Resource Brokering in Grid Computing

Grid Computing has emerged in the academia and evolved towards the bases of what is currently known as Cloud Computing and Internet of Things (IoT). The vast collection of resources that provide the nature for Grid Computing environment is very complex; multiple administrative domains control access and set policies to the shared computing resources. It is a decentralized environment with geographically distributed computing and storage resources, where each computing resource can be modeled as an autonomous computing entity, yet collectively can work together. This is a class of Cooperative Distributed Systems (CDS). We extend this by applying characteristic of open environments to create a foundation for the next generation of computing platform where entities are free to join a computing environment to provide capabilities and take part as a collective in solving complex problems beyond the capability of a single entity. This thesis is focused on modeling “Computing” as a collective performance of individual autonomous fundamental computing elements interconnected in a “Grid” open environment structure. Each computing element is a node in the Grid. All nodes are interconnected through the “Grid” edges. Resource allocation is done at the edges of the “Grid” where the connected nodes are simply used to perform computation. The analysis put forward in this thesis identifies Grid Computing as a form of computing that occurs at the resource level. The proposed solution, coupled with advancements in technology and evolution of new computing paradigms, sets a new direction for grid computing research. The approach here is a leap forward with the well-defined set of requirements and specifications based on open issues with the focus on autonomy, adaptability and interdependency. The proposed approach examines current model for Grid Protocol Architecture and proposes an extension that addresses the open issues in the diverged set of solutions that have been created

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Virtual Machine Image Management for Elastic Resource Usage in Grid Computing

Grid Computing has evolved from an academic concept to a powerful paradigm in the area of high performance computing (HPC). Over the last few years, powerful Grid computing solutions were developed that allow the execution of computational tasks on distributed computing resources. Grid computing has recently attracted many commercial customers. To enable commercial customers to be able to execute sensitive data in the Grid, strong security mechanisms must be put in place to secure the customers' data. In contrast, the development of Cloud Computing, which entered the scene in 2006, was driven by industry: it was designed with respect to security from the beginning. Virtualization technology is used to separate the users e.g., by putting the different users of a system inside a virtual machine, which prevents them from accessing other users' data. The use of virtualization in the context of Grid computing has been examined early and was found to be a promising approach to counter the security threats that have appeared with commercial customers. One main part of the work presented in this thesis is the Image Creation Station (ICS), a component which allows users to administer their virtual execution environments (virtual machines) themselves and which is responsible for managing and distributing the virtual machines in the entire system. In contrast to Cloud computing, which was designed to allow even inexperienced users to execute their computational tasks in the Cloud easily, Grid computing is much more complex to use. The ICS makes it easier to use the Grid by overcoming traditional limitations like installing needed software on the compute nodes that users use to execute the computational tasks. This allows users to bring commercial software to the Grid for the first time, without the need for local administrators to install the software to computing nodes that are accessible by all users. Moreover, the administrative burden is shifted from the local Grid site's administrator to the users or experienced software providers that allow the provision of individually tailored virtual machines to each user. But the ICS is not only responsible for enabling users to manage their virtual machines themselves, it also ensures that the virtual machines are available on every site that is part of the distributed Grid system. A second aspect of the presented solution focuses on the elasticity of the system by automatically acquiring free external resources depending on the system's current workload. In contrast to existing systems, the presented approach allows the system's administrator to add or remove resource sets during runtime without needing to restart the entire system. Moreover, the presented solution allows users to not only use existing Grid resources but allows them to scale out to Cloud resources and use these resources on-demand. By ensuring that unused resources are shut down as soon as possible, the computational costs of a given task are minimized. In addition, the presented solution allows each user to specify which resources can be used to execute a particular job. This is useful when a job processes sensitive data e.g., that is not allowed to leave the company. To obtain a comparable function in today's systems, a user must submit her computational task to a particular resource set, losing the ability to automatically schedule if more than one set of resources can be used. In addition, the proposed solution prioritizes each set of resources by taking different metrics into account (e.g. the level of trust or computational costs) and tries to schedule the job to resources with the highest priority first. It is notable that the priority often mimics the physical distance from the resources to the user: a locally available Cluster usually has a higher priority due to the high level of trust and the computational costs, that are usually lower than the costs of using Cloud resources. Therefore, this scheduling strategy minimizes the costs of job execution by improving security at the same time since data is not necessarily transferred to remote resources and the probability of attacks by malicious external users is minimized. Bringing both components together results in a system that adapts automatically to the current workload by using external (e.g., Cloud) resources together with existing locally available resources or Grid sites and provides individually tailored virtual execution environments to the system's users

Efficient multilevel scheduling in grids and clouds with dynamic provisioning

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 12-01-2016La consolidación de las grandes infraestructuras para la Computación Distribuida ha resultado en una plataforma de Computación de Alta Productividad que está lista para grandes cargas de trabajo. Los mejores exponentes de este proceso son las federaciones grid actuales. Por otro lado, la Computación Cloud promete ser más flexible, utilizable, disponible y simple que la Computación Grid, cubriendo además muchas más necesidades computacionales que las requeridas para llevar a cabo cálculos distribuidos. En cualquier caso, debido al dinamismo y la heterogeneidad presente en grids y clouds, encontrar la asignación ideal de las tareas computacionales en los recursos disponibles es, por definición un problema NP-completo, y sólo se pueden encontrar soluciones subóptimas para estos entornos. Sin embargo, la caracterización de estos recursos en ambos tipos de infraestructuras es deficitaria. Los sistemas de información disponibles no proporcionan datos fiables sobre el estado de los recursos, lo cual no permite la planificación avanzada que necesitan los diferentes tipos de aplicaciones distribuidas. Durante la última década esta cuestión no ha sido resuelta para la Computación Grid y las infraestructuras cloud establecidas recientemente presentan el mismo problema. En este marco, los planificadores (brokers) sólo pueden mejorar la productividad de las ejecuciones largas, pero no proporcionan ninguna estimación de su duración. La planificación compleja ha sido abordada tradicionalmente por otras herramientas como los gestores de flujos de trabajo, los auto-planificadores o los sistemas de gestión de producción pertenecientes a ciertas comunidades de investigación. Sin embargo, el bajo rendimiento obtenido con estos mecanismos de asignación anticipada (early-binding) es notorio. Además, la diversidad en los proveedores cloud, la falta de soporte de herramientas de planificación y de interfaces de programación estandarizadas para distribuir la carga de trabajo, dificultan la portabilidad masiva de aplicaciones legadas a los entornos cloud...The consolidation of large Distributed Computing infrastructures has resulted in a High-Throughput Computing platform that is ready for high loads, whose best proponents are the current grid federations. On the other hand, Cloud Computing promises to be more flexible, usable, available and simple than Grid Computing, covering also much more computational needs than the ones required to carry out distributed calculations. In any case, because of the dynamism and heterogeneity that are present in grids and clouds, calculating the best match between computational tasks and resources in an effectively characterised infrastructure is, by definition, an NP-complete problem, and only sub-optimal solutions (schedules) can be found for these environments. Nevertheless, the characterisation of the resources of both kinds of infrastructures is far from being achieved. The available information systems do not provide accurate data about the status of the resources that can allow the advanced scheduling required by the different needs of distributed applications. The issue was not solved during the last decade for grids and the cloud infrastructures recently established have the same problem. In this framework, brokers only can improve the throughput of very long calculations, but do not provide estimations of their duration. Complex scheduling was traditionally tackled by other tools such as workflow managers, self-schedulers and the production management systems of certain research communities. Nevertheless, the low performance achieved by these earlybinding methods is noticeable. Moreover, the diversity of cloud providers and mainly, their lack of standardised programming interfaces and brokering tools to distribute the workload, hinder the massive portability of legacy applications to cloud environments...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEsubmitte

Negotiated resource brokering for quality of service provision of grid applications

Grid Computing is a distributed computing paradigm where many computers often formed from different organisations work together so that their computing power may be aggregated. Grids are often heterogeneous and resources vary significantly in CPU power, available RAM, disk space, OS, architecture and installed software etc. Added to this lack of uniformity is that best effort services are usually offered, as opposed to services that offer guarantees upon completion time via the use of Service Level Agreements (SLAs). The lack of guarantees means the uptake of Grids is stifled. The challenge tackled here is to add such guarantees, thus ensuring users are more willing to use the Grid given an obvious reluctance to pay or contribute, if the quality of the services returned lacks any guarantees. Grids resources are also finite in nature, hence priorities need establishing in order to best meet any guarantees placed upon the limited resources available. An economic approach is hence adopted to ensure end users reveal their true priorities for jobs, whilst also adding incentive for provisioning services, via a service charge. An economically oriented model is therefore proposed that provides SLAs with bicriteria constraints upon time and cost. This model is tested via discrete event simulation and a simulator is presented that is capable of testing the model. An architecture is then established that was developed to utilise the economic model for negotiating SLAs. Finally experimentation is reported upon from the use of the software developed when it was deployed upon a testbed, including admission control and steering of jobs within the Grid. Results are presented that show the interactions and relationship between the time and cost constraints within the model, including transitions between the dominance of one constraint over the other and other things such as the effects of rescheduling upon the market

