RESOURCE AWARE
SCHEDULING IN APACHE STORM
Presented by Boyang Jerry Peng
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ABOUT ME• Apache Storm Committer and PMC member• Member of the Yahoo’s low latency Team
Data processing solutions with low latency • Graduate student @ University of Illinois, Urbana-Champaign
Research emphasis in distributed systems and stream processing
• Contact: [email protected]
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AGENDA•Overview of Apache Storm•Problems and Challenges •Introduction of Resource Aware Scheduler
•Results
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OVERVIEW• Apache Storm is an open source distributed real-time data stream
processing platform Real-time analytics Online machine learning Continuous computation Distributed RPC ETL
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STORM TOPOLOGY
• Processing can be represented as a directed graph• Spouts are sources of information• Bolts are operators that process data
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DEFINITIONS OF STORM TERMS• Stream
an unbounded sequence of tuples.• Component
A processing operator in a Storm topology that is either a Bolt or Spout
• Executors Threads that are spawned in
worker processes that execute the logic of components
• Worker Process A process spawned by Storm that
may run one or more executors.
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STORM ARCHITECTURE
Master Node
Cluster Coordinati
onWorker
processes
Worker
Nimbus
Zookeeper
Zookeeper
Zookeeper
Supervisor
Supervisor
Supervisor
Supervisor Worker
Worker
Worker
Launches workers
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LOGICAL VS PHYSICAL CONNECTION IN STORM
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OVERVIEW OF SCHEDULING IN STORM• Default Scheduling Strategy
Naïve round robin schedulerNaïve load limiter (Worker Slots)
• Multitenant SchedulerDefault Scheduler with multitenant capabilities (supported by security)
Can allocate a set of isolated nodes for topology (Soft Partitioning)
Resource Aware
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RUNNING STORM AT YAHOO - CHALLENGES• Increasing heterogeneous clusters
Isolation Scheduler – handing out dedicated machines• Low cluster overall resource utilization
Users not utilizing their isolated allocation very well• Unbalanced resource usage
Some machines not used, others over used• Per topology scheduling strategy
Different topologies have different scheduling needs (e.g. constraint based scheduling)
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RUNNING STORM AT YAHOO – SCALE
2012 2013 2014 2015 20160
5001000150020002500300035004000
0100200300400500600700800
600
2300
3500
120
300
680
Total Nodes Running Storm at YahooTotal Nodes Largest Cluster Size
Year
Nod
es
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RESOURCE AWARE SCHEDULING IN STORM• Scheduling in Storm that takes into account resource availability on machines and resource requirement of workloads when scheduling the topology Fine grain resource control Resource Aware Scheduler (RAS) implements this function
- Includes many nice multi-tenant features• Built on top of:
Peng, Boyang, Mohammad Hosseini, Zhihao Hong, Reza Farivar, and Roy Campbell. "R-storm: Resource-aware scheduling in storm." In Proceedings of the 16th Annual Middleware Conference, pp. 149-161. ACM, 2015
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RAS API• Fine grain resource control
Allows users to specify resources requirement for each component (Spout or Bolt) in a Storm Topology:
API to set component memory requirement:
API to set component CPU requirement:
Example of Usage:
public T setMemoryLoad(Number onHeap, Number offHeap)
public T setCPULoad(Number amount)
SpoutDeclarer s1 = builder.setSpout("word", new TestWordSpout(), 10);s1.setMemoryLoad(1024.0, 512.0);builder.setBolt("exclaim1", new ExclamationBolt(), 3) .shuffleGrouping("word").setCPULoad(100.0);
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CLUSTER CONFIGURATIONSconf/storm.yaml
.
.
.supervisor.memory.capacity.mb: 20480.0supervisor.cpu.capacity: 400.0
.
.
.
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RAS FEATURES – PLUGGABLE PER TOPOLOGY SCHEDULING STRATEGIES• Allows users to specify which scheduling strategy to use
• Default Strategy- Based on:
• Peng, Boyang, Mohammad Hosseini, Zhihao Hong, Reza Farivar, and Roy Campbell. "R-storm: Resource-aware scheduling in storm." In Proceedings of the 16th Annual Middleware Conference, pp. 149-161. ACM, 2015.
- Enhancements have been made (e.g. limiting max heap size per worker, better rack selection algorithm, etc)- Aims to pack topology as tightly as possible on machines to reduce communication latency and increase
utilization- Collocating components that communication with each other (operator chaining)
• Constraint Based Scheduling Strategy CSP problem solver
conf.setTopologyStrategy(DefaultResourceAwareStrategy.class);
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RAS FEATURES – RESOURCE ISOLATION VIA CGROUPS (LINUX PLATFORMS ONLY*)• Replaces resource isolation via isolated nodes• Resource quotas enforced on a per worker basis• Each worker should not go over its allocated resource quota• Guarantee QOS and topology isolation• Documentation:
https://storm.apache.org/releases/2.0.0-SNAPSHOT/cgroups_in_storm.html
*RHEL 7 or higher. Potential critical bugs in older RHEL versions.
