Misplaced Pages

#575424

58-448: CESGA also runs i-math, a Grid computing project with an initiative to approach Grid technologies to mathematics researchers, and operates the i-math Portal, a Grid Portal based on P-GRADE Portal technology. (See also: Grid computing ) The following table shows some of the systems installed in CESGA. Since some systems were installed around 10 years ago some information may be outdated. CESGA

116-682: A computer network (private or public) by a conventional network interface , such as Ethernet . This is in contrast to the traditional notion of a supercomputer , which has many processors connected by a local high-speed computer bus . This technology has been applied to computationally intensive scientific, mathematical, and academic problems through volunteer computing , and it is used in commercial enterprises for such diverse applications as drug discovery , economic forecasting , seismic analysis , and back office data processing in support for e-commerce and Web services . Grid computing combines computers from multiple administrative domains to reach

174-528: A network (private, public or the Internet ) by a conventional network interface producing commodity hardware, compared to the lower efficiency of designing and constructing a small number of custom supercomputers. The primary performance disadvantage is that the various processors and local storage areas do not have high-speed connections. This arrangement is thus well-suited to applications in which multiple parallel computations can take place independently, without

232-401: A commercial solution, though the cutting edge of each area is often found within specific research projects examining the field. For the segmentation of the grid computing market, two perspectives need to be considered: the provider side and the user side: The overall grid market comprises several specific markets. These are the grid middleware market, the market for grid-enabled applications,

290-481: A common goal, to solve a single task, and may then disappear just as quickly. The size of a grid may vary from small—confined to a network of computer workstations within a corporation, for example—to large, public collaborations across many companies and networks. "The notion of a confined grid may also be known as an intra-nodes cooperation whereas the notion of a larger, wider grid may thus refer to an inter-nodes cooperation". Coordinating applications on Grids can be

348-550: A complex task, especially when coordinating the flow of information across distributed computing resources. Grid workflow systems have been developed as a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, in the grid context. “Distributed” or “grid” computing in general is a special type of parallel computing that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to

406-641: A new computer to a distributed software application. An example might involve scaling out from one web server to three. High-performance computing applications, such as seismic analysis and biotechnology , scale workloads horizontally to support tasks that once would have required expensive supercomputers . Other workloads, such as large social networks, exceed the capacity of the largest supercomputer and can only be handled by scalable systems. Exploiting this scalability requires software for efficient resource management and maintenance. Scaling vertically (up/down) means adding resources to (or removing resources from)

464-606: A particular associated grid or for the purpose of setting up new grids. BOINC is a common one for various academic projects seeking public volunteers; more are listed at the end of the article . In fact, the middleware can be seen as a layer between the hardware and the software. On top of the middleware, a number of technical areas have to be considered, and these may or may not be middleware independent. Example areas include SLA management, Trust, and Security, Virtual organization management, License Management, Portals and Data Management. These technical areas may be taken care of in

522-460: A public utility, analogous to the phone system. CPU scavenging and volunteer computing were popularized beginning in 1997 by distributed.net and later in 1999 by SETI@home to harness the power of networked PCs worldwide, in order to solve CPU-intensive research problems. The ideas of the grid (including those from distributed computing, object-oriented programming, and Web services) were brought together by Ian Foster and Steve Tuecke of

580-503: A service (SaaS) is “software that is owned, delivered and managed remotely by one or more providers.” ( Gartner 2007) Additionally, SaaS applications are based on a single set of common code and data definitions. They are consumed in a one-to-many model, and SaaS uses a Pay As You Go (PAYG) model or a subscription model that is based on usage. Providers of SaaS do not necessarily own the computing resources themselves, which are required to run their SaaS. Therefore, SaaS providers may draw upon

638-453: A single node, typically involving the addition of CPUs, memory or storage to a single computer. Benefits to scale-up include avoiding increases management complexity, more sophisticated programming to allocate tasks among resources and handle issues such as throughput, latency, synchronization across nodes. Moreover some applications do not scale horizontally . Network function virtualization defines these terms differently: scaling out/in

SECTION 10

#1733085163576

696-408: A single warehouse for sorting, the system would not be as scalable, because one warehouse can handle only a limited number of packages. In computing, scalability is a characteristic of computers, networks, algorithms , networking protocols , programs and applications. An example is a search engine , which must support increasing numbers of users, and the number of topics it indexes . Webscale

