in sharing resources.
Index Terms— architecture, availability, Azure, cloud computing, cost management, database management system, DBMS, design, elastic database pool, elasticity, GDPR, General Data Protection Regulation, isolation, multitenancy, noisy neighbor, performance, isolation, provisioning, SaaS, scalability, security, service level agreement, (SLA), software-as-a-service, SQL, tenant, tutorial, WingTip
The foundation of cloud computing starts with vendors designing and deploying an application once and making available to customers for a fee for use. This is known as a software-as-a-service (SaaS) application. These application solutions can take a wide variety of forms depending on the business and security requirements of the customer and the databases used to house business-specific data. This can involve customers having their own dedicated application and database as well as a shared application and their database (or tenant) sharing provisioning resources, or somewhere in between.Cloud computing has changed the landscape of application architecture in significant ways. Concepts such as machine learning and big data were once only available to those with access to sophisticated hardware. Now, almost anyone with an internet connection can tap into computing power beyond the scope of their machines. Beyond computing power, users can access the same information in real time more efficiently than ever before. The market has grown because of this, with nearly a 1500% growth since 2008 .
The models we focus on are public cloud solutions where customers share a program but have their own unique application instance and require that their data remain isolated. These are popular among cloud computing practitioners and is the focus of this paper due to their nimble capabilities to adjust their size and scope depending on the usage patterns of the application. Because customers co-exist in the same application domain, some unique design considerations must be assured. Fundamental rights such as isolation and security are employed to ensure the data does not fall into unauthorized hands. Service level agreement (SLA) rights include scalability, availability and performance ensure at the quality-level agreed upon by the vendor and end user . Fundamental and SLA rights are non-trivial and lay the foundational criteria we use to assess the performance of our model.
To ensure our model meets the fundamental and service level agreement criteria, data practitioners must define a coherent, vendor-wide plan to handle data created, stored and managed by an organization. This is known as the Data Management Strategy (DBMS) . A possible data management solution includes co-locating all customers within a single or small number of databases and having each customer tenant sharing a single database resource pool. This strategy would be known as a multi-tenant application with multi-tenant pool databases. conversely, you can have different combinations of single tenant application and single tenant databases which involve individual databases and application instances for each customer. Many of the design option combinations either do not meet all the requirements of a SaaS system or cost too much – or both.
In order to design and build these database management solutions, we use Microsoft Azure’s cloud tools to access the computing resources needed in order to scale and test the performance, availability, isolation, and security. While Azure is a relatively new product, cloud computing has been around for decades and allows computer networks to connect and share finite resources to drive greater efficiency. Over time, vendors have turned cloud computing into a commodity with a business model that includes managed services, commodity hardware, metered pay-for-use, and on-demand resource provisioning.
While these business model characteristics all play a role, the concept of on-demand provisioning of resources help differentiate Elastic Database Pools from the other solutions. In a database pool, tenants share computing resources (such as bandwidth and CPU) known as provisioning and tenants only pay for what they use. The database pool design option is of great interest because it may provide a way to both meet key fundamental and SLA requirements while costing less overall than if we had separate provisioning settings for each database. Elastic database pools are also ideal for managing databases that have variable performance strain (or load) and have a high likelihood of scaling. Companies such as Cyberesa have used database pools in a similar fashion and saw costs decrease.
In this paper, we explore these issues and provide a tutorial of how to set up and use an elastic database pool in Microsoft Azure using Azure SQL databases. As a demonstration of our strategy in effect, we employed the Wingtip SaaS Ticketing application within Azure. As a prerequisite to the tutorial, we provide a detailed walkthrough of how to set up Azure and PowerShell. We then provide a guide of how to get started, establishing the tenants in your pool, loading our elastic database pool with database activity, restoring a lost tenant and accessing the cross-tenant reporting data. Using Elastic Pools, we tested the performance of the database at load scenarios at a scale that mimics real world conditions.
I. Software-as-a-Service (SaaS) Applications
The SaaS application model has grown in popularity  over the last several years. Using this model, vendors can deploy a software application once to make it available to customers. Customers license the software by paying a periodic fee, typically monthly and/or by number of users, in order to access the software. Examples of popular SaaS applications include: Adobe Creative Cloud, Microsoft Office 365, GitHub, and even the statistical software SAS On Demand. These clouds can be public or private, depending on the business goals . Public clouds are more popular and are ideal for applications with a high degree of elasticity and range of scalability needing a greater reach. Private clouds are more ideal for workloads with intense security and resource needs while being able to support a shared infrastructure. For this paper, we focus on public clouds as they are more popular and are better suited for varying elasticity.
Cloud technology is particularly useful for deploying a SaaS application because of its elasticity, multitenancy, and scalability. These features have grown to allow SaaS to be the standard bearer for modern enterprise computing and collaboration, and these can take many shapes with regard to how customers can have their own or shared database, tenant, and application to meet their business objectives.
One of the key features of SaaS applications is the access of users to databases owned and managed by the vendors they are using. For the purposes of this paper, we define databases as a structured set of data that is stored within a CPU and is accessible in various ways. In practice, customers can share a database with another customer or have their own .
Each database used in a SaaS model is known as a tenant  in much the same way that each resident of an apartment complex can be considered a tenant. In practice, tenants can share a database with another customer or have their own. A multi-tenant database strategy involves companies employing a third-party provider to house their data in a shared database management service (as opposed to a more traditional model wherein users install software independently from each other on their own computing and networking equipment).
The nature of a SaaS model starts with the concept of an application where a program is designed to address a customer’s service requirement. These applications can be built for a single customer per instance, built for a single instance to be shared by many customers, or built so that each customer shares an application, but is served a unique instance to them. While single tenant applications are still practical for large clients, one of the key flaws in the single tenant method was the overall low utilization averages for each database. One case showed that the CPU utilization can be as low as 4% across 200 database servers. Modern multi-tenant database models correct those utilization needs with their shared resource structure . These single-, multi-, and pooled-tenant databases can be paired with different combinations of single and multi-tenant applications. We focused on those needs and structure here.
II. SaaS Data Management Features
The database multitenancy nature of SaaS applications carries with it important offerings that must be factored into any SaaS application design. These offerings are necessary in order to ensure fundamental rights such as isolation and security as well as service level agreement (SLA) offerings like scalability, availability, and performance. Database management system practitioners need to be dynamic in how they provision resources so that they can meet their job needs without violating SLA offerings for a project to maximize revenue . Because customers have limited control over the infrastructure, database, and application deployment, the SaaS vendor must provide these service offerings to its customers.
Each customer of the SaaS application needs security. Security in this context includes protection from software exploits and common attacks. A security breach can carry significant commercial consequences. For example, in 2015, the makers of MacKeeper “acknowledged a breach that exposed the usernames, passwords, and other information of more than 13 million customers” . Customers might be exposed in ways that carry financial, reputational, and opportunity costs. Customers are connected by virtue of their all being part of the same application. As a result, if one customer is compromised, it is likely that all the others could be, too. Because a breach could easily impact many or all the customers who are subscribed to the SaaS service, market and regulatory consequences for the SaaS provider could be severe. Due to these factors, security is of paramount importance and must be considered carefully in the SaaS application’s design.
Isolation carries increasing importance for everyone and is necessary to safeguard privacy. Security breaches can compromise isolation, of course, but there are additional risks in a SaaS service. Since all customers are logically part of the same application, there might be risk of data leakage, wherein one customer’s information might inadvertently be made visible to another customer. Beyond the obvious market implications of a product that offers poor isolation protection, new regulations such as the General Data Protection Regulation (GDPR)  provide significant penalties for vendors to uphold database isolation. The importance of privacy therefore leads us to a key requirement of any SaaS system – that of customer complete isolation between customers.
