7586381
Dynamic Load Balancing for I/O-Intensive Tasks on Heterogeneous Clusters
Since I/O-intensive tasks running on a heterogeneous cluster need a highly effective usage of global I/O resources, previous CPU- or memory-centric load balancing schemes suffer significant performance drop under I/O-intensive workload due to the imbalance of I/O load. To solve this problem, we develop two I/O-aware load-balancing schemes, which consider system heterogeneity and migrate more I/O-intensive tasks from a node with high I/O utilization to those with low I/O utilization. If the workload is memory-intensive in nature, the new method applies a memory-based load balancing policy to assign the tasks. Likewise, when the workload becomes CPU-intensive, our scheme leverages a CPU-based policy as an efficient means to balance the system load. In doing so, the proposed approach maintains the same level of performance as the existing schemes when I/O load is low or well balanced. Results from a trace-driven simulation study show that, when a workload is I/O-intensive, the proposed schemes improve the performance with respect to mean slowdown over the existing schemes by up to a factor of 8. In addition, the slowdowns of almost all the policies increase consistently with the system heterogeneity.
16064075
Boosting performance of I/O-intensive workload by preemptive job migrations in a cluster system
Load balancing in a cluster system has been investigated extensively, mainly focusing on the effective usage of global CPU and memory resources. However, if a significant portion of applications running in the system is I/O-intensive, traditional load balancing policies that focus on CPU and memory usage may cause the system performance to decrease substantially. To solve this problem, a new I/O-aware load-balancing scheme with preemptive job migration is presented to sustain the high performance of a cluster with a diverse set of workload conditions. The proposed scheme dynamically detects I/O load imbalance on nodes of a cluster, and determines whether to preempt some running jobs on overloaded nodes and migrate them to other less- or under-loaded nodes. Besides balancing I/O load, the scheme takes into account both CPU and memory load sharing in clusters, thereby maintaining the same level of performance as existing schemes when I/O load is low or well balanced. Results from a trace-driven simulation show that, compared to the existing approaches that only consider I/O with nonpreemptive job migrations, the proposed schemes achieve the improvement in mean slowdown by up to a factor of 10.
10155852
Nexus: a novel weighted-graph-based prefetching algorithm for metadata servers in petabyte-scale storage systems
An efficient, accurate and distributed metadata-oriented prefetching scheme is critical to the overall performance in large distributed storage systems. In this paper, we present a novel weighted-graph-based prefetching technique, built on successor relationship, to gain performance benefit from prefetching specifically for clustered metadata servers, an arrangement envisioned necessary for petabyte-scale distributed storage systems. Extensive trace-driven simulations show that by adopting our new prefetching algorithm, the hit rate for metadata access on the client site can be increased by up to 13%, while the average response time of metadata operations can be reduced by up to 67%, compared with LRU and an existing state of the art prefetching algorithm.
20068677
A case study of parallel I/O for biological sequence search on Linux clusters
In this paper we analyze the I/O access patterns of a widely-used biological sequence search tool and implement two variations that employ parallel-I/O for data access based on PVFS (Parallel Virtual File System) and CEFT-PVFS (cost-effective fault-tolerant PVFS). Experiments show that the two variations outperform the original tool when equal or even fewer storage devices are used in the former. It is also found that although the performance of the two variations improves consistently when initially increasing the number of servers, this performance gain from parallel I/O becomes insignificant with further increase in server number. We examine the effectiveness of two read performance optimization techniques in CEFT-PVFS by using this tool as a benchmark. Performance results indicate: (1) doubling the degree of parallelism boosts the read performance to approach that of PVFS; and (2) skipping hot-spots can substantially improve the I/O performance when the load on data servers is highly imbalanced. The I/O resource contention due to the sharing of server nodes by multiple applications in a cluster has been shown to degrade the performance of the original tool and the variation based on PVFS by up to 10 and 21 folds, respectively; whereas, the variation based on CEFT-PVFS only suffered a two-fold performance degradation.
