1. Auburn University http://www.eng.auburn.edu/~xqin
COMP7500 Advanced Operating Systems I/O-Aware Load Balancing Techniques (2)
Dr. Xiao Qin
Auburn University
Spring, 2012

2. Current Solutions Disk I/O Systems Limitation Caching Prefetching
Parallel I/O
Limitation
Low level
Not Portable

3. Current Solutions (Cont.)
Scheduling/Load balancing
Space-sharing
(PBS,Backfilling)
Time-Sharing
Centralized Control
(PBS)
Distributed Control
Coordinated
Scheduling (Gang)
Non-I/O-aware
(Condor, Mosix, DQS, LSF)
Disk-I/O-aware
Network-I/O-aware
load balancing
Support Sequential Jobs
Support Parallel Jobs
Disk-I/O Buffer
Management
Support Homogeneous Clusters
Support Heterogeneous Clusters

4. System Architecture I/O-intensive jobs Client Services
Load Manager
Load Manager
Load Manager t1 t2 t3 t3 t4 t5 t6 t7
mem
disk
mem
disk
mem
disk
Workstation 1
Workstation 2
Workstation n
High Bandwidth network

5. Methodology I/O Intensive Applications User Specified Access Pattern
Data Storage Pattern
Measure I/O load
Predict Response
Time
Estimate Overhead
Make Decisions
Load Balancing Schemes
Dispatch and Migration

6. Outline
Motivations
A Disk-I/O-Aware Load Balancing Policy with Remote Execution
A Disk-I/O-Aware Load Balancing Policy with Preemptive Migration
Evaluation of the two Disk-I/O-Aware Policies
Load Balancing for Heterogeneous Clusters
Contributions and Conclusions

7. Load Balancing with Remote Execution
A newly arrived job
Remote Execution
Local Execution
Node j
Node i
Running jobs
Running jobs
High Bandwidth Network

8. The IOCM-RE Scheme A new parallel job Yes. Find candidate remote nodes
Select candidate nodes, balance I/O load
I/O overloaded ? no no
yes
Select candidate nodes,
balance memory load
mem overloaded ? no no
yes
Select candidate nodes,
balance CPU load
CPU overloaded ? no no
Remotely
Execute
Locally execute

9. Explicit I/O load
Explicit I/O load = I/O access rate  (1 - buffer hit rate)
Applications
Applications
data
data
Probability that data is NOT in the I/O buffer of node i
Disk I/O Buffer
Disk I/O Buffer or
data
Data is NOT in the buffer
Data is in the buffer

10. Implicit I/O load Given a task s running on node i:
Implicit I/O load induced by page faults
Given a task s running on node i:
Memory space requested by the running tasks
Available user memory space if
otherwise
Page fault rate of task s

11. Overall I/O load Implicit I/O load induced by page faults
Explicit I/O load resulting from tasks accessing disks.
I/O load index of node i
Implicit I/O load of task s running on node i
Explicit I/O requirement of task s