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Realizing Compositional Scheduling through Virtualization

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  1. Realizing Compositional Scheduling through Virtualization Jaewoo Lee, Sisu Xi, Sanjian Chen, Linh T.X. Phan Chris Gill, Insup Lee, Chenyang Lu, Oleg Sokolsky

  2. Virtualization Virtualization Platform Hypervisor Legacy System Legacy System Domains • The benefits of virtualization • Consolidate legacy systems • Integrate large, complex systems • Key challenges of virtualization for safety-critical systems • Temporal isolation • Real-time guarantee

  3. Compositional Scheduling Parent component • Periodic Resource Model (period, budget) Resource Model • Rate Monotonic Scheduler Periodic Tasks Resource Model Resource Model Scheduler Scheduler Workload Workload Component Child components • Compositional Scheduling Framework (CSF) • Provides temporal isolation and real-time guarantee • Computes the minimum-bandwidth resource model for the component • The gap betweenCSF theory and system • Realizing CSF though virtualization can bridge the gap

  4. Contributions • Compositional Scheduling Architecture (CSA) • Confederation of compositionalschedulingand virtualization • Enhancement to periodic server designin CSA • Extension to CSF for quantum-based platforms • Performance evaluation of CSA • Synthetic workloads and avionic workloads • First open-source real-time virtualization with CSF • Extensible with new domain-scheduling algorithms

  5. Overview of Our Work RT-Xen Compositional Scheduling Task Task Task Task App App App App Component Component Domain Domain PRM PRM PS PS Root Component Hypervisor Hardware • S. Xi, J. Wilson, C. Lu, C. Gill, RT-Xen: Real-Time Virtualization Based on Hierarchical Scheduling, EMSOFT, 2011 • Compositional Scheduling Architecture (CSA) • Component  domain • Periodic Resource Model (PRM)  Periodic Server (PS) • Task model: independent, CPU-intensive, periodic task • Scheduling algorithm: rate monotonic

  6. Theory Pessimism in CSF Interface Overhead • Interface considers the worst case: UPRM – UW ≥ 0 • Interface overhead leads to underutilization of the component • Resource model  periodic server in CSA • Periodic server does not fully utilize its budget • Slacks : tasks do not always execute for WCETs • Interface overhead • Underutilization of periodic server  long response times of real-time tasks • Using idle times, we propose enhanced periodic servers

  7. Periodic Server Design t Δ Task Release Current Domain Task Complete DH Budget time Execution of tasks in DH Budget DL Execution of tasks in DL • Purely Time-driven Periodic Server (PTPS) • If currently scheduled domain is idle, its budget is wasted • Not work-conserving

  8. Periodic Server Design t Δ Task Release Task Complete Current Domain DH Budget time Execution of tasks in DH Budget DL Execution of tasks in DL • Work-Conserving Periodic Server (WCPS) • If currently scheduled domain is idle, the hypervisor picks a lower-priority domain that has tasks to execute • Early execution of the lower-priority domainduring idle period does not affect schedulability

  9. Periodic Server Design t Δ Task Release Task Complete Current Domain DH Budget time Execution of tasks in DH Budget DL Execution of tasks in DL • Capacity Reclaiming Periodic Server (CRPS) • If currently scheduled domain is idle, wecan re-assign this idled budget to any other domain that has tasks to execute • Early execution of the other domainduring idle period does not affect schedulability

  10. CSF Extension for Quantum-based Platforms Task period Task execution time of resource model 3 tasks B/P: Real-number-based resource model P: of resource model To find the minimum-bandwidth resource model for workload W.

  11. CSF Extension for Quantum-based Platform Quantum-based resource model of resource model Non-decreasing B/P: the upper bound of the period to find min-BW resource model? Real-number-based resource model infeasible bandwidth Necessary condition for schedulability P: of resource model 2 1 To find the minimum-bandwidth resource model for workload W.

  12. CSF Extension for Quantum-based Platform Quantum-based resource model of resource model Min-BW resource model Non-decreasing B/P: the upper bound of the period to find min-BW resource model? Real-number-based resource model infeasible bandwidth Necessary condition for schedulability P: of resource model 2 1 To find the minimum-bandwidth resource model for workload W.