DRIVE: A Distributed Economic Meta-Scheduler for the Federation of Grid and Cloud Systems

The computational landscape is littered with islands of disjoint resource providers including commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers. These providers are independent and isolated due to a lack of communication and coordination, they are also often proprietary without standardised interfaces, protocols, or execution environments. The lack of standardisation and global transparency has the effect of binding consumers to individual providers. With the increasing ubiquity of computation providers there is an opportunity to create federated architectures that span both Grid and Cloud computing providers effectively creating a global computing infrastructure. In order to realise this vision, secure and scalable mechanisms to coordinate resource access are required. This thesis proposes a generic meta-scheduling architecture to facilitate federated resource allocation in which users can provision resources from a range of heterogeneous (service) providers. Efficient resource allocation is difficult in large scale distributed environments due to the inherent lack of centralised control. In a Grid model, local resource managers govern access to a pool of resources within a single administrative domain but have only a local view of the Grid and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to submit jobs to multiple resource managers, however they are most often deployed on a per-client basis and are therefore concerned with only their allocations, essentially competing against one another. In a federated environment the widespread adoption of utility computing models seen in commercial Cloud providers has re-motivated the need for economically aware meta-schedulers. Economies provide a way to represent the different goals and strategies that exist in a competitive distributed environment. The use of economic allocation principles effectively creates an open service market that provides efficient allocation and incentives for participation. The major contributions of this thesis are the architecture and prototype implementation of the DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler in which members of the VO collaboratively allocate services or resources. Providers joining the VO contribute obligation services to the VO. These contributed services are in effect membership “dues” and are used in the running of the VOs operations – for example allocation, advertising, and general management. DRIVE is independent from a particular class of provider (Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in federated environments composed of heterogeneous providers in vastly different scenarios. Protocol independence facilitates the use of arbitrary protocols based on specific requirements and infrastructural availability. For instance, within a single organisation where internal trust exists, users can achieve maximum allocation performance by choosing a simple economic protocol. In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used with a secure protocol which ensures the allocation is carried out fairly in the absence of trust. DRIVE establishes contracts between participants as the result of allocation. A contract describes individual requirements and obligations of each party. A unique two stage contract negotiation protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a distributed environment without requiring large scale dedicated resources. This thesis presents several other contributions related to meta-scheduling and open service markets. To overcome the perceived performance limitations of economic systems four high utilisation strategies have been developed and evaluated. Each strategy is shown to improve occupancy, utilisation and profit using synthetic workloads based on a production Grid trace. The gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications. The gRAVI toolkit has been extended for this thesis such that it creates economically aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring developer input. The final contribution of this thesis is the definition and architecture of a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources contributed by members of a Social network. The Social Cloud prototype is based on DRIVE and highlights the ease in which dynamic DRIVE markets can be created and used in different domains

Virtual Cluster Management for Analysis of Geographically Distributed and Immovable Data

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Scenarios exist in the era of Big Data where computational analysis needs to utilize widely distributed and remote compute clusters, especially when the data sources are sensitive or extremely large, and thus unable to move. A large dataset in Malaysia could be ecologically sensitive, for instance, and unable to be moved outside the country boundaries. Controlling an analysis experiment in this virtual cluster setting can be difficult on multiple levels: with setup and control, with managing behavior of the virtual cluster, and with interoperability issues across the compute clusters. Further, datasets can be distributed among clusters, or even across data centers, so that it becomes critical to utilize data locality information to optimize the performance of data-intensive jobs. Finally, datasets are increasingly sensitive and tied to certain administrative boundaries, though once the data has been processed, the aggregated or statistical result can be shared across the boundaries. This dissertation addresses management and control of a widely distributed virtual cluster having sensitive or otherwise immovable data sets through a controller. The Virtual Cluster Controller (VCC) gives control back to the researcher. It creates virtual clusters across multiple cloud platforms. In recognition of sensitive data, it can establish a single network overlay over widely distributed clusters. We define a novel class of data, notably immovable data that we call "pinned data", where the data is treated as a first-class citizen instead of being moved to where needed. We draw from our earlier work with a hierarchical data processing model, Hierarchical MapReduce (HMR), to process geographically distributed data, some of which are pinned data. The applications implemented in HMR use extended MapReduce model where computations are expressed as three functions: Map, Reduce, and GlobalReduce. Further, by facilitating information sharing among resources, applications, and data, the overall performance is improved. Experimental results show that the overhead of VCC is minimum. The HMR outperforms traditional MapReduce model while processing a particular class of applications. The evaluations also show that information sharing between resources and application through the VCC shortens the hierarchical data processing time, as well satisfying the constraints on the pinned data