1. CA-DAG: Communication-Aware Directed Acyclic Graphs for Modeling Cloud Computing Applications
Dzmitry Kliazovich University of Luxembourg, Luxembourg
Johnatan E. Pecero
Pascal Bouvry
Andrei Tchernykh CICESE Research Center, Mexico
Samee U. Khan North Dakota State University, U.S.A.
Albert Y. Zomaya University of Sydney, Australia

2. Cloud Computing Applications
Jul 1, 2013
Dzmitry Kliazovich

3. Resource Requirements of Cloud Applications
Computing
Network Bandwidth
Communication delays (tolerance)
Degree of interactivity
Storage
Jul 1, 2013
Dzmitry Kliazovich

4. Resource Requirements of Cloud Applications
Computing
Network Bandwidth
Communication delays (tolerance)
Degree of interactivity
Storage
Jul 1, 2013
Dzmitry Kliazovich

5. Cloud Computing Applications
Communication
resources
Jul 1, 2013
Dzmitry Kliazovich

6. Cloud Computing Applications
Traditional resource allocation and scheduling
Distribute incoming jobs to the pool of servers
Communication requirements and networking are not taken into account
Jul 1, 2013
Dzmitry Kliazovich

7. Scheduling in Data Centers
Network congestion!!!
Jul 1, 2013
Dzmitry Kliazovich

8. Scheduling in Data Centers
Network is balanced !!!
Jul 1, 2013
Dzmitry Kliazovich

9. Modeling Cloud Computing Applications

10. Modeling of Cloud Applications
Directed Acyclic Graphs (DAGs)
Vertices represent computing tasks of a job
Edges represent task dependencies and order of execution 1 2 3 4
How to model communication processes?
Jul 1, 2013
Dzmitry Kliazovich

11. Modeling of Cloud Applications
Communication-unaware model
Edges-based model
Jul 1, 2013
Dzmitry Kliazovich

12. Modeling of Cloud Applications
Communication-unaware model
Each vertex represents both computing and communication processes of a task 1
Computing job
of a task 2 3
Communication job
of a task 4
Main drawback
Having a single vertex for both computing and communications makes it impossible to make separate scheduling decisions
Jul 1, 2013
Dzmitry Kliazovich

13. Modeling of Cloud Applications
Edge-based model
DAG edges represent communication processes of a task 1
Computing job
of a task 2 3
Communication job
of a task 4
Main drawback
Two different computing tasks cannot have the same data transfer to receive input as a singe edge cannot lead to two different vertices
Jul 1, 2013
Dzmitry Kliazovich

14. Proposed Communication-Aware DAG model

15. Modeling of Cloud Applications
Proposed CA-DAG: Communication-Aware DAG model
Two types of vertices: one for computing and one for communications
Edges show define dependences between tasks and order of execution 1
Communication task
Computing task 2 3 4
Main advantage
Allows separate resource allocation decisions, assigning processors to handle computing jobs and network resources for information transmissions
Jul 1, 2013
Dzmitry Kliazovich

16. Modeling of Cloud Applications
Proposed CA-DAG: Communication-Aware DAG model
Represented by a directed acyclic graph 𝐺= 𝑉,𝐸,𝜔,𝜑
Set of vertices 𝑉= 𝑉 𝑐 , 𝑉 𝑐𝑜𝑚𝑚 is composed of computing tasks 𝑉 𝑐 and communication tasks 𝑉 𝑐𝑜𝑚𝑚
A computing task 𝑣 𝑖 𝑐 ∈ 𝑉 𝑐 is described by a pair 𝐼, 𝐷 𝑐 with the number of instructions 𝐼 (amount of work) that has to be executed within a specific deadline 𝐷 𝑐
A communication task 𝑣 𝑖 𝑐𝑜𝑚𝑚 ∈ 𝑉 𝑐𝑜𝑚𝑚 is described by parameters (𝑆, 𝐷 𝑐𝑜𝑚𝑚 ) and defined as the amount of information 𝑆 in bits that has to be successfully transmitted within a predefined deadline 𝐷 𝑐𝑜𝑚𝑚
The set of edges 𝐸 consists of directed edges e ij representing dependence between node 𝑣 𝑖 ∈𝑉 and node 𝑣 𝑗 ∈𝑉
Jul 1, 2013
Dzmitry Kliazovich

17. Modeling of Cloud Applications
Example of webmail cloud application
Step 1: Receive user request and process it
Step 2: Generate personalized advertisement
Step 3: Request list of messages from database
Step 4: Generate HTML pages and send it to the user
Jul 1, 2013
Dzmitry Kliazovich

18. CA-DAG: Communication-Aware DAG
Receive User Request
Step 1
Step 1: Receive user request and process it
Step 2: Generate personalized advertisement
Step 3: Request list of messages from database
Step 4: Generate HTML pages and send it to the user 1
Process User Data 2
Analyze user social profile 4
Request Database
Step 2
Step 3 3
Retrieve Personalized Ad 5
Generate list of messages 6
Group conversations 7
Generate HTML page
Step 4 8
Send output to user
Jul 1, 2013
Dzmitry Kliazovich

19. CA-DAG: Communication-Aware DAG
Network
Processor
Receive User Request
Receive User Request
Step 1 1
Process User Data 1
Process User Data 2
Analyze user social profile 2
Analyze user social profile 4
Receive User Request 4
Request Database
Step 2
Makespan: 7
Step 3 3
Retrieve Personalized Ad 3
Receive User Request 5
Generate list of messages 5
Generate list of messages 6
Group conversations 6
Group conversations 7
Generate HTML page 7
Generate HTML page
Step 4 8
Send output to user 8
Send output to user
Jul 1, 2013
Dzmitry Kliazovich

20. CA-DAG: Communication-Aware DAG
Receive User Request 1
Process User Data
Communication unaware model
Step 1
Step 1: Receive user request and process it
Step 2: Generate personalized advertisement
Step 3: Request list of messages from database
Step 4: Generate HTML pages and send it to the user 2
Analyze user social profile 3
Retrieve Personalized Ad 4
Request Database 5
Generate list of messages
Step 2
Step 3 6
Group conversations 7
Generate HTML page 8
Send output to user
Step 4
Jul 1, 2013
Dzmitry Kliazovich

21. CA-DAG: Communication-Aware DAG
Communication unaware model
Network
Processor
Receive User Request 1
Process User Data
Receive User Request 1
Process User Data
Receive User Request
Step 1 2
Analyze user social profile 3
Receive User Request 2
Analyze user social profile 3
Retrieve Personalized Ad 4
Receive User Request 5
Generate list of messages 4
Request Database 5
Generate list of messages 3
Receive User Request
Step 2
Step 3
Makespan: 9 4
Receive User Request 6
Group conversations 6
Group conversations 7
Generate HTML page 8
Send output to user 7
Generate HTML page 8
Send output to user
Step 4 8
Send output to user
Jul 1, 2013
Dzmitry Kliazovich

22. CA-DAG: Communication-Aware DAG
Edges-based model
Receive User Request
Step 1 1
Process User Data
Step 1: Receive user request and process it
Step 2: Generate personalized advertisement
Step 3: Request list of messages from database
Step 4: Generate HTML pages and send it to the user 4
Request Database 2
Analyze user social profile
Step 2
Step 3 5
Generate list of messages 6
Group conversations 3
Retrieve Personalized Ad 7
Generate HTML page
Step 4 8
Send output to user
Jul 1, 2013
Dzmitry Kliazovich

23. CA-DAG: Communication-Aware DAG
Edges-based model
Network
Processor
Receive User Request
Receive User Request
Receive User Request
Step 1 1
Process User Data 1
Process User Data 4
Request Database 4
Request Database 4
Request Database 2
Analyze user social profile 2
Analyze user social profile 3
Retrieve Personalized Ad
Step 2
Makespan: 8
Step 3 4
Request Database 5
Generate list of messages 5
Generate list of messages 6
Group conversations 6
Group conversations 3
Retrieve Personalized Ad 7
Generate HTML page 7
Generate HTML page
Step 4 8
Send output to user 8
Send output to user
Jul 1, 2013
Dzmitry Kliazovich

24. CA-DAG: Communication-Aware DAG
Comparison of schedules
CA-DAG model
Communication-unaware model
Edges-based model
Jul 1, 2013
Dzmitry Kliazovich

25. CA-DAG: Communication-Aware DAG
Comparison of models’ makespan
# of
Processors
Network links
Communication-unaware model
Edges-based model
Proposed CA-DAG model 1 9 8 7 2
Achieves minimum makespan with the least resources
Jul 1, 2013
Dzmitry Kliazovich

26. Properties of Communication Tasks/Vertices

27. Properties of Communication Tasks/Vertices
Task parallelization
Multipath routing
Task completion time
Available bandwidth
Jul 1, 2013
Dzmitry Kliazovich

28. Dzmitry Kliazovich (dzmitry.kliazovich@uni.lu)
Task Parallelization
Each communication task/vertex can be divided into 𝑛 different independent communication tasks that can be executed in parallel
The smallest size of communication task is one bit as all bits in the message are independent 1 1 2
2.1
2.2 …
2.n 3 4 3 4
Jul 1, 2013
Dzmitry Kliazovich

