CLOUD BATCH

A Batch Job Queuing System on Clouds with Hadoop and Hbase

Presents ByNiharika Potharam

Introduction

“With CouldBATCH, a complete shift to Hadoop for managing an entire cluster to cater for hybrid computing needs becomes feasible.”

It is difficult to manage the Hadoop Clusters due to hadoop’s Lack of functionality ,user access control, accounting, finegrain performance monitoring, etc.

Hadoop is Incompatible with existing cluster batch job queuing systems and requires a dedicated cluster under its full control.

Existing Solutions

Hadoop Schedulers:FIFO Queue: But, It does not guarantee fair resource allocation.

Hadoop on Demand:Running deamons on each computer node creates an Hadoop on demand .But, Data locality of the external HDFS is not exploited.

Desired Properties for

A. Job Queue Management:Cluster nodes can be assigned to queues with a minimum and maximum quantity and capacity guarantee for optimized

resource utilization.

B. Job Scheduling and Resource Utilization: Jobs with higher priority must be scheduled first and may

require preemption based on priority.And reservation for pre-scheduled jobs can be supported by putting a Threshold on job submission allowance for each

user.

C. User Access Control & Accounting: Access control must be supported at least at queue level

(Stateful job execution).

CloudBATCH

Defintion: Uses a set of Hbase tables globally accessible, to maintain Meta-data for jobs and runs job through Hadoop MapReduce.

Cloud BATCH - Architecture

Hbase Tables Check Status

poll poll

Submit Wrapper Submit Wrapper

Execute Job Execute Job

wrapper wrapper

job X wn job Y

ClientJob

TableUser Table

Queue Table

Reservation Table

Map Reduce job Job

Broker j

Job Broker i

Reserved Job

Serial Job

Monitor

Hbase Tables

Queue Table: Stores information such as type of jobs, queue

capacity, queue job priority, execution time limit, queue domain, list of users or groups allowed to access the queue .

QueueID

QueueType

Capacity

Job Priority

Job Length Limit

Domain User Alice

User Bob

Group Default

Serial Serial 50 1 1200000

Public Y

Bio Informatics

Map Reduce

10 600000 Private Y

Job Table: Contains extensive information about jobs. Jobs are identified by unique IDs, submitted

to queues and associated with the submitting users.

Cloud-BATCH currently accepts 3 types of job.->serial->MapReduce->Scheduled Time

Job Table

JobID

Start Time

End Time

Submit Time

Queued Time

JobLengthLimit

Status:submitted

Status:queued

Status:running

Status:failed

Status:succeeded

23 T1 1200000

Y

24 T4 T2 t3 600000 Y

25 t5 1200000

Y

JobID

Queue ID: Serial

Queue ID:bioinformatics

JobType: serial

JobType: MapReduce

JobType:ScheduledTime

Priority: 1

Priority :5

23 Y Y Y

24 Y Y Y

25 Y Y Y Y

Scheduled Time Table: When Scheduled Time Table receives the information,

then it sets up the status of the job in Job Table as Status:Submitted.

The value of “scheduled time” in Scheduled Tabled is used to set the “Submit Time” in the Job Table.

When a Broker sees a Scheduled job, It will not process until its “Submit Time”

Scheduled TimeID

UserBob Group bio Job ID Scheduled Time

Request SubmitTime

2 Y Y 25 T5 T0

UserID Simultaneous JobNumLimit

Individual JobPriorityLimit

Group default

Group bio Group space

UserAlice

20 5 Y

UserBob 30 8 Y Y

USER TABLE:

CLIENT: Submits Job

Job Broker: Handles the Job Policies

Wrapper

Executes job through Hadoop Map Reduce frame work.

When a wrapper starts at some node, it grabs job information from Hbase table and stages it to local machine

Now, It performs job execution, and updates job status to “status:Running” .

After execution , sets Job status to “Status:successful” or “Status:failed”

Monitor: Detects and Handles wrapper failures

A threshold T is set , Monitor Polls the job the table for “queued” status for a time period longer than T.

Conclusion

“CloudBatch” enables Hadoop to function as a traditional batch queuing system with enhanced management functionalities for cluster resource management.

Future Enhancements

Future work will be explored in the directionof further testing the system under multi-queue, multi-user situations with heavy load and refining the prototype implementation of the system for trial production deployment in solving real-world use cases.

CloudBATCH may also be exploited to make dedicated Hadoop clusters useful for the load balancing of legacy batch job submissions.

Bibilography

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