My research is in virtualized infrastructure domain. I aim at minimizing electricity consumption while improving application performance. To achieve the first goal, I work both at the entire datacenter level (by providing better VM placement strategies) and at the physical machine level (by providing better power management policies). Concerning the second goal, I work both at the VM monitor level (for minimizing its overhead) and at the VM's operating system (OS) level (for making it aware of the fact that it is virtualized). In this talk I present two contributions of my research team, one for each objective. The first contribution presents Drowsy-DC, a novel way to reduce data center power consumption inspired by smartphones. The second contribution presents XPV (eXtended Para-Virtualization), a new principle for well virtualizing NUMA machines.

1. Drowsy-DC: Power management in
datacenters inspired by smartphones
Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)
Professeur des Universités
Université de Nice Sophia Antipolis
(alain.tchana@unice.fr)
Joint work with EPFL
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

2. “La prévision est une chose difficile
surtout lorsqu’il s’agit de l’avenir.”
Winston Churchill
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

3. Energy consumption
Electricity cost
+50% of TCO [Computer’11, HP report]
Eolas (Grenoble): 5 employees to manage the data center
Basic approach: ACPI
ACPI defines both the performance level and the sleep modes of
the whole server and its internal components
S-, P-, C-, D-states
Thus, power management (PM) policies adapt the power
consumed by the server according to its utilization rate (e.g.
DVFS on CPUs)
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

4. Current situation on servers with ACPI
0 20 40 60 80 100
0
50
100
S5
S4S3
S0idle
Utilization rate
Consumedenergy(%)
S5: Off; S4: suspend to disk; S3: suspend to RAM; S0: idle.
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

5. Current situation on servers with ACPI
Reasons
Not all server components are individually manageable
PM policies are not easy to implement by sysadmins
Relation between utilization and energy consumption
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

6. Virtualization
Almost everywhere (data centers, SDN,
NFV, IoT, phones, 5G devices, etc.)
Allows
optimal resource utilization (colocation)
quick deployment (packaging)
maintenance with zero downtime (live migration)
fault tolerance (checkpointing, live migration)
scalability (dynamic resource management)
...⇒(time, square, staff, money) saving
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

7. Resource utilization
Observation
Machines are under-used
-20% (e.g., Twitter [ASPLOS’14], Amazon [EuroSys’10],
Microsoft Azure [SOSP’17], Eolas, etc.)
Because of
Resource oversizing
Workload variation
ACPI limitations
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

8. Resource utilization importance
Figure: M. Fontoura [SOSP’17]
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

9. VM Consolidation
1 Exploit the variability of the workload
2 Dynamically relocate VMs (using migration)
3 Turn-off empty machines
4 e.g., OpenStack Neat, VMware DRS/DPM
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

10. VM Consolidation
Limitations
Memory is the most demanded resource type
Working set size estimation and prediction is very hard task
Thus machines are still under-used (Alibaba traces in 2018,
Microsoft Azure [SOSP’17])
10 / 27
Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

11. 1 (Energy) Drowsy-DC: power mgt. inspired by smartphones [IPDPS’19]
Collaboration with EPFL
11 / 27
Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

12. Observation
Emergence of Long-Lived, Mostly Idle (LLMI) VMs
Always available services [EuroSys’16]
Very small audience or clear periodicity in usage
ski resort web servers, weekly report generation...
1 2 3 4 5 6
10
20
Days
Activity(%)
Figure: IBM cluster’s traces
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

13. Drowsy Data Center (Drowsy-DC): a new
consolidation paradigm
Basic idea
Instead of consolidating to put empty servers to sleep, consolidate to
maximize idle servers and put them to sleep.
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

14. Drowsy-DC: basic idea
Classic consolidation Drowsy-DC consolidation
Goal Put unused hosts to sleep Suspend idle hosts
Method Gather workload to Gather workload to
maximize unused hosts maximize idle periods
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

15. Architecture and prototype (open source)
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

16. Idleness-aware VM consolidation
Idleness Probability (IP) is computed
from the Idleness Model (IM)
Consolidate VMs with similar IPs during
the next hour
how likely is the VM to be idle the nex
hour?
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

