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  • 1.
    Armstrong, Django
    et al.
    University of Leeds.
    Espling, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tordsson, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Djemame, Karim
    University of Leeds.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Contextualization: dynamic configuration of virtual machines2015Inngår i: Journal of Cloud Computing - Advances, Systems and Applications, ISSN 2192-113X, Vol. 4, nr 17Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    New VM instances are created from static templates that contain the basic configuration of the VM to achieve elasticity with regards to capacity. Instance specific settings can be injected into the VM during the deployment phase through means of contextualization. So far this is limited to a single data source and data remains static throughout the lifecycle of the VM.

    We present a layered approach to contextualization that supports different classes of contextualization data available from several sources. The settings are made available to the VM through virtual devices. Inside each VM data from different classes are layered on top of each other to create a unified file hierarchy.

    Context data can be modified during runtime by updating the contents of the virtual devices, making our approach the first contextualization approach to natively support recontextualization. Recontextualization enables runtime reconfiguration of an executing service and can act as a trigger and key enabler of self-* techniques. This trigger provides a service with a mechanism to adapt or optimize itself in response to a changing environment. The runtime reconfiguration using recontextualization and its potential gains are illustrated in an example with a distributed file system, demonstrating the feasibility of our approach.

  • 2.
    Armstrong, Django
    et al.
    University of Leeds.
    Espling, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tordsson, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Djemame, Karim
    University of Leeds.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Runtime virtual machine recontextualization for clouds2013Inngår i: Euro-Par 2012: Parallel Processing Workshops / [ed] Ioannis Caragiannis et al., Springer Berlin/Heidelberg, 2013, Vol. 7640, s. 567-576Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We introduce and define the concept of recontextualization for cloud applications by extending contextualization, i.e. the dynamic configuration of virtual machines (VM) upon initialization, with autonomous updates during runtime. Recontextualization allows VM images and instances to be dynamically re-configured without restarts or downtime, and the concept is applicable to all aspects of configuring a VM from virtual hardware to multi-tier software stacks. Moreover, we propose a runtime cloud recontextualization mechanism based on virtual device management that enables recontextualization without the need to customize the guest VM. We illustrate our concept and validate our mechanism via a use case demonstration: the reconfiguration of a cross-cloud migratable monitoring service in a dynamic cloud environment. We discuss the details of the interoperable recontextualization mechanism, its architecture and demonstrate a proof of concept implementation. A performance evaluation illustrates the feasibility of the approach and shows that the recontextualization mechanism performs adequately with an overhead of 18% of the total migration time.

  • 3. Beco, S
    et al.
    Maraschini, A
    Pacini, F
    Biran, O
    Breitgand, O
    Meth, K
    Rochwerger, B
    Salant, E
    Silvera, E
    Tal, S
    Wolfsthal, Y
    Yehuda, M
    Caceres, J
    Hierro, J
    Emmerich, W
    Galis, A
    Edblom, Lennart
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Henriksson, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hernandez, Francisco
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tordsson, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hohl, A
    Levy, E
    Sampaio, A
    Scheuermann, B
    Wusthoff, M
    Latanicki, J
    Lopez, G
    Marin-Frisonroche, J
    Dorr, A
    Ferstl, F
    Huedo, E
    Llorente, I
    Montero, R
    Massonet, P
    Naqvi, S
    Dallons, G
    Pezz, M
    Puliafito, A
    Ragusa, C
    Scarpa, M
    Muscella, S
    Cloud Computing and RESERVOIR project2009Inngår i: Nuovo Cimento C, ISSN ISSN 1124-1896, Vol. 32, nr 2, s. 99-103Artikkel i tidsskrift (Fagfellevurdert)
  • 4. Ben Yehuda, M.
    et al.
    Biran, O.
    Breitgand, D.
    Meth, K.
    Rochwerger, B.
    Salant, E.
    Silvera, E.
    Tal, S.
    Wolfsthal, Y.
    Cáceres, J.
    Hierro, J.
    Emmerich, W.
    Galis, A.
    Edblom, Lennart
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Henriksson, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hernández, Francisco
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tordsson, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hohl, A.
    Levy, E.
    Sampaio, A.
    Scheuermann, B.
    Wusthoff, M.
    Latanicki, J.
    Lopez, G.
    Marin-Frisonroche, J.
    Dörr, A.
    Ferstl, F.
    Beco, S.
    Pacini, F.
    Llorente, I.
    Montero, R.
    Huedo, E.
    Massonet, P.
    Naqvi, S.
    Dallons, G.
    Pezzé, M.
    Puliato, A.
    Ragusa, C.
    Scarpa, M.
    Muscella, S.
    RESERVOIR: An ICT Infrastructure for Reliable and Effective Delivery of Services as Utilities2008Rapport (Annet vitenskapelig)
  • 5.
    Elmroth, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Henriksson, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Distributed usage logging for federated grids2010Inngår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 26, nr 8, s. 1215-1225Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present a non-intrusive solution to the increasingly important problem of shared logging for overlapping and federated Grid environments. The solution addresses three usage scenarios of hierarchical Grids, mutual cross-Grid resource utilization, and federated Cloud computing infrastructures. The approach is evaluated by extending the existing SweGrid Accounting System (SGAS) with a light-weight component that makes the system applicable to a wide range of usage scenarios. The proposed architecture is characterized by its simplicity, flexibility, and generality, and the new key component by its non-intrusiveness, flexibility, and ability to manage high load. We present requirements derived from three usage scenarios, and also include an in-depth description of the architecture and design, as well as the implementation and performance evaluation of a new component written for use with SGAS. We conclude from a performance evaluation that the sharing of usage data is not likely to be a limiting performance factor even in large-scale Grid scenarios.

  • 6.
    Elmroth, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Marquez, Fermin Galan
    Telef´onica Investigaci´on y Desarrollo, Spain.
    Henriksson, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ferrera, David Perales
    Telef´onica Investigaci´on y Desarrollo, Spain.
    Accounting and Billing for Federated Cloud Infrastructures2009Inngår i: Proceedings of the Eighth International Conference on Grid and Cooperative Computing (GCC 2009) / [ed] Juan. E. Guerrero, IEEE Computer Society Press , 2009, s. 268-275Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Emerging Cloud computing infrastructures provide computing resources on demand based on postpaid principles. For example, the RESERVOIR project develops an infrastructure capable of delivering elastic capacity that can automatically be increased or decreased in order to cost-efficiently fulfill established Service Level Agreements. This infrastructure also makes it possible for a data center to extend its total capacity by subcontracting additional resources from collaborating data centers, making the infrastructure a federation of Clouds. For accounting and billing, such infrastructures call for novel approaches to perform accounting for capacity that varies over time and for services (or more precisely virtual machines) that migrate between physical machines or even between data centers. For billing, needs arise for new approaches to simultaneously manage postpaid and prepaid payment schemes for capacity that varies over time in response to user needs. In this paper, we outline usage scenarios and a set of requirements for such infrastructures, and propose an accounting and billing architecture to be used within RESERVOIR. Even though the primary focus for this architecture is accounting and billing between resource consumers and infrastructure provides, future support for inter-site billing is also taken into account.

  • 7.
    Espling, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Enabling Technologies for Management of Distributed Computing Infrastructures2013Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Computing infrastructures offer remote access to computing power that can be employed, e.g., to solve complex mathematical problems or to host computational services that need to be online and accessible at all times. From the perspective of the infrastructure provider, large amounts of distributed and often heterogeneous computer resources need to be united into a coherent platform that is then made accessible to and usable by potential users. Grid computing and cloud computing are two paradigms that can be used to form such unified computational infrastructures.