754-411: A “grid” from the idle resources in a network of participants (whether worldwide or internal to an organization). Typically, this technique exploits the 'spare' instruction cycles resulting from the intermittent inactivity that typically occurs at night, during lunch breaks, or even during the (comparatively minuscule, though numerous) moments of idle waiting that modern desktop CPU's experience throughout

812-427: Is a computer architectural approach that brings the capabilities of large-scale cloud computing companies into enterprise data centers. In distributed systems , there are several definitions according to the authors, some considering the concepts of scalability a sub-part of elasticity , others as being distinct. According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload

870-667: Is a vital consideration for businesses aiming to meet customer expectations, remain competitive, and achieve sustainable growth. Factors influencing scalability include the flexibility of the production process, the adaptability of the workforce, and the integration of advanced technologies. By implementing scalable solutions, companies can optimize resource utilization, reduce costs, and streamline their operations. Scalability in industrial engineering and manufacturing enables businesses to respond to fluctuating market conditions, capitalize on emerging opportunities, and thrive in an ever-evolving global landscape. The Incident Command System (ICS)

928-468: Is also publicly accessible. There is speculation that dedicated fiber optic links, such as those installed by CERN to address the WLCG's data-intensive needs, may one day be available to home users thereby providing internet services at speeds up to 10,000 times faster than a traditional broadband connection. The European Grid Infrastructure has been also used for other research activities and experiments such as

986-421: Is another implementation of CPU-scavenging where special workload management system harvests the idle desktop computers for compute-intensive jobs, it also refers as Enterprise Desktop Grid (EDG). For instance, HTCondor (the open-source high-throughput computing software framework for coarse-grained distributed rationalization of computationally intensive tasks) can be configured to only use desktop machines where

1044-592: Is conceptually similar to the canonical Foster definition of grid computing (in terms of computing resources being consumed as electricity is from the power grid ) and earlier utility computing. In November 2006, Seidel received the Sidney Fernbach Award at the Supercomputing Conference in Tampa, Florida . "For outstanding contributions to the development of software for HPC and Grid computing to enable

1102-401: Is distinguished from conventional high-performance computing systems such as cluster computing in that grid computers have each node set to perform a different task/application. Grid computers also tend to be more heterogeneous and geographically dispersed (thus not physically coupled) than cluster computers. Although a single grid can be dedicated to a particular application, commonly a grid

1160-513: Is located in the University of Santiago de Compostela Campus at the following address: Avenida de Vigo, s/n Campus Sur 15705 Santiago de Compostela A Coruña - Spain Grid computing Grid computing is the use of widely distributed computer resources to reach a common goal. A computing grid can be thought of as a distributed system with non-interactive workloads that involve many files. Grid computing

1218-633: Is nearly constant." Serverless technologies fit this definition but you need to consider total cost of ownership not just the infra cost. In mathematics, scalability mostly refers to closure under scalar multiplication . In industrial engineering and manufacturing, scalability refers to the capacity of a process, system, or organization to handle a growing workload, adapt to increasing demands, and maintain operational efficiency. A scalable system can effectively manage increased production volumes, new product lines, or expanding markets without compromising quality or performance. In this context, scalability

SECTION 20

#1733085163576

1276-531: Is often advised to focus system design on hardware scalability rather than on capacity. It is typically cheaper to add a new node to a system in order to achieve improved performance than to partake in performance tuning to improve the capacity that each node can handle. But this approach can have diminishing returns (as discussed in performance engineering ). For example: suppose 70% of a program can be sped up if parallelized and run on multiple CPUs instead of one. If α {\displaystyle \alpha }

1334-443: Is referred to as the provision of grid computing and applications as service either as an open grid utility or as a hosting solution for one organization or a VO . Major players in the utility computing market are Sun Microsystems , IBM , and HP . Grid-enabled applications are specific software applications that can utilize grid infrastructure. This is made possible by the use of grid middleware, as pointed out above. Software as

1392-738: Is suitable when availability and responsiveness are rated higher than consistency, which is true for many web file-hosting services or web caches ( if you want the latest version, wait some seconds for it to propagate ). For all classical transaction-oriented applications, this design should be avoided. Many open-source and even commercial scale-out storage clusters, especially those built on top of standard PC hardware and networks, provide eventual consistency only, such as some NoSQL databases like CouchDB and others mentioned above. Write operations invalidate other copies, but often don't wait for their acknowledgements. Read operations typically don't check every redundant copy prior to answering, potentially missing