  13. System Architecture Xen Scheduling Framework Real-Time Sub Framework PTPS WCPS CRPS • Implemented in Xen 4.0 • only re-compile Xen, keep Kernel untouched • All source code available at RT-Xen website: https://sites.google.com/site/realtimexen/ • Current Limitations: • one VCPU per domain (single core) • CPU intensive workload

  14. Evaluation – Setup Scheduling Algorithms (PTPS, CRPS, WCPS) Parameters for each Domain Responsiveness: response time / deadline Deadline Miss Ratio Use Rate Monotonic within Domain IDLE App App App App Dom0 Dom1 Dom5 … VCPU VCPU VCPU … Schedulers (PTPS, CRPS, WCPS) HW Core 0 Core 1

  15. Evaluation – Synthetic workloads typical overloaded situation most interface overhead uniform distribute [wcet*ETF,wcet] extremely overloaded situation • Randomly generate task sets, then compute interface • Sources of idle time: • interface overhead: UPRM – UW • slacks: over-estimation of tasks’ execution time • Range workload periods -> different interface overhead • UW: 0.7, 0.8, 0.9, 1.0 • Periods: [550ms, 650ms], [350ms, 850ms], [100ms, 1100ms] • Range Execution Time Factor (ETF) -> different slacks • For all tasks in highest three priorities domains: 100%, 50%, 10% • Using period [550ms, 650ms], pick Uw from 0.7, 0.8, 0.9, 1.0

  16. Evaluation – Interface Overhead 0% CDF Plot, Probability deadline miss CRPS ≥ WCPS ≥PTPS 60% 100%

  17. Evaluation – ETF ( Response Time / Deadline ) for the Lowest Priority Domain PTPS: non work conservative, can not utilize slacks WCPS: consumes budget in parallel, still miss deadlines CRPS: ‘reclaim’ budget more aggressively, utilize slacks effectively

  18. Evaluation – ARINC-653 Workload Interface Overhead (8%) • UPRM = UW • if using real number parameters • Overheads comes from rounding up the budget • period is fixed • CRPS > WCPS > PTPS CDF Plot, Probability Response Time / Deadline A. Easwaran, I. Lee, O. Sokolsky, and S. Vestal, A Compositional Framework for Avionics (ARINC-653) Systems, Tech Report MS-CIS-09-04, 2009, University of Pennsylvania 7 harmonic workloads, each represent a set of domains scheduled on a single processor, with each domain consisting of a set of periodic tasks

  19. Conclusion • Compositional Scheduling Architecture (CSA) • Enhanced version of the Pure Time-driven Periodic Server (PTPS) • WCPS: work conserving, consume budget in parallel • CRPS: aggressively reclaiming budget • Extension of CSF for quantum-based platforms • Extensive evaluation on synthetic and avionics workloads • Open Source: • RT-XenWebsite: https://sites.google.com/site/realtimexen/

  20. Questions? RT-Xen https://sites.google.com/site/realtimexen/ or just Google RT-Xen *^_^*

  21. Backup : Interface overhead Interface Overhead Supplied resource 1st period of the resource supply UPRM – UW =2/3 – 1/3 = 1/3 0 1 2 3 4 5 6 0 1 2 3 4 5 6 2nd period of the resource supply Task Release Task Deadline Deadline miss Resource supply of resource model (3,1) Resource supply of resource model (3,2) • Interface considers the worst case: UPRM – UW ≥ 0 • For example, a task T= (p = 3, e = 1) in a component • Resource model (3, 1) cannot schedule T • Resource model (3, 2) can schedule T

  22. Backup : Simple rounding up does not work • The minimum-bandwidth resource model • CSFallow real numberin budget. • But, budget should be an integer multiple of the scheduling quantum in quantum-based platforms • Ex: • Optimal algorithm : (1,0.38) rounding up (1,1) • Only integer : • (1,1), (2,1), (3,2) , (4,2),… • Among feasible resource models, (5,2) is minimum bandwidth for quantum-based platforms

  23. Backup: Upper bound of the period Quantum-based resource model B/P A given bandwidth Non-decreasing Real-number-based resource model infeasible bandwidth P Necessary condition for schedulability The upper bound of the period for a given bandwidth We can easily find the upper bound of the period for a given bandwidth

  24. Backup : Difference from reservation-based system • CSA on RT-Xen virtualization • Support different local scheduler for each domain ( by running different guest OS) • Clean separation between local scheduler and global scheduler • Local OS does not know other task or other domain inside the system • Global scheduler does not know task information inside domain • Reservation-based native system • Local scheduler is a part of the operating system • We cannot provide a component with a local scheduler • No clean separation between local scheduler and global scheduler • Malformed local scheduler can affect global scheduler or other local schedulers

  25. Backup : Related Work • Crespo et al., “XtratuM”, EDDC ’10 • A bare VMM with a fixed cyclic scheduling policy • Cucinota et al., “Respecting Temporal Isolation...”, COMSAC ’09 • KVM with a hard reservation behavior • CSA is different from above two works • Only CSA support compositional scheduling • CSA is implemented on Xen, different architecture from KVM • KVM is integrated into the manager domain

  26. CSF Extension for Quantum-based Platforms