1 a grid-based middleware for processing distributed data streams liang chen advisor: gagan agrawal...
TRANSCRIPT
1
A Grid-Based Middleware for Processing Distributed Data
Streams
Liang ChenAdvisor: Gagan Agrawal
Computer Science & Engineering
2
Roadmap• Introduction
– Motivation– Our approach and challenges
• System Overview and Initial Evaluation– Introduce system architecture and design– Discuss the self-adaptation function
• Self-Adaptation Algorithm– Explain the algorithm– Evaluate the system by using two data mining applications
• Resource Allocation Schemes• Dynamic Migration
– Motivation– Light-weight summary structure (LSS)– How applications utilize the dynamic migration– Evaluation
• Adaptive Volume Rendering• Related work• Conclusion and Future work
3
Introduction-Motivation• What is data steam
– Data stream: data arrive continuously – Enormous volume and must be processed
online– Need to be processed in real-time– Data sources could be distributed
• Data Stream Applications:– Online network intrusion detection– Sensor networks– Network Fault Management system for
telecommunication network elements
4
Introduction-MotivationNetwork Fault Management System (NFM)
analyzing distributed alarm streams
Switch Network
X
NFM (Network Fault Management) System
5
Introduction-Motivation
Switch Network
X
• Challenges– Data and/or computation intensive– System can be easily overloaded
6
Introduction-Motivation • Possible solutions
– Grid computing technologies– Automatically adjust processing rate
Switch Network
7
Introduction-Motivation
• The needs for processing distributed data streams– A middleware running in Grid– Allocate Grid resources– Provide self-adaptation function
8
Introduction-Our Approach• We implemented a middleware to meet the nee
ds• Five contributions of our work
1. Utilizing existing grid standards Liang Chen, K. Reddy and G. Agrawal “GATES: A Grid-Based Middleware for
Processing Distributed Data Streams”.HPDC, 2004. 2. Providing self-Adaptation functionality Liang Chen and G. Agrawal “Supporting Self-Adaptation in Streaming Data M
ining Applications”. IPDPS, 2006.
3. Supporting automatic resource allocation Liang Chen and G. Agrawal “A Static Resource Allocation Framework for Gri
d-Based Streaming Applications”. Concurrency Computation: Practice and Experience Journal, Volume 18, Issue 6 , Pages 653 - 666.
4. Supporting efficient dynamic migration Liang Chen, Q. Zhu and G. Agrawal “A Supporting Dynamic Migration in Tig
htly Coupled Grid Applications”. SC 2006.
5. Studying adaptive rendering application
9
Roadmap• Introduction
– Motivation– Our approach and challenges
• System Overview and Initial Evaluation– Introduce system architecture and design– Discuss the self-adaptation algorithms
• Self-Adaptation Algorithm– Introduce the algorithm– Evaluate the system by using two data mining applications
• Resource Allocation Schemes• Dynamic Migration
– Motivation– Light-weight summary structure (LSS)– How applications utilize the dynamic migration– Evaluation
• Adaptive Volume Rendering• Related work• Conclusion and Future work
10
System Architecture and Design(Architecture)
• Use Globus Toolkit 3.0, built on OGSA
• Allows users to specify their algorithms implemented in Java
• Take care of plugging user-defined algorithms into the system and running them in Grid.
