Look Ahead Distributed Planning For Application Management In Cloud

In this paper, we propose and implement a distributed autonomic manager that maintains service level agreements (SLA) for each application’ scenario. The proposed autonomic manager supports SLAs by configuring the bandwidth ratios for each application scenario and uses an overlay network as an infrastructure. The most important aspect of the proposed autonomic manager is its scalability which allows us to deal with geographically distributed cloud-based applications and a large volume of computation. This can be useful in look ahead optimization and in adaptations using complex models, such as machine learning. We formally prove the safety and liveness properties of the implemented distributed algorithms. Through experiments on Amazon AWS cloud, using two different use cases, we demonstrate the elasticity and flexibility of the autonomic manager as a measure of its applicability to different cloud applications with different types of workloads. Experiments also demonstrate that increasing the size of a look ahead window, up to a certain size, improves the accuracy of the adaptation decisions up to 50%.

show abstract

Formally Verified Scalable Look Ahead Planning For Cloud Resource Management

Zaker

Litoiu

Shtern

2022

ACM Trans. Auton. Adapt. Syst.

View full text Add to dashboard Cite

show abstract

Near real-time optimization of fog service placement for responsive edge computing

2020

View full text Add to dashboard Cite

In recent years, computing workloads have shifted from the cloud to the fog, and IoT devices are becoming powerful enough to run containerized services. While the combination of IoT devices and fog computing has many advantages, such as increased efficiency, reduced network traffic and better end user experience, the scale and volatility of the fog and edge also present new problems for service deployment scheduling.Fog and edge networks contain orders of magnitude more devices than cloud data centers, and they are often less stable and slower. Additionally, frequent changes in network topology and the number of connected devices are the norm in edge networks, rather than the exception as in cloud data centers.This article presents a service scheduling algorithm, labeled “Swirly”, for fog and edge networks containing hundreds of thousands of devices, which is capable of incorporating changes in network conditions and connected devices. The theoretical performance is explored, and a model of the behaviour and limits of fog nodes is constructed. An evaluation of Swirly is performed, showing that it is capable of managing service meshes for at least 300.000 devices in near real-time.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Look Ahead Distributed Planning For Application Management In Cloud

Cited by 2 publications

References 18 publications

Formally Verified Scalable Look Ahead Planning For Cloud Resource Management

Formally Verified Scalable Look Ahead Planning For Cloud Resource Management

Near real-time optimization of fog service placement for responsive edge computing

Contact Info

Product

Resources

About