1. SProj 3 Libra: An Economy-Driven Cluster Scheduler Jahanzeb Sherwani Nosheen Ali Nausheen Lotia Zahra Hayat Project Advisor/Client: Rajkumar Buyya Faculty Advisor: Dr. Arif Zaman

2. Problem Statement Implementing a computational- economy based user-centric scheduler for clusters

3. A collection of workstations interconnected via a network technology, in order to take advantage of combined computational power and resources A collection of workstations interconnected via a network technology, in order to take advantage of combined computational power and resources An integrated collection of resources that can provide a single system image spanning all its nodes: a virtual supercomputer An integrated collection of resources that can provide a single system image spanning all its nodes: a virtual supercomputer Used for computation-intensive applications such as AI expert systems, nuclear simulations, and scientific calculations Used for computation-intensive applications such as AI expert systems, nuclear simulations, and scientific calculations What is a cluster?

4. Why clusters? Cost-effectiveness: low cost-performance ratio compared to a specialized supercomputer Cost-effectiveness: low cost-performance ratio compared to a specialized supercomputer Increase in workstation performance Increase in workstation performance Increase in network bandwidth Increase in network bandwidth Decrease in network latency Decrease in network latency Scalability higher than that of a specialized supercomputer Scalability higher than that of a specialized supercomputer Easier to integrate into an existing network than specialized supercomputers Easier to integrate into an existing network than specialized supercomputers

5. Computational Economy Traditional system-centric performance metrics Traditional system-centric performance metrics CPU Throughput CPU Throughput Mean Response Time Mean Response Time Shortest Job First Shortest Job First Computational economy is the inclusion of user-specified quality of service parameters with jobs so that resource management is user-centric rather than system-centric Computational economy is the inclusion of user-specified quality of service parameters with jobs so that resource management is user-centric rather than system-centric

6. Computational Economy (contd) Project focus: to implement a scheduler that aims to maximize user utility Project focus: to implement a scheduler that aims to maximize user utility Job parameters most relevant to user- centric scheduling Job parameters most relevant to user- centric scheduling Budget allocated to job by user Budget allocated to job by user Deadline specified by user Deadline specified by user

7. Computational Economy for Grids What is a grid? What is a grid? An infrastructure that couples resources such as computers (workstations or clusters ), software (for special purpose applications) and devices (printers, scanners) across the Internet and presents them as a unified integrated single resource that can be widely used An infrastructure that couples resources such as computers (workstations or clusters ), software (for special purpose applications) and devices (printers, scanners) across the Internet and presents them as a unified integrated single resource that can be widely used How a grid differs from a cluster How a grid differs from a cluster Wide geographical area Wide geographical area Non-dedicated resources Non-dedicated resources No centralized resource management No centralized resource management

8. Computational Economy for Grids Management of resources and scheduling computations in a grid environment is complex as the resources are Management of resources and scheduling computations in a grid environment is complex as the resources are geographically distributed geographically distributed heterogeneous in nature heterogeneous in nature owned by different individuals or organizations owned by different individuals or organizations have different access and cost models have different access and cost models resource discovery required resource discovery required security issues security issues Computational economy has been implemented for grids: the Nimrod/G resource broker is a global resource management and scheduling system that supports deadline and economy-based computations in grid-computing environments Computational economy has been implemented for grids: the Nimrod/G resource broker is a global resource management and scheduling system that supports deadline and economy-based computations in grid-computing environments

9. Computational Economy for Clusters Market-based Proportional Resource Sharing for Clusters: Brent Chun and David E. Culler, University of California at Berkeley, Computer Science Division Market-based Proportional Resource Sharing for Clusters: Brent Chun and David E. Culler, University of California at Berkeley, Computer Science Division a market-based approach based on the notion of a computational economy which optimizes for user value. It describes an architecture for market-based cluster resource management based on the idea of proportional resource sharing of basic computing resources. Cluster nodes act as independent sellers of computing resources while user applications act as buyers who purchase resources. Users are allocated credits/tickets-the more tickets they have, the greater their CPU share. Ticket allocation is on the basis of the amount the user is willing to pay: his valuation of the job a market-based approach based on the notion of a computational economy which optimizes for user value. It describes an architecture for market-based cluster resource management based on the idea of proportional resource sharing of basic computing resources. Cluster nodes act as independent sellers of computing resources while user applications act as buyers who purchase resources. Users are allocated credits/tickets-the more tickets they have, the greater their CPU share. Ticket allocation is on the basis of the amount the user is willing to pay: his valuation of the job Deadline not incorporated Deadline not incorporated

10. Cluster Architecture

11. Cluster Management Software Cluster Management Software is designed to administer and manage application jobs submitted to workstation clusters. Cluster Management Software is designed to administer and manage application jobs submitted to workstation clusters. Creates a Single System Image Creates a Single System Image When a collection of interconnected computers appear to be a unified resource, we say it possesses a Single System Image When a collection of interconnected computers appear to be a unified resource, we say it possesses a Single System Image The benefit of a Single System Image is that the exact location of the execution of a process is entirely concealed from the user. The user is offered the illusion of a single powerful computer The benefit of a Single System Image is that the exact location of the execution of a process is entirely concealed from the user. The user is offered the illusion of a single powerful computer Maintains centralized information about cluster status and resources Maintains centralized information about cluster status and resources

12. Cluster Management Software Commercial and Open-source Cluster Management Software Commercial and Open-source Cluster Management Software Open-source Cluster Management Software Open-source Cluster Management Software DQS (Distributed Queuing System ) DQS (Distributed Queuing System ) CONDOR CONDOR GNQS (Generalized Network Queuing System) GNQS (Generalized Network Queuing System) MOSIX MOSIX REXEC (Remote Execution) REXEC (Remote Execution) SGE (Sun Grid Engine) SGE (Sun Grid Engine) PBS (Portable Batch System) PBS (Portable Batch System)

13. Cluster Management Software Why SGE was rejected Why SGE was rejected lack of online support lack of online support lack of stability lack of stability Final choice of CMS: PBS(Portable Batch System ) Final choice of CMS: PBS(Portable Batch System )

14. Pricing the Cluster Resources Cost= a (Job Execution Time) + b (Job Execution Time / Deadline) Cost= a (Job Execution Time) + b (Job Execution Time / Deadline) Cost of using the cluster depends on job length and job deadline: the longer the user is prepared to wait for the results, the lower his cost Cost of using the cluster depends on job length and job deadline: the longer the user is prepared to wait for the results, the lower his cost Cost formula forces user to reveal his true deadline Cost formula forces user to reveal his true deadline

15. Scheduling Algorithm How to meet budget and deadline constraints? How to meet budget and deadline constraints? Ensuring low run-time for the algorithm Ensuring low run-time for the algorithm Greedy Algorithm Greedy Algorithm Complex solutions unfeasible Complex solutions unfeasible Test run of algorithm: Test run of algorithm: 5 jobs, arriving at time t=0, 5, 7, 9, 9, on a 3 node cluster 5 jobs, arriving at time t=0, 5, 7, 9, 9, on a 3 node cluster

16. LIBRA with PBS Portable Batch System (PBS) as the Cluster Management Software (CMS) Portable Batch System (PBS) as the Cluster Management Software (CMS) Robust, portable, effective, extensible batch job queuing and resource management system Robust, portable, effective, extensible batch job queuing and resource management system Supports different schedulers Supports different schedulers Job accounting Job accounting Technical Support Technical Support

17. Setting up the PBS Cluster Installation of Linux with Windows Installation of Linux with Windows Installation of SGE as well as PBS Installation of SGE as well as PBS Setting up a Network File System Setting up a Network File System Configuring GridSim in Java Configuring GridSim in Java Configuring PBSWeb Configuring PBSWeb Setting up the Apache WebServer Setting up the Apache WebServer PHP scripting for Apache PHP scripting for Apache Setting up PostgreSQL Setting up PostgreSQL Setting up SSH Setting up SSH

18. PBS Overview Main components of PBS Main components of PBS Job Server pbs_server Job Server pbs_server Job Scheduler pbs_sched Job Scheduler pbs_sched Job Executor & Resource Monitor pbs_mom Job Executor & Resource Monitor pbs_mom The server accepts commands and communicates with the daemons The server accepts commands and communicates with the daemons qsub - submit a job qsub - submit a job qstat - view queue and job status qstat - view queue and job status qalter - change jobs attributes qalter - change jobs attributes qdel - delete a job qdel - delete a job