17. Idleness-aware VM consolidation
Idleness Model (by studying 308 clusters from Nutanix)
Different time scales
hour in the day (e.g., morning)
day in the week (e.g., weekend)
day in the month (e.g., end of the month)
month in the year (e.g., winter)
Example
a national diploma results website is mostly used at some specific
hours (2 p.m., 3 p.m.) of a specific day (20th) of one month
(July), every year
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

18. Idleness-aware VM consolidation
IM is composed of Synthesized Idlenesses and Weights
Synthesized Idlenesses (SI∗):
(24 SId , 24 × 7 SIw , 24 × 31 SIm, 24 × 365 SIy )
SId (h): synthesized idleness during h regarding its position in the day
SIw (h, dw ): synthesized idleness depending on h and the day of the
week dw
SIm(h, dm): synthesized idleness depending on h and the day of the
month dm
SIy (h, dm, m): synthesized idleness depending on h, dm and the
month m of the year
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

19. Idleness-aware VM consolidation
IM is composed of Synthesized Idlenesses and Weights
Synthesized Idlenesses (SI∗):
(24 SId , 24 × 7 SIw , 24 × 31 SIm, 24 × 365 SIy )
Weight (wd , ww , wm, and wy )
the importance of the time scale in the IM
e.g., a national diploma results website is mostly used at some
specific hours (2 p.m., 3 p.m.) of a specific day (20th) of one month
(July), every year
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

20. Idleness-aware VM consolidation
IM is composed of Synthesized Idlenesses and Weights
Synthesized Idlenesses (SI∗):
(24 SId , 24 × 7 SIw , 24 × 31 SIm, 24 × 365 SIy )
Weight (wd , ww , wm, and wy )
IP(h, dw , dm, m) = wd · SId (h) + ww · SIw (h, dw )
+ wm · SIm(h, dm) + wy · SIy (h, dm, m)
(1)
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

21. Idleness-aware VM consolidation
Computation of w∗ and other details in [IPDPS’19]
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

22. Server wakeup optimizations
Envoi ordre réveil
Réception ordre réveil
Réveil matériel
Réveil OS
Firmware (470ms)
Réveil CPU
Réveil périphériques
Début réinitialisation carte réseau
OS opérationnel
Noyau (190ms)
Lien Ethernet établi
Reset carte réseau (1,3s)
Réseau opérationnel
Processus ordonnancé
50ms
Latence de réveil totale du service (~2s)
Optimizations
Kernel
select only needed drivers and CPUs
Network device
use "critical section" to eliminate reset
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

23. Low scale experiments
5 HP machines, consume about 10% energy in S3
6 LLMI VMs (VM3-8), initially spread
2 LLMU VMs (VM1-2), initially spread
1 2 3 4 5 6
10
20
Days
Activity(%)
VM3, VM4
VM6
Figure: IBM cluster’s traces
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

24. Low scale experiments: Results
Figure: Colocation percentage of each VM — black cells: V1 and V2
were LLMU VMs; dark gray cells: V3 and V4 received the same
workload. Last column is the number of migrations a VM experienced.
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

25. Low scale experiments: Results
Algorithm P2 P3 P4 P5 Global
Drowsy-DC 0 94 79 91 66
OpenStack Neat 89 7 8 93 49
Table: Fraction of time (percent) spent by hosts in suspended power
state, with Drowsy-DC and with Neat
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

26. Large scale experiments: Energy saving
on Google traces ( 12.000 machines)
10 20 30 40 50 60 70 80 90
2
4
6
8
·103
LLMI VM proportion in the datacenter (%)
Powerconsumption(kWh)
Neat Neat+S3 Oasis Drowsy-DC
Figure: Drowsy-DC: 76% - 81% energy savings
compared to OpenStack Neat
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)

27. “Ceux qui ne savent pas où ils vont ,
sont surpris d’arriver ailleurs.”
Pierre Dac
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Alain Tchana (Ne fais pas d’IA et ne souhaite pas en faire!)