Resource Aware Scheduling in Apache Storm

RESOURCE
AWARE
SCHEDULING IN
APACHE STORM
Presented by Boyang Jerry Peng

2
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:
 jerrypeng@yahoo-inc.com

3
AGENDA
•Overview of Apache Storm
•Problems and Challenges
•Introduction of Resource Aware
Scheduler
•Results

4
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

5
STORM TOPOLOGY
• Processing can be represented as a directed graph
• Spouts are sources of information
• Bolts are operators that process data

6
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.

7
STORM ARCHITECTURE
Master
Node
Cluster
Coordination
Worker
processes
Worker
Nimbus
Zookeeper
Zookeeper
Zookeeper
Supervisor
Supervisor
Supervisor
Supervisor Worker
Worker
Worker
Launches
workers

8
LOGICAL VS PHYSICAL CONNECTION IN STORM

9
OVERVIEW OF SCHEDULING IN STORM
• Default Scheduling Strategy
 Naïve round robin scheduler
 Naïve load limiter (Worker Slots)
• Multitenant Scheduler
 Default Scheduler with multitenant capabilities (supported by
security)
 Can allocate a set of isolated nodes for topology (Soft
Partitioning)
Resource Aware

10
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)

11
RUNNING STORM AT YAHOO – SCALE
600
2300
3500
120
300
680
0
100
200
300
400
500
600
700
800
0
500
1000
1500
2000
2500
3000
3500
4000
2012 2013 2014 2015 2016
Nodes
Year
Total Nodes Running Storm at Yahoo
Total Nodes Largest Cluster Size

12
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

13
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);

14
CLUSTER CONFIGURATIONS
conf/storm.yaml
.
.
.
supervisor.memory.capacity.mb: 20480.0
supervisor.cpu.capacity: 400.0
.
.
.

15
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);

16
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.

17
RAS FEATURES – PER USER RESOURCE
GUARANTEES
• Configurable per user resource guarantees

18
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 – 9
STAGING => 10 – 19
DEV => 20 – 29

19
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:
SNAPSHOT/Resource_Aware_Scheduler_overview.html

20
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:
SNAPSHOT/Resource_Aware_Scheduler_overview.html

21
SELECTED RESULTS (THROUGHPUT) FROM PAPER [1] – YAHOO
TOPOLOGIES
47% improvement!
50% improvement!
* Figures used [1]

22
SELECTED RESULTS (THROUGHPUT) FROM PAPER [1] – YAHOO
TOPOLOGIES

23
PRELIMINARY RESULTS IN YAHOO STORM CLUSTERS

24
PRELIMINARY RESULTS IN YAHOO STORM CLUSTERS

25
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

27
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

29
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

30
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
30

31
PROBLEM FORMULATION
• Qi : Throughput contribution of each node
• Assign tasks to a subset of nodes N’ ∈ N that minimizes the total resource waste:
31

32
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
32

33
SOFT CONSTRAINTS VS HARD CONSTRAINTS
• Soft Constraints
 CPU and Network Resources
 Graceful performance degradation with over subscription
• Hard Constraints
 Memory
 Oversubscribe -> Game over
Your date comes hereYour footer comes here33

34
OBSERVATIONS ON NETWORK LATENCY
1. Inter-rack communication is the slowest
2. Inter-node communication is slow
3. Inter-process communication is faster
4. Intra-process communication is the fastest

35
HEURISTIC ALGORITHM
35
• 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

36
HEURISTIC ALGORITHM

37
HEURISTIC ALGORITHM
Switch
1 2
3 4 5
6

38
HEURISTIC ALGORITHM
38
• 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.

Resource Aware Scheduling in Apache Storm

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