29. Dzmitry Kliazovich (dzmitry.kliazovich@uni.lu)
Multipath Routing
Most of existing solutions rely on static network topology and fixed pre-allocation which implies circuit switching and pre-defined routing
In reality, datacenter networks are packet switched with routing decisions taken at every hop
The availability of multiple paths is essential to benefit from parallelization property of communication tasks
Jul 1, 2013
Dzmitry Kliazovich

30. Dzmitry Kliazovich (dzmitry.kliazovich@uni.lu)
Task Completion Time
Execution of communication task involves
Packet transmissions on multiple links
Sequential processing, variable bitrates
Communication delay components
Processing delay 𝑑 𝑝𝑟𝑜𝑐
Queuing delay 𝑑 𝑞𝑢𝑒𝑢𝑒
Transmission delay 𝑑 𝑡𝑥
Propagation delay 𝑑 𝑝𝑟𝑜𝑝
Jul 1, 2013
Dzmitry Kliazovich

31. Dzmitry Kliazovich (dzmitry.kliazovich@uni.lu)
Available Bandwidth
Residual Bandwidth
Bandwidth left unoccupied
Available bandwidth
Bandwidth that a new flow can obtain (residual bandwidth + portion of the used bandwidth)
Utilization performance of communication protocols
TCP throughput
Total capacity
(includes buffers)
Link capacity
Used bandwidth
Jul 1, 2013
Dzmitry Kliazovich

32. Performance of CA-DAG Model

33. Performance of CA-DAG model
System Architecture
Only one node can communicate at a time
Jul 1, 2013
Dzmitry Kliazovich

34. Performance of CA-DAG model
Workloads
Winkler graph generator
DAGs with occasional and frequent communications
Communication-to-Computation Ratio (CCR)
0.1
Computationally
intensive DAGs 1
Balanced DAGs 2
Communication
intensive DAGs
Jul 1, 2013
Dzmitry Kliazovich

35. Performance of CA-DAG model
Scheduling Algorithm
Offline (deterministic) scheduling
Zero release time of DAGs
Clairvoyant execution and communication time
Adapted list scheduling is employed
A processor allowing minimum execution time is selected
Jul 1, 2013
Dzmitry Kliazovich

36. Performance of CA-DAG model
Scheduling Criteria
Schedule efficiency
Approximation factor
Jul 1, 2013
Dzmitry Kliazovich

37. Performance of CA-DAG model
Schedule efficiency
Ratio of sequential execution time to the makespan of the schedule by the number of computing resources
where 𝐶 𝑚𝑎𝑥 is a makespan
𝑚 is the number of computing resources
eff 𝑆 = 𝑖=1..,𝑛 𝑝 𝑖 𝐶 𝑚𝑎𝑥 ×𝑚
Jul 1, 2013
Dzmitry Kliazovich

38. Performance of CA-DAG model
Schedule efficiency
Apps. with occasional communications
Apps. with frequent communications
The higher the better
Jul 1, 2013
Dzmitry Kliazovich

39. Performance of CA-DAG model
Approximation factor
where 𝐶 𝑚𝑎𝑥 is a makespan
𝐶 𝑚𝑎𝑥 ∗ is the optimal makespan
max 𝑏𝑙𝑒𝑣𝑒𝑙 𝑡 𝑖 is a critical path of the graph
𝜌= 𝐶 𝑚𝑎𝑥 / 𝐶 𝑚𝑎𝑥 ∗
𝐶 𝑚𝑎𝑥 ∗ =𝑚𝑎𝑥 max 𝑏𝑙𝑒𝑣𝑒𝑙( 𝑡 𝑖 ) , 𝑖=1..,𝑛 𝑝 𝑖 𝑚 ,
Jul 1, 2013
Dzmitry Kliazovich

40. Performance of CA-DAG model
Approximation factor
Apps. with occasional communications
Apps. with frequent communications
The lower the better
Jul 1, 2013
Dzmitry Kliazovich

41. Dzmitry Kliazovich (dzmitry.kliazovich@uni.lu)
Conclusions
Cloud applications use communication resources excessively
New communication-aware model of cloud applications, named CA-DAG, is proposed
CA-DAG includes separate vertices to represent communication processes to allow making separate resource allocation decisions (computing jobs to processors, communication jobs to the network)
CA-DAG model enables the design of novel solutions with mixed scheduling policies optimized for cloud computing
Jul 1, 2013
Dzmitry Kliazovich

42. University of Luxembourg
Thank you!
Contact information:
Dzmitry Kliazovich
University of Luxembourg