The American Journal of Engineering and Technology
154
https://www.theamericanjournals.com/index.php/tajet
TYPE
Original Research
PAGE NO.
154-158
10.37547/tajet/Volume07Issue05-14
OPEN ACCESS
SUBMITED
24 March 2025
ACCEPTED
20 April 2025
PUBLISHED
19 May 2025
VOLUME
Vol.07 Issue 05 2025
CITATION
Alexandr Hacicheant. (2025). Ensuring the Availability of Critical Cloud
Services Through SRE Practices. The American Journal of Engineering and
Technology, 7(05), 154
–
158.
https://doi.org/10.37547/tajet/Volume07Issue05-14
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Ensuring the Availability of
Critical Cloud Services
Through SRE Practices
Alexandr Hacicheant
Software Engineer and Head of Reliability Engineering at Mayflower.
Author LinkedIn Profile: -
company's site: -
Abstract:
The availability of cloud services is a critical
factor in the success of digital products. Downtime in
essential systems
—
whether for fintech platforms or
major online retailers
—
can lead to substantial financial
losses and reputational damage. Meanwhile, modern
cloud infrastructures continue to grow in complexity.
Distributed architectures, automated scaling, and
frequent software releases all increase the risk of
system failures.
In this dynamic environment, companies are actively
seeking strategies to minimize incidents and mitigate
their impact. One of the most effective approaches is
Site Reliability Engineering (SRE)
—
a discipline
pioneered at Google that combines engineering best
practices with operational processes to enhance the
reliability and resilience of cloud services.
This article examines how Site Reliability Engineering
(SRE) principles address the challenges of maintaining
cloud services availability. Alexandr Hacicheant, Head
of Reliability Engineering at Mayflower, provides an
analysis of key issues in this field, the core
methodologies of SRE, and real-world applications that
contribute to minimizing downtime.
Keywords:
S
ite Reliability Engineering,
SRE, С
loud
services, Infrastructure, SLI
, SLO
Introduction:
Challenges in Cloud Infrastructure Availability
Modern cloud services function within distributed
environments characterized by fluctuating workloads
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and infrastructure spanning multiple data centers
worldwide. This complexity presents challenges in
scalability,
traffic
balancing,
and
maintaining
continuous service availability.
A primary challenge in managing distributed systems is
their inherent complexity. Cloud services operate
across thousands of nodes, each vulnerable to
hardware failures, network congestion, and request
processing delays. As demand increases, resources
must be rapidly scaled; however, dynamic scaling itself
can introduce instability.
Another significant risk factor is human error. When
resource scaling is performed manually in response to
rising demand, delays in provisioning additional servers
may lead to service downtime. Misconfigurations
—
such as incorrect hostnames, IP addresses, or port
settings
—
can also contribute to system failures.
Furthermore, erroneous changes may be applied to the
wrong environment, or critical resources, such as
virtual machines or databases, may be unintentionally
deleted. In the absence of well-defined testing
procedures and rollback mechanisms, such errors can
result in substantial business disruptions.
Service availability is also constrained by unpredictable
failures that cannot be entirely eliminated. These may
include sudden outages of major data centers, network
disruptions between regions, or failures in third-party
APIs on which a service relies. Even when systems are
designed with redundancy, unforeseen circumstances
can still result in service disruptions.
In this context, a key challenge for cloud infrastructure
is predicting failures and mitigating their impact.
Effectively
addressing
this
issue
requires
a
comprehensive strategy that integrates automation,
continuous
monitoring,
and
rapid
recovery
mechanisms.
How SRE Addresses These Challenges
Site Reliability Engineering (SRE) is an engineering-
driven discipline focused on ensuring system reliability
and high availability. At its core, SRE establishes
measurable
standards
for
evaluating
service
performance and systematically managing incidents.
Rather than aiming for absolute failure prevention, this
approach prioritizes minimizing the impact of
disruptions and utilizing incidents as opportunities for
continuous learning and process refinement.
Failures as an Expected Norm: Accepting the
Inevitability of Outages
Given the complexity of modern distributed systems,
eliminating failures entirely is infeasible. SRE
acknowledges failures as an inherent aspect of system
operations and shifts the focus from prevention to
mitigation. The objective is to minimize the effects of
failures and restore service functionality as efficiently
as possible.
