A Stratified Approach for Supporting High Throughput Event Processing Applications July 2009 Geetika...
-
date post
19-Dec-2015 -
Category
Documents
-
view
217 -
download
0
Transcript of A Stratified Approach for Supporting High Throughput Event Processing Applications July 2009 Geetika...
A Stratified Approach for Supporting High Throughput Event Processing Applications
July 2009
Geetika T. Lakshmanan Yuri G. Rabinovich Opher EtzionIBM T. J. Watson Research Center IBM Haifa Research Lab IBM Haifa Research Lab
2
Outline Motivation and Goal
Definitions
Related Work
Overview of our solution– Credit-Card Scenario
– Profiling and initial assignment of nodes to strata
– Stratification
– Load Distribution Algorithm
Algorithm optimizations and support for dynamic changes in event processing graph
Implementation and Results
Conclusion
3
Our GoalDevise a generic framework to maximize the overall input (and thus output) throughput of an event processing application which is represented as an EPN, given a specific set of resources (cluster of nodes with varying computational power) and a traffic model. The framework should be adaptive to changes either in the configuration or in the traffic model.
EPNEPNEvent
ProducerEvent
Consumer
Event
Producer
Event
Producer
Event
Consumer
Event
Consumer
Engine
EPAEPA
EPAEPA
Engine
EPAEPA
EPAEPA
Engine
EPAEPA
EPAEPA
Repository
4
Why is this an important problem? Quantity of events that a single application needs to process is constantly increasing (E.g. RFID events, Massive Online Multiplayer Games).
Manual partitioning is difficult (due to semantic dependencies between event processing agents) particularly when it is required to be adaptive and dynamic.
5
Event Processing Agent An event processing agent has input and output event channels.
In general it receives a collection of events as input, derives one or more events as output and emits them on one or more output channels.
The input channels are partitioned according to a context which partitions the space of events according to semantic partitions of relevance
Event Processing Agent
InputChannel
Context Agent SpecDerived Event
Definition
OutputChannel
Event Processing Agent
Filter Transform Detect Pattern
Translate
Route
Aggregate Split Enrich
6
Related Work Scalability in event processing
– Large scale event processing applications (E.g. Astrolobe, PIER, Sienna.)
– Kulkarni et al., Wu et al.
– More work needs to be done.
Numerous centralized implementations arising due to interdependencies among event processing agents.
Synergy between stream processing and event processing.– Load distribution techniques for streams proposed:
• Shah et al.• Mehta et al.• Gu et al.• Xing et al.• Zhou et al.
7
Is this a solved problem?
Scalable event processing
implementations
(Astrolobe, PIER, Sienna)
Centralized
event processing
ImplementationsLoad distribution algorithms
for scalable stream processingShah et al., Mehta et al., Gu et al., Xing et al.
Zhou et al., Liu et al. ………
Event-at-a-time Implementations Set-at-a-time Implementations
Centralized stream processing
Implementations
8
Overview of Our Solution1. Profiling
– Used to assign agents to nodes in order to maximize throughput
2. Stratification of EPN– Splitting the EPN into strata layers
– Based on semantic dependencies between agents
– Distributed implementation with event proxy to relay events between strata
3. Load Distribution– Distribute load among agents dynamically during runtime and
respect statistical load relationships between nodes
9
Distributed Event Processing Network Architecture Input: Specification of an Event Processing Application
Output: Stratified EPN (event processing operations event processing agents)
EP Node
Stratum 1
EPProxy
EP Node
EP Node
DB
EP Node
Stratum 1
EPProxy
EP Node
EP Node
DB
EP Node
Stratum 2
EPProxy
EP Node
EP Node
DB
EP Node
Stratum 2
EPProxy
EP Node
EP Node
DB
EP Node
Stratum 3
EPProxy
EP Node
EP Node
DB
EP Node
Stratum 3
EPProxy
EP Node
EP Node
DB
Events Events
Event Proxy receives input events and routes them to nodes in a stratum according to the event context.
Event proxy periodically collects performance statistics per node in a stratum.
10
Stratified Event Processing Graph1. Define the event processing application in the form of an Event
Processing Network Dependency Graph G=(V,E) (directed edges from event source to event target)
2. Overview of Stratification Algorithm Create partitions by finding sub graphs that are independent in
the dependency graph. For each sub graph, construct a network of EPAs. Push filters to the beginning of the network to filter out irrelevant
events. Iterate through graph and identify areas of strict interdependence.
(i.e. sub graphs with no connecting edges). For each sub graph define stratum levels.
11
Credit Card Scenario
Amount > 100
More Than5 OccurrencesWithin 1 Hour
Amount > 100
More Than3 OccurrencesWithin 1 Hour
Cancel FollowsDiscount
High VolumePurchase
Purchase
Cancel High VolumeCancel
Give Discount to Company
Discount Canceled
Cancel Discount to Company
Amount > 100
More Than5 OccurrencesWithin 1 Hour
Amount > 100
More Than3 OccurrencesWithin 1 Hour
Cancel FollowsDiscount
High VolumePurchasePurchase
CancelHigh Volume
Cancel
Give Discountto Company
Discount Canceled
Cancel Discount to Company
Stratum 1 Stratum 2 Stratum 3
Stratification algorithm
Event Processing Dependency Graph
Stratified Event Processing Graph
12
Initial Placement of Agents
Goal is to maximize throughput.
Assume agents in a single stratum are replicated on all nodes in that stratum.
Overall strategy:
1. Profiling. Determine maximum event processing capability of available nodes.
– ri : Maximum possible event processing rate (events/sec)
– di : Maximum possible derived event production rate (events/sec)
2. Assigning nodes to each stratum. Executing at a user set percentage of their capacity, these nodes can process all of the incoming events in their stratum level in parallel under peak event traffic conditions.– Compute ratio of events split between nodes– Iterative calculation starting with the first stratum.
13
Assigning Nodes to Each Stratum
1. Assigning nodes to each stratum. Executing at a user set percentage of their capacity ti, these nodes can process all of the incoming events in their stratum level in parallel under peak event traffic conditions.
– Compute ratio of events split between nodes– Iterative calculation starting with the first stratum.
Formulas
Example: Incoming event rate: 200,000/sec. Ti=0.95. Processing Capacity of node n: 36,000 events/sec.
((ti*ri)/m)i*100 mi*(di/ri)Stratum n Stratum n+1
Stratum n Stratum n+1
Percentage of event stream directed to node ni
Derived event production rate of nodes in stratum n
((0.95*36,000)/200,000)*100 = 17.1%
Percentage of event stream directed to node n
If (di/ri)=0.5, derived event production rate is 200,000*0.5=100,000 events/sec
Thus, 6 nodes will be needed in this stratum
14
Dynamic Load Distribution Strategy
Desirable qualities include:– Dynamic
– Observes Semantics of Agent Dependencies
– Observes Link Latency
– Can perform task splitting
– Observes load, average load, and load variance
– Observes state
15
Overview of Dynamic Load Distribution Algorithm
Event Proxy collects statistics and maintains a time series and makes the following decisions:1. Identify most heavily loaded node in a stratum (donor node).
2. Identify a heavy context to migrate from the donor node.
3. Identify recipient node for migrated load.
4. Estimate post migration utilization of donor and recipient nodes. If post migration utilization of recipient node are unsatisfactory, go back to step 3 and identify new recipient node. If post migration utilization of donor node is unsatisfactory, go back to step 2 and identify new context to migrate.
5. Execute migration and wait for x length time interval. Go to step 1.
Engine Queue AMIT
Engine Queue AMIT
Engine Queue AMIT
Engine Queue AMIT
Engine Queue AMIT
Engine Queue AMIT
EPProxy
Stratum n Stratum n+1
16
Overview of Dynamic Load Distribution Algorithm
Statistics collected by event Proxy:– Number of input events processed by execution of agents in a particular
context– Number of derived events produced by the execution of agents in this
context– Number of different agent executions evaluated in this context– Total amount of latency to evaluate all agents executed in this context
For these statistics, event proxy maintains a time series, and computes statistics such as mean, standard deviation, covariance and correlation coefficient.
These statistics dictate the choice of donor and recipient nodes.
Definition of load is purposely generic to incorporate different application priorities.
