A key problem in executing performance critical applications on distributed computing environments (e.g. the Grid) is the resource selection. Research related to rdquoautomatic resource selectionrdquo aims to select suitable resources on behalf of users to optimize the execution performance. However, most of current approaches are based on the static principle (i.e. resource selection is performed prior to execution) and need detailed application-specific information. In the paper, we introduce a novel on-line automatic resource selection approach. This approach is based on a simple control theory: the application continuously reports the execution satisfaction degree (ESD) to the middleware application agent (AA), which relies on the reported ESD values to learn the execution behavior and tune the execution environment by adding/replacing/deleting resources at runtime in order to satisfy users' performance requirements. We use a utility-based learning and tuning algorithm to enable the automatic resource tuning/selection. A typical 2-D heat equation application is used to validate the approach. Results show that without resource or application knowledge being provided in advance, the approach is able to find the best-effort resources to satisfy users' execution requirements, and classify resources according to their contribution made to the application.

Resource allocations are performed statically for traditional distributed/parallel applications prior to launching application executions. This limitation could cause long resource waiting time when multiple resources need to be co-allocated. In contrast, the Adaptive Distributed/Parallel Applications (ADA), allowing resources to be added and released during executions, can adapt to the dynamic feature of common distributed computing environments (e.g. the Grid). The goal of the work is to create a tool to allow users to easily develop and run ADAs without dealing with the underneath distributed resource environments. We introduce a novel software package, Application Agent (AA), to support the execution of ADAs, including automatic resource allocation, dynamic process deployment, and enabling process wide-area communication. An AA-enabled application can be started on any internet-connected machines and the AA will dynamically configure a virtual machine from the local machine to remote available machines to satisfy the execution. The AA is composed of two parts. The first part is a library of AA interface routines, which contains user-callable functions for developers to integrate their applications with the AA. This second part is the daemons, which dynamically collect computational resources on the Internet to create a wide-area virtual machine to execute an application.

A key problem in executing performance critical applications on distributed computing environments (e.g. the Grid) is the selection of resources for execution. A lot of research related to "automatic resource selection" has been made to allocate the best-effort resources on behalf of users to optimize the execution performance. However most of current approaches are based on the static principle (i.e. resource selection is performed prior to execution) and need detailed application specific information. In the paper, we introduce a lightweight approach for automatic resource selection/configuration. This approach is based on a simple control theory: the application continuously reports performance values to the middleware Application Agent (AA), which relies on the reported values to decide how to dynamically reconfigure the execution environment during the execution to ensure userspsila performance requirements (e.g. execution deadline, running N iteration per second). We divide the research into two paradigms: neglecting network latency and considering network latency. For the first paradigm, we use a linear prediction with Kalman filter to find the expected RC to satisfy certain performance requirement. For the second, we let AA probe possible RCs and rollback the bad RCs, to look for a local optimized RC that can provide applications highest performance.

Most resource management systems in large distributed environments rely on a centralised controller scheme, known as a meta-scheduler. Such a meta-scheduler is designed to have a complete control of all resources worldwide through a number of local schedulers on each participating site. Faced with a potential large number of sites, the central scheduling point represents a potential bottleneck for requests needing fast response. In this paper, we consider the efficiency of a localised two-tier system: the first tier selects appropriate computing resources from the worldwide pool and creates a virtual cluster. The second tier employs a local scheduler to distribute jobs/tasks from the applications among the resources that make up the cluster. The two-tier approach differentiates the tasks of securing a suitable resource level and managing their use. The aim is to ensure a rapid request response while at the same time optimising the system performance. To this end, we consider a management strategy that incorporates rental policy and scheduling schemes. We propose aggressive and conservative heuristic based policies and evaluate their performance to demonstrate the cost effectiveness of such approaches.

Rapid and unpredictable topology changes and resource constraints make delivering packets in a MANET (Mobile Ad hoc Network) a challenging task. Routing information has to be updated to reflect the topology changes and maintain the correctness of route selection. On the other hand, the dissemination of control messages has to be optimised for efficient resource usage and to alleviate channel contention problems. To solve this problem, this dissertation focuses on how to automatically tune routing performance for MANETs in terms of packet delivery ratio and control overhead. The impacts of soft state signalling, especially the refresh intervals, are studied under various scenarios. A variety of topology advertisement strategies are presented. Two self-tuning neighbour detection schemes are proposed, the Dynamic Timer algorithm and the Fast Neighbour Handshake algorithm, in order to enhance routing performance.

This paper presents a fast neighbour detection scheme for a proactive MANET routing protocol. Instead of using periodic HELLO messages, the proposed scheme adopts explicit handshake mechanism to reduce the latency in neighbour detection. In particular, two route handshake options are presented, namely the Broadcast based handshake (BHS) algorithm and Unicast based handshake (UHS) algorithm. Our simulation results show that the proposed scheme improves routing performance, especially in networks with moderate or high mobility. In low-density networks, the unicast option improves the routing throughput significantly without introducing extra control overhead.

In wireless mission-critical systems, systems may be resource-constrained including limited bandwidth, so minimising protocol overhead, whilst maintaining performance, is important. Proactive MANET routing protocols tend to provide smaller route discovery latency than on-demand protocols because they maintain route information to all the nodes in the network at all time. However, such protocols may impose excessive soft-state routing control overhead which is generated by disseminating periodic update messages. In order to mitigate the side effects of the soft-state control over-heads, we propose two adaptive proactive routing algorithms, namely DT_MIAD and DT_ODPU. By tuning the value of refresh intervals dynamically and automatically, refresh updates are triggered based on traffic conditions and node mobility. We show through simulations that the proposed adaptive routing algorithms can outperform a traditional proactive routing protocol (OLSR).

BACKGROUND: In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power. In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible. RESULTS: We show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains. CONCLUSION: This study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE.

Abstract Summary: The Genomic Threading Database currently contains structural annotations for the genomes of over 100 recently sequenced organisms. Annotations are carried out by using our modified GenTHREADER software and through implementing grid technology. Availability: http://bioinf.cs.ucl.ac.uk/GTD Supplementary information: http://bioinf.cs.ucl.ac.uk/GTD/demo.avi, http://bioinf.cs.ucl.ac.uk/GTD/summaryView.html, http://bioinf.cs.ucl.ac.uk/GTD/figure1.pdf, http://www.e-protein.org

A formula for the go-back-N ARQ (automatic repeat request) scheme applicable to Markov error patterns is derived. It is a generalization of the well-known efficiency formula p/(p+m(1-p)) (where m is the round trip delay in number of block durations and p is the block transmission success probability), and it has been successfully validated against simulation measurements. It is found that for a given error rate, error patterns having zero correlation between successive transmission generally fare better than those with negative correlation, and that error patterns with positive correlation fare better still. It is shown that the present analysis can be extended in a straightforward manner to cope with error patterns of a more complex nature. Simple procedures for numerical evaluation of efficiency under quite general error structures are presented.>