Computer Science Deadline Fair Scheduling: Bridging the Theory and Practice of Proportionate-Fair Scheduling in Multiprocessor Servers Abhishek Chandra Micah Adler Prashant Shenoy University of Massachusetts Amherst, USA

Computer Science Motivation  Applications hosted on Commodity (OTS) OS’s  Key Challenge: Design OS mechanisms for predictable Resource Management  Diverse multimedia applications popular  Streaming, games, etc.  Applications require delay/bandwidth guarantees Server Streaming Virtual Worlds Games

Computer Science Requirements for OS Resource Management  Fair, Proportionate Allocation Divide resource among applications based on requirement  Predictable Execution Provide soft real-time guarantees (delay, response, etc.)  Application Isolation Overloaded application should not affect others  Efficiency Utilize system resources, low overhead Focus: Achieving these objectives for CPU scheduling on multiprocessor machines

Computer Science Outline Motivation  Proportionate-Fairness  Deadline Fair Scheduling  Behavior in Commodity OS  Practical Enhancements  Experimental Evaluation  Concluding Remarks

Computer Science Upper bound Lower bound Proportionate-Fairness [Baruah et al.96]  Periodic Task needs e units of service every p units  Ideal service in duration t,  P-fairness: Ideal time CPU Service e=1, p=2 P-fair

Computer Science Deadline Fair Scheduling  Eligibility: Task not allowed to run more than its due  Deadlines: Task finishes each run on time Ideal time CPU Service DeadlineEligible Ineligible DeadlineEligible DFS e=1, p=2

Computer Science Deadline Fair Scheduling: Relative Allocation  Weight: For relative shares in a k-CPU system  Start tag: Service received by a task so far  Virtual time: Average service of all tasks in the system

Computer Science Deadline Fair Scheduling: Online Schedule  Eligibility criteria:  Comparison of start tag and virtual time  Deadlines:  In order of start tags

Computer Science Deadline Fair Scheduling: Proportionate Allocation time CPU Service 1 CPU, 3 Tasks with weights 2, 1, 1 Task 1 Task 2 Task 3 Task 1 (w1=2 => e=1, p=2) Task 2 (wt=1 => e=1, p=4) Task 3 (wt=1 => e=1, p=4) ED ED ED I I DE I E : Eligible I : Ineligible D : Deadline E E E

Computer Science Properties of DFS  System Model:  Multiprocessor system with k symmetric CPUs  A fixed set of n tasks  No arrivals and departures  All CPUs are synchronized  Lemma 1: DFS generates P-fair schedule  Lemma 2: DFS is work-conserving (no CPU is idle if runnable tasks in the system)

Computer Science Outline Motivation Proportionate-Fairness Deadline Fair Scheduling  Behavior in Commodity OS  Practical Enhancements  Experimental Evaluation  Concluding Remarks

Computer Science Issues in Commodity OS  Quantum durations are not fixed length  Due to blocking/pre-emption  CPUs are not synchronized:  Quanta on various CPUs are out of phase  Each CPU calls scheduler independently  Tasks can arrive and depart Question: How does this affect the work-conserving behavior of DFS?

Computer Science Non-Work Conserving Behavior  DFS becomes non-work conserving

Computer Science Enhancement 1: Fair Airport Scheduling [Goyal et al.97]  Goal: Utilize available bandwidth if runnable tasks Guaranteed Service Queue Rate Controller Eligible Queue Auxiliary Service QueueIneligible Queue DFS Ordered by deadlines Eligibility Criteria

Computer Science Enhancement 2: Incorporating Processor Affinities  Problem: How to utilize processor caches?  Solution: Give priority to tasks with affinity  New deadline D:  If task has affinity: D = d  If task has no affinity: D = d + α Here, d is the deadline of task, α is the affinity weight  Tasks scheduled in order of D  Trades fairness for performance

Computer Science Outline Motivation Proportionate-Fairness Deadline Fair Scheduling Behavior in Commodity OS Practical Enhancements  Experimental Evaluation  Concluding Remarks

Computer Science Proportionate Allocation and Application Isolation

Computer Science Performance of Real-Time and Multimedia Applications

Computer Science Related Work  P-fairness [Baruah96]:  PF Algorithm [Baruah-Gehrke96], Pfair Priorities [Anderson99]  Deadline-based scheduler [West99]  Generalized Processor Scheduling [Parekh92]  WFQ [Demers89], SFQ [Goyal96], BVT [Duda99]  Multiprocessor Scheduling:  Spring [Ramamritham84]  CPU Reservations [Jones99], SFS [Chandra00]

Computer Science Summary  P-fairness notion of proportionate scheduling  DFS can provide p-fairness in ideal system model  Two practical enhancements:  Fair Airport Scheduling  Incorporating processor affinities  Source code available at: