15655964
A partial-multiple-bus computer structure with improved cost effectiveness
This paper addresses the design and performance analysis of partial-multiple-bus interconnection networks. One such structure, called processor-oriented partial-multiple-bus (or PPMB), is proposed. It serves as an alternative to the conventional structure called memory-oriented partial-multiple-bus (or MPMB) and is aimed at higher system performance at less or equal system cost. PPMB's structural feature, which distinguishes itself from the conventional, is to provide every memory module with B paths to processors (where B is the total number of buses). This in contrast to the mere B/g paths provided in the conventional MPMB structure (where g is the number of groups), suggests a potential for higher system bandwidth. This potential is fully fulfilled by the load-balancing arbitration mechanism suggested, which in turn highlights the advantages of the proposed structure. As a result, it has been shown, both analytically and by simulation, that a substantial increase in system bandwidth (up to 20%) is achieved by the PPMB structure over the MPMB structure. In addition to the fact that the cost of PPMB is less than, or equal to, that of MPMB, its reliability is shown to be slightly increased as well.
16880603
Adaptive Load Balancing for Long-Range MD Simulations in A Distributed Environment
Molecular dynamics, a computationally intensive application is used by researchers in various fields. The inherent parallelism (Plimpton and Hendrickson, 1994) in the computations involved with this application can be exploited in parallel and distributed environments. However, in distributed environments such as the grid (Foster et al., 2001), the available resources, namely the network and computational power, are continually changing with respect to every available node. To optimally utilize these dynamic resources, a scheduler should be able to continually adapt to the changes and suitably vary the load scheduled to every available node. We propose one such scheduling algorithm. The proposed scheduling algorithm builds and continually updates a model of the distributed system, which it then uses to make decisions about how to optimally redistribute the load in the system at every time step of the MD simulation. The scheduling algorithm can additionally handle dynamic changes in the number of nodes available for computation at runtime. We then demonstrate the efficiency of our scheduling algorithm when applied to MD simulations in a distributed environment
11533778
Dynamic, reliability-driven scheduling of parallel real-time jobs in heterogeneous systems
In this paper, a heuristic dynamic scheduling scheme for parallel real-time jobs in a heterogeneous system is presented. The parallel real-time jobs studied in this paper are modelled by directed acyclic graphs (DAG). We assume a scheduling environment where parallel real-time jobs arrive at a heterogeneous system following a Poisson process. The scheduling algorithms developed in this paper take the reliability measure into account, in order to enhance the reliability of the heterogeneous system without any additional hardware cost. In addition, scheduling time and dispatch time are both incorporated into our scheduling scheme so as to make the scheduling result more realistic and precise. Admission control is in place so that a parallel real-time job whose deadline cannot be guaranteed is rejected by the system. The performance of the proposed scheme is evaluated via extensive simulations. The simulation results show that the heuristic algorithm performs significantly better than two other algorithms that do not consider reliability cost. Furthermore, results suggest that shortening the scheduling time results in a higher guarantee ratio. Hence, if parallel scheduling algorithm is devised and employed to shorten the scheduling time, the performance of the heterogeneous system will be further enhanced.
9864742
JOPI: a Java object-passing interface
Recently there has been an increasing interest in developing parallel programming capabilities in Java to harness the vast resources available in clusters, grids and heterogeneous networked systems. In this paper, we introduce a Java object-passing interface (JOPI) library. JOPI provides Java programmers with the necessary functionality to write object-passing parallel programs in distributed heterogeneous systems. JOPI provides an MPI-like interface that can be used to exchange objects among processes. In addition to the well-known benefits of the object-oriented development model, using objects to exchange information in JOPI is advantageous because it facilitates passing complex structures and enables the programmer to isolate the problem space from the parallelization problem. The run-time environment for JOPI is portable, efficient and provides the necessary functionality to deploy and execute parallel Java programs. Experiments were conducted on a cluster system and a collection of heterogeneous platforms to measure JOPI's performance and compare it with MPI. The results show good performance gains using JOPI.
26735928
I/O Response Time in a Fault-Tolerant Parallel Virtual File System
A fault tolerant parallel virtual file system is designed and implemented to provide high I/O performance and high reliability. A queuing model is used to analyze in detail the average response time when multiple clients access the system. The results show that I/O response time is with a function of several operational parameters. It decreases with the increase in I/O buffer hit rate for read requests, write buffer size for write requests and number of server nodes in the parallel file system, while higher I/O requests arrival rate increases I/O response time.
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%.