Customers using the SaaS application have different needs as time goes on. Commonly, seasonal spikes occur. For instance, consider a SaaS service that provides web sites that other companies, in turn, expose to their own customers for ecommerce purposes. Various industries tend to have differing load peaks, a phenomenon known as seasonality. For example, a retailer could experience spikes in volume around the Christmastime holidays. A flower shop might have spikes around Valentine’s Day and Mother’s Day. A lawn and garden supplier might see its highest business in the summer, and so on. The SaaS application needs to be able to dynamically scale in order to meet the demand of these usage spikes without affecting other tenants.
The addition of new customers in and of itself could cause scalability issues. The system must be able to dynamically provision additional resources as needed to meet the demand of new customers. Inversely, if customer load or demand falls, the system should deallocate resources in order to save on cost.
Performance needs to be consistent in accordance with the SaaS provider’s SLA. For instance, a seasonal spike at one customer should not only be met with enough performance to guarantee that customer’s requirements, but such a spike should also not affect any adjacent customers in the tenant. This is known as the “noisy neighbor” problem. It is the job of the SaaS application to manage this performance transparently to its users.
Availability is a service level agreement condition where a specific resource or value is available and accessible by the customer. Availability is measured in terms of a time percentage of the accessibility of the database, and it can also sometimes be measured in hours. Customers should be able to easily access the database while logged onto the database, so, in cloud computing, availability is paramount. Availability with fault tolerance in both accurate analytical querying and in transactional systems with Atomicity, Consistency, Isolation, and Durability (ACID) properties being ensured .
I. Potential SaaS Database Management Solutions
Meeting the offering requirements for a robust SaaS application involves careful design. A few options are available through varying combinations of single or multiple application and database tenancy, as discussed previously. These options, however, provide different services for the system. Others may meet requirements but at excessive cost. We weigh the various options in this section and determine a design strategy.
A. Multi-Tenant Application with Multi-Tenant Database
In this approach, a single application hosts all users of the system (i.e. a “multi-tenant” application), and a single database is used for all user data (i.e. a “multi-tenant” database). A key benefit of this approach is its low cost. If only one application instance and one database is required for everyone, then the SaaS vendor needs to only pay for those single resources.
However, this approach does not meet all the requirements. In fact, it risks not meeting any of the requirements. First, there is no isolation since all customers are saved in a single database. The risk of information leakage is high. In addition, there are risks to security (a breach could expose all customers in the single database) and performance (noisy neighbors could affect other customers). As a result, this is a poor design choice for a SaaS application.
B. Multi-Tenant Application with Single-Tenant Database
This strategy takes the previous design and provides a separate database for each tenant. Such a decision certainly provides isolation since each tenant is in its own database, and it also enhances security, since any data breach would affect only the customer who was breached. However, it is not perfect, and some data leaks can be caused through malicious tenants and virtual machine instances .
However, this solution would be very expensive. Database resources tend to be expensive, and if you need a separate database for each tenant, the cost grows very quickly. In addition to cost, scalability suffers because scaling might potentially need to occur at more than one – or all – of the databases at once. Such management overhead would be unwieldy. Imagine that a particular SaaS application has millions of users. You would need millions of databases in this scheme!
C. Single-Tenant Application with Multi-Tenant Database
If we flip the approach around and provide each customer with its own application but sharing a database, app isolation would help with security, and going back to a single (or few) databases reigns in cost. Scalability is also improved because we are only using a single database.
However, we are back to the original key problem with having a single database – no isolation. A coding error or security breach could easily expose one user’s data to others.
D. Single-Tenant Application with Single-Tenant Database
In this design, each customer gets its own application and database. Not only are all requirements met, but this solution provides maximum isolation for security and isolation. Unfortunately, scalability is challenging, as in other single tenant solutions. Worst of all, the cost would be greater in this model than in any of the other models!
E. Single-Tenant Application with Pooled Single-Tenant Database (EDP)
Now let’s examine using the previous solution with one modification: instead of using a separate actual database per user, we use a separate logical database per user. Those logical databases are grouped into one or more elastic database pools. These pools are groupings of databases that allow loads across all the databases to be averaged out.
Because the databases are still separated, all of the requirements we have discussed are met in this scenario. However, because they are billed and managed as a block, cost is far lower, and management is far easier.
II. Microsoft Azure Cloud Services
The origins of cloud computing date back to the 1960s. One of the first cases of a shared resource network comes from an IBM computer scientist, Bob Bemer, who designed a program that re-provisioned time that was not being used while other computer scientists and processors were waiting for an I/O and allocated it to users who needed it more urgently. This allowed multiple people to use the systems at the same time while also driving efficiency in lowering the average time needed per user. This time-sharing software eventually led to mainframe, transaction, and grid computing systems as the key resource categories for programmers to share. Azure is the overarching brand name for the cloud-computing service offered by Microsoft, which is one of the market leaders for this type of service along with Amazon’s AWS and Google’s GCC. Azure is an ever-expanding toolbox of various cloud computing services. Azure gives the freedom to build, manage, and deploy applications on a massive, global network using a selection tools and frameworks.
Cloud-native computing with Azure is defined with several key characteristics, such as enablement of a variety of managed platform services (such as virtual machines and data storage), constrained multitenant services running on commodity hardware, on-demand provisioning of resources, and metered pay-for-use in a short term rental model, which is enabled by horizontal cloud scaling .
With these characteristics comes many well-documented advantages, including cost management, elasticity, scalability, and multitenancy. Azure offers a solid implementation toolkit of an elastic database pool service that even allows users to scale their demonstration database to real world sizes. Utilizing the cloud service allows us to focus on the potential benefits of elastic pools rather than creating, installing, or managing the underlying technology. Such benefits in this context realize many of the key benefits of the cloud in general, especially that of multitenancy, elasticity, and scalability .
Among the key benefits of cloud computing, scalability is one of key difference makers to an advanced outsourcing solution. As computing power has grown more advanced over years, more computers are able to access sophisticated applications via virtual machines. The ability to link via a shared network and database is integral to the modern computing landscape. To achieve scalability in your database management strategy, the costs to ensure performance, availability, and scalability of each database are a real concern, and elastic databases are a practical solution to manage resource provisioning while also being easy to implement .
III. Elastic Database Pools (EDP)
As of November 2019, relational database management systems make up 75% of the popularity of all database types. EDP works with relational database managements systems , thus, pooling technology is extremely applicable given the current landscape of the database market. The basic idea behind an EDP is that one database pool “holds” many discrete logical databases. Each database is assigned to a single user or customer in order to meet SaaS requirements. Performance and provisioning occur at the pool level. The key observation that of which this product takes advantage is that not all users need all their database performance capabilities at the same time.
Following up on our earlier ecommerce example, consider the retailer who needs more resources at the end of the year and the flower shop who needs them in the spring. Because one user’s peak is the other’s normal or down time, capacity is available for both when they need it.
The same idea holds true for usage patterns in other time scales. For instance, perhaps one customer needs access during business hours so that their employees can do their jobs. Another customer might need more resources at night for batch processing. Because one user’s peak usage complements the others, we can pay for the average case of the resource from our cloud provider rather than the maximum (i.e. worst) case, which is precisely what was needed in the other models. Cost goes down as a result.
The underlying nature of the cloud supports scalability, and a cloud based EDP takes advantage of this feature to provide scalability when needed. For instance, consider if some non-trivial subset of users all needs their maximum-usage performance at the same time. Maybe a large percentage of customers are retailers, and its holiday time. The pool can be configured to dynamically scale as needed in this scenario.
This may seem counter-intuitive to the basic premise of averaging usage needs across customers; however, it would be naive to think that the system would never need to exceed its average case, so we should plan for such a contingency. Because we can scale beyond the average case when needed and return to the average case when the need has passed, we can meet SaaS SLAs and still save on cost because we only provision the extra resources when they are actually being used rather than all the time.
