Universität Stuttgart Institute of Parallel and Distributed Systems (IPVS) Universitaetsstr. 38 70569 Stuttgart Germany Improving the Efficiency of Cloud Infrastructures with Elastic Tandem Machines Sixth IEEE International Conference on Cloud Computing Santa Clara, CA, USA June 29th, 2013 Frank Dürr
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Improving the Efficiency of Cloud Infrastructures with Elastic Tandem Machines (IEEE Cloud 2013)
The presentation of our full paper presented at IEEE Cloud 2013.
Abstract: In this paper, we propose a concept for improving the energy efficiency and resource utilization of cloud infrastructures by combining the benefits of heterogeneous machine instances. The basic idea is to integrate low-power system on a chip (SoC) machines and high-power virtual machine instances into so-called Elastic Tandem Machine Instances (ETMI). The low-power machine serves low load and is always running to ensure the availability of the ETMI. When load rises, the ETMI scales up automatically by starting the high-power instance and handing over traffic to it. For the non-disruptive transition from low-power to high-power machines and vice versa, we present a handover mechanism based on software-defined networking technologies. Our evaluations show the applicability of low-power SoC machines to serve low load efficiently as well as the desired scalability properties of ETMIs.
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Universität Stuttgart
Institute of Parallel and
Distributed Systems (IPVS)
Universitaetsstr. 38
70569 Stuttgart
Germany
Improving the Efficiency of Cloud Infrastructures
with Elastic Tandem Machines
Sixth IEEE International Conference on Cloud Computing
Santa Clara, CA, USA
June 29th, 2013
Frank Dürr
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Overview
• Motivation
• System Model
• Elastic Tandem Machines
• Evaluation
• Summary
2
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Motivation
• Date centers contain up to tens of thousands of hosts
• Energy-efficiency one of the major challenges
• The ideal host is energy proportional [Barroso, Hölzle]
◦ Energy consumption should be proportional to utilization/load
3
power
consumption
utilization 100%
max
Ideal System Real System
0% (idle) 100% 0% (idle)
power
consumption
utilization
Efficiency
100%
Efficiency 0% Efficient area
of operation
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Goal
Building the ideal energy-proportional machine
• (Almost) no power consumption while being idle
• Elasticity: Scaling up to nominal (maximum) requested resources
4
100% idle
Fill this area of
inefficient operation!
power
consumption
utilization
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Contribution: Elastic Tandem Machines
System on a Chip (SoC)
Machine
• Low performance
• Low power consumption:
~ 2 Watt
Classic high power VM
on commodity PC Hardware
• High performance
• High power consumption
Elastic Tandem Machine: Best of both worlds
• Low power consumption in idle/weak load
• Scale up to maximum nominal resources
• Transparency: Clients see only one ideal machine
+
Transparent integration of heterogeneous hardware
100 Mbps
NIC
700 MHz ARM
512 MB RAM
16 GB
SD Card ~ 35$
[source: www.dell.com]
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Contributions in Detail
Show that SoCs can serve low load in realistic settings
• Web server in 3-tier system architecture
Concept for implementing Elastic Tandem Machines
• Handover concept to switch between SoC and VM
◦ Adaptive: based on dynamic load
◦ Transparent, seamless, non-disruptive
▪ Client just sees one “ideal” machine
▪ Existing (TCP) connections don‘t break during handover
◦ “In network” based on Software-defined Networking (SDN)
• Proof of concept implementation and evaluation
6
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Overview
• Motivation
• System Model
• Elastic Tandem Machines
• Evaluation
• Summary
7
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
System Model (1)
Target environment: Data center of IaaS provider
• SoC machines (Low-power Micro Instances; LPMI)
• Classic VMs on PC hosts (High-power Instances; HPI)
• One LPMI + one HPI = one Elastic Tandem Machine (ETMI)