debs presentation 2009 july62009

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

[email protected] [email protected] [email protected]

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Outline Why scalable event processing is an important problem?

Some terms (EPN, EPA)

What has been done already?

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

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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

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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, financial transactions).

Manual partitioning is difficult (due to semantic dependencies between event processing agents) particularly when it is required to be adaptive and dynamic.

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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

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Related Work Scalability in event processing

– Scalable event processing infrastructure (E.g. Astrolobe (2003), PIER (2003), Sienna (2000.))

– Controlled input load shedding (Kulkarni et al. (2008)).– CEP over streams (Wu et al. (2006)).– More work needs to be done.

Numerous centralized implementations arising due to interdependencies among event processing agents.

Synergy between stream processing and event processing.– Distributed stream processing techniques:

• Mehta et al., 1995• Shah et al., 2003• Balazinska et al., 2004• Kumar et al., 2005• Xing et al., 2005, 2006• Zhou et al., 2006• Pietzuch et al., 2006• Gu et al., 2007• Lakshmanan et al., 2008

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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

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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 design 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

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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.

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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.

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Example: Credit Card Scenario

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Stratum 1 Stratum 2 Stratum 3

Stratification algorithm

Event Processing Dependency Graph

Stratified Event Processing Graph

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Assigning Nodes to Each Stratum

Goal: Executing at a user set percentage of their capacity, nodes in a stratum can process all of the incoming events in their stratum level in parallel under peak event traffic conditions.

– Assume agents in a single stratum are replicated on all nodes in that stratum.

Overall strategy:1. Profiling nodes. Determine maximum event processing capability of available

nodes by observing performance under synthetic workload.

2. Compute ratio of events split between nodes for first stratum.

3. Determine number of nodes to assign to stratum.

4. Repeat for next stratum, and next, until done.

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Assigning Nodes to Each Stratum

– ti : User set percentage of node capacity

– mi : All the incoming events

– ri : Maximum possible event processing rate (events/sec)

– di : Maximum possible derived event production rate (events/sec)Formulas

Example: Incoming event rate: 200,000 ev/sec. Processing Capacity of node i: 36,000 events/sec. t i=0.95.

((ti*ri)/mi)*100 mi*(di/ri) Stratum ID: n+1

Stratum ID: n

Percentage of event stream directed to node i

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 i

If (di/ri)=0.5, total rate of derived events created by the stratum n nodes is 200,000*0.5=100,000 events/sec

Thus, 6 nodes will be needed in this stratum

Stratum ID: n

Stratum ID: n+1

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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 (between agents on the same node, and between contexts for the same agent).

These statistics dictate the choice of load donor and recipient nodes.

Definition of load is purposely generic to incorporate priorities and preferences of application priorities.

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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. (Also use load

correlation as a guiding factor).3. Identify recipient node for migrated load.4. Estimate post migration utilization of donor and recipient nodes. If post

migration utilization of recipient node is 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

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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.

If the average event arrival rate in time period t for context c is (c) and the average latency to evaluate context c is (c), then the load of this context in time period t can be defined as (c1)(c1).

Thus the post migration utilization, Ud, of the donor machine and Ur of the recipient machine after migrating a context c1, and where nd and nr are the total number of contexts on the donor and recipient respectively, is:

Post migration utilization of donor and recipient nodes must be less than preset quality thresholds.

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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.

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Stratum 1 Stratum 2 Stratum 3

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Results

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162000150000

0

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150000

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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

Synthetic workload: consists strictly of all events that trigger the generation of derived events. Number of nodes: 3, 5, 10. Heterogeneous mix of Type 1, 2 and 3. Ratios are selected to be “optimal.”

Y-axis: Maximum number of input event processing rate is computed as the sum of the average input event processing rate of all nodes in the network.

Illustrates the maximum performance that can be achieved by the event processing network when it is overloaded

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15000

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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.

Synthetic workload: consists strictly of all events that trigger the generation of derived events. Number of nodes: 3, 5, 10. Heterogeneous mix of Type 1, 2 and 3. Ratios are selected to be “optimal.”

Y-axis: Maximum number of derived event processing rate is computed as the sum of the average derived event processing rate of all nodes in the network.

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Results

Percentage of improvement in performance of

the stratified network relative to

a partitioned network

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100% - 5:4:1 12.5.% - 5:4:1

Percentage of Events Participating in Derived Events Production

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Improvement In Event Processing Rate Improvement In Derived Events Rate

Stratified network of ten nodes, where the proportion of nodes in three strata is 5:4:1

with ten nodes in a partitioned network.

All nodes used for this experiment are of Type 1.

Illustrates how changing the proportion of input events that participate in derived events production in the first stratum level impacts the input event processing rate and derived events production rate of the entire system.

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Results

Average input events processing rate per node in a stratified

network with different configurations

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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

Compares the average input events processing rate per processing node of a stratified network of ten nodes where the distribution of nodes among the three strata is 5:4:1 to an optimal configuration of nodes to strata.

Demonstrates that reconfiguration of the system with optimal ratio of nodes per stratum can improve performance and reacts effectively to changes in the proportion of input events that participate in derived events production in the first stratum level

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Largest Load First

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Results

Total throughput since the system started event processing

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Dynamic Load Distribution (Ours)

No Load Distribution

Mean throughput of load distribution

compared with LLF, Random and None for different number of nodes

Load is defined as the total number of agents' executions for a particular context.

0.5: load threshold for a node to initiate load distribution.

0.1: load threshold of a context’s contribution to the percentage of the total load on a node, where this context has the highest load correlation coefficient with respect to the remaining contexts on the same node.

0.85: acceptable post-migration utilization of a recipient node. 0.1 is the threshold for percentage decrease in utilization of a donor node to warrant a migration.

Periodically fluctuating workload.

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Support for Dynamic Changes in EP Graph

Our algorithm supports:– Addition of a new connected subgraph 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).

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Conclusion and Future Work

We demonstrate a novel architecture for distributed event processing that maximizes the throughput of the event processing system, and stratification algorithm to partition an event processing application on to a distributed set of nodes.

The experimental results illustrate the effectiveness of the stratification technique for achieving an initial partitioning of the event processing graph in a distributed event processing system that anticipates a high volume of agent triggering events.

Performance of a stratified network can be improved during runtime with the dynamic load distribution algorithm.

Future Work: – Investigate high availability– Techniques for optimizing stateful load migration between nodes

dynamically during runtime.– Investigate variations of stratification (currently in IBM HRL)

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Backup Slides

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Goal of Implementation

Explore benefits of event processing on stratified vs. centralized (single node) vs. partitioned network (single stratum in which load is distributed according to context) when system is under heavy load (when the number of incoming events that trigger the generation of derived events increases).

Compare stratification with partitioned approach when system is not heavily loaded.

Explore the effectiveness and scalability of the load distribution algorithm