Multi-objective reinforcement learning for responsive grids

The original publication is available at www.springerlink.comInternational audienceGrids organize resource sharing, a fundamental requirement of large scientific collaborations. Seamless integration of grids into everyday use requires responsiveness, which can be provided by elastic Clouds, in the Infrastructure as a Service (IaaS) paradigm. This paper proposes a model-free resource provisioning strategy supporting both requirements. Provisioning is modeled as a continuous action-state space, multi-objective reinforcement learning (RL) problem, under realistic hypotheses; simple utility functions capture the high level goals of users, administrators, and shareholders. The model-free approach falls under the general program of autonomic computing, where the incremental learning of the value function associated with the RL model provides the so-called feedback loop. The RL model includes an approximation of the value function through an Echo State Network. Experimental validation on a real data-set from the EGEE grid shows that introducing a moderate level of elasticity is critical to ensure a high level of user satisfaction

Admission control in Flow-Aware Networking (FAN) architectures under GridFTP traffic

This is the author’s version of a work that was accepted for publication in Optical Switching and Networking. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Optical Switching and Networking, 6, 9 (2009) DOI: 10.1016/j.osn.2008.05.003Selected papers from First International Symposium on Advanced Networks and Telecommunication Systems, ANTS 2007Computing and networking resources virtualization is the main objective of Grid services. Such a concept is already used in the context of Web-services on the Internet. In the next few years, a large number of applications belonging to various domains (biotechnology, banking, finance, car and aircraft manufacturing, nuclear energy etc.) will also benefit from Grid services. Admission control is a key functionality for Quality of Service (QoS) provision in IP networks, and more specifically for Grid services provision. Service differentiation (DS) is a widely deployed technique on the Internet. It operates at the packet level on a best-effort mode. Flow-Aware Networking (FAN) that operates at the scale of the IP flows relies on implicit flow differentiation through priority fair queuing (PFQ). It may be seen as an alternative to DS. A Grid session may be seen as a succession of parallel TCP/IP flows characterized by data transfers with much larger volume than usual TCP/IP flows. In this paper, we propose an extension of FAN for the Grid environment called Grid over FAN (GoFAN). We compare, by means of computer simulations, the efficiency of Grid over DS (GoDS) and GoFAN. Two variants of GoFAN architectures based on different fair queuing algorithms are considered. As a first step, we provide two short surveys on QoS for Grid environment and on QoS in IP networks respectively

FairGV: Fair and Fast GPU Virtualization

Increasingly high-performance computing (HPC) application developers are opting to use cloud resources due to higher availability. Virtualized GPUs would be an obvious and attractive option for HPC application developers using cloud hosting services. Unfortunately, existing GPU virtualization software is not ready to address fairness, utilization, and performance limitations associated with consolidating mixed HPC workloads. This paper presents FairGV, a radically redesigned GPU virtualization system that achieves system-wide weighted fair sharing and strong performance isolation in mixed workloads that use GPUs with variable degrees of intensity. To achieve its objectives, FairGV introduces a trap-less GPU processing architecture, a new fair queuing method integrated with work-conserving and GPU-centric co-scheduling polices, and a collaborative scheduling method for non-preemptive GPUs. Our prototype implementation achieves near ideal fairness (? 0.97 Min-Max Ratio) with little performance degradation (? 1.02 aggregated overhead) in a range of mixed HPC workloads that leverage GPUs

Cost Based Optimization of Job Allocation in Computational Grids

Computational grids are distributed systems composed of heterogeneous computing resources which are distributed geographically and administratively. These highly scalable systems are designed to meet the large computational demands of many users from scientific and business orientations. Grid computing is a powerful concept, its chief appeal being the ability to make sure all of a resource’s computing power is used. In a grid world, the idle time of hundreds or thousands of resources could be harnessed and rented out to anyone who needed a massive infusion of processing power. First, the architecture of a grid system is presented. The design gives a mathematical model of the grid system for efficiently allocating the grids resources. The challenges faced for optimal job allocation motivate the exploration in optimizing grid resource allocations. We have extensively surveyed the current state of art in this area. A grid server coordinates the job allocation for the grid users and helps to select the best resources for a job among different possible resource offers with the best prices offered. Interaction between grid users and the resources require a mediator that uses different paradigm to communicate the needs of the two parties in terms of performance requirements, timing constraints, price charged etc. A game theoretic bargaining approach is studied to agree upon standard prices. We have implemented various job allocation schemes in computational grids based on the mathematical modeling of the grid system and bargaining protocol with the objective function of optimizing the cost. The performance of the schemes have been analyzed and compared. A new model for job allocation in computational grids has been proposed, for job allocation based on the clustering of resources

Towards a lightweight generic computational grid framework for biological research

Background: An increasing number of scientific research projects require access to large-scale computational resources. This is particularly true in the biological field, whether to facilitate the analysis of large high-throughput data sets, or to perform large numbers of complex simulations – a characteristic of the emerging field of systems biology. Results: In this paper we present a lightweight generic framework for combining disparate computational resources at multiple sites (ranging from local computers and clusters to established national Grid services). A detailed guide describing how to set up the framework is available from the following URL: http://igrid-ext.cryst.bbk.ac.uk/portal_guide/. Conclusion: This approach is particularly (but not exclusively) appropriate for large-scale biology projects with multiple collaborators working at different national or international sites. The framework is relatively easy to set up, hides the complexity of Grid middleware from the user, and provides access to resources through a single, uniform interface. It has been developed as part of the European ImmunoGrid project