Tao Yang, UCSB CS 240B’03 Unix Scheduling Multilevel feedback queues –128 priority queues (value: 0-127) –Round Robin per priority queue Every scheduling event the scheduler picks the lowest priority non-empty queue and runs jobs in round-robin Scheduling events (when to schedule): –Clock interrupt –Process does a system call –Process gives up CPU,e.g. to do I/O

Tao Yang, UCSB CS 240B’03 UNIX Scheduling All processes assigned a baseline priority based on the type and current execution status: –swapper 0 –waiting for disk20 –waiting for lock35 –user-mode execution50 At scheduling events, all process’s priorities are adjusted:

Tao Yang, UCSB CS 240B’03 Multiprocessor Scheduling Targeted applications: Single-process jobs. Multi-process jobs. Multi-threaded jobs. Multiprogrammed vs. dedicated: running several applications concurrently? Granularity of parallelism. Scheduling policy?

Tao Yang, UCSB CS 240B’03 Granularity of Parallelism Independent parallelism: independent processes. No need to synchronize. Coarse-grained parallelism: relatively independent processes with occasional synchronization. Medium-grained parallelism. E.g. multi-threads which sync frequently. Fine-grained parallelism: synchronization every few instructions.

Tao Yang, UCSB CS 240B’03 Scheduling Challenges in Multiprogrammed Environments Why multiprogramming? –Improve system utilization Applications with independent tasks or coarse grained parallelism. –Regular OS scheduling policy Applications with medium grained parallelism –Special scheduling policy is needed. E.g. Consider a set of cooperative threads collectively.

Tao Yang, UCSB CS 240B’03 Assigning Processes to Processors Master/slave approach: Single scheduler. One processor is dedicated for processor assignment –Good: Centralized decision making –But: single point of failure. Not scalable. Peer assignment: OS can run any processor. Each processor does self- scheduling.

Tao Yang, UCSB CS 240B’03 Scheduler Organization in Peer Assignment Single global ready queue with locks. CPUs schedule jobs from this queue. –Use FCFS or other scheduling policies. –Limited scalability, but reasonable. One ready queue per CPU. Each CPU schedules its processes. –Static process assignment: cache friendly (Affinity), but difficult to balance load –Dynamic assignment with job stealing. may need process migration.

Tao Yang, UCSB CS 240B’03 Self Scheduling vs. Gang Scheduling Self-scheduling (load sharing): Pool of threads, pool of processors. –Better utilization –Cooperative threads may not run in parallel. Gang-scheduling (co-scheduling): Schedule related threads/processes at once on different CPUs. –Maximum concurrency for parallel applications. –Minimize intra-task communication latency. –More resource waste.

Tao Yang, UCSB CS 240B’03 Other Issues Time sharing and space sharing: –Space sharing: Should processors be statically or dynamically divided among applications? –Time sharing: should a processor be multiprogrammed? Resource allocation: –How many processors should be assigned to a process (a user job)? How can the OS know about the resource requirement of a job (process). In Solaris, a user needs to declare threads to be scheduled in a system scope. –Should the resource allocation be dynamically adjusted? A job may not utilize all processors assigned.

Tao Yang, UCSB CS 240B’03 Lottery Scheduling for Resource Share Allocation Carl A. Waldspurger and William E. Weihl. Lottery Scheduling: Flexible Proportional-Share Resource Management. In OSDI’94 Probabilistic approach for proportional resource sharing. –Assign a number of “lottery tickets” to each process. –The OS selects “winning ticket” randomly. –Tickets are transferable to among processes for share adjustment. –The # of tickets a process owns represents the percentage of its share.