Irena Bojanova - Home
Understanding Multi-Tenancy I
Irena Bojanova
APR 24, 2013 08:00 AM
A+ A A-
painting of cowboy wrangling wild horses

It's the High-Tech Wild, Wild West out there!
Although the Cloud Computing marketplace is still chaotic, it is:

  • Exciting
  • Fast-growing
  • Full of opportunities

NIST has defined five essential cloud characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service (please see the "Defining Cloud Computing" post and Figure 1 below).

Resource pooling is defined as follows: "The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, and network bandwidth."

Figure 1

Figure 1. The Cloud Model – Emphasis on Essential Characteristics

Multi-tenancy is tightly related to the resource pooling characteristic and is often discussed as an important element of cloud computing (CSA, Gartner). It implies use of same resources by multiple consumers/tenants from same organization or different organizations, as cloud services leverage shared infrastructure, data, metadata, services, and applications. Data and applications of one consumer/tenant may reside with data and applications of other consumers/ tenants. The impact is visibility/access to confidential residual data or trace of operations by other tenants through the shared platforms, storage, and networks. Keeping memory, storage, and network access isolated is essential in such a multi-tenant, resource sharing environment (please refer the examples in Table 1), as competing companies could be using the same cloud services and shoulder-to-shoulder running their workloads.

Table 1. Examples of Shared Resources by Service Model

Service Model

Shared Resources

Shared By


Same application or database

Different consumers


Same operating system, and supporting data and networking services

Different processes


Same hardware via a hypervisor

Different VMs

Multi-tenancy in cloud service models implies policy-driven enforcement, segmentation, isolation, governance, service levels, and chargeback/ billing models for different types of consumers. It should be considered for all cloud deployment models. Consumers may utilize a public cloud as individual users. A private cloud organization may segment users as different business units sharing common infrastructure. For example, Figure 2 (CSA) illustrates a private cloud company with three business units on shared infrastructure, where each unit has different security, SLA, governance, and chargeback policies.

Figure 2

Figure 2. Multi-Tenancy in a Private Cloud

From a provider's perspective, multi-tenancy implies scale, availability, management, segmentation, isolation, and operational efficiency. For example, Figure 3 (CSA) illustrates a public cloud provider with three business consumers on shared infrastructure, where each consumer has different security, SLA, governance, and billing policies.

Figure 3

Figure 3. Multi-Tenancy in a Public Cloud

When properly implemented multi-tenancy leads to significant economic efficiencies achieved through resources sharing on provider's side. Multi-tenancy can be achieved through the following methods: using a database, using virtualization, or through physical separation. One important research problem is related to building confidence that logical separation is a suitable substitute for physical separation.

In the next post, the risks from multi-tenancy and different methods for achieving multi-tenancy will be discussed.

Anyone have thoughts or sources that will help readers understand multi-tenancy? Please share here!

Irena BojanovaIrena Bojanova, Ph.D., is the Founding Chair of IEEE CS Cloud Computing STC, an associate editor of IEEE Transactions on Cloud Computing, and an editorial board member of IEEE CS IT Professional. She is a professor and program director, Information and Technology Systems, at University of Maryland University College, managed academic programs at Johns Hopkins University and PIsoft Ltd., and co-started OBS Ltd., (now CSC Bulgaria). Her current research interests include cloud computing, web-based systems, and educational innovations. She is a member of the IEEE and can be reached at

[%= name %]
[%= createDate %]
[%= comment %]
Share this:
Please login to enter a comment: