1.  Escalonamento e Migração de Recursos e Balanceamento de carga 27-04-20151 Carlos Ferrão Lopes nº M6935 Bruno Simões nº M6082 Celina Alexandre nº M6807 Paulo Cabral nº M6765 Quoc nº M6831 Tecnologias de Cloud e Datacenter Professor Doutor Nuno M. Garcia

2. Overview 1. Stagger 2. Migration 3. Load Balancing 27-04-20152

3. Overview 1. Stagger 2. Scheduling Tasks 2.1. Algorithms Hetergeneous Earliest Finish Time (HEFT) 2.2. Algorithms Path Clustering Heuristic (PCH) 2.3 Scheduling algorithms for the comparison study 27-04-20153

4. Overview (Cont.) 3. Scheduling Procedure 3.1. Process Scheduling in Distributed System 27-04-20154

5. What is Scheduling?  A system that uses multi-programming processes compete for processing, as they require dividing the execution time of each. 27-04-20155

6. Scheduling problem  Problem workflow scheduling a set of dependent services:  Order of precedence of services;  Costs of performance of the services;  Communication costs between services;  Resource processing capabilities;  Data transmission capabilities of network connections, which interconnect these resources; 27-04-20156

7. Scheduling algorithm Rating 27-04-20157 Rating scheduling methods

8. Scheduling tasks  dependent  independent 27-04-20158

9. Heterogeneous Earliest Finish Time Algorithm (HEFT)  Algorithm Workflow escalation  Using an acyclic digraph (DAG 1 ) for a limited number of heterogeneous computers.  Task scheduling problem is NP-hard 27-04-2015 1DAG - Directed acyclic graph 9

10. Heterogeneous Earliest Finish Time Algorithm (HEFT) (Cont.)  The HEFT consists of two main phases:  Prioritization phase  Selection phase 27-04-201510

11. Prioritization phase  Each task should be prioritized considering the length of the critical path;  The list of tasks to be performed is then sorted in descending order of the critical path length;  A topological sorting is produced tasks, preserving the precedence constraints of the DAG; 27-04-2015 11

12. Selection Phase  To select which processor will perform a given task, calculating the earliest time of completion of a given task. With HEFT algorithm, the search for a vacant time slot on a processor P starts from the moment that the processor P becomes vacant. 27-04-201512

13. Path Heuristic Clustering Algorithm (PCH)  Clustering uses the technique to create groups (clusters) tasks;  Groups the ways of the DAG, thus reducing communication costs between tasks;  The tasks of the same cluster are scheduled on the same resource; 27-04-201513

14. Path Heuristic Clustering Algorithm (PCH) (Cont.)  Uses some attributes calculated for each task, estimating the start times (DS = Earliest Start Time) and end (EFT - Estimated Finish Time) of tasks and process;  EST and EFT are calculated using information that is provided by the middleware and the programming model; 27-04-201514

15. Path Heuristic Clustering Algorithm (PCH) (Cont.) 27-04-201515 Example scheduling using the PCH

16. Scheduling algorithms for the comparison study  Applications used:  Application Montage 1  Application Epigenomics 2 27-04-2015 1 is used to generate personalized sky mosaics using multiple points of input images. 2 is used to mapping of epigenetic state of human cells on a large scale genomics. 16

17. Analysis Applications 27-04-201517 Representation of epigenomics application used in the simulation. Representation of montage application used in the simulation.

18. Architecture  Were also specified two sets: homogeneous and heterogeneous;  Have been implemented in addition to the scheduling algorithms HEFT, CPOP and PCH, a FIFO type of scheduling; 27-04-201518

19. Architecture (Cont.) 27-04-201519 Architecture

20. Architecture (Cont.) 27-04-201520 Computing power table

21. Experimental Results  Applications to the Montage  We observed that the FIFO strategy only performs well in homogeneous architecture.  The PCH algorithm does not perform well in both architectures.  The algorithms HEFT and CPOP perform well in both architectures with an almost negligible difference. 27-04-201521

22. Analysis Applications Workload simulation result Montage with a set Homogeneous Workload simulation result Montage with a set Heterogeneous 27-04-2015 Makespan – The time difference between the beginning and end of a sequence of jobs or tasks 22

23. Experimental Results  Applications to Epigenomics  The PCH algorithm improves performance in this type of application.  The CPOP algorithm preserves the good performance in both architectures.  The HEFT algorithm performs well in heterogeneous architecture, but in the homogeneous architecture not. 27-04-201523