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RAS FEATURES – PER USER RESOURCE GUARANTEES• Configurable per user resource guarantees
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RAS FEATURE – TOPOLOGY PRIORITY• Users can set the priority of a topology to indicate its importance
• The range of topology priorities can range form 0-29. The topologies priorities will be partitioned into several priority levels that may contain a range of priorities
conf.setTopologyPriority(int priority)
PRODUCTION => 0 – 9STAGING => 10 – 19DEV => 20 – 29
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RAS FEATURES – PLUGGABLE TOPOLOGY PRIORITY• Topology Priority Strategy
Which topology should be scheduled first? Cluster wide configuration set in storm.yaml Default Topology Priority Strategy
- Takes into account resource guarantees and topology priority- Schedules topologies from users who is the most under his or her
resource guarantee. - Topologies of each user is sorted by priority- More details:
https://storm.apache.org/releases/2.0.0-SNAPSHOT/Resource_Aware_Scheduler_overview.html
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RAS FEATURES – PLUGGABLE TOPOLOGY EVICTION STRATEGIES• Topology Eviction Strategy
When there is not enough resource which topology from which user to evict?
Cluster wide configuration set in storm.yaml Default Eviction Strategy
- Based on how much a user’s guarantee has been satisfied- Priority of the topology
FIFO Eviction Strategy- Used on our staging clusters. - Ad hoc use
More details:https://storm.apache.org/releases/2.0.0-SNAPSHOT/Resource_Aware_Scheduler_overview.html
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SELECTED RESULTS (THROUGHPUT) FROM PAPER [1] – YAHOO TOPOLOGIES
47% improvement!
50% improvement!
* Figures used [1]
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SELECTED RESULTS (THROUGHPUT) FROM PAPER [1] – YAHOO TOPOLOGIES
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PRELIMINARY RESULTS IN YAHOO STORM CLUSTERS
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PRELIMINARY RESULTS IN YAHOO STORM CLUSTERS
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CONCLUDING REMARKS AND FUTURE WORK• In Summary
Built resource aware scheduler• Migration Process
In the Progress from migrating from MultitenantScheduler to RAS
Working through bugs with Cgroups, Java, and Linux kernel• Future Work
Improved Scheduling Strategies Real-time resource monitoring Elasticity
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QUESTIONS
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REFERENCES• [1] Peng, Boyang, Mohammad Hosseini, Zhihao Hong, Reza Farivar, and Roy Campbell.
"R-storm: Resource-aware scheduling in Storm." In Proceedings of the 16th Annual Middleware Conference, pp. 149-161. ACM, 2015.
http://web.engr.illinois.edu/~bpeng/files/r-storm.pdf• [2] Official Resource Aware Scheduler Documentation
https://storm.apache.org/releases/2.0.0-SNAPSHOT/Resource_Aware_Scheduler_overview.htm
• [3] Umbrella Jira for Resource Aware Scheduling in Storm https://issues.apache.org/jira/browse/STORM-893
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EXTRA SLIDES
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PROBLEM FORMULATION• Targeting 3 types of resources
CPU, Memory, and Network• Limited resource budget for each node • Specific resource needs for each task
Goal:Improve throughput by maximizing utilization and minimizing network
latency
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PROBLEM FORMULATION• Set of all tasks Ƭ = {τ1 , τ2, τ3, …}, each task τi has resource demands
CPU requirement of cτi
Network bandwidth requirement of bτi
Memory requirement of mτi
• Set of all nodes N = {θ1 , θ2, θ3, …} Total available CPU budget of W1
Total available Bandwidth budget of W2
Total available Memory budget of W3
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PROBLEM FORMULATION
• Qi : Throughput contribution of each node• Assign tasks to a subset of nodes N’ ∈ N that minimizes the total resource waste:
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PROBLEM FORMULATION
Quadratic Multiple 3D Knapsack Problem We call it QM3DKP! NP-Hard!
• Compute optimal solutions or approximate solutions may be hard and time consuming• Real time systems need fast scheduling
Re-compute scheduling when failures occur
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SOFT CONSTRAINTS VS HARD CONSTRAINTS• Soft Constraints
CPU and Network Resources Graceful performance degradation with over subscription
• Hard Constraints Memory Oversubscribe -> Game over
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OBSERVATIONS ON NETWORK LATENCY1. Inter-rack communication is the slowest2. Inter-node communication is slow3. Inter-process communication is faster4. Intra-process communication is the fastest
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HEURISTIC ALGORITHM
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• Greedy approach• Designing a 3D resource space
Each resource maps to an axis Can be generalized to nD resource space Trivial overhead!
• Based on: min (Euclidean distance) Satisfy hard constraints
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HEURISTIC ALGORITHM
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HEURISTIC ALGORITHM
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HEURISTIC ALGORITHM
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• Our proposed heuristic algorithm has the following properties:1) Tasks of components that communicate will each other will have the highest priority to be scheduled in close
network proximity to each other. 2) No hard resource constraint is violated.3) Resource waste on nodes are minimized.
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