Grid Resource Management: Challenges, Approaches, & Solutions Dr. Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Lab. The University of Melbourne Melbourne, Australia

2 Agenda Grid Challenges Revisited Foundations of Resource Management Challenges Decentralised Grid Scheduling Approach Service Oriented-Grid Architecture Market-Oriented Grid Middleware On-Demand Assembly of UtilityGrids Summary SchedulingEconomics Grid Grid Economy

3 Grid Challenges: RM and Scheduling Security Resource Allocation & Scheduling Data locality Network Management System Management Resource Discovery Uniform Access Computational Economy Application Construction

4 Open-Source Grid Middleware Projects

5 Driving Theme: Community vs. Utility Grids Type Feature Community GridsUtility Grids User QoSBest effortContract/SLA Service Pricing Not considered / free access Usage, QoS level, Market supply and demand Example Middleware Globus, Condor, OMII, Unicore Nimrod-G, Gridbus, & many inspired efforts (CatNets, Sun Grid Market, IBM..)

6 Why “ integrate ” Scalable Architecture, Business Models, and Optimal Allocation WWG Pushes Grid computing into mainstream computing Gridbus

Foundations of Grid Resource Management

8 Resource Management Systems (RMSs): General Goals Manage the Supply and Demand for Resources Allocate Resources such that: They are allocated on fairly They are effectively utilised Most users are satisfied High priority jobs are given prominence In a wide-area systems such as Grids: Additionally, we need to make sure that Resource Providers are given appropriate “ incentive ” for their contribution and to ensure sustained resource sharing. Therefore, Resource Management is a Challenging Task due to their complex characteristics and goals.

9 Resources in Grid Environment: Characteristics Autonomous Each have their own resource allocation policy no central control Heterogeneous and substrate: Each resource can be different – SMPs, Clusters, Linux, UNIX, Windows, Intel, etc. Resource owners have their own policies or scheduling mechanisms. Varying Availability Resource allocation/co-allocation challenge The amount of resource available various with time

10 Resources in Grid Environment: Characteristics Size (large number of nodes, providers, consumers) Heterogeneity of: resources (PCs, Workstatations, clusters, and supercomputers) fabric management systems (single system image OS, queuing systems, etc.) fabric management polices applications (scientific, engineering, and commerce) application requirements (CPU, I/O, memory, and/or network intensive) demand patterns (peak, off-peak) Geographic distribution and different time zones Differing goals (producers and consumers have different objectives and strategies) Unsecure and Unreliable environment

11 RMS Architecture Alternatives R1R1 R2R2 RnRn R R1R1 RnRn.. R R R1R1 RnRn.. ……. Centralized (Single Resource) Decentralized (Hierarchical) (Multiple Domains) Centralized (Multiple Resources) (Single/Multiple Domains) Decentralized (Self coordinated or Job Pool) (Multiple Domains) R Scheduler ModelScheduler ArchitectureExample System Unix, Linux, Windows OS Multi-clusters/ Grid Systems: AppLes, SGE-E Nimrod-G, Gridbus Broker Enterprise Grids: Alchemi, 1. Cooperative Clusters (exchange workload) 2. P2P systems (no exchange of jobs) Cluster systems: PBS, LSF, SGE, Condor (Resource Broker or Grid Scheduler) (Job Pool) (Jobs) (Queue) (Local Scheduler)

12 Centralized Vs. Decentralized Resource Management in Grids? Traditional systems use centralised policy that need complete state-information and common fabric management policy or decentralised consensus-based policy. Due to too many heterogenous parameters in the Grid it is impossible to define/get: system-wide performance matrix and common fabric management policy that is acceptable to all. Therefore, “ decentralised ” approach towards management of resource is advocated. Question is: Market-oriented approaches found to be very effective in managing complexities of decentralisation present in “ human ” economy, can they be applied to Grid Resource Management?

A Case for Economy-based Grid Resource Management Service-Oriented Grid Architecture

14 What do Grid players want? Grid Consumers Execute jobs for solving varying problem size and complexity Benefit by utilizing distributed resources wisely Tradeoff timeframe and cost Strategy: minimise expenses Grid Providers Contribute resources for executing consumer jobs Benefit by maximizing resource utilisation Tradeoff local requirements & market opportunity Strategy: maximise return on investment

15 What do Grid players want & require? Grid Service Consumers (GSCs): - minimize expenses, meet QoS How do I express QoS requirements ? How do I trade between timeframe & cost ? How do I discover services and map jobs to meet my QoS needs? How do I manage Grid dynamics and get my work done? … Grid Service Providers (GSPs):– maximise ROI How do I decide service pricing models ? How do I specify them ? How do I translate them into resource allocations ? How do I enforce them ? How do I advertise & attract consumers ? How do I do accounting and handle payments? … They need mechanisms, tools and technologies that help them in value expression, value translation, and value enforcement.