1450-465: Is that the computers which are actually performing the calculations might not be entirely trustworthy. The designers of the system must thus introduce measures to prevent malfunctions or malicious participants from producing false, misleading, or erroneous results, and from using the system as an attack vector. This often involves assigning work randomly to different nodes (presumably with different owners) and checking that at least two different nodes report

1508-693: Is the ability to scale by adding/removing resource instances (e.g., virtual machine), whereas scaling up/down is the ability to scale by changing allocated resources (e.g., memory/CPU/storage capacity). Scalability for databases requires that the database system be able to perform additional work given greater hardware resources, such as additional servers, processors, memory and storage. Workloads have continued to grow and demands on databases have followed suit. Algorithmic innovations include row-level locking and table and index partitioning. Architectural innovations include shared-nothing and shared-everything architectures for managing multi-server configurations. In

1566-413: Is the fraction of a calculation that is sequential, and 1 − α {\displaystyle 1-\alpha } is the fraction that can be parallelized, the maximum speedup that can be achieved by using P processors is given according to Amdahl's Law : Substituting the value for this example, using 4 processors gives Doubling the computing power to 8 processors gives Doubling

1624-622: Is the largest of any FP6 integrated project. Of this, 15.7 million is provided by the European Commission and the remainder by its 98 contributing partner companies. Since the end of the project, the results of BEinGRID have been taken up and carried forward by IT-Tude.com . The Enabling Grids for E-sciencE project, based in the European Union and included sites in Asia and the United States,

1682-469: Is the property of a system to handle a growing amount of work. One definition for software systems specifies that this may be done by adding resources to the system. In an economic context, a scalable business model implies that a company can increase sales given increased resources. For example, a package delivery system is scalable because more packages can be delivered by adding more delivery vehicles. However, if all packages had to first pass through

1740-658: Is used by emergency response agencies in the United States. ICS can scale resource coordination from a single-engine roadside brushfire to an interstate wildfire. The first resource on scene establishes command, with authority to order resources and delegate responsibility (managing five to seven officers, who will again delegate to up to seven, and on as the incident grows). As an incident expands, more senior officers assume command. Scalability can be measured over multiple dimensions, such as: Resources fall into two broad categories: horizontal and vertical. Scaling horizontally (out/in) means adding or removing nodes, such as adding

1798-522: Is used for a variety of purposes. Grids are often constructed with general-purpose grid middleware software libraries. Grid sizes can be quite large. Grids are a form of distributed computing composed of many networked loosely coupled computers acting together to perform large tasks. For certain applications, distributed or grid computing can be seen as a special type of parallel computing that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to

CESGA - Misplaced Pages Continue

1856-500: Is “to establish effective routes to foster the adoption of grid computing across the EU and to stimulate research into innovative business models using Grid technologies”. To extract best practice and common themes from the experimental implementations, two groups of consultants are analyzing a series of pilots, one technical, one business. The project is significant not only for its long duration but also for its budget, which at 24.8 million Euros,

1914-545: The University of Chicago , and Carl Kesselman of the University of Southern California 's Information Sciences Institute . The trio, who led the effort to create the Globus Toolkit, is widely regarded as the "fathers of the grid". The toolkit incorporates not just computation management but also storage management , security provisioning, data movement, monitoring, and a toolkit for developing additional services based on

1972-593: The framework programmes of the European Commission . BEinGRID (Business Experiments in Grid) was a research project funded by the European Commission as an Integrated Project under the Sixth Framework Programme (FP6) sponsorship program. Started on June 1, 2006, the project ran 42 months, until November 2009. The project was coordinated by Atos Origin . According to the project fact sheet, their mission

2030-508: The utility computing market, and the software-as-a-service (SaaS) market. Grid middleware is a specific software product, which enables the sharing of heterogeneous resources, and Virtual Organizations. It is installed and integrated into the existing infrastructure of the involved company or companies and provides a special layer placed among the heterogeneous infrastructure and the specific user applications. Major grid middlewares are Globus Toolkit, gLite , and UNICORE . Utility computing

