Managing server load in global memory systems
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
Parallel programming: techniques and applications using networked workstations and parallel computers
Availability and utility of idle memory in workstation clusters
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
File Assignment in Parallel I/O Systems with Minimal Variance of Service Time
IEEE Transactions on Computers
Improved Strategies for Dynamic Load Balancing
IEEE Concurrency
IEEE Transactions on Computers
Storage-Aware Caching: Revisiting Caching for Heterogeneous Storage Systems
FAST '02 Proceedings of the Conference on File and Storage Technologies
Faster Collective Output through Active Buffering
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
An analytical comparison of nearest neighbor algorithms for load balancing in parallel computers
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Improving Distributed Workload Performance by Sharing Both CPU and Memory Resources
ICDCS '00 Proceedings of the The 20th International Conference on Distributed Computing Systems ( ICDCS 2000)
Towards Communication-Sensitive Load Balancing
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
The Home Model and Competitive Algorithms for Load Balancing in a Computing Cluster
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Dynamic Load Balancing and Efficient Load Estimators for Asynchronous Iterative Algorithms
IEEE Transactions on Parallel and Distributed Systems
Agent-Based Load Balancing on Homogeneous Minigrids: Macroscopic Modeling and Characterization
IEEE Transactions on Parallel and Distributed Systems
An Adaptive Energy-Conserving Strategy for Parallel Disk Systems
DS-RT '08 Proceedings of the 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications
DARAW: a new write buffer to improve parallel I/O energy-efficiency
Proceedings of the 2009 ACM symposium on Applied Computing
Dynamic load balancing for I/O-intensive applications on clusters
ACM Transactions on Storage (TOS)
An adaptive load balancing management technique for RFID middleware systems
Software—Practice & Experience
Quality of security adaptation in parallel disk systems
Journal of Parallel and Distributed Computing
Task partitioning, scheduling and load balancing strategy for mixed nature of tasks
The Journal of Supercomputing
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Load balancing techniques play a critically important role in developing high-performance cluster computing platforms. Existing load balancing approaches are concerned with the effective usage of CPU and memory resources. Due to imbalance in disk I/O resources under I/O-intensive workloads, the previous CPU- or memory-aware load balancing schemes suffer significant performance drop. To remedy this deficiency, in this paper we propose a novel load-balancing algorithm (hereinafter referred to as IOLB) for clusters, which aims at maintaining high resource utilization under a wide range of workload conditions. Specifically, IOLB is conducive to reducing the average slowdown of all parallel jobs submitted to a cluster by balancing load in disk resources. This can, in turn, not only achieve the effective usage of global disk resources but also reduce response times of I/O-intensive parallel jobs. To theoretically study the optimization of the IOLB algorithm, we qualitatively comparing IOLB with two conventional CPU- and memory-aware load-balancing schemes. We prove that when the workloads become CPU- or memory-intensive in nature, IOLB gracefully degrades towards the existing load-balancing schemes. Experimental results based on trace-driven simulations demonstratively show that the IOLB algorithm significantly improves the resource utilization of a cluster under I/O-intensive workloads. Furthermore, our results confirm that IOLB is able to maintain the same level of performance as the two existing approaches, because IOLB improves CPU and memory utilization under CPU- and memory-intensive workloads.