Autonomic Systems

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Autonomic Systems

by James G. Barr

Docid: 00018019

Publication Date: 2204

Publication Type: TUTORIAL


With everything we as human beings have to think about and do, imagine if we
had to consciously instruct our lungs to breathe air or our hearts to pump
blood. Fortunately, we’re free to ignore these life-sustaining processes because
our autonomic nervous system performs these basic
functions for us. The autonomic nervous system is important in a computing
context because it has served as the inspiration – and developmental model –
for “autonomic systems” or “autonomic computing,” a modern data processing
paradigm that emphasizes system self-management and self-development.

Report Contents:

Executive Summary

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With everything we, as human beings, have to think about and do, imagine
if we had to consciously instruct our lungs to breathe air or our hearts
to pump blood. Fortunately, we’re free to ignore these life-sustaining
processes because our autonomic nervous system (ANS) performs these basic
functions for us. This system, which connects our brain stem and spinal cord with our
internal organs, regulates internal body processes without any conscious
effort. The ANS has two divisions: the parasympathetic division, which
maintains normal body functions during normal situations, and the
sympathetic division, which prepares the body for stressful situations –
the so-called “fight or flight” response.1


Faulkner Reports
Robotic Process Automation
Automated Patch Management

The autonomic nervous system is important in a computing context because
it has served as the inspiration and developmental model for
“autonomic systems” or “autonomic computing,” a modern data processing
paradigm that emphasizes system self-management and self-development.

As reported by analyst Ken Hess, IBM is credited with launching the field
of autonomic computing: Big Blue’s Paul Horn coined the term in 2001 and
in 2004 IBM Press published a 336-page Autonomic Computing volume that
described systems that “install, heal, protect themselves, and adapt to
your needs – automatically.”2

The concept of autonomic computing is popular because it promises to
eliminate or, at least, mitigate the human element that is responsible
for poor systems, particularly patch management, leaving systems exposed
to ransomware and other threats. It also reduces the human overhead
associated with systems management, saving money while improving
productivity and performance.

One popular implementation of autonomic computing is IBM’s Db2 autonomic
computing environment, which the vendor describes as a “self-configuring,
self-healing, self-optimizing, and self-protecting” database program. “By
sensing and responding to situations that occur, autonomic computing
shifts the burden of managing a computing environment from database
administrators to technology.”

Autonomic Systems Are Trending

Gartner has identified autonomic systems as one of the “Top Strategic
Technology Trends for 2022,” defining autonomic systems, rather
expansively, as “self-managed physical or software systems that learn from
their environments, and dynamically modify their own algorithms in real
time to optimize their behavior in complex ecosystems. Autonomic systems
create an agile set of technology capabilities that are able to support
new requirements and situations, optimize performance, and defend against
attacks without human intervention.”3

Autonomic Systems Overview

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As IBM neatly summarizes, “Autonomic computing helps to address
[computing] complexity by using [computing] technology to manage
[computing] technology.”4

This brief overview of autonomic systems is intended to introduce their
self-managing attributes, the business imperatives that drive autonomic
investment, and how a mainstream database product, IBM’s Db2, has been
enhanced by injecting autonomic functionality.

Autonomic System Attributes

Autonomic, or “self-adaptive”5, systems – whether hardware,
like servers or storage units. or software, like operating systems or
business applications – share the same four attributes. They are:

Self-configuring – They can “dynamically adapt to changing
environments,” usually according to established IT policies. Such changes
might include the deployment of new components, or the removal of existing

Self-healing – They can “discover, diagnose, and react to
disruptions,” initiating policy-based corrective actions. Such actions
might include quarantining or otherwise isolating a compromised component.

Self-optimizing – They can “monitor and tune resources
automatically,” most commonly by reallocating resources to accommodate
changing workloads.

Self-protecting – They can “anticipate, detect, identity, and
protect against threats from anywhere.” In today’s environment, common
threat vectors include unauthorized access, malware (including ransomware)
infections, and distributed denial of service (DDoS) attacks.6

Autonomic System Imperatives

The demand for autonomic systems, which has grown slowly but steadily
over the past two decades, is poised to explode owing to a combination of
market and technology imperatives.

