Comprehensive Evaluation of Software Ageing Indicators in OpenStack Cloud Deployments

Core Concepts

Software ageing is a phenomenon that leads to gradual performance degradation and increased failure rate in complex computational systems over time. This study comprehensively evaluates two key software ageing indicators, memory usage and request response time, in OpenStack cloud deployments under varying configurations and workload concurrency levels.

Abstract

The paper presents a comprehensive analysis of software ageing in OpenStack cloud deployments. It focuses on evaluating two key ageing indicators: memory usage (including swap memory) and request response time.
The study was conducted using an accelerated testing approach, where OpenStack was subjected to increased workload concurrency to expedite the ageing process. Experiments were performed on both single-node and multi-node OpenStack configurations, with concurrency levels ranging from 1 to 64.
The analysis revealed several key insights:

Failure analysis: The authors identified different types of failures in OpenStack, including ageing errors that leave behind leftover entities and non-ageing errors that do not impact the ageing process. These failures were found to significantly contribute to the ageing of the system.

Ageing evaluation: The study quantified the ageing and rejuvenation effects on the two ageing indicators using statistical techniques like the Mann-Kendall test and Sen's slope. The results showed that increased workload concurrency led to faster software ageing.

Ageing indicators evaluation: The analysis of memory usage and request response time as ageing indicators provided valuable insights. Memory usage, including swap memory, exhibited clear ageing trends, while response time showed more complex patterns, sometimes contradicting the expected ageing behavior.

The authors also discussed the implications of their findings on system performance, reliability, and the need for effective software rejuvenation strategies in OpenStack cloud environments.

Stats

The OpenStack system failed due to the exhaustion of physical space on the compute nodes.
In scenario 6, the system successfully executed workloads for the initial 14 hours, continuously operating in an overloaded state.
In scenario 5, OpenStack managed to execute only 25 workloads before experiencing a failure within the first 10 minutes due to exhaustion of its instance capacity.

Quotes

"Software ageing is a phenomenon in which prolonged usage of complex computational systems leads to the fatigue of their components [6]. It can lead to an increased failure rate, degrade system performance, and even result in premature system failures."
"Memory consumption is used to calculate time before resource exhaustion, while response time is used as a direct performance degradation metric. Studying the degradation trends in response time and memory consumption against failure frequency can serve to predict an increase in failures."

Key Insights Distilled From

On Software Ageing Indicators in OpenStack

by Yevhen Yazvi... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16446.pdf

Deeper Inquiries

How can the proposed methodology be extended to incorporate the waiting phase from the SWARE approach, and how would this impact the analysis of ageing indicators?

To incorporate the waiting phase from the SWARE approach into the proposed methodology, we can introduce a period of system inactivity after the stress phase and before rejuvenation. During this waiting phase, the system would be idle, allowing for the observation of ageing indicators in a quiescent state. This waiting phase would provide valuable insights into the baseline behaviour of the system without any active workload execution, helping to differentiate between the effects of ageing and immediate workload demands on the indicators.
The inclusion of the waiting phase would impact the analysis of ageing indicators by offering a clearer distinction between the effects of ageing and transient workload influences. By comparing the indicators during the stress phase, waiting phase, and rejuvenation phase, researchers can better understand how ageing manifests in the system over time. This extended methodology would provide a more comprehensive view of the system's performance degradation and rejuvenation patterns, enhancing the accuracy of ageing analysis and rejuvenation strategies.

What are the potential implications of the observed computing inconsistency in OpenStack deployments, and how can this be further investigated to improve the reliability of cloud systems?

The observed computing inconsistency in OpenStack deployments can have significant implications for system reliability and performance. Inconsistencies in workload duration, memory usage, and error patterns can lead to unpredictable behaviour, potential system failures, and reduced overall efficiency. These inconsistencies may stem from various factors such as resource limitations, workload variability, or software errors, highlighting the complexity of managing cloud systems.
To improve the reliability of cloud systems and address computing inconsistencies, further investigation is crucial. This can be achieved through in-depth analysis of error patterns, workload characteristics, and system behaviour under different configurations and workloads. Implementing advanced monitoring tools, automated error detection mechanisms, and anomaly detection algorithms can help identify and mitigate computing inconsistencies in real-time. Additionally, conducting controlled experiments with varying parameters and stress levels can provide valuable insights into the root causes of inconsistencies and inform strategies for system optimization and reliability enhancement.

Given the complex interactions between ageing and overload observed in the study, how can these two phenomena be more effectively distinguished and analyzed to provide better insights for cloud system management?

To more effectively distinguish and analyze the complex interactions between ageing and overload in cloud systems, a multi-faceted approach is necessary.

Advanced Monitoring and Analytics: Implementing sophisticated monitoring tools that track ageing indicators, system performance metrics, and workload characteristics in real-time can help identify patterns and correlations between ageing and overload. Utilizing advanced analytics techniques such as machine learning algorithms can provide deeper insights into the relationships between these phenomena.

Scenario-Based Experiments: Conducting controlled experiments with varying levels of workload intensity, system configurations, and rejuvenation strategies can help isolate the effects of ageing and overload on system performance. By systematically varying parameters and observing the system's response, researchers can gain a better understanding of how these phenomena interact and impact each other.

Root Cause Analysis: Investigating the root causes of system failures, errors, and performance degradation can shed light on the underlying factors contributing to ageing and overload. By identifying specific triggers and failure patterns, cloud system managers can implement targeted solutions to mitigate the impact of these phenomena.

Continuous Improvement: Implementing a continuous improvement process based on the findings from the analysis can help optimize system performance, enhance reliability, and proactively address ageing and overload issues. Regularly reviewing and updating system configurations, workload management strategies, and rejuvenation techniques can lead to more efficient cloud system management.

By combining these approaches and leveraging data-driven insights, cloud system managers can effectively distinguish between ageing and overload, leading to better decision-making and improved system performance.