2088-1528: The Internet. The project ran on about 3.1 million machines before its close in 2007. Today there are many definitions of grid computing : List of grid computing projects Scalability Collective intelligence Collective action Self-organized criticality Herd mentality Phase transition Agent-based modelling Synchronization Ant colony optimization Particle swarm optimization Swarm behaviour Social network analysis Small-world networks Centrality Motifs Graph theory Scaling Robustness Systems biology Dynamic networks Evolutionary computation Genetic algorithms Genetic programming Artificial life Machine learning Evolutionary developmental biology Artificial intelligence Evolutionary robotics Reaction–diffusion systems Partial differential equations Dissipative structures Percolation Cellular automata Spatial ecology Self-replication Conversation theory Entropy Feedback Goal-oriented Homeostasis Information theory Operationalization Second-order cybernetics Self-reference System dynamics Systems science Systems thinking Sensemaking Variety Ordinary differential equations Phase space Attractors Population dynamics Chaos Multistability Bifurcation Rational choice theory Bounded rationality Scalability

2146-449: The amount of trust “client” nodes must place in the central system such as placing applications in virtual machines. Public systems or those crossing administrative domains (including different departments in the same organization) often result in the need to run on heterogeneous systems, using different operating systems and hardware architectures . With many languages, there is a trade-off between investment in software development and

2204-473: The choice of whether to deploy onto a dedicated cluster, to idle machines internal to the developing organization, or to an open external network of volunteers or contractors. In many cases, the participating nodes must trust the central system not to abuse the access that is being granted, by interfering with the operation of other programs, mangling stored information, transmitting private data, or creating new security holes. Other systems employ measures to reduce

2262-481: The collaborative numerical investigation of complex problems in physics; in particular, modeling black hole collisions." This award, which is one of the highest honors in computing, was awarded for his achievements in numerical relativity. Also, as of March 2019, the Bitcoin Network had a measured computing power equivalent to over 80,000 exaFLOPS (Floating-point Operations Per Second). This measurement reflects

2320-441: The context of scale-out data storage , scalability is defined as the maximum storage cluster size which guarantees full data consistency, meaning there is only ever one valid version of stored data in the whole cluster, independently from the number of redundant physical data copies. Clusters which provide "lazy" redundancy by updating copies in an asynchronous fashion are called 'eventually consistent' . This type of scale-out design

2378-590: The day ( when the computer is waiting on IO from the user, network, or storage ). In practice, participating computers also donate some supporting amount of disk storage space, RAM, and network bandwidth, in addition to raw CPU power. Many volunteer computing projects, such as BOINC , use the CPU scavenging model. Since nodes are likely to go "offline" from time to time, as their owners use their resources for their primary purpose, this model must be designed to handle such contingencies. Creating an Opportunistic Environment

CESGA - Misplaced Pages Continue

2436-401: The early 1990s as a metaphor for making computer power as easy to access as an electric power grid . The power grid metaphor for accessible computing quickly became canonical when Ian Foster and Carl Kesselman published their seminal work, "The Grid: Blueprint for a new computing infrastructure" (1999). This was preceded by decades by the metaphor of utility computing (1961): computing as

2494-469: The environment of a supercomputer, which may have a custom operating system, or require the program to address concurrency issues. If a problem can be adequately parallelized, a “thin” layer of “grid” infrastructure can allow conventional, standalone programs, given a different part of the same problem, to run on multiple machines. This makes it possible to write and debug on a single conventional machine and eliminates complications due to multiple instances of

2552-565: The keyboard and mouse are idle to effectively harness wasted CPU power from otherwise idle desktop workstations. Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. It can be used to manage workload on a dedicated cluster of computers as well or it can seamlessly integrate both dedicated resources (rack-mounted clusters) and non-dedicated desktop machines (cycle scavenging) into one computing environment. The term grid computing originated in

2610-547: The need for continuous network connectivity) and reassigning work units when a given node fails to report its results in the expected time. Another set of what could be termed social compatibility issues in the early days of grid computing related to the goals of grid developers to carry their innovation beyond the original field of high-performance computing and across disciplinary boundaries into new fields, like that of high-energy physics. The impacts of trust and availability on performance and development difficulty can influence

2668-437: The need to communicate intermediate results between processors. The high-end scalability of geographically dispersed grids is generally favorable, due to the low need for connectivity between nodes relative to the capacity of the public Internet. There are also some differences between programming for a supercomputer and programming for a grid computing system. It can be costly and difficult to write programs that can run in