19. Xpbs – GUI for PBS

20. Xpbs --- GUI for PBS

21. Job Scheduling in PBS

22. The Libra Scheduler Default FIFO Scheduler in PBS Default FIFO Scheduler in PBS FIFO - sort jobs by job queuing time running the earliest job first FIFO - sort jobs by job queuing time running the earliest job first Fair share: sort & schedule jobs based on past usage of the machine by the job owners Fair share: sort & schedule jobs based on past usage of the machine by the job owners Round-robin - pick a job from each queue Round-robin - pick a job from each queue By key - sort jobs by a set of keys: shortest_job_first, smallest_memory_first By key - sort jobs by a set of keys: shortest_job_first, smallest_memory_first

23. The Libra Scheduler Job Input Controller Job Input Controller Adding parameters at job submission time Adding parameters at job submission time deadline deadline budget budget executionTime executionTime Defining new attributes of job Defining new attributes of job Job Acceptance and Assignment Controller Job Acceptance and Assignment Controller Budget checked through cost function Budget checked through cost function Admission control through deadline scheduling Admission control through deadline scheduling Execution host with the minimum load and ability to finish job on time selected Execution host with the minimum load and ability to finish job on time selected Equal Share instead of Minimum Share Equal Share instead of Minimum Share

24. The Libra Scheduler Job Execution Controller Job Execution Controller Job run on the best node according to algorithm Job run on the best node according to algorithm Cluster and node status updated Cluster and node status updated runTime runTime cpuLoad cpuLoad Job Querying Controller Job Querying Controller Server, Scheduler, Exec Host, and Accounting Logs Server, Scheduler, Exec Host, and Accounting Logs

25. PBS-Libra Web --- Front-end for the Libra Engine

26. PBS-Libra Web

35. Simulations Goal: Goal: Measure the performance of Libra Scheduler Measure the performance of Libra Scheduler Performance = ? Performance = ? Maximize user satisfaction Maximize user satisfaction

36. Simulations Simulation Software Simulation Software Alter GridSim (grid resource management simulation) Alter GridSim (grid resource management simulation)

37. GridSim Class Diagram

38. Simulations Methodology Methodology Workload Workload 120 jobs with deadlines and budgets 120 jobs with deadlines and budgets Job lengths: 1000 to 10000 Job lengths: 1000 to 10000 Resources Resources 10 node, single processor (MIPS rating: 100) homogenous cluster 10 node, single processor (MIPS rating: 100) homogenous cluster

39. Simulations Assumptions Assumptions Strict deadlines Strict deadlines Ignores processing overhead due to scheduler and clock interrupt Ignores processing overhead due to scheduler and clock interrupt Scheduler simulated as a function Scheduler simulated as a function Input: job size, deadline, budget Input: job size, deadline, budget Output: accept/reject, node #, share allocated Output: accept/reject, node #, share allocated

40. Simulations Compared: Compared: Proportional Share Proportional Share FIFO FIFO Experiments: Experiments: 120 jobs, 10 nodes 120 jobs, 10 nodes Increasing workload to 150 and 200 Increasing workload to 150 and 200 Increasing cluster size to 20 Increasing cluster size to 20

41. Simulation Results 120 jobs, 20 did not meet budget 120 jobs, 20 did not meet budget

42. 100 Jobs, 10 Nodes FIFO: 23 rejected - Proportional Share: 14 rejected

43. Simulation Results Increase workload to 200 jobs on the same 10 node cluster Increase workload to 200 jobs on the same 10 node cluster

44. 200 Jobs, 10 Nodes FIFO: 105 rejected - Proportional Share: 93 rejected

45. Simulation Results Scale the cluster up to 20 nodes Scale the cluster up to 20 nodes

46. 200 Jobs, 20 Nodes FIFO: 35 rejected - Proportional Share: 23 rejected

47. Simulation Results

51. Conclusion & Future Work Succesfully implemented a Linux-based cluster that schedules jobs using PBS with our economy-driven Libra scheduler, and PBS-Libra Web as the front end. Succesfully implemented a Linux-based cluster that schedules jobs using PBS with our economy-driven Libra scheduler, and PBS-Libra Web as the front end. Successfully tested our scheduling policy Successfully tested our scheduling policy Proportional Share delivers more value to users Proportional Share delivers more value to users Exploring other pricing mechanisms Exploring other pricing mechanisms Expanding the cluster with more nodes and with support for parallel jobs Expanding the cluster with more nodes and with support for parallel jobs