1. Resource Management of Grid Computing
-- Juan Chen 陈娟
Ying Tao 陶莺

2. Overview Grid Computing Resource Management Instance --Nimrod/G
Local Resource Management
Global Resource Management
Scheduling
Instance --Nimrod/G

3. Grid Computing (1)
A Grid is a very large-scale, generalized distributed Network Computing system that can scale to Internet size environments with machines distributed across multiple organizations and administrative domains.
There are four main aspects characterize a grid
Multiple Administrative Domains and Autonomy
Heterogeneity
Scalability
Dynamicity or Adaptability
For a Grid to efficiently support a variety of applications, the resource management system (RMS) that is central to its operation must address the above issues in addition to issues such as fault-tolerance and stability

4. Grid Computing (2)
Grid computing is concerned with coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations.
The key concept is the ability to negotiate resource-sharing arrangements among a set of participating parties and then to use the resulting resource pool for some purpose. So we can see that resource management system is the central component of grid computing systems.

5. Designing a Grid architecture is challenging due to:
supporting adaptability, extensibility, and scalability;
allowing systems with different administrative policies to inter-operate while preserving site autonomy;
co-allocating resources;
supporting quality of service;
economy of computations

6. A layered Grid architecture and components

7. Basic library and supported softwares
Globus Architecture
Applications
Basic library and supported softwares
mpich, PSEs
GIISs
collective
GRAM, GRIS, GSS
Globus
resource
GIS, GAA
connectivity
Low-level (Fabric)
interface
local scheduler, PBS, Condor, SQMS

8. Traditional Resource Management
Designed and operated under the assumption that:
– They have complete control over a resource
– They can implement the mechanisms and policies needed for effective use of that resource in isolation
This is not the case for Grid Resource management
– Separate administrative domains
– Resource Heterogeneity
– Lack of control of different policies

9. What is Grid Resource Management?
Identifying application requirements, resource specification
Matching resources to applications
Allocating/scheduling and monitoring those resources and applications over time in order to run as effectively as possible.

10. Resource Management System

11. RMS system abstract structure

12. Grid Resource Management System consists of :
Local resource management system (Resource Layer)
Basic resource management unit
Provide a standard interface for using remote resources
e.g. GRAM, etc.
Global resource management system (Collective Layer)
Coordinate all Local resource management system within multiple or distributed Virtual Organizations (VOs)
Provide high-level functionalities to efficiently use all of resources
Job Submission
Resource Discovery and Selection
Scheduling
Co-allocation
Job Monitoring, etc.
e.g. Meta-scheduler, Resource Broker, etc.

13. Local Resource Management
Globus Resource Allocation Manager (GRAM) is responsible for
1. processing RSL specifications representing resource requests, by either denying the request or by creating one or more processes (a \job") that satisfy that request;
2. enabling remote monitoring and management of jobs created in response to a resource request
3. periodically updating the MDS information service with information about the current availability and capabilities of the resources that it manages.

14. Major components of the GRAM implementation

15. Resource co-allocator
it is often the case that a metacomputing application requires that several resources be allocated simultaneously. In these cases, a resource broker produces a multirequest and co-allocation is required.
the role of a co-allocator is to split a request into its constituent components, submit each component to the appropriate resource manager, and then provide a means for manipulating the resulting set of resources as a whole

16. Types of co-allocation
a range of different co-allocation service can be constructed.
require all resources to be available before the job is allowed to proceed, and fail globally if failure occurs at any resource;
allocate at least N out of M requested resources then return
return immediately, but gradually return more resources as they become available.

17. Scheduling Scheduling is the matching of application
requirements and available resources.
System-level schedulers---focus on throughput and generally do not consider application requirements in scheduling decisions.
Application-specific schedulers---have been very successful for individual applications, but are not easily applied to new applications.

