The EPIKH Project (Exchange Programme to advance e-Infrastructure Know-How) Giuseppe Andronico INFN Sez. CT / Consorzio COMETA Beijing, Grid and cloud computing

Outline Computing and distributed computing Grid computing Cloud computing Grid and Cloud computing together?

Computing The computing era started with Mainframes Big central CPU, memory, storage used at the same time from different users and batch jobs

Computing Major improvements: Multiple CPUs Faster clock speed, buses and circuits Wider instruction and data paths Faster disk access More and faster memory

Computing: multiprocessing Reasons Increase the processing power of a system Parallel processing Types of multiprocessor systems Tightly coupled systems Master-slave multiprocessing Symmetrical multiprocessing Loosely coupled systems Shared-nothing model Shared-disk model

Computing Introduction of personal computers changed computing

Distributed computing Ever and ever powerful personal computers and the introduction of networking made easy to implement loosely coupled systems, known as clusters

Distributed computing Externally, clusters appear as a single computing unit. Network nodes are individually identifiable. Workload on a cluster is determined by cluster administration and load-balancing software. Network workload cannot be controlled using the above method.

Distributed computing Major improvements High performance networking Parallel computing with clusters Distributed and networking file systems Beowulf and beowulf like clusters In this way was possible to front ever and ever complex numerical problems

Distributed computing Remote Procedure calls (RPC) Concept of service registry Beginnings of service oriented architecture Object oriented approaches Java Remote Method Invocation (RMI) CORBA (Common Request Broker Architecture) Cluster computing Software Techniques: Computing platforms: Parallel computers Geographically distributed computers (Grid computing in the broadest sense) Web services

Grid & Cloud computing “If computers of the kind I have advocated become the computers of the future, then computing may someday be organized as a public utility just as the telephone system is a public utility... The computer utility could become the basis of a new and important industry.” John McCarthy, at the MIT Centennial in 1961

Grid computing Some problems arose that were to complex to build a single cluster in only one place to front them. An example is Large Hadron Collider, an experiment producing tens of PetaBytes of data to be analyzed every year. Or the analysis of the human genoma. The winning solutions was to adopt grid computing

Grid computing Grid computing is about collaborating and resource sharing as much as it is about high performance computing Resource to be shared: Storage Sensors for experiments at particular sites Application Software Databases Network capacity, …

Grid computing Ingredients: High capacity and high speed networks Computers and other resources Middle ware, the software to share resources Authorization and authentication system Virtual Organizations

Cloud Computing Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. European Telecommunications Standards Institute (ETSI) The NIST Definition of Cloud Computing

Cloud computing Why only now? Broadband networks Fast penetration of virtualization technology for x86- based servers –Virtual appliances Adoption of Software as a Service –Salesforce.com –Web 2.0 mindset General purpose on-line virtual machines that can do almost anything

Cloud computing Main ingredients: Network Storage resources Computer resources Virtualization layer Provisioning, billing, accounting

Grid vs Cloud Massive scale resource sharing over the Internet, sounds a lot like grid computing, yet the driving force are different hence solutions are different too Grid Highly specialized resources that need to be shared by thousands [of researchers] Large data sets In many cases, providers are also consumers Driven by the need to increase performance (FLOPs) Cloud Reducing CAPEX, OPEX, time to market Millions of users that share to save not for the sake of sharing Providers want market share and customer lock-in Driven by the need to reduce cost (€£¥$) Grid computing is more a computing paradigm, while cloud computing is a business model

Grid & Cloud You do not need a grid to have a cloud Today a cluster with recent virtualization enabled hardware is enough to start Usually most of the resources in a working structure (research departments or business units) can be used to set up a cloud Simply adding a virtualization hypervisor (XEN, KVM, VirtualBox,…) and a cloud environment (OpenNebula, Eucaliptus, Nimbus, …) the game is done Having a grid you can provide a cloud Usually in a grid you have lot of resources Adding storage virtualization and computing virtualization you can handle provisioning Improving accounting you can provide billing

Grid & Cloud In this workshop will be explored 3 approaches to having a cloud interface to grid resources: 1.Integrating a cloud environment in a grid middleware 2.Configuring LRMS and modifying a part of the middleware to implement a cloud interface 3.Developing a different approach to a cloud environment, minimally invasive and easily interacting with clusters or grids