C. Identifying Candidates for Pooling
It’s important to consider usage patterns when designing the system. For instance, if it is likely that all customers might have similar peaks and valleys in their needs, then pooling might not provide as much cost savings. A better approach might be the single database model with automatic scaling, though, of course, the cost would be higher.
The best scenario is one where users roughly balance out each other’s needs. One user’s needs are high while another’s is low and vice versa. It may be possible, especially with a large SaaS implementation, to place complementary usage patterns on the same server. Additionally, provisioned servers can replicate that pattern with other complementary users, and so on.
Even if usage can’t be predicted up front, a well-written application can monitor usage patterns and migrate users (in their off-peak times) automatically to other servers to gain the benefits of average-case usage. This sort of automatic balancing should be a component of a robust SaaS application. The general rule of thumb is that spikes of at least 1.5 times the average are good candidates for an EDP.
D. Proactive SaaS Resource Allocation
Once the initial SaaS application is designed, the next step should be to add algorithms that can proactively allocate future resources based on historical demand, accounting for the fact that each tenant has its own demand patterns.
Historically, database administrators used prediction models in attempt to forecast the need for database provisioning on a proactive basis. This involved machine learning techniques to extrapolate future data using historical performance data. Over time, this has been adapted to use in conjunction with reactive elastic database pools to identify which variables are key predictors of peak or depression performance .
Among previous works that discuss elasticity in multi-tenant databases, in a study on predictive replication , authors introduce and propose an approach that uses forecasting methods to provision future workloads on cloud database systems. With this prediction, they are able to algorithmically allocate or disseminate resources accordingly to reduce costs and maintain performance SLA requirements.
After using prediction models to characterize the workload of a cloud database system, they were able to compare those results to a more reactive approach. Between the two approaches on a multi-tenant database, the forecasting approach reduced the number of SLA violations by way of elastic replication of database resources. With these results, a key limitation was that the change in resource needs can make prediction difficult to pin down reliably.
Incorporating these techniques into a SaaS application takes advantage of the elasticity of cloud computing and predictive power of machine learning models to make sure resources are available when they are most needed and deallocated when they are not to save on cost.
IV. Actual EDP SaaS Implementation Example
To get a sense of the benefits of EDPs in the real world, let’s consider Cyberesa , a Tunisian software vendor for the hospitality industry. This company processes over 10,000 bookings a day, and any downtime could result in a lost sale. At scale, that could mean a loss of $240k on a high season. Cyberesa used Azure Elastic Databases to help provision the load of the database activity between daytime and nighttime. This allowed them to auto scale settings based on CPU performance, use automatic backups, and use staging slots for an easy, streamlined deployment cycle. The use of Azure improved performance and allowed Cyberesa to reduce each single database requirement from 50 DTUs to 20.
V. EDP Tutorial
It is useful to see this technology in action, so the tutorial includes a written walk-though, a short live demo, and a more in-depth recorded demo, which is available here: https://youtu.be/3XYWxCjOefI.
To demonstrate EDP, we utilized a sample Azure application written by the vendor (i.e. Microsoft) for this purpose. This application is called Wingtip SaaS. The repository can be found here: https://github.com/Microsoft/WingtipTicketsSaaS-DbPerTenant. This which allows the interested reader to reproduce or expand upon the demonstrations of this tutorial. Wingtip simulates a ticket-processing service and demonstrates EDP for several types of ticket-processing vendors.
The demonstration covers creation, provisioning, schema management, monitoring, and support activities. In addition, we demonstrate how EDPs can be used to add users and scale resources. The tutorial conveys the benefits of using this technology, design considerations (nothing is free, after all), and possible downsides as well as a good sense of how to get started and next steps to implement EDP in other SaaS applications.