Total Cost of Ownership – Autonomic systems lower the costs of
technology ownership by:

  • Increasing system utility (through self-configuring and
  • Decreasing system failure (through self-healing and self-protecting);
    and, perhaps most significantly,
  • Reducing the number of skilled technicians committed to system
    maintenance and management.

The Expanding System Universe – Until the mid-1990s, computing was
largely confined to enterprise spaces like data centers and office
buildings. Today, however, computing is everywhere, fueled by innovations
like high-speed networks, smartphones, cloud computing, and, most
recently, edge computing. And it’s not just a geographic expansion, the
total number of information systems has grown from millions to billions
through developments like mobile telephony and, most recently, the
Internet of Things (IoT). In this environment, the notion of deploying
systems that rely on human-level maintenance and management is,
increasingly, a non-starter. Autonomic systems will play an
ever-increasing role in IT deployments.

The Cloud Computing Advantage – According to Engati, a leader in conversational
, “Autonomic computing is widely used in cloud computing
environments because it brings self-monitoring, self-repairing, and
self-optimizing capabilities that improve the whole performance of the
cloud system. Even though autonomic computing can be used in pretty much
any environment, it has shown the ability to bring unmatched levels of
performance improvement in the cloud environments because of it’s
dynamism, scalability, and complex behavior.”7


As a prominent example of an autonomic system, IBM’s Db2 data warehouse
product features an “autonomic computing environment” that is
self-configuring, self-healing, self-optimizing, and self-protecting. As detailed by the vendor, Db2’s autonomic, or automatic, features

Self-tuning memory for single-partition databases – “This feature
responds to significant changes in workload by automatically and
iteratively adjusting the values of several memory configuration
parameters and the sizes of the buffer pools, thus optimizing

Automatic storage – “The automatic storage feature simplifies
storage management for table spaces. When you create a database, you
specify the storage paths for the default storage group where the database
manager places your table space data. Then, the database manager manages
the container and space allocation for the table spaces as you create and
populate them.”

Data compression – “Both tables and indexes can be compressed to
save storage. Compression is fully automatic; once you specify that a
table or index should be compressed.”

Automatic database backups – “A database can become unusable due
to a wide variety of hardware or software failures. Ensuring that you have
a recent, full backup of your database is an integral part of planning and
implementing a disaster recovery strategy for your system.”

Automatic reorganization – “After many changes to table data, the
table and its indexes can become fragmented. The automatic reorganization
process periodically evaluates tables and indexes that have had their
statistics updated to see if reorganization is required, and schedules
such operations whenever they are necessary.”

Automatic statistics collection – “Automatic statistics collection
helps improve database performance by ensuring that you have up-to-date
table statistics. The database manager determines which statistics are
required by your workload and which statistics must be updated.”

Configuration Advisor – “When you create a database, this tool is
automatically run to determine and set the database configuration
parameters and the size of the default buffer pool. This initial automatic
tuning means that your database performs better than an equivalent
database that you could create with the default values.”

Health monitor – “The health monitor is a server-side tool that
proactively monitors situations or changes in your database environment
that could result in a performance degradation or a potential outage. If a
health risk is encountered, the database manager informs you and advises
you on how to proceed.”

Utility Throttling – “This feature regulates the performance
impact of maintenance utilities so that they can run concurrently during
production periods. Currently, you can throttle statistics collection,
backup operations, rebalancing operations, and asynchronous index

Autonomic Security Management

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Not surprisingly, one of the first priorities for autonomic system
developers has been security management, which ticks both the self-healing
and self-protecting boxes, and addresses the number one concern of most
enterprise executives.

An early, and often underrated, success in the field of autonomic
security management is the Windows Update utility, which periodically
searches for functional patches and security fixes to the Windows
operating systems, and then automatically downloads and applies them –
thus avoiding a manual patching process which is often incomplete and
always tardy, and prolongs the exposure of Windows users to potential
cyber attacks.