17
Post Migration Utilization Calculation
We need to determine whether this migration will lead to overload. If it triggers other migrations then the system will become unstable. Therefore compute the post migration utilization of the donor and recipient machines.
Thus the post migration utilization, Ud, of the donor machine and Ur of the recipient machine after migrating an task t1, and where nd and nr are the total number of tasks on the donor and recipient respectively, is:
)
)(
)(1('
1
1
dni
ii
dd
tL
tLUU )
)(
)(1('
1
1
rni
ii
rr
tL
tLUU
Post migration utilization of donor must be less than preset quality threshold
Post migration utilization of recipient must be less than preset quality threshold
18
Support for Dynamic Changes in EP Graph
Our algorithm supports:– Addition of a new connected sub graph to the existing EPN.
– Addition of an agent to the graph in the EPN.
– Deletion of agents from the graph
– Failure of one or more nodes in a stratum level.
Algorithm is also amenable to agent-level optimizations (E.g. coalescing of neighboring agents).
19
Implementation
Used nodes running IBM Active Middleware Technology (AMiT), a CEP engine that serves as a container for event processing agents.
Event processing scenario: credit card scenario
Node hardware characteristics:
– Type 1: Dual Core AMD Opteron 280 2.4 GHz and 1GB memory.
– Type 2: Intel Pentium D 3.6 Ghz and 2GB memory.
– Type 3: Intel Xeon 2.6 Ghz and 2 GB memory.
20
Goal of Implementation
Explore benefits of event processing on stratified vs. centralized vs. partitioned network (single stratum in which load is distributed according to context).
Explore benefit of stratified approach under heavy load (when the number of incoming events that trigger the generation of derived events increases).
Explore the effectiveness and scalability of the load distribution algorithm
21
Results
398000
90000
300000
81000
162000150000
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
1:1:1 = 3 Machines 2:2:1 = 5 Machines 5:4:1 = 10 Machines
System Type
Eve
nts
/Sec
Stratified Input Rate Partitioned Input RateCentralized = 30,000
Input events processing rate by stratified versus partitioned event processing networks
22
15000
21000
9000
4,500
7500
4500
0
5,000
10,000
15,000
20,000
25,000
1:1:1 = 3 Machines 2:2:1 = 5 Machines 5:4:1 = 10 Machines
System Type
Eve
nts
/Sec
Stratified Derived Rate Partitioned Derived RateCentralized = 1,500
Results
Derived events production rate by stratified versus partitioned event processing networks.
23
Results
Percentage of improvement in performance of the stratified network relative to a partitioned network
40.0032.67
-32.08 -31.75
-40.00
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
40.00
50.00
100% - 5:4:1 12.5.% - 5:4:1
Percentage of Events Participating in Derived Events Production
Imp
rove
men
t (P
erce
nta
ge)
Improvement In Event Processing Rate Improvement In Derived Events Rate
24
Results
Average input events processing rate per node in a stratified network with different configurations
44100
49083
52800
3443834813
3737539800 39800
0
10000
20000
30000
40000
50000
60000
100% - 5:4:1 50% - 6:3:1 25% - 8:3:1 12.5.% - 11:3:1
Percenatage of Events Participating in Derived Events Productions
Eve
nts
/Sec
5:4:1Ratio Optimal Ratio for Percentage
25
0
10000
20000
30000
40000
50000
60000
70000
5 10 15 20 25 30
Number of Nodes
Mea
n T
hro
ug
hp
ut
Dynamic Load Distribution (Ours)
Largest Load First
Random
No Load Distribution
Results
Throughput results for the load distribution algorithm
0
50000
100000
150000
200000
250000
300000
0 200 400 600 800 1000 1200
Time (sec)
To
tal T
hro
ug
hp
ut
Dynamic Load Distribution (Ours)
No Load Distribution
Scalability of load distribution algorithm
26
Conclusion and Future Work
Demonstrated stratified, load distribution for scalable event processing
Future Work: Investigate high availability
Future Work: Investigate other objectives in addition to scalability
Future Work: Execution of multiple strata within a single nodes cluster.
Future Work: Techniques for effective load migration between nodes.