Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Greening web servers: A case for ultra low-power web servers

Authors: Benoy Varghese; Anirban Mahanti; Prashant Shenoy; Guillaume Jourjon; Niklas Carlsson;

Greening web servers: A case for ultra low-power web servers

Abstract

This paper studies the feasibility and benefits of greening Web servers by using ultra-low-power micro-computing boards to serve Web content. Our study focuses on the tradeoff between power and performance in such systems. Our premise is that low-power computing platforms can provide adequate performance for low-volume Websites run by small businesses or groups, while delivering a significantly higher request per Watt. We use the popular Raspberry Pi platform as an example low-power computing platform and experimentally evaluate our hypothesis for static and dynamic Web content served using this platform. Our result show that this platform can provide comparable response times to more capable server-class machines for rates up to 200 requests per second (rps); however, the scalability of the system is reduced to 20 rps for serving more compute-intensive dynamic content. Next, we study the feasibility of using clusters of low-power systems to serve requests for larger Websites. We find that, by utilising low-power multi-server clusters, we can achieve 17x to 23x more requests per Watt than typical tower server systems. Using simulations driven by parameters obtained from our real-world experiments, we also study dynamic multi-server policies that consider the tradeoff between power savings and overhead cost of turning servers on and off.

Related Organizations
  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    5
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Average
Upload OA version
Are you the author? Do you have the OA version of this publication?