Static process scheduling
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Static Process Scheduling . Yi Sun. Overview. Before execution, processes need to be scheduled and allocated with resources Objective Enhance overall system performance metric Process completion time and processor utilization In distributed systems: location and performance transparency

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  • Before execution, processes need to be scheduled and allocated with resources

  • Objective

    • Enhance overall system performance metric

      • Process completion time and processor utilization

    • In distributed systems: location and performance transparency

  • In distributed systems

    • Local scheduling (on each node) + global scheduling

    • Communication overhead

    • Effect of underlying architecture

    • Dynamic behavior of the system

Process interaction models
Process Interaction Models

  • Precedence process model: Directed Acyclic Graph (DAG)

    • Represent precedence relationships between processes

    • Minimize total completion time of task (computation + communication)

  • Communication process model

    • Represent the need for communication between processes

    • Optimize the total cost of communication and computation

  • Disjoint process model

    • Processes can run independently and completed in finite time

    • Maximize utilization of processors and minimize turnaround time of processes

Process models

Communication overhead

Process Models

Partition 4 processes onto two nodes

System performance model
System Performance Model

Attempt to minimize the total completion time of (makespan) of a set of interacting processes

System performance model cont
System Performance Model (Cont.)

  • Related parameters

    • OSPT: optimal sequential processing time; the best time that can be achieved on a single processor using the best sequential algorithm

    • CPT: concurrent processing time; the actual time achieved on a n-processor system with the concurrent algorithm and a specific scheduling method being considered

    • OCPTideal: optimal concurrent processing time on an ideal system; the best time that can achieved with the concurrent algorithm being considered on an ideal n-processor system(no inter-communication overhead) and scheduled by an optimal scheduling policy

    • Si: the ideal speedup by using a multiple processor system over the best sequential time

    • Sd: the degradation of the system due to actual implementation compared to an ideal system

System performance model cont1
System Performance Model (Cont.)




Pi: the computation time ofthe concurrent algorithm onnode i


(RP  1)











System performance model cont2
System Performance Model (Cont.)

(The smaller, the better)

(The larger, the better)

System performance model cont3
System Performance Model (Cont.)

  • RP: Relative processing (algorithm)

    • How much loss of speedup is due to the substitution of the best sequential algorithm by an algorithm better adapted for concurrent implementation but which may have a greater total processing need

    • Loss of parallelism due to algorithm conversion

    • Increase in total computation requirement

  • Sd

    • Degradation of parallelism due to algorithm implementation

  • RC: Relative concurrency (algorithm?)

    • How far from optimal the usage of the n-processor is

    • RC=1  best use of the processors

    • Theoretic loss of parallelism

  • : loss of parallelism when implemented on a real machine (system architecture + scheduling)

Efficiency loss
Efficiency Loss

Impact factors: scheduling, system, and communication

Efficiency loss cont
Efficiency Loss  (Cont.)

Workload distribution
Workload Distribution

  • Performance can be further improved by workload distribution

  • Loading sharing: static workload distribution

    • Dispatch process to the idle processors statically upon arrival

    • Corresponding to processor pool model

  • Load balancing: dynamic workload distribution

    • Migrate processes dynamically from heavily loaded processors to lightly loaded processors

    • Corresponding to migration workstation model

  • Model by queuing theory: X/Y/c

    • Proc. arrival time distribution:X; Service time distribution:Y; # of servers: c

    • : arrival rate; : service rate; : migration rate

    • : depends on channel bandwidth, migration protocol, context and state information of the process being transferred.

Processor pool and workstation queueing models
Processor-Pool and Workstation Queueing Models

Static Load Sharing

Dynamic Load Balancing

M for Markovian distribution

Comparison of performance for workload sharing
Comparison of Performance for Workload Sharing

(Communication overhead)

(Negligible Communication overhead)

Static process scheduling1
Static Process Scheduling

  • Static process scheduling: deterministic scheduling policy

    • Scheduling a set of partially ordered tasks on a non-preemptive multi-processor system of identical processors to minimize the overall finishing time (makespan)

      • Optimize makespan  NP-complete

      • Need approximate or heuristic algorithms…

    • Attempt to balance and overlap computation and communication

    • Mapping processes to processors is determined before the execution

      • Once a process starts, it stays at the processor until completion

      • Need prior knowledge about process behavior (execution time, precedence relationships, communication patterns)

      • Scheduling decision is centralized and non-adaptive

Precedence process and communication system models
Precedence Process and Communication System Models

Communication overhead for A(P1) and E(P3)= 4 * 2 = 8

Communication overhead for one message

Execution time

No. of messagesto communicate

Precedence process model
Precedence Process Model

  • Precedence Process Model – NP-complete

    • A program is represented by a DAG (Figure 5.5 (a))

      • Node: task with a known execution time

      • Edge: weight showing message units to be transferred

    • Communication system model: Figure 5.5 (b)

  • Scheduling strategies

    • List Scheduling (LS): no processor remains idle if there are some tasks available that it could process (no communication overhead)

    • Extended List Scheduling (ELS): LS first + communication overhead

    • Earliest Task First (ETF) scheduling: the earliest schedulable task is scheduled first

  • Critical path: longest execution path

    • Lower bound of the makespan

    • Try to map all tasks in a critical path onto a single processor

Communication process model
Communication Process Model

  • Communication process model

    • Maximize resource utilization and minimize inter-process communication

    • Undirected graph G=(V,E)

      • V: Processes

      • E: weight = amount of interaction between processes

    • Cost equation

      • e = process execution cost (cost to run process j on processor i)

      • C = communication cost (C==0 if i==j)

      • Again!!! NP-Complete

Stone s two processor model to achieve minimum total execution and communication cost
Stone’s two-processor model to achieve minimum total execution and communication cost

  • Example: Figure 5.7 (Don’t consider execution cost)

    • Partition the graph by drawing a line cutting through some edges

      • Result in two disjoint graphs, one for each process

      • Set of removed edges  cut set

        • Cost of cut set  sum of weights of the edges

          • Total inter-process communication cost between processors

      • Of course, the cost of cut sets is 0 if all processes are assigned to the same node

        • Computation constraints (no more k, distribute evenly…)

  • Example: Figure 5.8 (Consider execution cost)

    • Maximum flow and minimum cut in a commodity-flow network

      • Find the maximum flow from source to destination

Computation cost and communication graphs
Computation Cost and Communication Graphs execution and communication cost

Minimum cost cut
Minimum-Cost Cut execution and communication cost

Only the cuts that separate A and Bare feasible

Discussion static process scheduling
Discussion – Static Process Scheduling execution and communication cost

  • Once a process is assigned to a processor, it remain there until its execution has been completed

  • Need prior knowledge of execution time and communication behavior

    • Not realistic

Reference execution and communication cost

  • Distributed Operating Systems & Algorithms, by Randy Chow and Theodore Johnson, 1997