18. Grid Application Development Software Project (GrADS) Scheduling
Launch-time scheduling is the pre-execution determination of an initial matching of application requirements and available resources.
Rescheduling involves making modifications to that initial matching in response to dynamic system or application changes.
Meta-scheduling involves the coordination of schedules for multiple applications running on the same Grid at once.
The Grid Application Development Software Project provides a system to simplify Grid application development. I will focus on application sheduling in it and present the three scheduling approaches developed in GrADS:

19. Grid Application Development Software Architecture
The Program Preparation System (PPS) handles application development, composition, and compilation. To begin the development process, the user interacts with a high-level interface called a problem solving environment (PSE) to assemble their Grid application source code and integrate it with software libraries. The resulting application is passed to a specialized GrADS compiler. The compiler performs application analysis and partial compilation into intermediate representation code and generates a configurable object program (COP). This work may be performed
off-line, as the COP is a long-lived object that may be re-used for multiple runs of the application. The COP must encapsulate all results of the PPS phase for later usage; for example, application performance models and partially compiled code called intermediate representation code.
The Program Execution System (PES) provides on-line resource discovery, scheduling, binding, application performance monitoring, and rescheduling. To execute an application, the user submits parameters of the problem such as problem size to the GrADS system. The application COP is retrieved and the PES is invoked. At this stage the scheduler interacts with the Grid run-time system to determine which resources are available and what performance can be expected of those resources. The scheduler then uses application requirements specified in
the COP to select an application-appropriate schedule (resource subset and a mapping of the problem data or tasks onto those resources).
The binder is then invoked to perform a final, resource-specific compilation of the intermediate representation code. Next, the executable is launched on the selected Grid resources and a real-time performance monitor is used to track program performance and detect violation of performance guarantees. Performance guarantees are formalized in a performance contract [35]. In the case of a performance contract violation, the rescheduler is invoked to evaluate alternative schedules.

20. Launch-time scheduling
The launch-time scheduler is called just before application launch to determine how the current application execution should be mapped to available Grid resources.
The resulting schedule specifies the list of target machines, the mapping of virtual application processes to those machines, and the mapping of application data to processes.

21. GrADS launch-time scheduling architeture
Launch-time scheduling is a central component in GrADSoft and is key to achieving application performance; This architecture is designed to work in concert with many other GrADS components. Launch-time scheduling proceeds as follows. A user submits a list of machines to be considered for this application execution (the machine list). The list is passed to the information services client; this component contacts the MDS and the NWS to retrieve information about Grid resources. The Monitoring and Discovery Service (MDS) and the Network Weather Service (NWS) [36] are two on-line services that collect and store Grid information that may be of interest to tools such as schedulers. The resulting Grid information is passed to the filter resources component. This component retrieves the application requirements and filters out resources that can not be used for the application. The remaining Grid information is then passed to the search procedure. This procedure searches through available resources to find application-appropriate subsets; this process is aided by the application performance model and mapper. The search procedure eventually selects a final schedule, which is then used to launch the application on the selected Grid resources.

22. The drawbacks of launch-time scheduling
When two applications are submitted to GrADS at the same time, scheduling decisions will be made for each application ignoring the presence of the other.
If the launch-time scheduler determines there are not enough resources for the application, it can not make further progress.
A long running job in the system can severely impact the performance of numerous new jobs entering the system
The root cause of these and other problems is the absence of a metascheduler

23. Rescheduling
Rescheduling, can include changing the machines on which the application is executing or changing the mapping of data and/or processes to those machines according to the change of load or application requirements.
Rescheduling can be implemented via two ways:
Application Migration
Process Swapping

