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  1. Proceedings of the Third International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing (ARMS-CC'16)
  2. Cloud Elasticity: going beyond demand as user load
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Computation Offloading from Mobile Devices: Can Edge Devices Perform Better Than the Cloud?
Performance of Approximate Causal Consistency for Partially Replicated Systems
Modelling the Scalability of Real-Time Online Interactive Applications on Clouds
The Impact on the Performance of Co-running Virtual Machines in a Virtualized Environment
A Gossip-Based Dynamic Virtual Machine Consolidation Strategy for Large-Scale Cloud Data Centers
Internet of Things data management in the cloud for Bluetooth Low Energy (BLE) devices
I2oT: Inexactness in IoT
Cloud Elasticity: going beyond demand as user load
Cloud live streaming System based on Auto-adaptive Overlay for Cyber Physical Infrastructure

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Cloud Elasticity: going beyond demand as user load

Content Provider ACM Digital Library
Author Constantin, Alexandru Popa, Dan Dobre, Ciprian Chilipirea, Cristian Crintea, Octavian
Abstract Cloud computing systems have become not only popular, but extensively used. They are supported and exploited by both industry and academia. Cloud providers have diversified and so did the software offered by their systems. Infrastructure as a Service (IaaS) clouds are now available from single virtual machine use cases, such as a personal server, to specialized high performance or machine learning engines. This popularity has been brought by the low-cost and risk-free feature of renting computing resources instead of buying them, in a large, one-time investment. Furthermore, clouds permit their clients the use of elasticity. Elasticity is the most relevant feature of cloud computing. It refers to the clients' ability to easily change the number of rented resources in a live environment. This permits the entire system to handle differences in load. Most cloud clients serve web applications or services to third parties. In these cases, load differences can be correlated to the number of users for the service and elasticity is used to handle differences in what is called user load. Most of the scientific literature approaches elasticity looking solely at user load. To give a clearer understanding, the majority of cloud frameworks in use today work as follows: they start a number of worker nodes, and tasks are assigned to them for execution. Only when the user load changes, the number of workers is adjusted, if any. In this paper, we propose an alternative approach, where the number of workers depends on the actual requirements coming from the different execution steps of an application. We show such an idea can be achieved for several workflows from different fields and that it can bring significant benefits to execution time and cost.
Starting Page 46
Ending Page 51
Page Count 6
File Format PDF
ISBN 9781450342278
DOI 10.1145/2962564.2962570
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-07-25
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Cloud computing Elasticity Scheduling Cost
Content Type Text
Resource Type Article
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