9730668
Broadening the Semantic Coverage of Agent Communicative Acts
Communicative acts-based ACLs specify domain-independent information about communication and relegate domain-dependent information to an unspecified content language. This is reasonable, but the ACLs cover only a small fraction of the domain-independent information possible. As a key element of modern ACLs, the set of communicative acts needs to be as complete as possible to allow agents to communicate the widest range of information with agreed-upon semantics. This paper describes a new approach to broaden the semantic coverage of agent communicative acts. It provides agents with the ability to express more of the semantics of human languages and yields a more powerful ACL. We first describe the main meaning categories and semantics for an ACL, which we derive from prior work on speech-act classifications. Next, we prove the resultant semantic coverage. Finally, we present some example applications, which demonstrate that the approach can combine the benefits of the FIPA ACL with Ballmer and Brennenstuhl’s speech act classification, resulting in a more expressive and efficient ACL.
18051966
Design and analysis of adaptive strategies for locating internet-based servers in MANETs
A critical problem in providing Internet access to Mobile Ad Hoc Networks (MANETs) is how the mobile hosts can locate Internet-based servers efficiently in a dynamic, unstructured network. This paper studies adaptive strategies that combine both proactive advertising by the servers and on-demand discovery by the mobile hosts. The adaptive strategies determine the relative rate of proactive advertising and on-demand discovery according to system characteristics such as host mobility level and offered load. Our simulation study reveals that, compared to the previous proactive strategies and reactive strategies, the adaptive strategies reduce network traffic by several times when the network has a moderate offered load and a low or high level of host mobility.
16906364
Scalable and Robust Aggregation Techniques for Extracting Statistical Information in Sensor Networks
Wireless sensor networks have stringent constraints on system resources and data aggregation techniques are critically important. However, accurate data aggregation is difficult due to the variation of sensor readings and due to the frequent communication failures. To address these difficulties, we propose a scalable and robust data aggregation algorithm. The novelty of our work includes two aspects. First, our algorithm exploits the mixture model and the Expectation Maximization (EM) algorithm for parameter estimation. Hence, it captures the effects of aggregation over different scales while keeping the communication cost low. Second, our algorithm exploits loss-tolerant multi-path routing schemes. Hence, it obtains accurate statistical information even in the presence of high link and node failure rates. We demonstrate that our techniques reduce communication cost while retaining the precious statistical information otherwise neglected by other aggregation techniques. Our evaluation shows the proposed techniques are robust against link and node failures, and perform consistently well.
14525964
Adaptive strategies for efficiently locating internet-based servers in MANETs
Providing Internet access to Mobile Ad hoc Networks (MANETs) can greatly extend their applications, increase their scalability, and improve the quality of service. However, a critical problem is how the mobile hosts can locate Internet-based servers efficiently in a dynamic, unstructured network. Neither reactive strategies, where the hosts initiate on-demand server discovery, nor proactive strategies, where the servers periodically advertise their availability information, are optimal. To that end, this paper studies adaptive strategies that (1) combine both proactive advertising by the servers and on-demand discovery by the mobile hosts, and (2) determine the relative rate of proactive advertising and on-demand discovery adaptively according to the network characteristics including the host mobility level and the offered load. We propose and evaluate two novel, integrated algorithms. First, to determine the rate of proactive advertising, we propose an exponential backoff algorithm to probe the optimal operating point. Second, to reduce the network traffic due to reactive (on-demand) discovery, we propose a novel controlled flooding algorithm. Our simulation study reveals that, compared to the previous proactive strategies and reactive strategies, our adaptive strategies reduce network traffic for locating the servers by several times when the network has a moderate offered load and a low or high level of host mobility.
2540895
Large margin hidden Markov models for speech recognition
In this paper, motivated by large margin classifiers in machine learning, we propose a novel method to estimate continuous-density hidden Markov model (CDHMM) for speech recognition according to the principle of maximizing the minimum multiclass separation margin. The approach is named large margin HMM. First, we show this type of large margin HMM estimation problem can be formulated as a constrained minimax optimization problem. Second, we propose to solve this constrained minimax optimization problem by using a penalized gradient descent algorithm, where the original objective function, i.e., minimum margin, is approximated by a differentiable function and the constraints are cast as penalty terms in the objective function. The new training method is evaluated in the speaker-independent isolated E-set recognition and the TIDIGITS connected digit string recognition tasks. Experimental results clearly show that the large margin HMMs consistently outperform the conventional HMM training methods. It has been consistently observed that the large margin training method yields significant recognition error rate reduction even on top of some popular discriminative training methods