16 Principle 1: Service Oriented Architecture (SOA) A SOA is a contractual architecture for offering and consuming software as services. There are four entities that make up an SOA service provider, service registry, and service consumer (also known as service requestor). The functions or tasks that the service provider offers, along with other functional and technical information required for consumption, are defined in the service definition or contract. provider registry consumer contract

17 Principle 2: Market-Oriented (Grid) Computing- (a) Sustained Resourced Sharing and (b) Effective Management of Shared Resources Grid Economy

18 Market-based Systems = Self-managed and Self-regulated systems. Manage Complexity Supply and Demand Enhance Utility penalty

19 Cost Grid site X Non-uniform costing Encourages the use of local resources first Real accounting system can control machine usage User 5 Machine 1 User 1 Machine 5 Resource Cost = Function (cpu, memory, disk, network, software, QoS, current demand, etc.) Simple: price based on peaktime, offpeak, discount when less demand,..

20 Market-based Computing Systems Need to Support: To enable users (GSPs and GSCs) to realise economic value, market-based systems need to provide mechanisms for: Value Expression a means to express their requirements, valuations, and objectives Value Translation scheduling policies to translate them to resource allocations Value Enforcement mechanisms to enforce the selection and allocation of differential services, and dynamic adaptation to changes in their availability at runtime Market mechanisms, accounting and payment, Reservation of resources.

21 Grid Node N Service-Oriented Grid Architecture Grid Service Consumer Programming Environments Grid Resource Broker Grid Service Providers Grid Explorer Schedule Advisor Trade Manager Job Control Agent Deployment Agent Trade Server Resource Allocation Resource Reservation R1R1 Misc. services Information Service R2R2 RmRm … Pricing Algorithms Accounting Grid Node1 … Core Middleware Services … … Health Monitor Grid Market Services JobExec Info ? Secure Trading QoS Storage Sign-on Grid Bank Applications Data Catalogue

22 Market-Oriented Grid Software: A union of Gridbus and other technologies AIX Solaris WindowsLinux.NET Grid Fabric Software Grid Applications Core Grid Middleware User-Level Middleware Grid Bank Grid Exchange & Federation JVM Grid Scheduling: Task, Parametric, and Components Programming Gridbus Resource Broker MPI CondorSGETomcatPBS Aneka Workflow APIs IRIXOSF1 Mac Libra GlobusUnicore … … Grid Market Directory PDBCDB Worldwide Grid Grid Fabric Hardware Grid PortalsScienceCommerceEngineering … … Collaboratories … … Grid Storage Economy Grid Economy NorduGridXGrid ExcellGrid Grid Workflow Engine APIs/Tools:

23 On Demand Assembly of Services in Market- Oriented Grid Environments ASP Catalogue Grid Info Service Grid Market Directory GSP (Accounting Service) Gridbus GridBank GSP (e.g., UofM) PE GSP (e.g., VPAC) PE GSP (e.g., IBM) CPU or PE Grid Service (GS) (Globus) Aneka GS GTS Cluster Scheduler Job 8 Grid Resource Broker 2 Visual Application Composer Application Code Explore data Results 97 Results+ Cost Info Bill 12 Data Catalogue

24 On Demand Assembly of Services in Market- Oriented Grid Environments: Putting Them All Together ASP Catalogue Grid Info Service Grid Market Directory GSP (Accounting Service) Gridbus GridBank GSP (e.g., UofM) PE GSP (e.g., VPAC) PE GSP (e.g., IBM) CPU or PE Grid Service (GS) (Globus) Aneka GS GTS Cluster Scheduler Job 8 Grid Resource Broker 2 Visual Application Composer Application Code Explore data Results 97 Results+ Cost Info Bill 12 Data Catalogue

25 Summary Resource management is a complex undertaking as systems need to be adaptive, scalable, competitive, …, and driven by QoS. A “ computational economy ”-based resource allocation helps in regulating the supply-and- demand for resources. The use of economic paradigm for resource management and scheduling is essential for pushing Grids into mainstream computing. Next: Nimrod-G Broker, Globus middleware, Aneka Cloud Middleware