• Applications need be broken down into a number of pipelined stages
11
A B C
Stage A Stage B Stage C
:GATES services
:Stages of an application :Queues between Grid services
:Buffers for applications
System Architecture and Design(Architecture)
Application
Stage A
Stage B
Stage C
12
Public class Second-Stage implements StreamProcessing{ … void work(buffer in, buffer out) {
… while(true) { DATA = GATES.getFromInputBuffer(in); Inter-Results = Processing(Data); GATES.putToOutputBuffer (out, Inter-Results); }
}}
System Architecture and Design
(GATES API Functions)
13
Adaptation Parameter• Definition:
– A parameter in an application– Changing the parameter’s value can change
processing rate of the application, also impact accuracy of the processing
• Two kinds of adaptation parameters– Performance parameter– Accuracy parameter
– Example• Sampling rate is an accuracy parameter
AccuracyProcessing rateAccuracy Parameter
AccuracyProcessing ratePerformance Parameter
14
Pseudo Codes Again with Self-adaptation API Functions
Public class Second-Stage implements StreamProcessing{ … //Initialize sampling-rate Sampling-rate = (Max+ Min)/2; void work(buffer in, buffer out) {
GATES.specifyAccuracyPara(Sampling-rate, Max, Min);
while(true) { DATA = GATES.getFromInputBuffer(in); Inter-Results = Processing(Data, Sampling-rate); GATES.putToOutputBuffer (out, Inter-Results); Sampling-rate = GATES.getSuggestedValue(); }
}}
15
Roadmap• Introduction
– Motivation– Our approach and challenges
• System Overview and Initial Evaluation– Introduce system architecture and design– Discuss the self-adaptation function
• Self-Adaptation Algorithm– Explain the algorithm– Evaluate the system by using two data mining applications
• Resource Allocation Schemes• Dynamic Migration
– Motivation– Light-weight summary structure (LSS)– How applications utilize the dynamic migration– Evaluation
• Adaptive Volume Rendering• Related work• Conclusion and Future work
16
• View the system as a pipeline
• To ensure real-time processing, a balanced pipeline is needed
• When average queue length is too small or too large, queue is under or over loaded. Pipeline is not balanced.
Self-Adaptation Algorithm
A B C
• When GATES.getSuggestedValue() is invoked, use the heuristic way to determine a new value for the adaptation parameter according to the measured lengths
• Measure the average lengths of the queues in the pipeline
17
Self-adaptation Algorithm
• The way we measure average queue length
• the heuristic way to adjust an adaptation parameter
– Should the adaptation parameter be modified, and if so, in which direction?
– How to find a new value (update the value) of the adaptation parameter
))(*)(*),(*(*)1(~
*~
33222111 dPPttPadad BB
18
Self-adaptation Algorithm
• Should the adaptation parameter be modified, and if so, in which direction?– The answer is related to the pipeline’s
load state.
19
Self-adaptation AlgorithmPerformance Parameter BP
A B C
A B C
A B C
A B C
A B C
A B C
A B C
A B C
Convergent States
Non-Convergent States
:Overloaded
:Properly-loaded
:lightly-loaded
A B C
A B C
20
Self-adaptation Algorithm
Summary of Load States
21
Self-adaptation Algorithm
• How to determine a new value for the adaptation parameter– Linear update: increase or decrease
by a fixed value
• Hard to find a proper fixed value
– Binary search
BPBP
PPP )(P
P
22
Self-adaptation Algorithm
Left Border
Current Value
Right Border
New Value
Left Border
Current Value
Right Border
23
24
Self-adaptation Algorithm
• Two Data mining applications– Clustream: Clustering data-points in stre
ams
25
Data Mining Applications &
System Evaluation• Dist-Freq-Counting: finding frequent i
temsets from distributed streams
26
Data Mining Applications &
System Evaluation
27
Data Mining Applications &
System Evaluation
28
Data Mining Applications &
System Evaluation
29
Data Mining Applications &
System Evaluation
30
Data Mining Applications &
System Evaluation
31
Data Mining Applications &
System Evaluation
Data Mining Applications &
System Evaluation
32
Data Mining Applications &
System Evaluation
Data Mining Applications &
System Evaluation
33
Data Mining Applications &
System Evaluation
Data Mining Applications &
System Evaluation
34
Roadmap• Introduction
– Motivation– Our approach and challenges
• System Overview and Initial Evaluation– Introduce system architecture and design– Discuss the self-adaptation algorithms
• Self-Adaptation Algorithm– Explain the algorithm– Evaluate the system by using two data mining applications
• Resource Allocation Schemes• Dynamic Migration
– Motivation– Light-weight summary structure (LSS)– How applications utilize the dynamic migration– Evaluation
• Adaptive Volume Rendering• Related work• Conclusion and Future work
35
Resource Allocation Schemes
• Problem Definition– Grid resource allocation for pipelined applicati
ons that process distributed streaming data in real-time is challenging