While programs like Windows Update have helped reduce the risks to PCs,
an even more formidable problem has emerged: IoT smart spaces. As analysts
Changyuan Lin, Hamzeh Khazael, Andrew Walenstein, and Andrew Malton
explain, “Embedded sensors and smart devices have turned the environments
around us into smart spaces that could automatically evolve, depending on
the needs of users, and adapt to the new conditions. While smart spaces
are beneficial and desired in many aspects, they could be compromised and
expose privacy, security, or render the whole environment a hostile space
in which regular tasks cannot be accomplished anymore. In fact, ensuring
the security of smart spaces is a very challenging task due to the
heterogeneity of devices, vast attack surface, and device resource

Figure 1. A Universe of IoT Smart Spaces Requiring Autonomic Security Management

Figure 1. A Universe of IoT Smart Spaces Requiring Autonomic Security Management

Source: Pixabay

For their part, Lin, Khazael, Walenstein, and Malton have designed “an
autonomic security manager that can maintain the security of smart spaces
adaptively.”9 This is one of many such developments that will
rely on autonomic system attributes to secure a sprawling Internet of
Things (IoT) and Industrial Internet of Things (IIoT) information

Google Autonomic Security Operations

Interesting for its ambition, Google’s entry into the autonomic security
management space is Autonomic Security Operations, which strives for
exponential improvement in four critical domains;

People – “[Enhancing] the abilities and effectiveness of your

Processes – “[Distributing and automating] your security processes
and workflows.”

Technology – “[Leveraging] cloud-native technologies that can
operate at planet scale with minimal operational overhead to focus on
solving security challenges.”

Influence – “[Having] a deep integration and significant influence
across your organization to improve the efficacy of your preventive
defenses to minimize the amount of threats [to which] your team has to …

The Future of Autonomic Systems

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AI Boost

Autonomic systems are, by nature, smart systems, and should be conducive
to smart technology. According to experts, however, “research into
integrating artificial intelligence (AI) and machine learning (ML) to
improve resource autonomy and performance at scale continues to be a
fundamental challenge.”

Fortunately, these same experts believe that “AI- and ML-based autonomic
computing will become prevalent with increasing scale and
inter-connectivity of our systems, making manual administration and
adaptation of such systems challenging and expensive.

“We expect AI- and ML-based autonomic computing will be the norm in
the future – with human users still able to influence the behavior of
these systems through the use of judiciously integrated interfaces.
Crucially, with the advent of cyber-physical systems and digital twins,
quality-assured and mission-critical adaptations will become mandatory
because the self-adaptive software will be responsible for physical
assets, such as the unit operations of a processing plant.”11

Systems Development

In recent years, the software development industry has expanded its
DevOps practices, which combine software development with IT operations,
to insert a third crucial element, security; thus creating DevSecOps. The
intent was to integrate security design (such as incorporating wide-scale
data encryption) and security testing (like conducting QA-administered
functionality and performance exercises) into both software development
and operations; in effect, building security into the entire software
development lifecycle (SDLC).

The systems development sector is due for a similar transformation, this
time autonomic. “When building a new [product/service], it is important to
keep in mind the notion of self-evaluation … based on data from embedded
sensors. A business intelligence solution must be at the heart of an
integral model to product or service introduction. Employing an embedded
intelligent system and a machine learning algorithm model is a major

Enterprise Maintenance

Just as enterprise planners came to terms with the pervasive nature of
cybersecurity threats – committing to the implementation of anti-malware
and other protective measures from the mainframe to the IoT edge – the
challenge of maintaining millions of distributed systems and connected
components demands an autonomic approach, one that favors self-help in the
form of self-configuring, self-healing, self-optimizing, and
self-protecting capabilities. Over time, system maintenance protocols and
practices will be organized around autonomic systems and autonomic

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About the Author

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James G. Barr is a leading business continuity analyst
and business writer with more than 40 years’ IT experience. A member of
“Who’s Who in Finance and Industry,” Mr. Barr has designed, developed, and
deployed business continuity plans for a number of Fortune 500 firms. He
is the author of several books, including How to Succeed in Business
BY Really Trying
, a member of Faulkner’s Advisory Panel, and a
senior editor for Faulkner’s Security Management Practices.
Mr. Barr can be reached via e-mail at

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