24. GrADS rescheduling architecture
Figure 1.6 shows rescheduling architecture and depict an application already executing on N processors. The performance contract specifies the performance expected for the current application execution. While the application is executing, application sensors are co-located with application processes to monitor application progress. Meanwhile, resource sensors are located on the machines on which the application is executing, as well as the other machines available to the user for rescheduling. Application sensor data and the performance contract are passed to the contract monitor, which compares achieved application performance against expectations. When performance falls below expectations, the contract monitor signals a violation and contacts the rescheduler. Upon a contract violation, the rescheduler must determine whether rescheduling is profitable, and if so, what new schedule should be used. Data from the resource sensors can be used to evaluate various schedules, but the rescheduler must also consider (i) the cost of moving the application to a new execution schedule and (ii) the amount of work remaining in the application that can benefit from a new schedule. To initiate schedule modification, the rescheduler contacts the rescheduling actuators located on each processor. These actuators use some mechanism to initiate the actual migration or load balancing.
Application support for migration or load balancing is the most important part of the rescheduling system. The most transparent migration solution would involve an external migrator that, without application knowledge, freezes execution, records important state such as register values and message queues, and restarts the execution on a new processor. Unfortunately, this is not yet feasible as a general solution in heterogeneous environments. Specifically, the application developer simply adds a few lines to their source code and then links their application against our MPI rescheduling libraries in addition to their regular MPI libraries.

25. Metascheduling
The goal of metascheduling is to investigate scheduling policies that take into account both the needs of the application and the overall performance of the system.
The metascheduler possesses global knowledge of all applications in the system and tries to balance the needs of the applications.
The metascheduler is implemented by the addition of four components, namely database manager, permission service, contract negotiator and rescheduler

26. Metascheduler and interactions
The metascheduler is implemented by the addition of four components, namely database manager, permission service, contract negotiator and rescheduler. The database manager acts as a repository for information about all applications in the system. As shown in Figure, the application stores information in the database manager at different stages of execution. Other metascheduling components query the database manager for information to make scheduling decisions. In metascheduling, after the filter resources component generates a list of resources suitable for problem solving, this list is passed, along with the problem parameters, to the permission service. The permission service determines if the filtered resources have adequate capacities for problem solving and grants or rejects permission to the application for proceeding with the other stages of execution. If the capacities of the resources are not adequate for problem solving, the permission service tries to proactively stop a large resource consuming application to accommodate the new application. before launching the application, the final schedule along with performance estimates are passed as a performance contract to the contract developer, which in turn passes the information to the metascheduling component, contract negotiator. The contract negotiator is the primary component that balances the needs of different applications. It can reject a contract if it determines that the application has made a scheduling decision based on outdated resource information. The contract negotiator also rejects the contract if it determines that the approval of the contract can severely impact the performance of an
executing application. Finally, the contract negotiator can also preempt an executing application to improve the performance contract of a new application. Thus the major objectives of the contract negotiator is to provide high performance to the individual applications and to increase the throughput of the system.

27. Introduction of Nimrod
A large-scale parameter study of a simulation is well suited to high-throughput computing. It involves the execution of a large number of tasks (task farms) over a range of parameters.
The Nimrod system is designed to address the complexities associated with parametric computing on clusters of distributed systems.
However, Nimrod is unsuitable as implemented in the large-scale dynamic context of computational grids, where resources are scattered across several administrative domains, each with their own user policies, employing their own queuing system, varying access cost and computational power.
Nimrod/G: An architecture for a Resource Management and Scheduling System in a Global Computational Grid.

28. Nimrod/G
Shortcomings of Nimrod are addressed by a new system called Nimrod/G
It uses the Globus middleware services for dynamic resource discovery and dispatching jobs over computational grids.
The architecture of Nimrod/G and its key components are shown as follows:

29. The architecture of Nimrod/G

30. Scheduling and Computational Economy of Nimrod/G
Nimrod/G system has integrated computational economy as part of a scheduling system, It can be handled in two ways:
systems can work on the user’s behalf and try to complete the assigned work within a given deadline and cost. (the early prototype of Nimrod/G)
the user can enter into a contract with the system and pose requests. The advantage of this approach is that the user knows before the experiment is started whether the system can deliver the results and what the cost will be. (rather complex and need grid middleware services for resource reservation, broker services for negotiating cost )

31. The important parameters of computational
economy that can influence the way resource
scheduling is done are:
Resource Cost (set by its owner)
Price (that the user is willing to pay)
Deadline (the period by which an application execution need to completed)
The scheduler can use all sorts of information
gathered by a resource discoverer and also
negotiate with resource owners to get the best “value
for money”.

32. Thank you