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Universität Stuttgart
Institute of Parallel and
Distributed Systems (IPVS)
Universitaetsstr. 38
70569 Stuttgart
Germany
Im...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Overview
• Motivation
• System Model
• Elastic Tandem Mach...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Motivation
• Date centers contain up to tens of thousands ...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Goal
Building the ideal energy-proportional machine
• (Alm...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Contribution: Elastic Tandem Machines
System on a Chip (So...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Contributions in Detail
Show that SoCs can serve low load ...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Overview
• Motivation
• System Model
• Elastic Tandem Mach...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
System Model (1)
Target environment: Data center of IaaS p...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
System Model (2)
3-Tier web service
• ETMI runs web server...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Overview
• Motivation
• System Model
• Elastic Tandem Mach...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Basic Concept: Overview
• HTTP requests either forwarded t...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
System Components
Handover Controller:
• Switches between ...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
System Components
Load Monitors:
• Notify controller of
LP...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
• Problem: Simple switching breaks existing TCP connection...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Client Controller
Connection
Monitor LPMI
SYN
Web Server
L...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Overview
• Motivation
• System Model
• Elastic Tandem Mach...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Evaluation Setup
• Elastic Tandem machine with:
◦ Low-powe...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
LPMI (SoC) Performance for Static Web Pages
18
20 requests...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
ETMI Performance for Static Web Pages
Handover LPMI  HPI
...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Energy Efficiency (Static Web Page Scenario)
Idle mode pow...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Energy Efficiency – Comparison with
Virtualization (Static...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
(Closest) Related Work (more see paper)
SoC & host integra...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Summary and Future Work
Elastic Tandem Machine
• Concept f...
Universität Stuttgart
IPVS
Research Group
“Distributed Systems”
Discussion
24
Full paper:
http://goo.gl/Vkdmfc
Contact:
Fr...
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Improving the Efficiency of Cloud Infrastructures with Elastic Tandem Machines (IEEE Cloud 2013)

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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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Transcript of "Improving the Efficiency of Cloud Infrastructures with Elastic Tandem Machines (IEEE Cloud 2013)"

  1. 1. 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
  2. 2. Universität Stuttgart IPVS Research Group “Distributed Systems” Overview • Motivation • System Model • Elastic Tandem Machines • Evaluation • Summary 2
  3. 3. 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 utilization100% max Ideal System Real System 0% (idle) 100%0% (idle) power consumption utilization Efficiency 100% Efficiency 0% Efficient area of operation
  4. 4. 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
  5. 5. 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]
  6. 6. 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
  7. 7. Universität Stuttgart IPVS Research Group “Distributed Systems” Overview • Motivation • System Model • Elastic Tandem Machines • Evaluation • Summary 7
  8. 8. 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) • Network: ◦ Core switches SDN-enabled ◦ Programmable forwarding tables SDN Controller Core Switches Client Data center Top of Rack Switches … Internet ETMI DB HPILPMI
  9. 9. Universität Stuttgart IPVS Research Group “Distributed Systems” System Model (2) 3-Tier web service • ETMI runs web server (middle tier) ◦ One public IP address for ETMI  Transparency ◦ One web server instance on LPMI and HPI • File/DB servers in backend ◦ Store all persistent data and state ◦ Not part of optimization! 9 SDN Controller Core Switches Client Data center Top of Rack Switches … HPILPMI Internet DB ETMI Public Service IP
  10. 10. Universität Stuttgart IPVS Research Group “Distributed Systems” Overview • Motivation • System Model • Elastic Tandem Machines ◦ Overview ◦ System Components ◦ Seamless handover concept • Evaluation • Summary 10
  11. 11. Universität Stuttgart IPVS Research Group “Distributed Systems” Basic Concept: Overview • HTTP requests either forwarded to … ◦ … LPMI during low load ◦ … HPI during high load  SDN-based programming of network (forwarding tables) • LPMI always running ◦ Service always available • HPI booted on demand on LPMI overload • HPI shutdown if current load would not overload LPMI SDN Controller Core Switches … Internet ETMI Low load path to LPMI
  12. 12. Universität Stuttgart IPVS Research Group “Distributed Systems” System Components Handover Controller: • Switches between LPMI and HPI based on their load • Programs core switches using OpenFlow • Boots or shuts down HPI via Virtual Machine Manager • Hysteresis and “ignore period” to prevent oscillation 12 Core Switches Top of Rack Switches … Handover Controller OpenFlow MAC Address re-writing: • If destination IP matches public IP write MAC of LPMI (or HPI) in frame IP aliasing: • NICs configured with (same) public IP address of service • Private IPs used for communication with controller
  13. 13. Universität Stuttgart IPVS Research Group “Distributed Systems” System Components Load Monitors: • Notify controller of LPMI overload and HPI under-load • Load metric: Incoming data rate • Threshold scheme (overload, under-load thresholds) • Offline benchmarking to define LPMI overload threshold 13 Core Switches Top of Rack Switches … Internet Load MonitorLoad Monitor Overload!
