Energy-Efficient and SLA-Aware Virtual Machine Selection Algorithm for Dynamic Resource Allocation in Cloud Data Centers

Moghaddam, Seyedhamid Mashhadi; Piraghaj, Sareh Fotuhi; O'Sullivan, Michael J.; Walker, Cameron; Unsworth, Charles P.

doi:10.1109/ucc.2018.00019

Cited by 15 publications

(4 citation statements)

References 16 publications

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“…The resources of the identified software containers are predicted. Many studies have been conducted to predict the resource utilization of software containers using ML, and this area of study covers a broad range of topics [20][21][22][23]. Our study utilized multiple deep-learning models, including bidirectional LSTM, CNN LSTM, convolutional LSTM, and stacked LSTM, all implemented using the Keras library.…”

Section: Replacement Strategymentioning

confidence: 99%

Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure

Rabieyan,

Yahyapour,

Jahnke

2024

J Supercomput

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This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems.

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Section: Replacement Strategymentioning

confidence: 99%

Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure

Rabieyan,

Yahyapour,

Jahnke

2024

J Supercomput

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“…This led to the development of application-specific tools for optimal instance selection with the aim to maximize performance [34][35][36][37][38][39][40]. These approaches often rely on detailed application profiling prior to execution [20,[41][42][43][44][45]. In some cases, they rely on historical training data from similar applications running on similar hardware for performance optimization [46][47][48][49][50][51][52][53].…”

Section: Related Workmentioning

confidence: 99%

Ribbon

Roy

Patel

et al. 2021

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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Deep learning model inference is a key service in many businesses and scientific discovery processes. This paper introduces Ribbon, a novel deep learning inference serving system that meets two competing objectives: quality-of-service (QoS) target and costeffectiveness. The key idea behind Ribbon is to intelligently employ a diverse set of cloud computing instances (heterogeneous instances) to meet the QoS target and maximize cost savings. Ribbon devises a Bayesian Optimization-driven strategy that helps users build the optimal set of heterogeneous instances for their model inference service needs on cloud computing platformsand, Ribbon demonstrates its superiority over existing approaches of inference serving systems using homogeneous instance pools. Ribbon saves up to 16% of the inference service cost for different learning models including emerging deep learning recommender system models and drug-discovery enabling models. CCS CONCEPTS• Computer systems organization → Cloud computing; Heterogeneous (hybrid) systems; • General and reference → Performance; • Computing methodologies → Machine learning.

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“…Previous works such as Paragon [10] and TetriSched [11] have focused on optimizing heterogeneous resource utilization [24][25][26][27], but their resource heterogeneity is pre-determined and sub-optimal, and their target applications are long-running jobs in datacenters, which is different from online inference tasks. Some other previous works have relied on tuning by expertise [28][29][30][31], prior profiling [32][33][34][35], or historical training data from similar applications [36][37][38][39], and cannot be used to solve the Kairos problem.…”

Section: Introductionmentioning

confidence: 99%

Building Heterogeneous Cloud System for Machine Learning Inference

Li¹,

Samsi²,

Gadepally³

et al. 2022

Preprint

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Online inference is becoming a key service product for many businesses, deployed in cloud platforms to meet customer demands. Despite their revenue-generation capability, these services need to operate under tight Quality-of-Service (QoS) and cost budget constraints. This paper introduces Kairos, a novel runtime framework that maximizes the query throughput while meeting QoS target and a cost budget. Kairos designs and implements novel techniques to build a pool of heterogeneous compute hardware without online exploration overhead, and distribute inference queries optimally at runtime. Our evaluation using industry-grade deep learning (DL) models shows that Kairos yields up to 2× the throughput of an optimal homogeneous solution, and outperforms state-of-the-art schemes by up to 70%, despite advantageous implementations of the competing schemes to ignore their exploration overhead.

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Energy-Efficient and SLA-Aware Virtual Machine Selection Algorithm for Dynamic Resource Allocation in Cloud Data Centers

Cited by 15 publications

References 16 publications

Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure

Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure

Ribbon

Building Heterogeneous Cloud System for Machine Learning Inference

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