Workload Management for Power Efficiency in Heterogeneous Data Centers

Ruiu, Pietro; Scionti, Alberto; Nider, Joel; Rapoport, Mike

doi:10.1109/cisis.2016.107

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…It is interesting to notice that the Oasis Topology and Orchestration Specification for Cloud Applications (TOSCA) (2013) standard has not been used by any of the primary studies for designing microservice architectures. TOSCA is a standard that can be used for representing portable cloud applications and supporting their life-cycle management ( Bergmayr et al, 2018 ), and is a promising candidate for the microservice architectures ( Ruiu et al, 2016;Lipton et al, 2018;Shalom, 2017 ).…”

Section: Support For Architectingmentioning

confidence: 99%

Architecting with microservices: A systematic mapping study

Francesco

Lago

Malavolta

2019

Journal of Systems and Software

193

103

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Context: A microservice architecture is composed of a set of small services, each running in its own process and communicating with lightweight mechanisms. Many aspects on architecting with microservices are still unexplored and existing research is still far from being crispy clear. Objective: We aim at identifying, classifying, and evaluating the state of the art on architecting with microservices from the following perspectives: publication trends, focus of research, and potential for industrial adoption. Method: We apply the systematic mapping methodology. We rigorously selected 103 primary studies and we defined and applied a classification framework to them for extracting key information for subsequent analysis. We synthesized the obtained data and produced a clear overview of the state of the art. Results: This work contributes with (i) a classification framework for research studies on architecting with microservices, (ii) a systematic map of current research of the field, (iii) an evaluation of the potential for industrial adoption of research results, and (iv) a discussion of emerging findings and implications for future research. Conclusion: This study provides a solid, rigorous, and replicable picture of the state of the art on architecting with microservices. Its results can benefit both researchers and practitioners of the field.

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Section: Support For Architectingmentioning

confidence: 99%

Architecting with microservices: A systematic mapping study

Francesco

Lago

Malavolta

2019

Journal of Systems and Software

193

103

View full text Add to dashboard Cite

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“…The ASCETiC project [21] holds similarities it focused upon energy efficiency and software adaptation but in the context of clouds. A highly scalable model for developing applications, exploiting hardware heterogeneity in cloud data centres while at the same time considering the aspect of energy efficiency is presented in [22]. In such model applications are expressed in the form of interconnected microservices which are automatically scheduled for execution on the most suitable heterogeneous computing elements.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

Kavanagh

Djemame

Ejarque

et al. 2020

IEEE Trans. Sustain. Comput.

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Hardware in High Performance Computing environments in recent years have increasingly become more heterogeneous in order to improve computational performance. An additional aspect of such systems is the management of power and energy consumption. The increase in heterogeneity requires middleware and programming model abstractions to eliminate additional complexities that it brings, while also offering opportunities such as improved power management. In this paper we explore application level self-adaptation including aspects such as automated configuration and deployment of applications to different heterogeneous infrastructure and for their redeployment. This therefore not only mitigates complexities associated with heterogeneous devices but aims to take advantage of the heterogeneity. The overall result of this paper is a self-adaptive framework that manages application Quality of Service (QoS) at runtime, which includes the automatic migration of applications between different accelerated infrastructures. Discussion covers when this migration is appropriate and quantifies the likely benefits.

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“…Note that in lines 5,12,18,27 in the pseudo codes stipulate that the resulting load of the destination PM is still subject to the load constraint, i.e., a PM should not be extra-highly-loaded. Load fairness is a very important metric to evaluate a virtual machine consolidation algorithm, which indicates the resource utilization of a system [27], [28]. We consider d as the measure of load fairness.…”

Section: The Coalitional Game-theoretic Approachmentioning

confidence: 99%

A Workload-Aware VM Consolidation Method Based on Coalitional Game for Energy-Saving in Cloud

et al. 2019

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Virtual Machine (VM) consolidation technique plays an important role in energy management and load-balancing of cloud computing systems. Dynamic VM consolidation is a promising consolidation approach in this direction, which aims at using least active physical machines (PMs) through appropriately migrating VMs to reduce resource consumption. The resulting optimization problem is well-acknowledged to be NP-hard optimization problems. In this paper, we propose a novel merge-and-split-based coalitional game-theoretic approach for VM consolidation in heterogeneous clouds. The proposed approach first partitions PMs into different groups based on their workload levels, then employs a coalitional-game-based VM consolidation algorithm (CGMS) in choosing members from such groups to form effective coalitions, performs VM migrations among the coalition members to maximize the payoff of every coalition, and finally keeps PMs running in a high energy-efficiency state. The simulation results based on three scenarios clearly suggest that our proposed approach outperforms traditional ones in terms of energy-saving, and also achieve a fair level of load balance.

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Workload Management for Power Efficiency in Heterogeneous Data Centers

Cited by 12 publications

References 17 publications

Architecting with microservices: A systematic mapping study

Architecting with microservices: A systematic mapping study

Energy-Aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

A Workload-Aware VM Consolidation Method Based on Coalitional Game for Energy-Saving in Cloud

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