Reliability at the Edge: SRE for Distributed Cloud and IoT Platforms

As computer paradigms travel to the edge to provide resilience, scalability, and uptime in more distributed environments, site reliability engineering (SRE) is redefining itself. This work explores the development of SRE approaches to fit distributed cloud and Internet of Things (IoT) platforms which are distinguished by fragmented architectures, varied network circumstances, and different hardware which are marked by fragmented architectures, varied network circumstances, and different hardware. Site Reliability Engineering (SRE) is the application of software engineering approaches in operations aimed at optimal availability and performance. While distributed clouds span several sites to encourage flexibility and scalability, edge computing puts processing resources closer to the data source to reduce latency. IoT systems link physically active data-generating devices needing fast response. Taken together, these technologies create new dependability problems like limited local observability, edge node failures, intermittent connection, and regional variability. This paper defines these difficulties coupled with plausible SRE solutions based on federated configuration management, autonomous remediation, localized alerting and metrics aggregation, and resilient rollout strategies. One important component is a useful case study on an edge-driven smart logistics platform where adaptive load balancing driven by artificial intelligence-driven predictive maintenance greatly reduced demand and hence improved resource use by way of reducing downtime. This case shows how SRE ideas blameless postmortems, error budgets, and Service Level Objectives (SLOs) adapted to edge-centric ecosystems. The story still stays anchored in pragmatic language, highlighting the dependability of position as a human and commercial issue instead of merely a technical one. Though their main goal is still to provide smooth and consistent digital experiences over a vast, intelligent infrastructure, readers will be well aware of how conventional SRE techniques are evolving to accommodate the complexity of edge activities

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Publication Details

Journal
International Journal of Emerging Research in Engineering and Technology
Published
2025-01-01
DOI
https://doi.org/10.63282/3050-922x.ijeret-v6i2p106
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
3.25
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article

Reliability at the Edge: SRE for Distributed Cloud and IoT Platforms

Hitesh Allam
1 citations
International Journal of Emerging Research in Engineering and Technology
Cloud Computing and Resource Management
3.25
article

Reliability at the Edge: SRE for Distributed Cloud and IoT Platforms

Hitesh Allam
article en
1 citations

Abstract

As computer paradigms travel to the edge to provide resilience, scalability, and uptime in more distributed environments, site reliability engineering (SRE) is redefining itself. This work explores the development of SRE approaches to fit distributed cloud and Internet of Things (IoT) platforms which are distinguished by fragmented architectures, varied network circumstances, and different hardware which are marked by fragmented architectures, varied network circumstances, and different hardware. Site Reliability Engineering (SRE) is the application of software engineering approaches in operations aimed at optimal availability and performance. While distributed clouds span several sites to encourage flexibility and scalability, edge computing puts processing resources closer to the data source to reduce latency. IoT systems link physically active data-generating devices needing fast response. Taken together, these technologies create new dependability problems like limited local observability, edge node failures, intermittent connection, and regional variability. This paper defines these difficulties coupled with plausible SRE solutions based on federated configuration management, autonomous remediation, localized alerting and metrics aggregation, and resilient rollout strategies. One important component is a useful case study on an edge-driven smart logistics platform where adaptive load balancing driven by artificial intelligence-driven predictive maintenance greatly reduced demand and hence improved resource use by way of reducing downtime. This case shows how SRE ideas blameless postmortems, error budgets, and Service Level Objectives (SLOs) adapted to edge-centric ecosystems. The story still stays anchored in pragmatic language, highlighting the dependability of position as a human and commercial issue instead of merely a technical one. Though their main goal is still to provide smooth and consistent digital experiences over a vast, intelligent infrastructure, readers will be well aware of how conventional SRE techniques are evolving to accommodate the complexity of edge activities

International Journal of Emerging Research in Engineering and TechnologyVol. 6
Software (Spain) (ES)
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Cloud Computing and Resource Management
3.25
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