    Resources from several independent infrastructure providers can be joined to form large-scale decentralized infrastructures. The primary advantage of doing this is that it increases the scale of the available resources, making it possible to address more complex problems or to run a greater number of services on the infrastructures. In addition, there are advantages in terms of factors such as fault-tolerance and geographical dispersion. Such multi-domain infrastructures require sophisticated management processes to mitigate the complications of executing computations and services across resources from different administrative domains.

    This thesis contributes to the development of management processes for distributed infrastructures that are designed to support multi-domain environments. It describes investigations into how fundamental management processes such as scheduling and accounting are affected by the barriers imposed by multi-domain deployments, which include technical heterogeneity, decentralized and (domain-wise) self-centric decision making, and a lack of information on the state and availability of remote resources.

    Four enabling technologies or approaches are explored and developed within this work: (I) The use of explicit definitions of cloud service structure as inputs for placement and management processes to ensure that the resulting placements respect the internal relationships between different service components and any relevant constraints. (II) Technology for the runtime adaptation of Virtual Machines to enable the automatic adaptation of cloud service contexts in response to changes in their environment caused by, e.g., service migration across domains. (III) Systems for managing meta-data relating to resource usage in multi-domain grid computing and cloud computing infrastructures. (IV) A global fairshare prioritization mechanism that enables computational jobs to be consistently prioritized across a federation of several decentralized grid installations.

    Each of these technologies will facilitate the emergence of decentralized computational infrastructures capable of utilizing resources from diverse infrastructure providers in an automatic and seamless manner.

  • 8.
    Espling, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Metadata Management in Multi-Grids and Multi-Clouds2011Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Grid computing and cloud computing are two related paradigms used to access and use vast amounts of computational resources. The resources are often owned and managed by a third party, relieving the users from the costs and burdens of acquiring and managing a considerably large infrastructure themselves. Commonly, the resources are either contributed by different stakeholders participating in shared projects (grids), or owned and managed by a single entity and made available to its users with charging based on actual resource consumption (clouds). Individual grid or cloud sites can form collaborations with other sites, giving each site access to more resources that can be used to execute tasks submitted by users. There are several different models of collaborations between sites, each suitable for different scenarios and each posing additional requirements on the underlying technologies.

    Metadata concerning the status and resource consumption of tasks are created during the execution of the task on the infrastructure. This metadata is used as the primary input in many core management processes, e.g., as a base for accounting and billing, as input when prioritizing and placing incoming task, and as a base for managing the amount of resources allocated to different tasks.

    Focusing on management and utilization of metadata, this thesis contributes to a better understanding of the requirements and challenges imposed by different collaboration models in both grids and clouds. The underlying design criteria and resulting architectures of several software systems are presented in detail. Each system addresses different challenges imposed by cross-site grid and cloud architectures:

    • The LUTSfed approach provides a lean and optional mechanism for filtering and management of usage data between grid or cloud sites.

    • An accounting and billing system natively designed to support cross-site clouds demonstrates usage data management despite unknown placement and dynamic task resource allocation.

    • The FSGrid system enables fairshare job prioritization across different grid sites, mitigating the problems of heterogeneous scheduling software and local management policies.

    The results and experiences from these systems are both theoretical and practical, as full scale implementations of each system has been developed and analyzed as a part of this work. Early theoretical work on structure-based service management forms a foundation for future work on structured-aware service placement in cross- site clouds. 

  • 9.
    Espling, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Armstrong, Django
    University of Leeds.
    Tordsson, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Djemame, Karim
    University of Leeds.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Contextualization: Dynamic Configuration of Virtual MachinesManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Virtual Machines (VMs) are commonly used as building blocks of IaaS cloud services. The number of running VM instances can be adjusted during runtime to achieve elasticity in the capacity of the service. New VM instances are based on templates that contain the basic configuration of the VM. Instance specific settings, settings unique to the infrastructure to which the instance is being deployed, are normally injected to the VM during the deployment phase through means of contextualization. In this work we present a layered approach to contextualization that supports different classes of contextualization data through the use of virtual devices. Inside each VM, data from different classes are layered on top of each other to create a unified file hierarchy using a small, custom file system. Context data can be updated during runtime by updating the contents of the virtual devices, making this approach the first contextualization approach to natively support recontextualization. Recontextualization enables run-time reconfiguration of a running service and can act as a trigger and key enabler of self-* techniques running inside the VM, allowing the service itself an unambiguous trigger for, e.g., further optimization in response to a changing environment. The runtime reconfiguration using recontextualization and its potential gains are shown in an example with a distributed file system, demonstrating the feasibility of the approach.

