WOSE workshop, Edinburgh
• Title: Average-Based Workload Allocation
Strategy for QoS-Constrained Jobs In A
Web Service-Oriented Grid)
• Authors: Yash Patel and John Darlington
Previous Work
• Recent WOSE related work presented at All
Hands Meeting in September
– Grid Workflow Scheduling in WOSE
• Similar work presented at Grid 2006 Conference,
Barcelona, Spain
– QoS Support for Workflows in a Volatile Grid
• Both works focus on satisfying QoS requirements
and scheduling individual workflows
• And use stochastic programming technique to
tackle uncertainty
Previous Work - Drawbacks
• Overhead for scheduling workflows one by
one
• One needs to gather information about Grid
services more frequently (leads to
monitoring overheads)
• May be impractical when workflow arrival
rate is high
Extension of previous work
• Advantages over previous work
– Collectively schedule workflows
– Information about states of Grid services need
to be obtained only periodically
• Use of
– Queueing theory
– Mathematical programming
Overview
•
•
Web services emerging as a powerful
mechanism to achieve loosely coupled
distributed computing
Grid users can effectively compose web
services in the form of workflows and
tools such as BPEL engine can execute
their workflows
Applications
•
•
•
•
Financial services industry. E.g. portfolio
optimisation, risk analysis
News/weather/stock price etc services are
web services
Complex tasks can be interfaced through
web services. E.g. GridSAM
Basically any complex piece of code can
be interfaced through a web service
Our Approach
• Problem: Satisfy QoS requirements of end-users
in dynamic environments such as Grid
• Motivation: Develop an effective method that
doesn’t rely on obtaining real-time information to
make scheduling decisions
• Solution: Formulate scheduling problem of
workflows as a MINLP + model a web service as
a G/G/k queue
Our Approach
• MINLP: Mixed-Integer Non-linear Program
– Objective and constraints may be non-linear
and both real (continuous) and integer variables
in the optimisation program
• G/G/k queue
– General distribution of inter-arrival times and
general distribution of service times and k
processing threads
Why this approach
• MINLP: Mixed-Integer Non-linear Program
– Embed the non-linear equations arising from
G/G/k analysis into the program
• G/G/k queue
– Provides a general enough model
– No need for assuming specific distributions e.g.
M/M/k
Scheduling Problem as MINLP
• MINLP:
– minimise penalty
– Subject To:
• Deadline Constraint (deadlines allocated to
workflow tasks)
• Cost Constraint (budget allocated to workflow tasks)
• Reliability Constraint (reliability requirements of
workflow tasks)
MINLP
Penalty
Variables
penalty
Deadline constraint
Cost constraint
Reliability constraint
Task assignments should
be less than arrival rate
Stable queue requirement
Response Time for G/G/k queue
Calculation of diy and eiy
• Calculation of deadline and cost allocations for workflow
tasks
• diy = (Upper bound of the 95th confidence interval of the
workflow task y) * (Remaining workflow Deadline) /
(sum of upper bound of the 95th confidence interval of all
workflow tasks along workflow path starting with task y)
Similarly scaling with respect to remaining cost budget we can
calculate eiy
MINLP drawbacks
• NP-hard as apart from being non-linear it also falls under
combinatorial optimisation
• Solution time may increase exponentially with increase in
the number of variables / constraints
• How to get around the above problems:
– Linearise the MINLP model to MILP or LP
– Or reduce the number of variables
Doing so may not lead to good enough representations of original
problems
Experimental Evaluation
• We want to compare the ability to satisfy QoS
requirements for different scheduling strategies
with our developed strategy
• Next
–
–
–
–
–
–
Simulation in a nutshell
Scheduling Strategies
Workflows used
Simulation Setup
Experimental Results
Summary of Results
Simulation Summary
• Simulation developed in SimJava
• Web services, brokering service etc are SimJava
objects
• Workflows arrive with a general inter-arrival time
distribution
• Statistics (mean response time, cost, failures,
utilisation etc) collected for 1000 jobs following
500 jobs that require system initiation
• Workflows have overall deadline and cost
requirements apart from individual workflow tasks
having reliability requirements
Simulation in a nutshell
Payment Service
Workflow
QoS Document
Web Services
BROKER
End-User
DISCOVERY
SCHEDULER
Web Service-Oriented GRID
Performance
Repository
Web Services
Scheduling Strategies
• GWA: Global Weighted Allocation
• MINLP based workload allocation scheme
(FF)
• RTLL: Real Time based Least Loaded
Scheme
• Comparison: Workflow failures (workflows
that fail to meet either their deadlines or
budget)
Experimental Setup
• Next
– Workflows Used
– Simulation Setup
– Summary of results
Workflows used
GENERATE
MATRIX (1)
PRE-PROCESS TRANSPOSE
INVERT
MATRIX (2)
MATRIX (3) MATRIX (4)
Workflow Type 1
ALLOCATE INITIAL
RESOURCES (1)
CHECK IM LIFECYCLE
EXISTS (3)
YES
RETRIEVE A DAQ
MACHINE (2)
JOIN (5)
CREATE IM
COMMAND (7)
YES
EXECUTE
COMMAND (8)
CHECK IF COMMAND
EXECUTED (9)
NO
THROW IM COMMAND
EXCEPTION (13)
1
2
4
6
7
5
NO
CREATE IM
LIFECYCLE (4)
Workflow 1
CHECK IF
SUCCESSFUL
JOIN (6)
NO
THROW IM
LIFECYCLE
EXCEPTION (12)
1
2
3
4
5
1
2
3
4
5
6
7
8
Workflow 2
Workflow 3
Heterogenous Workload
YES
3
XDAQ APPLIANT
(10)
MONITOR DATA
ACQUISITION (11)
Workflow Type 2
Simulation Setup
Simulation
WS per task
Arrival rate (per sec)
Task Mean
Task CV
WS Cost per sec
WS Reliability (%)
Workflows
Workflow Deadline
Workflow Cost
Task Reliability (%)
1
6-24
2
6-12
3
6-24
1.5-10
3-12
0.2-2.0
0.1-2.0
3-10
0.2-1.4
1.5-3.6
3-12
0.2-2.0
0.07-0.7
50-100
Type 1
0.07-0.7
50-100
Type 2
0.07-0.7
50-100
HW
40-60
1-5
60-95
80-100
1-5
60-95
40-60
1-5
60-95
Failures (%)
Failures vs Arrival Rate [Low CV]
100
90
80
70
60
50
40
30
20
10
0
RTLL
FF
GWA
1.5
2
2.5
3
Arrival Rate (jobs/sec)
3.5
Failures (%)
Failures vs Arrival Rate [High CV]
100
90
80
70
60
50
40
30
20
10
0
RTLL
FF
GWA
1.5
2
2.5
3
Arrival Rate (jobs/sec)
3.5
Results
• The workload allocation strategy performs considerably
better than the algorithms that do not use these strategies
• Workflow and workload nature don't change the
performance of the scheme notably
• When arrival rates are low, performance is nearly similar
to RTLL
• Execution time variability does not change the
performance of the workload allocation strategy
significantly for both low are high arrival rates
• Don’t require to schedule individual workflows
• Doesn’t require real time information of web services
Future Work
• Experiment with workflows having slack periods
• Investigate techniques to linearise the optimisation
program and/or develop pre-optimisation
strategies that help to reduce the number of
unknowns in the MINLP
• Overhead analysis of RTLL and our approach
Thank You