Published on February 19, 2009
Market-Oriented Cloud Computing Srikumar Venugopal Grid Computing and Distributed Systems (GRIDS) Laboratory Dept. of Computer Science and Software Engineering The University of Melbourne, Australia E: email@example.com
Agenda Motivation for Market-based Computing A vision for Market-based Clouds Current research in GRIDS Lab Aneka: A resource provider Brokering of resources and negotiation Conclusion and Open Questions 2
Salient Features of Cloud Computing Abstracted Infrastructure Using Resources without reference to their location Fully Virtualized Servers are virtual instances Dynamic Can add, delete new instances dynamically Pay by Consumption No fixed long-term contracts Configurable Any application or OS can be provided Forrester Research, “Is Cloud Computing Ready For The Enterprise?”, 3 March 2008
Yet.. Cloud SLAs are still in their infancy Limited options for higher Quality of Service Flat Pricing Model Amazon Cloud provisioning is not a core product Amazon is an e-commerce company Google is a search company Need innovative business models A larger marketplace 4
The Gridbus Project @ Melbourne: Enable Leasing of ICT Services on Demand WWG Gridbus Pushes Grid computing into mainstream computing 5
http://www.gridbus.org The Gridbus Project @ GRIDS Lab, The University of Melbourne: The Gridbus Project @ GRIDS Lab, The University of Melbourne: Toolkit for Creating and Deploying e-* Applications on Utility Grids Toolkit for Creating and Deploying e-* Applications on Utility Grids • Gridbus is a “open source” Grid R&D Distributed Data project with focus on Grid Economy, Utility Grids and Service Oriented Computing. • Gridbus Middleware components include: – Aneka: .NET-based Enterprise Grid – Grid Market Directory and Web Services Gridbus – Grid Bank: Accounting and Transaction Management – Visual Tools for Creation of Distributed Applications – Grid Service Broker and Scheduling – Workflow Management Engine – Libra: SLA-based Resource Allocation – GridSim Toolkit 6
Participants, Goals, Requirements Consumers: - 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? … Providers:– maximise ROI and profit 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. Service Level Agreements (SLAs) 8
Market Mechanisms for Clouds: A vision Broker1 Cloud Consumer Request Negotiate/Bid Compute Storage Resources Cloud Cloud Directory .... Bank Auctioneer MARKET Enterprise Compute Resource BrokerN Cloud Manager (Proxy) Storage Cloud 9
Aneka: a resource provider for parallel and distributed applications Applications Container Thread Task Dataflow MPI Map Other SLA Model Model Model Model Reduce Models Negotiation Persistence Allocation Manager Message Handler / Dispatcher Security Communication Layer 10
Advance Reservations Commitment of a guaranteed share of a resource ahead of usage time Resources : Nodes, Bandwidth, Storage Advantages: Lowers risk for user Easier capacity planning for provider Assured income Applications : workflow, multimedia applications, etc. Are a form of SLA 11
Aneka’s SLA-View for Resource Allocation User/Broker Enterprise Grid Negotiation Protocol Engine Master Node Membership Scheduling Reservation Service Service Service Node Pricing Membership Reservation Task Selection Policy Store Store Store Policy Execution Execution Execution Node Node Node Execution Allocation Service Service Time Slot Reservation Task Selection Store Store Policy Ack: C.S. Yeo 12
Pricing of Reservations Dynamic pricing based on utilization level p ax by Where p is the unit price, x is the static component (base price), and y = load factor * z, is the dynamic component a and b are the relative weights b can be set higher when resource availability is low and vice versa Serves as a method of admission control takes advantage of market conditions C.S.Yeo, S. Venugopal, X. Chu, and R. Buyya, Autonomic Metered Pricing for a Utility Service, Technical Report, GRIDS-TR-2008-16, GRIDS Laboratory. 13
Revenue vs Strategy 14
Cloud Provider Architecture Users/ Brokers Service R equest Examiner and A dmission Control - Customer-driven Service Management - Computational R isk Management - Autonomic Resource Management SLA Resource Allocator Pricing Accounting VM Service R equest Monitor Monitor Dispatcher Virtual Machines (VMs) Physical Machines 15
Gridbus Broker: Abstracting Resource Access Home Node/Portal Credential Repository MyProxy Portlets batch() Gridbus -PBS -Condor Broker -SGE fork() Data Catalog Globus Aneka Data Store Unicore SSH Job manager Access Technology fork() Gateway batch() fork() batch() SRB -PBS Grid FTP -PBS Gridbus Gridbus -Condor -Condor agent agent -SGE -SGE -XGrid 16
Broker-Provider Negotiation Provider Broker Negotiation Negotiation Module Module Advance Reservation Resource Allocation Scheduler Manager Job Submission and Monitoring Broker acts as a user agent Broker translates user requirements to resource requirements However, the negotiation process is invisible to the end user. 17
The Negotiation Protocol 18
Effect of deadline urgency S. Venugopal, X. Chu, and R. Buyya, “A Negotiation Mechanism for Advance Resource Reservation using the Alternate Offers Protocol”, IWQoS 2008. 19
MetaCDN: Brokering Cloud Storage Providers Dr. James Broberg, University of Melbourne, http://www.metacdn.org 20
Open Questions How to commoditise cloud services ? What would be the structure of the Cloud services market ? What are the accounting and payment mechanisms available ? How to monitor and enforce the SLAs arrived at by negotiation ? Who arbitrates the process ? 21
Thank You Questions? http://www.gridbus.org
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Title: Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities