Energy efficiency in microservices is primarily treated as an operational optimisation problem rather than a lifecycle-spanning architectural concern, highlighting the need for earlier architectural integration and improved measurement practices.
Abstract
Context. Microservice architectures are widely adopted for building scalable cloud-native systems, enabling independent deployment, fine-grained service composition, and operational elasticity. Problem. Despite growing interest in sustainable software, research on energy efficiency in microservices spans operational, infrastructural, and architectural perspectives, but these are typically addressed in isolation. Existing studies focus on optimisation techniques or measurement approaches, with limited synthesis of how energy efficiency is considered, measured, and addressed at the architectural level. Goal. This study synthesises research on energy-efficient microservices by examining where energy efficiency is considered, how it is measured, and which architectural solutions have been proposed. Method. We conduct a systematic literature review following Kitchenham's guidelines, screening publications from four major digital libraries through a six-stage process with backward and forward snowballing, resulting in 40 primary studies. Results. Energy efficiency is predominantly addressed at runtime through monitoring, scheduling, and resource management, while design-time integration remains limited. Measurement practices are largely infrastructure-oriented and rely on model-based estimation and coarse-grained monitoring. Conclusion. Energy efficiency in microservices is primarily treated as an operational optimisation problem rather than a lifecycle-spanning architectural concern, highlighting the need for earlier architectural integration and improved measurement practices.
The engineering of scalable cloud systems has matured from empirical practice into a discipline grounded in formal scalability theory, distributed systems research, and production-derived architectural principles. Existing treatments address load scalability, microservices consistency, autoscaling, and production ML reliability as separate problems, each evaluated through single-mechanism studies conducted under stationary or weakly non-stationary conditions that diverge from production environments where these problems interact simultaneously. This review closes that gap by tracing a structural pattern common to all four domains: each first-order engineering solution introduces a second-order problem of comparable difficulty, a regularity not previously consolidated across the scalability, decomposition, resource optimization, and ML deployment literatures. Its added value over prior reviews lies in connecting formal theoretical constraints, including Amdahl’s Law, the Universal Scalability Law, and Conway’s Law, with production-derived quantitative benchmarks, rather than treating theory and practice as separate registers.The source pool combines foundational theoretical works, empirical studies published in IEEE and ACM venues between 2018 and 2024, and systematic reviews and large-sample case study collections, most notably Velepucha and Flores (2023), covering 71 primary studies of microservices migration, and Paleyes et al. (2022), synthesizing 209 machine learning deployment case studies. Inclusion required that a source report a formal theoretical result, an empirical measurement obtained under stated experimental conditions, or a synthesis of multiple primary studies; sources offering only prescriptive guidance without supporting measurement were excluded. Synthesis proceeded by extracting, for each domain, the mechanism, experimental conditions, and quantitative outcome, then identifying structural patterns recurring across domains.The review’s practical engineering contribution is a set of decision criteria for consistency protocol selection, autoscaling architecture design, and ML deployment monitoring that translate these findings into guidance actionable by practitioners operating production cloud systems.
Antonov Sergey Viktorovich· American Journal of Interdis...· 0 citations
Cloud computing is gaining popularity by giving access to seemingly unlimited virtual resources. However, Cloud data centres are built with physical resources and their electricity consumption has been continuously growing over the past decades. Microservices are an important building block of Cloud applications, calling for new solutions to observe their energy consumption. Distributed tracing is widely deployed to diagnose latency and failures in microservice-based applications, yet it does not expose the energy cost of individual end-user requests. Such a gap limits energy-aware debugging, accountability, and control. This paper presents Spanergy, an energy-aware distributed tracing approach that correlates permicroservice power measurements with traces and that attributes measured energy consumption to request segments, i.e. trace spans. We showcase Spanergy with synchronous request chains and asynchronous interactions across microservices. We present a rigorous experimental protocol and statistical analysis plan to quantify overhead and to validate conservation and coverage properties on realistic configurations. Enabling OpenTelemetry tracing increased total experiment energy by 59.1% relative to the uninstrumented baseline, and Spanergy post-processing added 15.2% of the baseline energy. Hence, Spanergy's incremental energy cost is smaller than the energy overhead of enabling tracing itself, making the approach lightweight in practice. Spanergy also reveals that a non-negligible fraction of request energy comes from spans outside the latency-critical path. These results show that energy-aware tracing is feasible at modest overhead and provides actionable insights for energy-efficient microservices.