6280246
A dynamic load balancing scheme for I/O-intensive applications in distributed systems
In this paper, a new I/O-aware load-balancing scheme is presented to improve overall performance of a distributed system with a general and practical workload including I/O activities. The proposed scheme dynamically detects I/O load imbalance on nodes of a distributed system and determines whether to migrate the I/O requests of some jobs from overloaded nodes to other less- or under-loaded nodes, depending on data migration cost and remote I/O access overhead. Besides balancing I/O load, the scheme judiciously takes into account both CPU and memory load sharing in distributed systems, thereby maintaining the same level of performance as the existing schemes when I/O load is low or well balanced. Results from a trace-driven simulation study show that, compared with the existing schemes that only consider CPU and memory, the proposed scheme reduces the mean slowdown by up to 54.5% (with an average of 39.9%). On the other hand, when compared to the existing approaches that only consider I/O, the proposed scheme reduces the mean slowdown by up to 57.2% (with an average of 31.6%). More importantly, the new scheme improves over a very recent algorithm found in the literature that considers all the three resources by up to 49.6% (with an average of up to 41.9%).
5775560
Hierarchical Bloom filter arrays (HBA): a novel, scalable metadata management system for large cluster-based storage
An efficient and distributed scheme for file mapping or file lookup scheme is critical in decentralizing metadata management within a group of metadata servers. This work presents a technique called HBA (hierarchical Bloom filter arrays) to map file names to the servers holding their metadata. Two levels of probabilistic arrays, i.e., Bloom filter arrays, with different accuracies are used on each metadata server. One array, with lower accuracy and representing the distribution of the entire metadata, trades accuracy for significantly reduced memory overhead, while the other array, with higher accuracy, caches partial distribution information and exploits the temporal locality of file access patterns. Extensive trace-driven simulations have shown our HBA design to be highly effective and efficient in improving performance and scalability of file systems in clusters with 1,000 to 10,000 nodes (or superclusters).
17203024
A channel-aware adaptive control to the MAC protocol in rate adaptive wireless LANs
Due to the low efficiency of legacy IEEE 802.11 MAC protocol, it has been the main bottleneck of wireless local area networks (WLANs). In this paper, we focus on multi-rate WLANs and propose an adaptive transmission control scheme, which adapts the transmission probability according to the number of active stations, transmission rates, and channel conditions. At first, an analytical model is built in terms of the proposed scheme to explore the throughput of the network. Then, by means of the characteristics of practical system, a heuristic algorithm is developed to approach the maximum throughput on-line. Extensive numerical calculations and simulations based on NS-2 are implemented to evaluate the performance of the proposed algorithm and the impact of inaccurate estimation of parameters. The results show that our algorithm outperforms the existing adaptive algorithms in imperfect channel, and insensitive to inaccurate estimation of parameters. Copyright © 2005 John Wiley & Sons, Ltd.
35564489
Accelerated 2D Image Processing on GPUs
Graphics processing units (GPUs) in recent years have evolved to become powerful, programmable vector processing units. Furthermore, the maximum processing power of current generation GPUs is roughly four times that of current generation CPUs (central processing units), and that power is doubling approximately every nine months, about twice the rate of Moore's law. This research examines the GPU's advantage at performing convolutionbased image processing tasks compared to the CPU. Straight-forward 2D convolutions show up to a 130:1 speedup on the GPU over the CPU, with an average speedup in our tests of 59:1. Over convolutions performed with the highly optimized FFTW routines on the CPU, the GPU showed an average speedup of 18:1 for filter kernel sizes from 3x3 to 29x29.
14174120
Overlay Multicast with Inferred Link Capacity Correlations
We model the overlay using linear capacity constraints, which accurately and succinctly capture overlay link correlations. We show that finding a maximum-bandwidth multicast tree in an overlay with LCC is NP-complete and propose an efficient distributed heuristics algorithm.
1667264
Path extendability of claw-free graphs
Let G be a connected, locally connected, claw-free graph of order n and x,y be two vertices of G. In this paper, we prove that if for any 2-cut S of G, S@?{x,y}=@A, then each (x,y)-path of length less than n-1 in G is extendable, that is, for any path P joining x and y of length h(
28041688
Real-Time GPU-Based Simulation of Dynamic Terrain
Although a great deal of research effort has been devoted to the realistic and efficient rendering of terrain data, the existing research mainly focuses on displaying static terrain. The realistic simulation of dynamic terrain, including vehicle-terrain interactions, is absent in most 3D graphical applications. In this paper, we present a new GPU-based algorithm for dynamic terrain simulation. A key component of this algorithm is a Dynamically-Displaced Height Map (DDHM) which is generated and manipulated on the GPU. In addition, our method achieves real-time performance by using new features of the latest graphics hardware and shader technology. We demonstrate our algorithm by simulating a ground vehicle traveling on soft terrain. Our algorithm is particularly useful for ground based visual simulation applications as well as 3D games.