Rather than adopting a purely reactive approach, SRE
teams rely on comprehensive monitoring and proactive
alerting mechanisms to anticipate potential system
degradation. For instance, if API response latency
surpasses a predefined threshold, an automated scaling
process can be triggered to allocate additional
resources before users experience any perceptible
decline
in
service
quality.
Moreover, instead of allowing an outage to fully disrupt
operations, systems can be designed to degrade
gracefully, maintaining partial functionality under
adverse conditions. For example, if a database becomes
temporarily unavailable, the system can retrieve
cached query results, ensuring that users continue to
receive responses while engineers resolve the
underlying issue.
A Unified Culture of Responsibility
A key limitation of traditional development models is
the separation of responsibilities: developers primarily
focus on building new features, while operations
engineers are tasked with maintaining system stability.
This division often leads to inefficiencies and delays in
deploying updates. SRE addresses this challenge by
integrating these roles, fostering a collaborative culture
that enhances consistency and accelerates the
deployment
of
new
functionality
without
compromising reliability.
Under this model, developers assume accountability
for the operational performance of their code. Rather
than limiting their responsibilities to writing and
shipping software, they also monitor its real-world
behavior in production environments. This approach
improves the quality of releases, reduces unexpected
failures, and encourages a proactive stance toward
system reliability. Conversely, SRE engineers actively
contribute to the development process by designing
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robust and scalable systems from the outset, ensuring
that reliability is embedded at every stage of the
software lifecycle.
Error Budget: A Risk Management Tool
While the complete elimination of failures is
unattainable, their frequency can be managed within
acceptable limits. To achieve this, SRE introduces the
concept of an error budget
—
a predefined threshold
that specifies the permissible amount of downtime
within a given period.
For example, if a service has an availability target of
99.95%, its error budget allows for 0.05% downtime per
month. This mechanism provides flexibility in release
management: when a system demonstrates high
reliability, updates can be deployed more frequently.
Conversely, if the error budget is depleted due to a
series of incidents, deployments are temporarily
paused to prioritize system stabilization. Furthermore,
error budgets help teams assess acceptable risk levels
and reinforce a reliability-first mindset across
development and operations.
Rapid Detection and Isolation of Issues
Minimizing downtime requires a structured approach
to failure recovery, with Mean Time to Recovery
(MTTR) serving as a critical performance metric. To
reduce MTTR, automated rollback strategies are
employed, enabling the system to revert to a previously
stable version without requiring manual intervention in
the event of a failed deployment. Additionally, feature
flags allow teams to selectively disable problematic
features rather than rolling back an entire release,
thereby preserving system stability while addressing
localized issues.
Another widely adopted strategy for risk mitigation is
canary releases, wherein new features are initially
deployed to a limited subset of users. If the system
remains stable, the deployment is incrementally
expanded to a broader audience. This controlled rollout
minimizes the potential impact of defects while
facilitating continuous innovation.
Measuring Reliability with Metrics
●
Service Level Objectives (SLO)
define target
reliability levels that organizations commit to
maintaining. These objectives help establish clear
risk thresholds based on key performance
indicators such as uptime, response times, and
request
success
rates.
For instance, an SLO may specify that a system must
maintain 99.9% availability per month, meaning
total downtime should not exceed 43 minutes. If
real-time monitoring data indicates a deviation
from this target, engineers receive alerts, often
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through tools such as Prometheus Alertmanager,
facilitating a rapid response.
●
Service Level Indicators (SLI)
provide an empirical
assessment of system health by tracking metrics
such as response latency, API success rates, and
error
frequencies.
These
indicators
allow
organizations to detect anomalies and address
potential reliability concerns before they escalate
into major incidents. Analytical tools such as
Grafana and Kubernetes’ built
-in monitoring
capabilities are commonly used to collect and
analyze SLI data.
Practical Example: Configuring SLI and SLO Metrics
Implementing SRE best practices requires a structured
approach to measuring and improving service
reliability. This process involves defining key
performance indicators, monitoring system behavior,
and continuously refining infrastructure to minimize
downtime.
Consider a large international marketplace that
processes millions of orders each month across
multiple regions. Critical service scenarios include
product catalog browsing, search functionality, cart
operations, and payment processing.
For the payment system, an SLI may be defined as the
percentage of successful transactions, transaction
processing time, and service uptime. Similarly, for
search functionality, SLIs could include request success
rates, response times, and the accuracy of retrieved
product metadata.
To establish performance benchmarks, historical data
must be collected using tools such as Sentry Distributed
Tracing or Jaeger for request routing analysis, Web
Vitals for frontend performance insights, and ELK stack-
based log management systems for in-depth incident
analysis.
Based on this data, baseline performance indicators can
be defined. For example:
●
Search requests demonstrate an availability of
99.92%, with a response time of 350 ms in 95% of
cases.
●
Cart operations exhibit 99.95% availability, with a
response time of 240 ms in 95% of cases.
●
Payments are successfully processed in 99.93% of
cases, with a transaction time of 3.1 seconds in 95%
of cases.
From these baselines, SLOs can be established in
alignment with business objectives. For instance, an
SLO for search functionality might require 99.95%
availability (with an error budget allowing 22 minutes
of downtime per month), response times below 400 ms
for 95% of requests. For the payment system, the SLO
could specify a transaction success rate of 99.95%, a
processing time of no more than 3.5 seconds for 95% of
transactions, and system availability of at least 99.98%
(error budget: 8.6 minutes per month).
Properly configured SLI and SLO metrics enable
marketplaces to scale while maintaining service
stability, ensuring an optimal user experience even as
traffic volumes grow.
Limitations of SRE
Despite its advantages, implementing SRE presents
several challenges. One of the primary concerns is
cost
—
establishing
robust
monitoring
systems,
automating operational processes, and developing
incident response frameworks require substantial
upfront investment and dedicated team efforts.
From a technical standpoint, selecting appropriate SLIs
is critical. Poorly defined metrics can result in alert
fatigue, leading to either excessive false positives or
insufficient incident detection. For example, setting an
overly aggressive response time threshold may
generate an overwhelming number of alerts, causing
teams to overlook genuinely critical issues.
Cultural resistance also poses a significant challenge.
Successful SRE adoption requires fundamental shifts in
organizational processes, including close collaboration
between development and operations teams.
However, in companies with rigidly established
workflows, this shift may be met with resistance.
Additionally, some organizations may have punitive
cultures that discourage open discussions about
failures, making it difficult to implement post-mortem
analyses effectively.
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To address this issue, implementing a blameless culture
can be beneficial. This approach focuses on systemic
problems rather than individuals, viewing failures as a
consequence of system imperfections rather than
mistakes made by specific employees.
With this mindset, the approach to errors becomes
proactive: they are seen as opportunities to improve
the system and foster team growth. Early detection of
failures is encouraged, leading to open discussions and
knowledge sharing among teams.
For example, Google follows a postmortem culture:
after each incident, a document is created to analyze
the root causes without pointing fingers. The principle
of "Learning, not blaming" is applied, emphasizing
lessons learned from each incident and preventing
future
occurrences
through
actionable
steps.
Additionally, the Error Budget serves as an objective
tool for balancing development speed and system
stability.
Amazon, on the other hand, follows the "Two-Pizza
Team" approach, which involves forming small,
autonomous teams with full ownership of their
services. These teams participate in every stage of the
product
lifecycle,
from
gathering
business
requirements to delivering the final solution into
production.
CONCLUSION
This article has examined the key challenges associated
with ensuring high availability in cloud environments
and demonstrated how the adoption of SRE
methodologies can mitigate downtime and enhance
infrastructure resilience. By integrating error budgets,
proactive
monitoring,
automated
recovery
mechanisms, and retrospective incident analyses,
organizations can systematically improve reliability
while maintaining operational efficiency.
Despite requiring significant investment in training,
process restructuring, and automation, the long-term
benefits
—
such as reduced failure rates, faster incident
resolution, and enhanced user satisfaction
—
position
SRE as an industry standard in modern cloud
computing. Future research directions include refining
failure prediction models, developing self-healing
systems, and integrating SRE methodologies with
emerging architectural paradigms such as serverless
computing and edge infrastructure.
REFERENCES
Impact of Site Reliability Engineering on Manufacturing
Operations: Improving Efficiency and Reducing
Downtime, IJSRP, 2020
https://www.arxiv.org/abs/2008.06717
Site
Reliability
Engineering
(SRE),
SITE RELIABILITY ENGINEERING A MODERN APPROACH
TO ENSURING CLOUD SERVICE UPTIME AND
RELIABILITY, IJCET, 2024
Evaluating the Impact of Site Reliability Engineering on
Cloud Services Availability, WJAETS, 2020
Using Cloud-Native and SRE Principles to Achieve Speed
and
Resiliency,
IBM
