Improving Responsiveness for Virtualized Networking Under Intensive Computing Workloads

RTLWS 2011
October 21st, Prague

Improving Responsiveness for Virtualized
Networking Under Intensive Computing
Workloads

Tommaso Cucinotta, Fabio Checconi, Dhaval Giani
Cucinotta,
Real-Time Systems Lab (RETIS)
Scuola Superiore Sant'Anna, Pisa (Italy)

Context

Tommaso Cucinotta – ReTiS Lab – Scuola Superiore Sant'Anna – Pisa – Italy 2

What is Real-Time
Drive assistance
 Engine control, brakes, stability,
speed, parking
 Trajectory and set-up control
Defence, army, space

Tommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore Sant'Anna Sant’Anna
© 2007 Scuola Superiore 3

What is Real-Time
Control of chemical and nuclear plants
Control of productive processes and
industrial automation
Traffic control


What is Real-Time
Multimedia, videosurveillance
Augmented virtual reality
Telecommunications
Environment monitoring


Our Focus
We focus on systems
 With non-critical soft real-time requirements
 Where the use of a GPOS is desirable and feasible
• As opposed to a traditional RTOS
 Where virtualization is desirable and feasible
Application scenarios
 Multimedia
 Embedded systems
 QoS-enabled Cloud Computing
 Web servers with QoS assurance

© 2007 Scuola Superiore

Motivations


Virtualization

Virtualization is a key technology
For IaaS providers (Cloud Computing)
Enables server consolidation

Physical Host
Physical Host
OS
OS
...

Physical Host
Physical Host
OS
OS

Virtualization

Virtualization is a key technology
For IaaS providers (Cloud Computing)
Enables server consolidation

Physical Host
Physical Host Physical Host
Physical Host
OS
OS VM/OS
VM/OS
... ...

Physical Host VM/OS
VM/OS
Physical Host
OS
OS

Need for Performance Isolation

Resource sharing
→ Temporal interference

Physical Host
Physical Host
VM
VM

VM
VM

VM
VM

VM
VM



Resource sharing ~30ms
VM Alone


Physical Host
Physical Host
VM
VM

VM
VM

VM
VM

VM
VM



Resource sharing ~30ms
VM Alone


Physical Host
Physical Host
VM
VM
τ1 = (30ms, 150ms)
τ2 = (50ms, 200ms)
VM
VM
~120ms
2 VMs

VM
VM

VM
VM


Possible Solutions

Hardware replication and Physical Host
Physical Host
static partitioning VM
VM
Computing
• Multi-core (1 core per VM)
• NUMA awareness VM
VM
Networking
• Multiple network adapters VM
VM
(1 network adapter per VM)
• Multi-queue adapters
VM
VM
Drawbacks
Limitation of flexibility
Under-utilization of resources
• e.g., with real-time/interactive workloads (cloud computing)

Possible Solutions

Another approach
Let multiple VMs use the same resources
Use proper resource scheduling strategies
For example
Computing
• Xen credit-based and SEDF schedulers
Networking
• QoS-aware protocols (IntServ, MPLS, WF2Q+)
Advantages
Increased flexibility
Increased resource saturation levels
Reduced infrastructure costs

Background on
Deadline-based Scheduling


Real-Time Schedulers for
Linux
SCHED_SPORADIC
 Additional scheduling policy within sched_rt.c
 Single-threaded applications
 API based on system call: sched_setscheduler()
SCHED_DEADLINE
 Independent scheduling class
 Single-threaded control applications
 API based on system call: sched_setscheduler()
IRMOS (a.k.a., EDF Throttling)
 Rewrite of RT throttling in sched_rt.c: from limits to guarantees
 Multi-threaded complex software components
• KVM, JVM, Apache+Tomcat, …
 API based on cgroups interface

IRMOS Scheduler

Slice the available computing power into
reservations

CPU 0
CPU 1
CPU 2
CPU 3


Hierarchical Scheduling

EDF
EDF
Max RT Prio
…
FP
FP FP
FP

Min RT Prio T1
T1 T2
T2 T3
T3 T4
T4

Needed operations
 create & destroy reservations
 attach & detach tasks ↔ reservations
 list tasks attached to reservations (and list reservations)
 Standard operations: get & set parameters


Background
Scheduling KVM VMs with
resource reservations
(deadline-based scheduling)
 VM1 granted Q1 time units every P1
. . .

