Synchronous Parallel Processing of Big-Data Analytics Services to Optimize Performance in Federated Clouds

Gueyoung Jung, Nathan Gnanasambandam, and Tridib Mukherjee

International Conference on Cloud Computing 2012

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Introduction Related Work Problem Statement Maximally Overlapped Cloud-Bursting

(MOBB) approach Experimental Evaluation Conclusion

Outline

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Introduction Related Work Problem Statement Maximally Overlapped Cloud-Bursting

(MOBB) approach Experimental Evaluation Conclusion

Outline

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Collected data can exceed hundreds of terabytes and continuously generated◦ sensors, social media, click-stream, log files,

and mobile devices

The solution: Cloud Computing◦ Analyze big-data by leveraging vast amounts of

computing resources available on demand with low resource usage cost

Big Data

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Parallel data mining◦ topic mining, pattern mining◦ analyze large amounts of unstructured data◦ time constraint

Big-data are partly analyzed on local private resources while rest of big-data are transferred to external computing nodes◦ more flexible and obvious cost benefits

Parallel Data Mining on Cloud

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The considerations for optimizing parallel data mining◦Node determination◦Synchronized completion◦Data partition determination

Maximally Overlapped Bin-packing driven Bursting (MOBB)

Optimization of Parallel Data Mining

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The goals of MOBB algorithm◦Balancing across computing nodes◦Time overlap between data transfer

delay and computation time in each computing node

Goals

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Introduction Related Work Problem Statement Maximally Overlapped Cloud-Bursting

(MOBB) approach Experimental Evaluation Conclusion

Outline

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Load distribution◦ the overhead of data transfer

Maximum overlap between data transfer and computation◦ determine the order of different sizes of data

chunks transferred to each node

Task scheduling among computing nodes◦ load-balancing (CometCloud)◦ heterogeneous clouds

Related Work

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Introduction Related Work Problem Statement Maximally Overlapped Cloud-Bursting

(MOBB) approach Experimental Evaluation Conclusion

Outline

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Number of Nodes vs Execution Time

SLA: Service Level Agreement

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Data Allocation to Clouds

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Ideal Time Overlap

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Introduction Related Work Problem Statement Maximally Overlapped Cloud-Bursting

(MOBB) approach Experimental Evaluation Conclusion

Outline

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Determination of Computing Nodes

made by the unit of data

min ()

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Estimation of computation time◦ Response surface model◦ Queueing model

Estimation of data transfer delay◦ more dynamic than computation time◦ Auto-regressive moving average (ARMA) model

Estimation of Data Computation and Transfer Delay

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MOBB Approach

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Determination of bucket size of each node Sorting of data chunks in descending order Sorting node bucket sizes in descending

order (high delay = lower bucket size)

1. Pre-processing

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The size of data given to particular node depends on the average delay of task on a node

For ideal parallelization,

Determination Bucket Size

where is denoted as total data size assigned to node , and is denoted as the delay for a unit of data

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Assume the overall execution time of mining task is

Let , then

Determination Bucket Size (Cont.)

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Thus, data assigned to each node is limited by an upper bound given as follow,

Determination Bucket Size (Cont.)

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Weighted load distribution

Delay-based preference

Buckets are completely filled one at a time◦ reduce fragmentation of

buckets

2. Greedy Bin-packing

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Organize the sequence of chunks for maximizing the overlap between data transfer and computation

3. Post-processing

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Assume and is the transfer delay and the computation time per unit of data on a node , respectively◦ Ideal: ◦ Type 1: unavailability of data computation ()◦ Type 2: delay incurred by queueing ()

Complete parallelization

Maximizing the Overlap between Data Transfer and Computation

and are the size of data chunk and assigned to node

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Maximally Overlapped Bin-packing

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Introduction Related Work Problem Statement Maximally Overlapped Cloud-Bursting

(MOBB) approach Experimental Evaluation Conclusion

Outline

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Frequent Pattern Mining◦ A phone call log obtained from a call center and

web access log

◦ Size: 200 GB (collected for one year)

◦ Objective: Obtain patterns of each user activities on human resource information systems

Experimental Setup - FPM

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Four computing nodes◦ Low–end Local Central node (LLC)

5 VMs, each has two 2.8 GHz cores, 1GB memory, 1TB hard drive

◦ Low-end Local Worker (LLW) similar to LLC

◦ High-end Local Worker (HLW) 6 non-virtualized servers, each has 24 2.6 GHz cores,

48GB memory, 10 TB hard drive Shared by other applications

◦ Mid-end Remote Worker (MRW) 9 VMs, each has two 2.8 GHz, 4 GB memory, 1 TB hard

drive

Experimental Setup – Computing Nodes

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Performance

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Impact of Data Transfer Delay

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Effect of Cloud-Bursting

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Comparison of Different Algorithms

HLW+MRW

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Ideal optimal data allocation ◦ The slack time must be 0

Comparison of Different Algorithms (Cont.)

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Introduction Related Work Problem Statement Maximally Overlapped Cloud-Bursting

(MOBB) approach Experimental Evaluation Conclusion

Outline

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A cloud-bursting based on maximally overlapped load-balancing algorithm which is to optimize the performance of big-data analytics is proposed

Results shows the performance can be improved by 20% to 60% against other approaches

Conclusion

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Thank you~