2726-588: The number of FLOPS required to equal the hash output of the Bitcoin network rather than its capacity for general floating-point arithmetic operations, since the elements of the Bitcoin network (Bitcoin mining ASICs ) perform only the specific cryptographic hash computation required by the Bitcoin protocol. Grid computing offers a way to solve Grand Challenge problems such as protein folding , financial modeling , earthquake simulation, and climate / weather modeling, and

2784-450: The number of platforms that can be supported (and thus the size of the resulting network). Cross-platform languages can reduce the need to make this tradeoff, though potentially at the expense of high performance on any given node (due to run-time interpretation or lack of optimization for the particular platform). Various middleware projects have created generic infrastructure to allow diverse scientific and commercial projects to harness

2842-514: The open-source Berkeley Open Infrastructure for Network Computing (BOINC) platform are members of the World Community Grid . One of the projects using BOINC is SETI@home , which was using more than 400,000 computers to achieve 0.828 TFLOPS as of October 2016. As of October 2016 Folding@home , which is not part of BOINC, achieved more than 101 x86-equivalent petaflops on over 110,000 machines. The European Union funded projects through

2900-415: The preceding write operation. The large amount of metadata signal traffic would require specialized hardware and short distances to be handled with acceptable performance (i.e., act like a non-clustered storage device or database). Whenever strong data consistency is expected, look for these indicators: Indicators for eventually consistent designs (not suitable for transactional applications!) are: It

2958-615: The processing power has only sped up the process by roughly one-fifth. If the whole problem was parallelizable, the speed would also double. Therefore, throwing in more hardware is not necessarily the optimal approach. In distributed systems , you can use Universal Scalability Law (USL) to model and to optimize scalability of your system. USL is coined by Neil J. Gunther and quantifies scalability based on parameters such as contention and coherency. Contention refers to delay due to waiting or queueing for shared resources. Coherence refers to delay for data to become consistent. For example, having

SECTION 50

#1733085163576

3016-494: The same answer for a given work unit. Discrepancies would identify malfunctioning and malicious nodes. However, due to the lack of central control over the hardware, there is no way to guarantee that nodes will not drop out of the network at random times. Some nodes (like laptops or dial-up Internet customers) may also be available for computation but not network communications for unpredictable periods. These variations can be accommodated by assigning large work units (thus reducing

3074-403: The same infrastructure, including agreement negotiation, notification mechanisms, trigger services, and information aggregation. While the Globus Toolkit remains the de facto standard for building grid solutions, a number of other tools have been built that answer some subset of services needed to create an enterprise or global grid. In 2007 the term cloud computing came into popularity, which

3132-448: The same program running in the same shared memory and storage space at the same time. One feature of distributed grids is that they can be formed from computing resources belonging to one or multiple individuals or organizations (known as multiple administrative domains ). This can facilitate commercial transactions, as in utility computing , or make it easier to assemble volunteer computing networks. One disadvantage of this feature

3190-568: The simulation of oncological clinical trials. The distributed.net project was started in 1997. The NASA Advanced Supercomputing facility (NAS) ran genetic algorithms using the Condor cycle scavenger running on about 350 Sun Microsystems and SGI workstations. In 2001, United Devices operated the United Devices Cancer Research Project based on its Grid MP product, which cycle-scavenges on volunteer PCs connected to

3248-499: The utility computing market. The utility computing market provides computing resources for SaaS providers. For companies on the demand or user side of the grid computing market, the different segments have significant implications for their IT deployment strategy. The IT deployment strategy as well as the type of IT investments made are relevant aspects for potential grid users and play an important role for grid adoption. CPU-scavenging , cycle-scavenging , or shared computing creates

3306-729: Was a follow-up project to the European DataGrid (EDG) and evolved into the European Grid Infrastructure . This, along with the Worldwide LHC Computing Grid (WLCG), was developed to support experiments using the CERN Large Hadron Collider . A list of active sites participating within WLCG can be found online as can real time monitoring of the EGEE infrastructure. The relevant software and documentation

3364-471: Was integral in enabling the Large Hadron Collider at CERN. Grids offer a way of using information technology resources optimally inside an organization. They also provide a means for offering information technology as a utility for commercial and noncommercial clients, with those clients paying only for what they use, as with electricity or water. As of October 2016, over 4 million machines running

#575424