Desktop Grid Platform Research Articles

Trust model for reliable node allocation based on daily computer usage behavior

SummaryResource volatility is a major challenge on desktop grid platforms with characteristics that primarily depend on human computer usage behavior. This study proposes a trust model based on Dempster–Shafer theory that predicts the relative reliability of nodes using information on daily computer usage behavior based on the historical information from a desktop grid platform for a desktop grid. In the proposed trust model, called TM‐DG, a degree of trustworthiness is introduced for the nodes to quantify their reliability. Dempster's rule of combination is also applied to exploit two bodies of independent evidence: 1) current node availability as actively probed by a special test node and 2) proportion of allocated tasks successfully completed. The simulations performed on a lightweight desktop grid platform illustrate how TM‐DG can fully utilize the most reliable nodes for a given computation, leading to a reduction in the communication overhead and an improvement in the computing power of the platform.

Using the High-Level Based Program Interface to Facilitate the Large Scale Scientific Computing

This paper is to make further research on facilitating the large-scale scientific computing on the grid and the desktop grid platform. The related issues include the programming method, the overhead of the high-level program interface based middleware, and the data anticipate migration. The block based Gauss Jordan algorithm as a real example of large-scale scientific computing is used to evaluate those issues presented above. The results show that the high-level based program interface makes the complex scientific applications on large-scale scientific platform easier, though a little overhead is unavoidable. Also, the data anticipation migration mechanism can improve the efficiency of the platform which needs to process big data based scientific applications.

Parallelization of Littlewood-Richardson Coefficients Computation and its Integration into the BonjourGrid Meta-Desktop Grid Middleware

This paper shows how to parallelize a compute intensive application in mathematics (Group Theory) for an institutional Desktop Grid platform coordinated by a meta-grid middleware named BonjourGrid. The paper is twofold: it shows how to parallelize a sequential program for a multicore CPU which participates in the computation; and it demonstrates the effort for launching multiple instances of the solutions for the mathematical problem with the BonjourGrid middleware. BonjourGrid is a fully decentralized Desktop Grid middleware. The main results of the paper are: a) an efficient multi-threaded version of a sequential program to compute Littlewood-Richardson coefficients, namely the Multi-LR program and b) a proof of concept, centered around the user needs, for the BonjourGrid middleware dedicated to coordinate multiple instances of programsfor Desktop Grids and with the help of Multi-LR. In this paper, the scientific work consists in starting from a model for the solution of a compute intensive problem in mathematics, to incorporate the concrete model into a middleware and running it on commodity PCs platform managed by an innovative meta Desktop Grid middleware.

Parallel processing on block-based Gauss-Jordan algorithm for desktop grid

Two kinds of parallel possibilities exist in the block-based Gauss-Jordan (BbGJ) algorithm, which are intra-step and inter-steps based parallelism. But the existing parallel paradigm of BbGJ algorithm just aiming at the intra-step based parallelism, can?t meet the requirement of dispatching simultaneously as many tasks as possible to computing nodes of desktop grid platform exploiting thousands of volunteer computing resources. To overcome the problem described above, this paper presents a hybrid parallel paradigm for desktop grid platform, exploiting all the possible parallelizable parts of the BbGJ algorithm. As well known to us all, volatility is the key issue of desktop grid platform and faults are unavoidable during the process of program execution. So the adapted version of block BbGJ algorithm for desktop grid platform should take the volatility into consideration. To solve the problem presented above, the paper adopts multi-copy distribution strategy and multi-queue based task preemption method to ensure the key tasks can be executed on time, thus ensure the whole tasks can be finished in shorter period of time.

COHESION — A microkernel based Desktop Grid platform for irregular task-parallel applications

We present Cohesion, a novel approach to Desktop Grid Computing. A major design goal of Cohesion is to enable advanced parallel programming models and application specific frameworks. We focus on methods for irregularly structured task-parallel problems, which require fully dynamic problem decomposition. Cohesion overcomes limitations of classical Desktop Grid platforms by employing peer-to-peer principles and a flexible system architecture based on a microkernel approach. Arbitrary modules can be dynamically loaded to replace default functionality, resulting in a platform that can easily adapt to application specific requirements. We discuss two representative example applications and report on the results of performance experiments that especially consider the high volatility of resources prevailing in a Desktop Grid.

DGMonitor: A Performance Monitoring Tool for Sandbox-Based Desktop Grid Platforms

Accurate, continuous resource monitoring and profiling are critical for enabling performance tuning and scheduling optimization. In desktop grid systems that employ sandboxing, these issues are challenging because (1) subjobs inside sandboxes are executed in a virtual computing environment and (2) the state of this virtual environment within the sandboxes is reset to an initial empty state after a subjob completion. DGMonitor is a monitoring tool which builds a global, accurate, and continuous view of real resource utilization for desktop grids with sandboxing. Our monitoring tool measures performance unobtrusively and reliably, uses a simple performance data model, and is easy to use. Our measurements demonstrate that DGMonitor can scale to large desktop grids (up to 12000 PCs) with low monitoring overhead in terms of resource consumption (less than 0.1% per machine). Though we originally developed DGMonitor with the Entropia DCGrid platform, our tool is easily portable and integrated into other desktop grid systems. In all of these systems, DGMonitor data can support existing and novel information services, particularly for performance tuning and scheduling. In this paper, the high scalability and monitoring power of DGMonitor are demonstrated with the Entropia DCGrid platform and the BOINC platform respectively.