wose workshop, edinburgh
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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
Deadline constraint
Cost constraint
Reliability constraint
Penalty Variables
Stable queue requirement
Task assignments should be less than arrival rate
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
Web Service-Oriented GRIDDISCOVERY
SCHEDULER
BROKER
Workflow
QoS Document
End-User
Web Services
Payment Service
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 MATRIX (2)
TRANSPOSE MATRIX (3)
INVERT MATRIX (4)
Workflow Type 1
1 2 3 4 5
6 7
1 2 3 4 5
1 2 3 4 5
6 7 8
Workflow 1
Workflow 2
Workflow 3
Heterogenous Workload
ALLOCATE INITIAL RESOURCES (1)
RETRIEVE A DAQ MACHINE (2)
CHECK IM LIFECYCLE EXISTS (3)
CREATE IM LIFECYCLE (4)
YESNO
JOIN (5)CHECK IF
SUCCESSFUL JOIN (6)
CREATE IM COMMAND (7) THROW IM
LIFECYCLE EXCEPTION (12)
YES NO
EXECUTE COMMAND (8)
CHECK IF COMMAND EXECUTED (9)
XDAQ APPLIANT (10)
THROW IM COMMAND EXCEPTION (13)
YES
NO MONITOR DATA ACQUISITION (11)
Workflow Type 2
Simulation Setup
Simulation 1 2 3
WS per task 6-24 6-12 6-24
Arrival rate (per sec) 1.5-10 0.1-2.0 1.5-3.6
Task Mean 3-12 3-10 3-12
Task CV 0.2-2.0 0.2-1.4 0.2-2.0
WS Cost per sec 0.07-0.7 0.07-0.7 0.07-0.7
WS Reliability (%) 50-100 50-100 50-100
Workflows Type 1 Type 2 HW
Workflow Deadline 40-60 80-100 40-60
Workflow Cost 1-5 1-5 1-5
Task Reliability (%) 60-95 60-95 60-95
Failures vs Arrival Rate [Low CV]
0
10
20
30
40
50
60
70
80
90
100
1.5 2 2.5 3 3.5
Arrival Rate (jobs/sec)
Fai
lure
s (%
)
RTLL
FF
GWA
Failures vs Arrival Rate [High CV]
0
10
20
30
40
50
60
70
80
90
100
1.5 2 2.5 3 3.5
Arrival Rate (jobs/sec)
Fai
lure
s (%
)
RTLL
FF
GWA
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