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%).
10945986
Improving the performance of I/O-intensive applications on clusters of workstations
Load balancing in a workstation-based 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 can cause system performance to decrease substantially. In this paper, two I/O-aware load-balancing schemes, referred to as IOCM and WAL-PM, are presented to improve the overall performance of a cluster system with a general and practical workload including I/O activities. The proposed schemes dynamically detect I/O load imbalance of nodes in a cluster, and determine whether to migrate some I/O load from overloaded nodes to other less- or under-loaded nodes. The current running jobs are eligible to be migrated in WAL-PM only if overall performance improves. Besides balancing I/O load, the scheme judiciously takes into account both CPU and memory load sharing in the system, thereby maintaining the same level of performance as existing schemes when I/O load is low or well balanced. Extensive trace-driven simulations for both synthetic and real I/O-intensive applications show that: (1) Compared with existing schemes that only consider CPU and memory, the proposed schemes improve the performance with respect to mean slowdown by up to a factor of 20; (2) When compared to the existing approaches that only consider I/O with non-preemptive job migrations, the proposed schemes achieve improvements in mean slowdown by up to a factor of 10; (3) Under CPU-memory intensive workloads, our scheme improves the performance over the existing approaches that only consider I/O by up to 47.5%.
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.
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).
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.
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.
42202570
Improving ASM Search Using Mixture Models for Grey-Level Profiles
The use of Active Shape Models (ASM) has been shown to be an efficient approach to image interpretation and pattern recognition. In ASM, grey-level profiles at landmarks are modelled as a Gaussian distribution. Mahalanobis distance from a sample profile to the model mean is used to locate the best position of a given landmark during ASM search. We present an improved ASM methodology, in which the profiles are modelled as a mixture of Gaussians, and the probability that a sample is from the distribution is calculated using the probability density function (pdf) of the mixture model. Both improved and original ASM methods were tested on synthetic and real data. The performance comparison demonstrates that the improved ASM method is more generic and robust than the original approach.
27692910
Dominating cycles in regular 3-connected graphs
Abstract Let G be a 3-connected, k -regular graph on at most 4 k vertices. We show that, for k ⩾63, every longest cycle of G is a dominating cycle. We conjecture that G is in fact hamiltonian.
15428033
On Optimal Physical Carrier Sensing: Theoretical Analysis and Protocol Design
Traditional Physical Carrier Sensing (PCS), which aims at eliminating hidden terminals in wireless networks, causes too many exposed terminals and deteriorates the throughput per user seriously. Some existing work has proven that an aggressive PCS can improve the throughput by balancing the tradeoff between hidden terminals and exposed terminals. However, little work has been conducted to optimize the PCS according to the network conditions. In this paper, we develop an analytical model to study the behaviors of a user in the aggressive PCS scenario, with which the optimal PCS threshold can be derived. Then, to avoid the complicated computation, we present a heuristic algorithm, in which the parameters required for PCS tuning are estimated by the carrier sensing mechanism employed in IEEE 802.11. Simulation results show that the proposed heuristic algorithm can make the PCS threshold approach to the theoretical optimal one. Moreover, the proposed heuristic algorithm can obtain significant throughput gain compared to the traditional PCS threshold setting solutions.
16266615
NeuroQL: A Domain-Specific Query Language for Neuroscience Data
In this paper, we propose a domain-specific query language called NeuroQL for the neuroscience domain. NeuroQL is designed primarily for neuroinformatics database users and aims to enable users to directly interact with neuroscience databases in their professional concepts and terms with the help of a conceptual data model. NeuroQL is DBMS independent and can be translated into traditional query language such as SQL, OQL and XQuery. It integrates some object-oriented features, and supports neuron domain-specific data types and query operators, which can dynamically evolve when the underlying database schema evolves.
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.