  14. 14. Universität Stuttgart IPVS Research Group “Distributed Systems” • Problem: Simple switching breaks existing TCP connections ◦ HTTP 1.1: Multiple requests send over same TCP connection! • Solution: “Pinning” of existing connections to old instance ◦ Controller queries instances for accepted or established connections ▪ Connection monitor (ss or netstat) ◦ Inserts high priority entry into core switch forwarding table: ▪ (client IP, client port, public IP, public port)  MAC_rewriting(instance MAC) Seamless Handover 14 … Internet Connection Monitor Connection Monitor Connections? connection pinning
  15. 15. Universität Stuttgart IPVS Research Group “Distributed Systems” Client Controller Connection Monitor LPMI SYN Web Server LPMI query open connections ACK / SYN pin open connections t1 t2 ... Seamless Handover • It‘s not that simple! ◦ There‘s a race condition • Solution: Block connection requests before querying ◦ Controller programs firewalls on LPMI/HPI ◦ Unblock after flow re-direction Connection accepted after query (t1)!
  16. 16. Universität Stuttgart IPVS Research Group “Distributed Systems” Overview • Motivation • System Model • Elastic Tandem Machines • Evaluation • Summary 16
  17. 17. Universität Stuttgart IPVS Research Group “Distributed Systems” Evaluation Setup • Elastic Tandem machine with: ◦ Low-power Instance (SoC): Raspberry Pi ▪ 700MHz ARM CPU, 512 MB RAM, 100 Mbps Ethernet NIC ◦ High-power Instance (PC): ▪ AMD Athlon 64 X2 Dual Core 4.2 GHz 2 GB RAM, 1 Gbps Ethernet NIC ◦ Running Apache Web server, PHP, Tomcat servlet engine • Backend: NFS file server, MySQL • Core switch: PC with Open vSwitch and multiport NIC ◦ Line rate forwarding (no bottleneck) • SDN handover controller based on Floodlight 17 NFS MySQL LPMI Apache, Tomcat HPI Apache, Tomcat ETMI Handover Controller OpenFlow HTTP-Client
  18. 18. Universität Stuttgart IPVS Research Group “Distributed Systems” LPMI (SoC) Performance for Static Web Pages 18 20 requests/s Increase avg. request rate by 1 request/s every 50 s (Poisson distr.) Low-power SoC can serve realistic low-load (too slow for processing-intensive jobs  paper) Scenario: • Real static web pages from: http://www.netsys2013.de/ Performance: • Throughput: ◦ Max. 26 pages/s • Response time ◦ Significant increase at 20 requests/s (> 150 ms) ◦ Performance limit
  19. 19. Universität Stuttgart IPVS Research Group “Distributed Systems” ETMI Performance for Static Web Pages Handover LPMI  HPI Increase request rate by 1 request/s every 50 s until 2500s, Then decrease rate at 1 request every 50 s ETMI scales up transparently Configuration: • Switch between LPMI and HPI at data rate Toverload = 80 KB/s Tunderload = 53 KB/s Performance: • Scales to maximum HPI performance • Seamless handover ◦ No broken HTTP connections Handover HPI  LPMI
  20. 20. Universität Stuttgart IPVS Research Group “Distributed Systems” Energy Efficiency (Static Web Page Scenario) Idle mode power consumption: • SoC: 1.85 W PC host: 141.22 W 20 The SoC area (left figure) PC HostSoC
  21. 21. Universität Stuttgart IPVS Research Group “Distributed Systems” Energy Efficiency – Comparison with Virtualization (Static Web Page Scenario) Idle mode power consumption: • SoC: 1.85 W • PC host: 141.22 W  76 (idle) VMs per host for same energy efficiency Fair comparison: PC host must serve same load as 76 SoCs • At 76x4 request/s = 304 request/s: ◦ 76 SoCs: 76 x 1.89W = 143.65 W ◦ PC host: 1 x 184.46 W • At 76x8 request/s = 608 request/s: ◦ 76 SoCs: 76 x 1.92 W = 145.92 W ◦ PC hosts: 2 x 184.46 W = 368.92 W Our PC host could only serve max. 300 request/s!!! 22% energy savings 60% energy savings
  22. 22. Universität Stuttgart IPVS Research Group “Distributed Systems” (Closest) Related Work (more see paper) SoC & host integration • B.-G. Chun, G. Iannaccone, R. Katz, G. Lee, and L. Niccolini, ACM SIGOPS Operating Systems Review, 44(1), 2010 ◦ Integration of discrete server systems as one design option ◦ Our handover mechanism is one (network centric) technical solution for a transparent integration Load balancing mechanisms • R. Wang, D. Butnariu, and J. Rexford, Hot-ICE 2011 ◦ SDN-based approach for keeping TCP connections alive ▪ Approach 1: Re-directs packets to controller (possibly high load on controller) ▪ Approach 2: Timeout heuristic (problem of setting timeout) ◦ We utilize readily available end-system information about connections ◦ We handle dynamic state consistently through firewall “locks” 22
  23. 23. Universität Stuttgart IPVS Research Group “Distributed Systems” Summary and Future Work Elastic Tandem Machine • Concept for transparent integration of SoCs and classic VMs • Low power consumption at weak load • Elasticity: Scale up to nominal resources • SDN-based seamless handover concept Future work • Integrating more than two machine types ◦ Micro instance, small instance, large instance, … • Predictive load/performance models to plan handover in advance 23
  24. 24. Universität Stuttgart IPVS Research Group “Distributed Systems” Discussion 24 Full paper: http://goo.gl/Vkdmfc Contact: Frank Dürr email: frank.duerr@ipvs.uni-stuttgart.de WWW: http://goo.gl/o6u2A
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