A review on prediction based autoscaling techniques for heterogeneous applications in cloud environment

Radhika, E. G.; Sadasivam, G. Sudha

doi:10.1016/j.matpr.2020.11.789

Cited by 17 publications

(19 citation statements)

References 22 publications

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“…However, several challenges need to be addressed such as the inability to identify the appropriate conditions to trigger scaling actions due to lack of training, the requirement for better monitoring metrics as well as the need for the historical data structure of the services 6 . Lastly, there are also hybrid scaling strategies implemented in a cloud environment that combine the two approaches 35 and several existing research that implement this strategy in microservice autoscaling address the SLA‐conflict 36 and QoS (i.e., response time) factors 32 …”

Section: Microservice Autoscalingmentioning

confidence: 99%

A review of microservices autoscaling with formal verification perspective

2022

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The process of scaling microservices is a challenging task, especially in maintaining optimum resource provisioning while respecting QoS constraints and SLA.Many research works have proposed autoscaling approaches for microservices, however, less likely concerned with the correctness guarantee of the proposed algorithms. Hence, it is significant to gather and summarize these approaches to foster future innovation. Meanwhile, a few reviews have been published concerning microservices from different aspects. Therefore, our review complements the existing by focusing on autoscaling with verification perspectives. This study highlights the recent contributions in three inter-related main topics that were published within the year 2017 to 2022, namely, microservice, verification, and autoscaling. Due to limited resources on verification for microservice autoscaling, we widen the perspective by considering the verification for autoscaling in cloud-based systems. Based on our findings, we found that the formal method is not a new thing in verifying the autoscaling policies in cloud-based systems, and one recent study that implements the formal method in the microservices area has been identified. Apart from the autoscaling techniques, we have also determined several factors that have been a concern in scaling the microservices as well as the relatable metrics. Meanwhile, from a verification perspective, we identified that probabilistic model checking is the common formal verification technique used to verify microservices and cloud autoscaling. Finally, we recommend open challenges from two perspectives which highlight the verification for existing microservice autoscaling and verification for ML-based microservice autoscaling.

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Section: Microservice Autoscalingmentioning

confidence: 99%

A review of microservices autoscaling with formal verification perspective

2022

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“…Recently, proactive autoscaling methods have been increasingly studied to provide elastic resource provisioning [22]. Gias et al.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, proactive autoscaling methods have been increasingly studied to provide elastic resource provisioning [22]. Gias et al [11] used the Layered Queuing Network (LQN) to model the performance of microservices and obtained the QoS estimation under the current resource usage conditions through the performance model to execute the optimal expansion and contraction scheme.…”

Section: Related Workmentioning

confidence: 99%

Cushion : A proactive resource provisioning method to mitigate SLO violations for containerized microservices

et al. 2022

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Deploying microservices in container-based cloud environments increases the agility of resource scaling. However, the delay in autoscaling for microservices caused by container cold start results in response time service-level objectives (SLO) violations under burst workloads. This paper proposes Cushion, a proactive resource provisioning method for containerized microservices to mitigate SLO violations caused by burst workloads, which promptly schedules workloads to reserved container instances when workloads suddenly increase and meanwhile steadily scales the container instances according to the SLOs of the microservices. Cushion was evaluated through prototype-based experiments in a containerized testbed using a benchmark microservice and four web workload traces by comparing it against existing methods. The experimental results show 9.12x times lower SLO violations, 23.75% higher throughput with 24.35% more CPU usage, and 4.85% more memory usage compared to the baseline methods.

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“…For the sake of clarity, we will use the following terminology from now on: Cloud consumer: a person or organization that maintains a business relationship with, and uses the service from, a cloud provider 2 . It is the user of the services offered by the cloud provider (i.e., IaaS, PaaS or SaaS) 10,11 Cloud provider: a person, an organization, or an entity responsible for making a service available to cloud consumers 2 .…”

Section: Problem Statementmentioning

confidence: 99%

From SLA to vendor‐neutral metrics: An intelligent knowledge‐based approach for multi‐cloud SLA‐based broker

Rampérez

Soriano

Lizcano

et al. 2021

Int J of Intelligent Sys

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Cloud computing has been consolidated as a support for the vast majority of current and emerging technologies. However, there are some barriers that prevent the exploitation of the full potential of this technology. First, the major cloud providers currently put the onus of implementing the mechanisms that ensure compliance with the desired service levels on cloud consumers. However, consumers do not have the required expertise. Since each cloud provider exports a different set of low‐level metrics, the strategies defined to ensure compliance with the established service‐level agreement (SLA) are bound to a particular cloud provider. This fosters provider lock‐in and prevents consumers from benefiting from the advantages of multi‐cloud environments. This paper presents a solution to the problem of automatically translating SLAs into objectives expressed as metrics that can be measured across multiple cloud providers. First, we propose an intelligent knowledge‐based system capable of automatically translating high‐level SLAs defined by cloud consumers into a set of conditions expressed as vendor‐neutral metrics, providing feedback to cloud consumers (intelligent tutoring system). Secondly, we present the set of vendor‐neutral metrics and explain how they can be measured for the different cloud providers. Finally, we report a validation based on two use cases (IaaS and PaaS) in a multi‐cloud environment formed by leading cloud providers. This evaluation has demonstrated that, thanks to the complementarity of the two solutions, cloud consumers can automatically and transparently exploit the multi‐cloud in many application domains, as endorsed by the cloud experts consulted in the course of this study.

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A review on prediction based autoscaling techniques for heterogeneous applications in cloud environment

Cited by 17 publications

References 22 publications

A review of microservices autoscaling with formal verification perspective

A review of microservices autoscaling with formal verification perspective

Cushion : A proactive resource provisioning method to mitigate SLO violations for containerized microservices

From SLA to vendor‐neutral metrics: An intelligent knowledge‐based approach for multi‐cloud SLA‐based broker

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