Networking Threads

KVM

Computing Thread


Problem Presentation


Focus on compute-intensive VM
Schedule with VM alone
 Unresponsiveness frame: P-Q

Well-known worst-case schedule
 Unresponsiveness frame: 2(P-Q)


Packet arrival in the unresponsiveness frame
 Its handling deferred till next activation period
Mixed workload inside VM
packet
 Batch compute-intensive workload
 Responsive application(s)
• e.g., triggered by arriving packet
Compute-intensive workload
 Period as large as possible
• For minimizing overheads
Responsive workload
 Period tuned on interactivity requirements
• VM unresponsive for a time-frame as high as 2(P-Q)


Proposed Approach
On packet arrival with null budget
 Attach the VM to a temporary reservation
• With period Ps << P
• With budget Qs sufficient to “respond” to the request
– Including both kernel-level and user-space level operations
 Temporary reservation lasts
• till budget exhaustion or original budget replenishment
Worst-case latency (assump.
of sporadic requests)
 From 2(P-Q) to 2(Ps-Qs)
Bandwidth occupation
 From to


Experimental Results
Experimental set-up
 Periodic ping every 1s
 Normal reservation: (40ms, 100ms)
 Spare reservation: (4ms, 10ms)
Batch activity
 Fake loop logging experienced loops/s


Achieved results
 Reservation (40,100) and no spare reservation
• Response-time ~ 120ms (top curve)
• Throughput ~ 1,11 cycles/us
 Reservation (40,100) with spare reservation (4,10)
• Response-time ~ 16ms (bottom curve)
 Reservation (4,10)
• Throughput ~ 0,56 cycles/us


Implementation Details


Modifications to the
Scheduler
Proper flags into the cgroup filesystem to tag
 Spare bandwidth provider groups
 Spare bandwidth client groups
Modifications to the networking receive path
 if packet goes to VM within client group
(and budget is null),
 then attach VM to (any matching) provider group
Spare bandwidth clients/providers matching
 Based on numerical identifier (clients can attach only to
servers with the same identifier)
 Allows for realizing spare bandwidth pools


Future Work
More extensive evaluation
 Presented results are very preliminary
 Need to test with user-space application, not only ping
Servers pooling
 All VMs need the spare reservation at the same time
• With a very low probability
• So, leverage statistical multiplexing and use #providers < #clients
• Lowers spare bandwidth requirements
VM
VM VM
VM

VM
VM S S VM
VM
S S

VM
VM VM
VM


Related Publications

 Hierarchical Multiprocessor CPU Reservations for the Linux Kernel
F. Checconi, T. Cucinotta, D. Faggioli, G. Lipari
OSPERT 2009, Dublin, Ireland, June 2009
 An EDF Scheduling class for the Linux kernel
D. Faggioli, F. Checconi, M. Trimarchi, C. Scordino
RTLWS 2009, Dresden, October 2009
 Self-tuning Schedulers for Legacy Real-Time Applications
T. Cucinotta, F. Checconi, L. Abeni, L. Palopoli
EuroSys 2010, Paris, April 2010
 Respecting temporal constraints in virtualised services
T. Cucinotta, G. Anastasi, L. Abeni
RTSOAA 2009, Seattle, Washington, July 2009


Thanks for your attention

Questions ?

http://retis.sssup.it/people/tommaso


Improving Responsiveness for Virtualized Networking Under Intensive Computing Workloads

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