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Improving Responsiveness for Virtualized Networking Under Intensive Computing Workloads
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Improving Responsiveness for Virtualized Networking Under Intensive Computing Workloads

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In this paper the problem of providing network response guarantees to multiple Virtual Machines (VMs) co-scheduled on the same set of CPUs is tackled, where the VMs may have to host both responsive ...

In this paper the problem of providing network response guarantees to multiple Virtual Machines (VMs) co-scheduled on the same set of CPUs is tackled, where the VMs may have to host both responsive real-time applications and batch compute-intensive workloads. When trying to use a real-time reservation-based CPU scheduler for providing stable performance guarantees to such a VM, the compute-intensive workload would be scheduled better with high time granularities, to increase performance and reduce system overheads, whilst the real-time workload would need lower time granularities in order to keep the response-time under acceptable levels. The mechanism that is proposed in this paper mixes both concepts, allowing the scheduler to dynamically switch between fine-grain and coarse-grain scheduling intervals depending on whether the VM is performing network operations or not. A prototype implementation of the proposed mechanism has been realized for the KVM hypervisor when running on Linux, modifying a deadline-based real-time scheduling strategy for the Linux kernel developed previously. The gathered experimental results show that the proposed technique is effective in controlling the response-times of the real-time workload inside a VM while at the same time it allows for an efficient execution of the batch compute-intensive workload.

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Improving Responsiveness for Virtualized Networking Under Intensive Computing Workloads Improving Responsiveness for Virtualized Networking Under Intensive Computing Workloads Presentation Transcript

  • RTLWS 2011 October 21st, PragueImproving Responsiveness for Virtualized Networking Under Intensive Computing Workloads Tommaso Cucinotta, Fabio Checconi, Dhaval Giani Cucinotta, Real-Time Systems Lab (RETIS) Scuola Superiore SantAnna, Pisa (Italy)
  • ContextTommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 2
  • What is Real-Time Drive assistance  Engine control, brakes, stability, speed, parking  Trajectory and set-up control Defence, army, spaceTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 3
  • What is Real-Time Control of chemical and nuclear plants Control of productive processes and industrial automation Traffic controlTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 4
  • What is Real-Time Multimedia, videosurveillance Augmented virtual reality Telecommunications Environment monitoringTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 5
  • 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 assuranceTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore
  • MotivationsTommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 7
  • VirtualizationVirtualization is a key technology For IaaS providers (Cloud Computing) Enables server consolidation Physical Host Physical Host OS OS ... Physical Host Physical Host OS OS Tommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 8
  • VirtualizationVirtualization 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 Tommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 9
  • Need for Performance IsolationResource sharing → Temporal interference Physical Host Physical Host VM VM VM VM VM VM VM VM Tommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 10
  • Need for Performance IsolationResource sharing ~30ms VM Alone → Temporal interference Physical Host Physical Host VM VM VM VM VM VM VM VM Tommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 11
  • Need for Performance IsolationResource sharing ~30ms VM Alone → Temporal interference Physical Host Physical Host VM VM τ1 = (30ms, 150ms) τ2 = (50ms, 200ms) VM VM ~120ms 2 VMs VM VM VM VM Tommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 12
  • Possible SolutionsHardware replication and Physical Host Physical Hoststatic 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 VMDrawbacks Limitation of flexibility Under-utilization of resources • e.g., with real-time/interactive workloads (cloud computing) Tommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 13
  • Possible SolutionsAnother approach Let multiple VMs use the same resources Use proper resource scheduling strategiesFor 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 Tommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 14
  • Background on Deadline-based SchedulingTommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 15
  • 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 interfaceTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore
  • IRMOS Scheduler Slice the available computing power into reservations CPU 0 CPU 1 CPU 2 CPU 3Tommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore
  • 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 parametersTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 18
  • Background Scheduling KVM VMs with resource reservations (deadline-based scheduling)  VM1 granted Q1 time units every P1  VM2 granted Q2 time units every P2  VM3 granted Q3 time units every P3 . . . Networking Threads KVM Computing ThreadTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 19
  • Problem PresentationTommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 20
  • 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)Tommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 21
  • Problem Presentation 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)Tommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 22
  • 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 toTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 23
  • Experimental ResultsExperimental set-up  Periodic ping every 1s  Normal reservation: (40ms, 100ms)  Spare reservation: (4ms, 10ms)Batch activity  Fake loop logging experienced loops/sTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 24
  • 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/usTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 25
  • Implementation DetailsTommaso Cucinotta – ReTiS Lab – Scuola Superiore SantAnna – Pisa – Italy 26
  • 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 poolsTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 27
  • 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 VMTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 28
  • 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 2009Tommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore
  • Thanks for your attention Questions ? http://retis.sssup.it/people/tommasoTommaso Cucinotta – Real Time Systems Laboratory (ReTiS) – Scuola Superiore SantAnna Sant’Anna © 2007 Scuola Superiore 30