PASCAL: An architecture for proactive auto-scaling of distributed services

Lombardi, Federico; Muti, Andrea; Aniello, Leonardo; Baldoni, Roberto; Bonomi, Silvia; Querzoni, Leonardo

doi:10.1016/j.future.2019.03.003

Cited by 19 publications

(19 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lombardi et al 98 proposed PASCAL for automatic scaling of distributed applications. PASCAL is combined with a performance model attempting to predict incoming workloads and a system for estimating the number of computing resources to be provisioned.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Self‐adaptation on parallel stream processing: A systematic review

Vogel

Griebler

Danelutto

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

A recurrent challenge in real-world applications is autonomous management of the executions at run-time. In this vein, stream processing is a class of applications that compute data flowing in the form of streams (e.g., video feeds, images, and data analytics), where parallel computing can help accelerate the executions. On the one hand, stream processing applications are becoming more complex, dynamic, and long-running. On the other hand, it is unfeasible for humans to monitor and manually change the executions continuously. Hence, self-adaptation can reduce costs and human efforts by providing a higher-level abstraction with an autonomic/seamless management of executions. In this work, we aim at providing a literature review regarding self-adaptation applied to the parallel stream processing domain. We present a comprehensive revision using a systematic literature review method. Moreover, we propose a taxonomy to categorize and classify the existing self-adaptive approaches.Finally, applying the taxonomy made it possible to characterize the state-of-the-art, identify trends, and discuss open research challenges and future opportunities.

show abstract

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…Consequently, reactive approaches may eventually violate QoS, but they tend to respond better to fluctuating workloads. References 37,47,63,65,66,74,75,82,98 claimed to be proactive.…”

Section: Adaptation Decisionsmentioning

confidence: 99%

Self‐adaptation on parallel stream processing: A systematic review

Vogel

Griebler

Danelutto

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…PASCAL [12] is a proactive autoscaling architecture for distributed streaming applications and datastores. It consists of a profiling and an autoscaling phase.…”

Section: Related Workmentioning

confidence: 99%

Towards autoscaling of Apache Flink jobs

Varga

Balassi²,

Kiss

2021

Acta Universitatis Sapientiae, Informatica

View full text Add to dashboard Cite

Data stream processing has been gaining attention in the past decade. Apache Flink is an open-source distributed stream processing engine that is able to process a large amount of data in real time with low latency. Computations are distributed among a cluster of nodes. Currently, provisioning the appropriate amount of cloud resources must be done manually ahead of time. A dynamically varying workload may exceed the capacity of the cluster, or leave resources underutilized. In our paper, we describe an architecture that enables the automatic scaling of Flink jobs on Kubernetes based on custom metrics, and describe a simple scaling policy. We also measure the e ects of state size and target parallelism on the duration of the scaling operation, which must be considered when designing an autoscaling policy, so that the Flink job respects a Service Level Agreement.

show abstract

“…In this article, the author performed the application profiling using micro services. [83] designed the approach using application profiling.…”

Section: Survey On Auto-scaling Techniquesmentioning

confidence: 99%

Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Singh

Gupta

Jyoti

et al. 2019

SCPE

View full text Add to dashboard Cite

Cloud computing emerging environment attracts many applications providers to deploy web applications on cloud data centers. The primary area of attraction is elasticity, which allows to auto-scale the resources on-demand. However, web applications usually have dynamic workload and hard to predict. Cloud service providers and researchers are working to reduce the cost while maintaining the Quality of Service (QoS). One of the key challenges for web application in cloud computing is auto-scaling. The auto-scaling in cloud computing is still in infancy and required detail investigation of taxonomy, approach and types of resources mapped to the current research. In this article, we presented the literature survey for auto-scaling techniques of web applications in cloud computing. This survey supports the research community to find the requirements in auto-scaling techniques. We present a taxonomy of reviewed articles with parameters such as auto-scaling techniques, approach, resources, monitoring tool, experiment, workload, and metric, etc. Based on the analysis, we proposed the new areas of research in this direction.

show abstract

PASCAL: An architecture for proactive auto-scaling of distributed services

Cited by 19 publications

References 47 publications

Self‐adaptation on parallel stream processing: A systematic review

Self‐adaptation on parallel stream processing: A systematic review

Towards autoscaling of Apache Flink jobs

Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Contact Info

Product

Resources

About