A. Prerequisite – Azure
Since we are using Azure for the tutorials, it is necessary to have a basic understanding of the technology. This tutorial assumes no prior knowledge of Azure or cloud services. It begins by showing how to log in to the Azure portal and how to obtain some free Azure credit in order to follow along with the demonstrations.
The scope is limited to those services that are used as part of the tutorials. This includes the demonstration web application (which uses App Services, App Service Plan, and Traffic Manager), Azure Resource Manager (ARM) templates, and the demo databases (which use Azure SQL Database and SQL Elastic Pools). It also includes certain limited features of those services, such as provisioning, monitoring, and configuration.
B. Prerequisite – PowerShell
The tutorials rely on PowerShell scripts to accurately execute the demo steps, and many of those steps involve repetitive and/or time-consuming activities, such as provisioning dozens of databases. Because of this, it is necessary to have a basic understanding of PowerShell to follow along. No prior experience in PowerShell is assumed; however, it is helpful to have some basic experience in any programming language or environment in order to understand the basic concepts.
The tutorial begins with how to launch and work with the PowerShell environment. Then, it describes some basic commands and their syntactical structures. Next, scripting staples like variables and control structures are demonstrated. Finally, basic data structures used in the demo scripts like arrays and hashes are shown.
C. Getting Started
This tutorial shows how to get the Wingtip Ticket Processing SaaS demo application set up and usable for all the tutorials to come. It covers provisioning the complete Azure environment for the Wingtip application using PowerShell. Once provisioned, a brief tour of the newly-created artifacts provides hands-on illustration of the starting state of the application, which initially consists of three tenants. Then, a load is placed on the application, and a new tenant is provisioned while the load is applied to demonstrate that the system can stay “up” while tenants are created.
D. Tenant Provisioning
For the tutorials to come, there needs to be at least 20 or so tenants running in the application. This tutorial takes advantage of this need by including how to automate the provisioning of new tenants in PowerShell. It also goes into greater depth on exactly how each tenant is being provisioned in PowerShell. Once all of the new tenants are provisioned, a moderate, steady load is placed on all of them, and we explain how the load is generated using SQL and PowerShell.
E. Pool Load Management
This tutorial ultimately demonstrates how to scale elastic pools up and out. It also shows how to scale individual databases in the pool. However, in order to see how variations in load affect the performance of the pool, it is first necessary to understand what monitoring tools are available. Therefore, this tutorial begins by showing how to monitor the pool as a whole and individual databases within that pool. The monitors use the Database Transaction Unit (DTU), an amalgamation of multiple performance measures including CPU and disk times.
Next, the tutorial demonstrates how to set up alerts and goes through a few of the metrics available for alerting as well as various thresholds and communication options. With the sample alert in place, load is increased to about double its previous level along with long spikes. The result of this load is shown on the pool and databases, and the alert is shown to have been triggered.
With the pool at its maximum performance capability, the tutorial demonstrates scaling up by showing how to double the DTU capacity of the pool and the effect this has on better handling the heavier load. Then, the tutorial adds a second pool and moves about half of the databases into it to show scaling out. Finally, a very heavy load is placed on a single database, and the tutorial shows how to scale up just that one database without affecting the rest of the pool.
F. Tenant Recovery
This tutorial demonstrates how to recover a tenant without bringing any other tenants down or affecting any other parts of the application. First, some events in the application are shown, and then those events deleted and verified as being gone. Then, the backup database that Azure automatically keeps is restored for that tenant only. All other tenants are running with load at the same time. Finally, the missing event is shown to have been restored.
G. Cross Tenant Reporting
This tutorial shows how to report across tenants for application-wide insight. To do this, hundreds of tickets are first automatically purchased for each of the tenants in the system. We then validate that the tickets were successfully purchased and that the information is available in a special database in the catalog database server. This database has information from all the tenants (each of which have their own independent databases), which allows the information to be queried collectively. Finally, queries are executed against the catalog reporting database, and the resulting execution plans are examined to see how the database executes queries across tenants.
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