– The scheme consists of two parts– Static Part: allocate resources before an applic
ation runs– Dynamic Part: re-allocate resources in run-time– A framework to monitor resources and support
dynamic resource allocation
36
Static Allocation Scheme
Destinationm1.cluster2.edu
Data Source 1162.9.23.1
Data Source 278.29.242.8
Data source 3192.168.2.8
Data Source 4123.97.61.9
Placement 1 Placement n1
Placement n2Placement 1
Placement 1 Placement n3
Stage 2:
Stage 3:
Stage 4:
Static allocation problem: determining a deployment configurationObjective: Automatically generate a deployment configuration according to the information of available resources
The number of data sources and their location
The destination The number of stages
consisting of a pipeline? The number of instances
of each stage? How the instances
connect to each other? The node where each
instance is placed
37
Roadmap• Introduction
– Motivation– Our approach and challenges
• System Overview and Initial Evaluation– Introduce system architecture and design– Discuss the self-adaptation algorithms
• Improved Self-Adaptation– self-adaptation algorithm– Evaluate the system by using two data mining applications
• Resource Allocation Schemes• Dynamic Migration
– Motivation– Light-weight summary structure (LSS)– How applications utilize the dynamic migration– Evaluation
• Adaptive Volume Rendering• Related work• Conclusion and Future work
38
Dynamic Migration-Motivation– Grid resources vary frequently– Dynamically allocating new resources and migrating applications to
the new resources improve performance – Checkpointing is a classic method to support dynamic migration
• A snapshot of system’s running state• Transmit to a remote site• Restore execution context and restart processes
– Disadvantages of checkpointing• Platform dependent• Inefficient• Involve lots of implementation efforts
– Our approach is base on Light-weight Summary Structure (LSS)
39
Dynamic Migration-LSS• Processing Structure:
...
while(true){
read_data_from_streams();process_data();accumulate_intermediate_results();reset_auxiliary_structures();
}...
• Data structure storing summary information is Light-weight summary structure
• Others are Auxiliary structures
40
Dynamic Migration-LSS• Two observations with respect to LSS
– The size of LSS is much smaller than that of the total memory
– Auxiliary structures are usually reset at the end of each loop. Unnecessary to migrate auxiliary structures when migration occurs at the end of a loop
• LSS can be used to support dynamic migration– GAETS provides an API function to allocate a block
of memory to be LSS– An application stores summary information to LSS– transmit only LSS at the end of the loop to a new
node and restore the LSS at the new node
41
Dynamic Migration–supported by GATES
... while(true) { ... //check if migration is needed
if(GATES.ifMigrationNeeded()) { GATES.migrate(lss); break; } }
Codes running atRemote Computing Node
42
Dynamic Migration
• Advantages of using LSS– Efficient, only LSS is migrated– Not impact the accuracy of processing– Support migration across
heterogeneous platforms– Reduce application developers’ efforts
on making application capable of migration
43
Dynamic Migration
44
Dynamic Migration
• Evaluation– Three applications
• Counting sample– LSS stores intermediate top M frequently occurrin
g numbers• Clustream, clustering data points in streams
– LSS stores micro-clusters computed at the second stage
• Dist-Freq-Counting, finding frequent itemsets in distributed streams.
– LSS stores unprocessed itemsets
45
Dynamic Migration
• Memory usage of LSS
46
Dynamic Migration• Migration using LSS is efficient
47
Dynamic Migration• Migration using LSS is efficient
48
Dynamic Migration• Benefits of migration in a dyamic envi
ronment
49
Dynamic Migration
• Memory usage of LSS
50
Dynamic Migration• Migration using LSS is efficient
51
Dynamic Migration• Migration using LSS is efficient
52
Dynamic Migration• Benefits of migration in a dynamic
environment
53
Dynamic Migration• LSS migration does not impact
processing accuracy– The counting sample application was
used– Compared the average accuracy of
the processing results from the non-migration and the migration versions, they are 97.28% and 97.51% accurate
54
Roadmap• Introduction
– Motivation– Our approach and challenges
• System Overview and Initial Evaluation– Introduce system architecture and design– Discuss the self-adaptation algorithms
• Self-Adaptation Algorithm– Explain the algorithm– Evaluate the system by using two data mining applications
• Resource Allocation Schemes• Dynamic Migration
– Motivation– Light-weight summary structure (LSS)– How applications utilize the dynamic migration– Evaluation
• Adaptive Volume Rendering• Related work• Conclusion and Future work
55
Adaptive Volume Rendering