César Perdigão Batista, D. Conan, S. Chabridon· IEEE/ACM International Sympo...· 1 citation
Review synthesizes research on "Serverless and disaggregated database architectures: performance optimization,
cost efficiency, scalability, historical evolution, current trends, practical applications in industries, comparison with
traditional database systems" to address the knowledge gap regarding how emerging paradigms reshape database
management in cloud-native environments. The review aimed to taxonomies architectural designs, evaluate performance
and cost-efficiency strategies, benchmark scalability, identify industry applications, and compare historical evolution with
current trends. A systematic analysis of empirical studies, prototypes, and theoretical works published up to mid-2024 was
conducted, focusing on cloud-native deployments leveraging technologies such as RDMA, persistent memory, and function
orchestration. Key findings reveal that these architectures enable elastic scaling and significant cost reductions through
pay-as-you-go models and resource pooling, while performance optimization benefits from AI-driven scheduling and
hardware co-design; however, challenges persist in cold-start latency, orchestration complexity, and consistency
management. Industry adoption spans finance, retail, and IoT, demonstrating operational gains but constrained by
migration complexity and tooling maturity. The evolution from monolithic to serverless and disaggregated systems is
marked by innovations in decoupled resource management and multi-cloud strategies. These findings collectively
underscore the transformative potential and practical limitations of serverless and disaggregated databases, informing
future research and guiding effective industrial adoption.
Khagendra Mishra, Suresh Gautam· International Journal of Inn...· 0 citations
This research will perform a comparative evaluation of monolithic architecture and microservices architecture with a focus on their performance and cost characteristics to help developers to choose one of the styles considering its performance and cost characteristics.
R. Rautela, Abhisekh Mishra, Aditya Pandey et al.· International Journal of All...· 0 citations
This paper analyzes the two software architectural approaches namely Monolithic Architecture vs Microservices Architecture when considering scalable software development, and puts the following into consideration: empirical trade-offs, scalability consideration, organizational implications, and development complexity.
Theophilus Bamise Ajala, A. Oduroye, I. Ayoade et al.· Asian journal of applied sci...· 0 citations
Modern cloud-native microservice systems face significant challenges when deployed in ultra-large-scale, missioncritical environments such as civil aviation passenger service platforms. Existing SDK-based frameworks suffer from tight coupling and limited cross-language flexibility, while Service Mesh solutions introduce scalability bottlenecks due to globally synchronized service state and configuration overhead. This paper presents a partition-based microservice architecture that introduces a hierarchical control model separating local traffic interception from global routing intelligence. The system is built upon a decoupled Sidecar-Router design, where Sidecar Proxies handle lightweight request interception and partition-level forwarding, while Service Routers execute instance-level routing and traffic governance. By introducing partition-bounded service discovery and physical-IP-based cross-cluster communication, the proposed architecture eliminates global state synchronization of high-frequency service metadata and ensures constant resource overhead at the data plane. The system has been deployed in a large civil aviation PSS environment, supporting over 1,800 microservices and more than 6,000 instances, with a sustained production throughput of over 100,000 TPS and 60% business coverage via the traffic access platform. The results demonstrate that partitioned routing and decoupled control significantly improve scalability, fault isolation, and operational efficiency in large-scale distributed microservice systems.
H. Cui, Zhongyi Liu, Mengxiao Li et al.· Fall Joint Computer Conferen...· 0 citations
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