Cost-efficient auto-scaling of container-based elastic processes

Sheganaku, Gerta; Schulte, Stefan; Waibel, Philipp; Weber, Ingo

doi:10.1016/j.future.2022.09.001

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…In addition, Sheganaku et al (2023) [9] investigated the design and implementation of strategies to optimize resource allocation and scale decisions to minimize operational expenses. By exploring various auto-scaling techniques, this study systematically analyzed their effectiveness in dynamically adjusting resource provisioning to satisfy varying workload demands.…”

Section: Methodologies and Tactics About Auto-scalingmentioning

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: Methodologies and Tactics About Auto-scalingmentioning

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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“…After carefully examining the corresponding scientific literature, the authors of this work were able to construct Table 2 that illustrates how these features correspond to the aforementioned key aspects for security in cloud-native services. These features are the following: [77], and facilitates continuous compliance. It also integrates threat intelligence for proactive threat hunting and faster incident response.…”

Section: Key Features For Security In Cloud-native Servicesmentioning

confidence: 99%

Security in Cloud-Native Services: A Survey

Theodoropoulos,

Rosa,

Benzaid

et al. 2023

JCP

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Cloud-native services face unique cybersecurity challenges due to their distributed infrastructure. They are susceptible to various threats like malware, DDoS attacks, and Man-in-the-Middle (MITM) attacks. Additionally, these services often process sensitive data that must be protected from unauthorized access. On top of that, the dynamic and scalable nature of cloud-native services makes it difficult to maintain consistent security, as deploying new instances and infrastructure introduces new vulnerabilities. To address these challenges, efficient security solutions are needed to mitigate potential threats while aligning with the characteristics of cloud-native services. Despite the abundance of works focusing on security aspects in the cloud, there has been a notable lack of research that is focused on the security of cloud-native services. To address this gap, this work is the first survey that is dedicated to exploring security in cloud-native services. This work aims to provide a comprehensive investigation of the aspects, features, and solutions that are associated with security in cloud-native services. It serves as a uniquely structured mapping study that maps the key aspects to the corresponding features, and these features to numerous contemporary solutions. Furthermore, it includes the identification of various candidate open-source technologies that are capable of supporting the realization of each explored solution. Finally, it showcases how these solutions can work together in order to establish each corresponding feature. The insights and findings of this work can be used by cybersecurity professionals, such as developers and researchers, to enhance the security of cloud-native services.

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Joint Autoscaling of Containers and Virtual Machines for Cost Optimization in Container Clusters

Entrialgo,

García,

García

et al. 2024

J Grid Computing

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Autoscaling enables container cluster orchestrators to automatically adjust computational resources, such as containers and Virtual Machines (VMs), to handle fluctuating workloads effectively. This adaptation can involve modifying the amount of resources (horizontal scaling) or adjusting their computational capacity (vertical scaling). The motivation for our work stems from the limitations of previous autoscaling approaches, which are either partial (scaling containers or VMs, but not both) or excessively complex to be used in real systems. This complexity arises from their use of models with a large number of variables and the addressing of two simultaneous challenges: achieving the optimal deployment for a single scheduling window and managing the transition between successive scheduling windows. We propose an Integer Linear Programming (ILP) model to address the challenge of autoscaling containers and VMs jointly, both horizontally and vertically, to minimize deployment costs. This model is designed to be used with predictive autoscalers and be solved in a reasonable time, even for large clusters. To this end, improvements and reasonable simplifications with respect to previous models have been carried out to drastically reduce the size of the resource allocation problem. Furthermore, the proposed model provides an enhanced representation of system performance in comparison to previous approaches. A tool called Conlloovia has been developed to implement this model. To evaluate its performance, we have conducted a comprehensive assessment, comparing it with two heuristic allocators with different problem sizes. Our findings indicate that Conlloovia consistently demonstrates lower deployment costs in a significant number of cases. Conlloovia has also been evaluated with a real application, using synthetic and real workload traces, as well as different scheduling windows, with deployment costs approximately 20% lower than heuristic allocators.

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Cost-efficient auto-scaling of container-based elastic processes

Cited by 8 publications

References 38 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

Security in Cloud-Native Services: A Survey

Joint Autoscaling of Containers and Virtual Machines for Cost Optimization in Container Clusters

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