• Motivation – Grid computing is needed• Visualization involves large volumes of dataset • We focus on streaming volume data• Interactively visualizing volume data in real-time is
needed– Computationally intensive– Resources consumed– Real-time processing can not be guaranteed
• The places where data are generated are distributed
• Typical client-server architecture is not scalable– Network bandwidths of wide-area networks are low– Computing capability of normal desktop is not enough
• Grid techniques would be a good solution– Divide the procedure into stages organized in a
pipeline – Allocate nodes close to data source to pre-process
volume data– The size of intermediate results is much smaller
56
Adaptive Volume Rendering
• Motivation – GATES is desirable– Automatic adaptation is desirable
• Volume rendering algorithms running on a grid need to be highly adaptive
• Adaptation usually achieved by manually adjusting adaptation parameters
• Such manual parameter adaptation is very challenging in a grid environment
– Automatic resource allocation is desirable• Grid environment is highly changeable
– The GATES middleware could fulfill the needs• Grid-based• Provide the self-adaptation function to applications• Automatically allocate Grid resources
57
• Overall design– Two pipelined steps – the first step:
• Build octrees from volume data– Octree is a tree data structure, in which each internal no
de has up to 8 children– Here, we use an octree to represent multiresolution info
rmation for a volume– Procedure to build an octree for a volume is as follows:
» Divide volume space into 8 subvolumes and create 8 children nodes
» For each subvolume, calculate standard deviation of all voxels in the subvolume, and store the deviation to the corresponding child node
» If the deviation is larger than a pre-defined value, divide the subvolume, repeat the above procedure. Otherwise, stop
Adaptive Volume Rendering
58
Adaptive Volume Rendering
• Overall design– Two pipelined steps – the second step:
• Use an octree and its corresponding volume to render images
• Provided an error tolerance (or user-defined resolution), use DFS to traverse the octree and stop at the nodes where the deviation is less than the resolution or error tolerance.
• Project the corresponding 3D-subvolumes to an image
59
Adaptive Volume Rendering
60
Adaptive Volume Rendering
• Make the rendering self-adaptive
– Two adaptation parameters used in the third stage• Error Tolerance – performance parameter• Image Size – accuracy parameter
– Only one adaptation parameter can be adjusted by GATES. So we fix one and adjust the other
61
62
Adaptive Volume Rendering
• Experiment 1
63
Adaptive Volume Rendering
100kbps150kbps
200kbps 250kbps
64
Adaptive Volume Rendering
• Experiment 2
65
Adaptive Volume Rendering
• Experiment 3: compare the performance of two implementations– Java-imple– C-imple
66
Adaptive Volume Rendering
• Experiment 3: compare the performance of two implementations
67
Related Work• Middleware for data stream processing
– Data cutter, Stampede– Differences: in a cluster, no self-adaptation, no specificall
y for real-time processing• Continuous query systems
– STREAM, dQUOB, TelegraphCQ, NiagraCQ– Differences: centralized, no adaptation supports
• Distributed continuous query systems– Aurora*, Medusa, Borealis– Differences: continuous queries, not in Grid environment
• In-Network aggregation in sensor network• Stream-based overlay networks
68
Related work• Grid Resource Allocation
– Condor, Realtor, ACDS– Main Differences: our work focus on Grid resou
rce allocation for workflow applications• Adaptation Through a Middleware
– Cheng et al.’s adaptation framework, SWiFT, Conductor, DART, ROAM
– Main Differences: our work focus on general supports for adaptation in run-time
• Dynamic Migration in Grid Environment– Condor, XCATS, Charm++– Main Differences: our work use LSS
69
Conclusion• Grid computing could be an effective
solution for distributed data stream processing
• GATES – Distributed processing– Exploit grid web services– Self-adaptation to meet the real-time
constraints– Grid resource allocation schemes and
dynamic migration
70
Future Work • CPU cycles and Network bandwidths
– Currently, only network bandwidth is considered a constraint when scheduling Grid resources
– Few related work proposes a metric to integrate both for pipelined appliations
• Port GATES from GT3 to GT4• Support fault-tolerance and high availability• Further relieve programming burdens from
application develops– Specify meta-data
• Support distributed continuous queries– Specify a set of query operators
71
Acknowledgements
• My advisor, Prof. Agrawal, proposed the idea of implementing the middleware, and gave lots advices for the directions of my research
• Prof. Shen gave lots of helps on implementing the render application, and provided lots of write-up for the chapter 7
72
Questions?• No more questions? Thanks!