  • 10.
    Espling, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Larsson, Lars
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Li, Wubin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tordsson, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Modeling and Placement of Cloud Services with Internal Structure2016Inngår i: IEEE Transactions on Cloud Computing, ISSN 2168-7161, Vol. 4, nr 4, s. 429-439Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Virtual machine placement is the process of mapping virtual machines to available physical hosts within a datacenter or on a remote datacenter in a cloud federation. Normally, service owners cannot influence the placement of service components beyond choosing datacenter provider and deployment zone at that provider. For some services, however, this lack of influence is a hindrance to cloud adoption. For example, services that require specific geographical deployment (due e.g. to legislation), or require redundancy by avoiding co-location placement of critical components. We present an approach for service owners to influence placement of their service components by explicitly specifying service structure, component relationships, and placement constraints between components. We show how the structure and constraints can be expressed and subsequently formulated as constraints that can be used in placement of virtual machines in the cloud. We use an integer linear programming scheduling approach to illustrate the approach, show the corresponding mathematical formulation of the model, and evaluate it using a large set of simulated input. Our experimental evaluation confirms the feasibility of the model and shows how varying amounts of placement constraints and data center background load affects the possibility for a solver to find a solution satisfying all constraints within a certain time-frame. Our experiments indicate that the number of constraints affects the ability of finding a solution to a higher degree than background load, and that for a high number of hosts with low capacity, component affinity is the dominating factor affecting the possibility to find a solution.

  • 11.
    Espling, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Östberg, Per-Olov
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Integration and Evaluation of Decentralized Fairshare Prioritization (Aequus)Manuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Fairshare is commonly one of the factors used by cluster resource management systems to prioritize jobs during scheduling. Despite the grid vision of a transparent and unified infrastructure, fairshare is normally calculated and enforced at the local cluster level rather than at a grid-wide scale. Aequus is a self-contained decentralized system for grid-wide fairshare job prioritization. Using Aequus, detailed global share policies can be combined with local cluster policies to offer a unified grid fairshare prioritization system where local administrations retain control over their clusters. This work shows how Aequus can be integrated with local resource management systems such as SLURM and Maui with minimal intrusion. Early results from production use are presented, and the system is further tested and evaluated for use at a nation-wide scale. Statistical models are created based on historical national grid usage data, and synthetic traces based on these models are used to create a diverse input set used to exemplify system behavior. The system is shown to behave consistently despite great variations in job arrival patterns and partial participation of some of the collaborating installations.

  • 12.
    Espling, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Östberg, Per-Olov
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Integration and evaluation of decentralized fairshare prioritization (Aequus)2014Inngår i: Proceedings of the IEEE 28th International Parallel & Distributed Processing Symposium Workshops IPDPSW 2014, IEEE Computer Society, 2014, s. 1198-1207Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Fairshare is commonly one of the factors used by cluster resource management systems to prioritize jobs during scheduling. Despite the grid vision of a transparent and unified infrastructure, fairshare is normally calculated and enforced at the local cluster level rather than at a grid-wide scale. Aequus is a self-contained decentralized system for grid-wide fairshare job prioritization. Using Aequus, detailed global share policies can be combined with local cluster policies to offer a unified grid fairshare prioritization system where local administrations retain control over their clusters. This work shows how Aequus can be integrated with local resource management systems such as SLURM and Maui with minimal intrusion. Early results from production help assess the maturity of the system, and the system is further tested and evaluated for use at a nation-wide scale using workload modeling techniques. Statistical models are created based on historical national grid usage data, and synthetic traces based on these models are used to create a diverse input set used to exemplify system behavior. The system is shown to behave consistently despite great variations in job arrival patterns and partial participation of some of the collaborating installations.

  • 13. Katsaros, Gregory
    et al.
    Subirats, Josep
    Fito, J. Oriol
    Guitart, Jordi
    Gilet, Pierre
    Espling, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    A service framework for energy-aware monitoring and VM management in Clouds2013Inngår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 29, nr 8, s. 2077-2091Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The monitoring of QoS parameters in Services Computing as well as in Clouds has been a functionality provided by all contemporary systems. As the optimization of energy consumption becomes a major concern for system designers and administrators, it can be considered as another QoS metric to be monitored. In this paper, we present a service framework that allows us to monitor the energy consumption of a Cloud infrastructure, calculate its energy efficiency, and evaluate the gathered data in order to put in place an effective virtual machine (VM) management. In that context, a simulation scenario of an eco-driven VM placement policy resulted in a 14% improvement of the infrastructure's energy efficiency. In total, the proposed approaches and implementations have been validated against a testbed, producing very promising results regarding the prospect of energy efficiency as an important quality factor in Clouds. (C) 2012 Elsevier B.V. All rights reserved.

  • 14.
    Larsson, Lars
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Högpresterande beräkningscentrum norr (HPC2N).
    Henriksson, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Högpresterande beräkningscentrum norr (HPC2N).
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Högpresterande beräkningscentrum norr (HPC2N).
    Scheduling and Monitoring of Internally Structured Services in Cloud Federations2011Inngår i: 2011 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2011, s. 173-178Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Cloud infrastructure providers may form Cloud federations to cope with peaks in resource demand and to make large-scale service management simpler for service providers. To realize Cloud federations, a number of technical and managerial difficulties need to be solved. We present ongoing work addressing three related key management topics, namely, specification, scheduling, and monitoring of services. Service providers need to be able to influence how their resources are placed in Cloud federations, as federations may cross national borders or include companies in direct competition with the service provider. Based on related work in the RESERVOIR project, we propose a way to define service structure and placement restrictions using hierarchical directed acyclic graphs. We define a model for scheduling in Cloud federations that abides by the specified placement constraints and minimizes the risk of violating Service-Level Agreements. We present a heuristic that helps the model determine which virtual machines (VMs) are suitable candidates for migration. To aid the scheduler, and to provide unified data to service providers, we also propose a monitoring data distribution architecture that introduces cross-site compatibility by means of semantic metadata annotations.

  • 15. Lindner, M
    et al.
    Marquez, F. G.
    Chapman, C
    Clayman, S
    Henriksson, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    The Cloud Supply Chain: A Framework for Information, Monitoring, Accounting and Billing2011Inngår i: 2nd International ICST Conference on Cloud Computing (CloudComp 2010), 2011Konferansepaper (Fagfellevurdert)
  • 16.
    Östberg, Per-Olov
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Espling, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Decentralized scalable fairshare scheduling2013Inngår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 29, nr 1, s. 130-143Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This work addresses Grid fairshare allocation policy enforcement and presents Aequus, a decentralized system for Grid-wide fairshare job prioritization. The main idea of fairshare scheduling is to prioritize users with regard to predefined resource allocation quotas. The presented system builds on three contributions: a flexible tree-based policy model that allows delegation of policy definition, a job prioritization algorithm based on local enforcement of distributed fairshare policies, and a decentralized architecture for non-intrusive integration with existing scheduling systems. The system supports organization of users in virtual organizations and divides usage policies into local and global policy components that are defined by resource owners and virtual organizations. The architecture realization is presented in detail along with an evaluation of the system behavior in an emulated environment. In the evaluation, convergence noise types (mechanisms counteracting policy allocation convergence) are characterized and quantified, and the system is demonstrated to meet scheduling objectives and perform scalably under realistic operating conditions.

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