Maximizing performance in a striped disk array
ISCA '90 Proceedings of the 17th annual international symposium on Computer Architecture
Clustering Algorithms
The Vision of Autonomic Computing
Computer
The SDSC storage resource broker
CASCON '98 Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research
Replica Selection in the Globus Data Grid
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Grid Datafarm Architecture for Petascale Data Intensive Computing
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Eliminating Replica Selection - Using Multiple Replicas to Accelerate Data Transfer on Grids
ICPADS '04 Proceedings of the Parallel and Distributed Systems, Tenth International Conference
Storage Device Performance Prediction with CART Models
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
RepStore: A Self-Managing and Self-Tuning Storage Backend with Smart Bricks
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
A Mass Storage System Administrator Autonomic Assistant
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Using Regression Techniques to Predict Large Data Transfers
International Journal of High Performance Computing Applications
A new formalism for dynamic reconfiguration of data servers in a cluster
Journal of Parallel and Distributed Computing - Special issue: Design and performance of networks for super-, cluster-, and grid-computing: Part I
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Design and analysis of a load balancing strategy in data grids
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Observer: keeping system models from becoming obsolete
HotAC II Hot Topics in Autonomic Computing on Hot Topics in Autonomic Computing
Future Generation Computer Systems
A high performance suite of data services for grids
Future Generation Computer Systems
Finding order in chaos: a behavior model of the whole grid
Concurrency and Computation: Practice & Experience - Grid Computing, High Performance and Distributed Application
Dynamic QoS-aware data replication in grid environments based on data "importance"
Future Generation Computer Systems
Autonomic storage system based on automatic learning
HiPC'04 Proceedings of the 11th international conference on High Performance Computing
Riding Out the Storm: How to Deal with the Complexity of Grid and Cloud Management
Journal of Grid Computing
An approach for constructing private storage services as a unified fault-tolerant system
Journal of Systems and Software
| Hi-index | 0.00 |
Data grid services have been used to deal with the increasing needs of applications in terms of data volume and throughput. The large scale, heterogeneity and dynamism of grid environments often make management and tuning of these data services very complex. Furthermore, current high-performance I/O approaches are characterized by their high complexity and specific features that usually require specialized administrator skills. Autonomic computing can help manage this complexity. The present paper describes an autonomic subsystem intended to provide self-management features aimed at efficiently reducing the I/O problem in a grid environment, thereby enhancing the quality of service (QoS) of data access and storage services in the grid. Our proposal takes into account that data produced in an I/O system is not usually immediately required. Therefore, performance improvements are related not only to current but also to any future I/O access, as the actual data access usually occurs later on. Nevertheless, the exact time of the next I/O operations is unknown. Thus, our approach proposes a long-term prediction designed to forecast the future workload of grid components. This enables the autonomic subsystem to determine the optimal data placement to improve both current and future I/O operations.