24. Analysis Applications Workload simulation result Epigenomics with a set Homogeneous Workload simulation result Epigenomics with a set Heterogeneous 27-04-2015 Makespan – The time difference between the beginning and end of a sequence of jobs or tasks 24

25. Conclusions  A FIFO strategy is appropriate only in homogeneous architectures.  In heterogeneous architectures it takes a study of both computational resources and tasks.  The HEFT and CPOP algorithms showed good performance and at the same time like in Montage application, while SHP not.  In applications with equipment that prevents the performance in a continuous process, such as montage application, it is not convenient grouping of tasks. 27-04-201525

26. Scheduling Procedure  A multi-programming computer has multiple processes running simultaneously, however, that all processes can not access the CPU at a time and so there is a fair way of implementation of these processes, an algorithm was created to organize tasks. 27-04-201526

27. Process Scheduling in Distributed System  Heterogeneity of the nodes prevents equal distribution;  In addition to the tasks to be performed it is necessary to:  Processing power;  Unemployment level;  If you are in possession of information:  Weight of tasks;  Estimated processing time; 27-04-201527

28. Information nodes  Physical characteristics: speed, memory, processor type  Degree of idleness of each node: the processing power is being used; 27-04-201528

29. Measure the level of unemployment  Average waiting time in the run queue  Problem: Priority difference of tasks;  Generate CPU utilization rate  Problem: to test specific process from time to time; 27-04-201529

30. Measure the level of unemployment (Cont.)  Problem General: update frequency  Excessive Update: scheduling algorithm becomes main consumer;  Insufficient Update: wrong choice of a node;  Algorithms:  Static scheduling;  Scheduling dynamic  Adaptive scheduling; 27-04-201530

31. Scheduling static  Assessment of conditions of the nodes is done once;  Data are used during the entire process; 27-04-201531

32. Dynamic Scheduling  Constant analysis of the nodes characteristics:  Waiting time in the CPU queue: little processing;  CPU Utilization: Small background processes making continuous testing; 27-04-201532

33. Adaptive Scheduling:  Dynamic Scheduling special case;  You can adjust the intensity of CPU consumption:  May fail to perform some tests 27-04-201533

34. Overhead  Distributed memory: high cost in exchange of information between nodes;  Can occur over communications and performance problems:  Nodes are communicating longer than processing; 27-04-201534

35. Algorithms of classification according to the policy  You can sort scheduling algorithms under policies:  Transfer policy: determining a suitable node to receive tasks;  Selection Policy: determine the most appropriate task to be sent to a node;  Location Policy: determine the most appropriate node to receive a given task;  Information Policy: determine storage location of information and frequency of update. 27-04-201535

36. Transfer Policy  Determine a receiver node: node is working below its processing capacity;  Is defined maximum load supported by each node; 27-04-201536

37. Transfer Policy (Cont.)  Problem: donor sends a task to a receiver that immediately becomes a donor:  Task switching is done indefinitely.  Is defined maximum load supported by each node;  Solution: Consider a donor node whose load is less than the limit and will continue to be less than or equal after receiving the task; 27-04-201537

38. Selection Policy  Determine a task to be sent when a node is overloaded or eligible to receive;  In an overloaded node:  Select the task that caused the overload; 27-04-201538

39. Selection Policy (Cont.)  Preemptive scheduling:  A task already running can be transferred to another node;  High transfer costs of the task context (ex.: data in memory);  Usually picks up recently started tasks;  Non-Preemptive Scheduling:  Only tasks that have not yet implemented can be transferred; 27-04-201539

40. Location Policy  Select node to receive a task;  Random method (most commonly used):  Node randomly chooses another node;  Problem: chances of choosing a node in the same situation;  Immediate solution: test if selected node is receiver;  Solution "better": vote between selected nodes. Generates excessive communication. 27-04-201540

41. Information Policy  Corresponds to the exchange of information on their status across multiple nodes:  Local storage: a specific node receives all the status messages of others.  Broadcast: each node sends to others;  Sub-demand: each node has information about you. Information about others only when it becomes donor or recipient. 27-04-201541

42. Scheduling algorithms  Started by the donor:  The node that has a donor in accordance with the transfer policy utilized, looking for a receiver;  Algorithm Random (most common), making the choice of a single receptor twice.  Sub-demand: each node has information about you. Information about others only when it becomes donor or recipient. 27-04-201542

43. Scheduling algorithms (Cont.)  Started by the receiver:  The receiver node calls tasks to process;  Most advantageous approach: unoccupied node has more time to choose a suitable donor; 27-04-201543