A Scheduling Service Oriented Approach for Workflow Scheduling

byConan Fan Li

Supervisor: Dr. Wendy MacCaullCommittee member: Dr. Man LinCommittee member: Dr. Iker Gondra

A SSO approach for workflow scheduling Initiatives

AIF project: building decision-support through dynamic workflow systems, academia and industry working together for better healthcare

Workflow engines are not naturally built for scheduling problems

What is a workflow? A workflow or a workflow model is a depiction

of a business process composed of a sequence of operations (tasks).

Tasks are connected in the form of a directed graph to provide an abstraction of the real work for further assessment.

What is a workflow?

What is a workflow?

Why workflow? Abstraction & visualization Clarity & consistency Automation

What is scheduling? The process of making decisions about the

allocation of resources for a number of tasks to achieve one or more objectives?

Two main applications

Application of schedulingManufacturing (e.g., a car factory)

Application of schedulingService industry (e.g., Gate Assignments at an

Airport)

Basics of Scheduling A scheduling problem can be described by a

triplet α | β | γ.

α describes the machine environment β describes the processing characteristics and

constraints γ describes the objective

The triplet Jm | prec | Cmax describes a job shop scheduling problem with precedence constraints and an objective to minimize the makespan

A Job Shop Example

The SSO approach An approach that maps a scheduling problem

onto a workflow model that consists of tasks with built-in services for scheduling.

Definitions A schedule has unforced idleness if some machines

idle when there are jobs waiting for processing. A schedule is non-delay if unforced idleness is prohibited.

Examples of possible objective functions to be minimized are: Makespan: completion time of the last job to leave the

system. Maximum lateness: the worst violation of the due dates. Total weighted completion time: the sum of the weighted

completion times of the n jobs. ...

A multi-instance task is a task that may have multiple distinct execution instances running concurrently within the same workflow case.

Definitions We say a job is waiting when it is not

assigned to any machine or finished. We say a job is independent when it does

not have a precedence constraint or the precedence constraint is satisfied (i.e., the preceding job is finished).

We say a job is machine-ready when its required machine is free.

We say a job is enabled when it is waiting, independent and machine-ready.

Definitions Schedule-flow is a framework that has a

predisposition to model scheduling processes in workflow. Schedule-flow patterns are an extension to the workflow language formalism - Workflow Patterns. By assembling and modifying the existing workflow patterns, schedule-flow introduces new patterns that carries particular responsibilities and services in scheduling systems.

Definitions Schedule-flow is a framework that has a

predisposition to model scheduling processes in workflow. It is an extension to the workflow language formalism - Workflow Patterns. By assembling and modifying the existing workflow patterns, schedule-flow introduces new patterns that carries particular responsibilities and services in scheduling systems.

A* search uses a distance-plus-cost evaluation function (f(x)) to determine the order in which the search visits nodes in the fringe. The distance-plus-cost heuristic is a sum of two functions: the cost function, which is the cost from the starting node to

the current node (usually denoted g(x)) and an "heuristic estimate" of the distance to the goal (h(x)).

Definitions We say an event e=(m,j,start,end) is a future

event of schedule S if e.start >= S.clock

Relation Scheduling is a ___ and a workflow is to

represent a ___. Why use workflow to model scheduling?

Workflow is concise, comprehensive and high-level Scheduling is diverse, technical and low-level We want to visualize the scheduling process

Workflow has a wider range of audience than scheduling does. That is why we need to bridge

them.

Attempt Case: a simple job shop scheduling problem

Objective: minimize makespan(Cmax)

Attempt1. Messy

What you see is messy

What you do not see is messier

Attempt

Attempt

We may choose Job0 to process first. Why does this seem like a smart choice?

2. multi-instance tasks are

confusing and high-maintenance

3. Need to incorporate smart

choices

Attempt

Finish Job0Clock+=7

Job0.waiting=False

Attempt

We want to assign as many

jobs as possible before

processing

4. Options for

unforced idleness

Attempt

We may choose Job4 to

process

Attempt

Finish job1Clock=7+9=16

Job1.waiting=False(Note, job4 has

been processed for 9 time units)

Attempt

No other jobs are available to

process except Job4

Attempt

Finish Job4Clock=16+(15-9)=22Job4.waiting=False

Attempt

Assign job2

Attempt … we get this:(57, {<0,0,0,7>, <0,4,7,22>, <0,2,22,43>,<1,1,7,16>, <1,5,22,52>, <1,3,52,57>})

An event:<machine, job,start,end>

clock attribute of a schedule

Attempt … we get this:(57, {<0,0,0,7>, <0,4,7,22>, <0,2,22,43>,<1,1,7,16>, <1,5,22,52>, <1,3,52,57>})

Gantt chart

Forced idleness

Problems The size issue

The size of the resulting workflow grows in accordance to the size of the problem (number of jobs and number of machines). There are also too many variables to configure inside the workflow. This is neither concise or comprehensive.

The complication of multi-instance tasks Users may not understand when to use them

The lack of heuristic incorporation When several jobs are presented to a machine,

there should be a mechanism to decide which one appears to be the best option

Problems The lack of options for unforced idleness

There should be an easy way of expressing that we do not want to allow unforced idleness, that is, when a machine is free, we always try to assign a job to it if possible.

The lack of comparison and sorting We do not want the workflow to stop as soon as it

finds one feasible schedule. Instead, we need it to compare all the schedules and present the best one. There should be a mechanism to easily compare and sort the schedules.

Proposal The current workflow components are clearly

not sufficient for constructing sophisticated schedulers. Therefore, we need a set of new workflow patterns to provide the services we need in scheduling. We call the extension schedule-flow patterns.

Schedule-flow Present the data (jobs and machines) in a single file

instead of mapping each one of them to a task in the workflow. This way, the size of the workflow will not be proportional to the size of the scheduling problem. More importantly, the same workflow can now work with different sets of data.

Eliminate the usage of multi-instance tasks. Instead, we use a data structure called “fringe”(a collection of ideas, see A*) which is implemented as a priority queue. Different execution instances (schedules) will be evaluated first and then pushed into the fringe.

Heuristics may be given by users regarding the preference of assignment. For example, we may want to assign the jobs with the least processing times first (SPT).

Schedule-flow By default, we do not allow unforced idleness.

We would like to keep the machines as busy as possible. “For many the models that have regular objective

functions, there are optimal schedules that are non-delay”

The fringe provides options for its priority rule, which is the order that the schedules are sorted.

Schedule-flowFor the same problem, use schedule-flow:

Built-in variables: fringe, current

Schedule-flow task components Creation

Does nothing Pop

current=fringe.pop() Selection

By default, select jobs that are enabled (or independent, machine-ready…)

Allocation Generate schedules by assigning jobs to corresponding machines

Process Generate one schedule by advancing time until any job is

finished Push

fringe.push(schedules)

Schedule-flow condition components Exist

Tests if the input exists Continue

By default, tests if the fringe is empty. Other options may be used such as limiting the execution time or the number of schedules generated…

Use Schedule-flow in practice Design a schedule-flow with a graphic editor

We used YAWL in this case.

Use Schedule-flow in practice Provide the files

Job file (e.g., “jobs.csv”) Machine file Schedule-flow XML file generate by the graphic

editor Parse the schedule-flow file and detect

schedule-flow components by matching names. We used a Python script to parse the YAWL’s XML

file.

<task id="POP_93"> <name>POP</name> <flowsInto> <nextElementRef id="SELECT_87" /> </flowsInto> <join code="xor" /> <split code="and" /> </task>

Use Schedule-flow in practice Configure the components, objective and

heuristic In this case, every component uses the default

setting Objective is set to minimize the makespan.

Therefore, the cost function g(S)=S.clock Heuristic is initially 0 (f(x)=g(x), see A*). For this

case, we set the heuristic function to return the processing time of the future event (see future event) with the earliest end time

Bind: automatically connect the components according the schedule-flow

Run the schedule-flow

Schedule-flow simulationChanges Values

fringe = [S0]S0 is the initial

schedule where no jobs are assigned.

S0={ }

Schedule-flow simulationChanges Valuescurrent=fringe.pop() current=S0

fringe=[]

Schedule-flow simulationChanges ValuesSELECT.output=[0,2,4] current=S0

fringe=[]SELECT.output=[0,2,4]

Schedule-flow simulationChanges ValuesEXIST returns True current=S0

fringe=[]

SELECT.output=[0,2,4]

Schedule-flow simulationChanges ValuesALLOCATE.output={S1,S2,S3} current=S0

fringe=[]S1: (0,{<0,0,0,7>})S2:(0,{<0,2,0,21>})S3:(0,{<0,4,0,15>})

Schedule-flow simulationChanges Valuesfringe.push(ALLOCATE.output) current=S0

fringe=[S1,S3,S2]

f(S1)=0+7=7f(S3)=0+15f(S2)=0+21

S1: (0,{<0,0,0,7>})S3:(0,{<0,4,0,15>})S2:(0,{<0,2,0,21>})

Schedule-flow simulationChanges ValuesCONTINUE returns True current=S0

fringe=[S1,S3,S2]

Schedule-flow simulationChanges Valuescurrent=fringe.pop() current=S1=(0,{<0,0,0,7>})

fringe=[S3,S2]

Schedule-flow simulationChanges ValuesSELECT.output=[] current=S1=(0,{<0,0,0,7>})

fringe=[S3,S2]SELECT.output=[]No jobs are enabled in

S1!

Schedule-flow simulationChanges ValuesEXIST returns False current=S1=(0,{<0,0,0,7>})PROCESS.output=S4=(7,{<0,0,0,7>}) fringe=[S3,S2]

Schedule-flow simulationChanges Valuesfringe.push(S4) current=S1=(0,{<0,0,0,7>})

fringe=[S4,S3,S2]

f(S4)=7+0f(S3)=15f(S2)=21

S4: (7,{<0,0,0,7>})S3:(0,{<0,4,0,15>})S2:(0,{<0,2,0,21>})

Schedule-flow simulationChanges ValuesCONTINUE returns True current=S1=(0,{<0,0,0,7>})

fringe=[S4,S3,S2]

Schedule-flow simulation Many more iterations… … we get this:(57, {<0,0,0,7>, <0,4,7,22>, <0,2,22,43>,<1,1,7,16>, <1,5,22,52>, <1,3,52,57>})

Gantt chart

FAQ Why is schedule-flow a framework?

Like many other frameworks (e.g., openGL, Joomla...), schedule-flow is “an abstraction in which software providing generic functionality can be selectively changed by user code, thus providing application specific software. It is a collection of software libraries providing a defined application programming interface (API)”.

How to use schedule-flow with a particular workflow language (e.g., NOVA workflow). The translation between workflow languages and schedule-flow

should be left to the applications. The translation could be as simple as extracting the names and dependencies of the tasks in the workflow like what we did with YAWL. However, for a more sophisticated and user-friendly software, advanced configurations such as heuristic function may be also incorporated into the workflow graphic editor to provide better user experience.

FAQ Why do we need to use workflow to model

schedulers anyway? To be concise, comprehensive and visual. If we can see

each step in the scheduling process, it is easier to optimize and make changes.

What problems does schedule-flow solve? By cutting the direct mapping between jobs and

workflow tasks, schedule-flow provides more flexibility so that the same structure can be used for more than just one problem. Moreover the size of the resulting workflow is significantly reduced.

By introducing the concepts of heuristics, schedule-flow makes “smart” choices instead of random permutation for job assignments.

Tool: PYSF We developed a tool called PYSF (Python

schedule-flow) for experiments Demo…