Load balancing techniques for I/O intensive tasks on heterogeneous clusters

Load balancing schemes in a cluster system play a critically important role in developing highperformance cluster computing platform. Existing load balancing approaches are concerned with the effective usage of CPU and memory resources. 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, Zhang et al. developed 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, their 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

Designing a scalable dynamic load -balancing algorithm for pipelined single program multiple data applications on a non-dedicated heterogeneous network of workstations

Dynamic load balancing strategies have been shown to be the most critical part of an efficient implementation of various applications on large distributed computing systems. The need for dynamic load balancing strategies increases when the underlying hardware is a non-dedicated heterogeneous network of workstations (HNOW). This research focuses on the single program multiple data (SPMD) programming model as it has been extensively used in parallel programming for its simplicity and scalability in terms of computational power and memory size.;This dissertation formally defines and addresses the problem of designing a scalable dynamic load-balancing algorithm for pipelined SPMD applications on non-dedicated HNOW. During this process, the HNOW parameters, SPMD application characteristics, and load-balancing performance parameters are identified.;The dissertation presents a taxonomy that categorizes general load balancing algorithms and a methodology that facilitates creating new algorithms that can harness the HNOW computing power and still preserve the scalability of the SPMD application.;The dissertation devises a new algorithm, DLAH (Dynamic Load-balancing Algorithm for HNOW). DLAH is based on a modified diffusion technique, which incorporates the HNOW parameters. Analytical performance bound for the worst-case scenario of the diffusion technique has been derived.;The dissertation develops and utilizes an HNOW simulation model to conduct extensive simulations. These simulations were used to validate DLAH and compare its performance to related dynamic algorithms. The simulations results show that DLAH algorithm is scalable and performs well for both homogeneous and heterogeneous networks. Detailed sensitivity analysis was conducted to study the effects of key parameters on performance

Development of Cluster Computing –A Review

This paper presents the review work of “Cluster Computing” in depth and detail.  Cluster Computing: A Mobile Code Approach by R.B.Patel and Manpreet Singh (2006); Performance Evaluation of Parallel Applications Using Message Passing Interface In Network of Workstations Of Different Computing Powers by Rajkumar Sharma, Priyesh Kanungo and Manohar Chandwani (2011); On the Performance of MPI-OpenMP on a 12 nodes Multi-core Cluster by Abdelgadir Tageldin, Al-Sakib Khan Pathan , Mohiuddin Ahmed (2011); Dynamic Load Balancing in Parallel Processing on Non-Homogeneous Clusters by Armando E. De Giusti, Marcelo R. Naiouf, Laura C. De Giusti, Franco Chichizola (2005); Performance Evaluation of Computation Intensive Tasks in Grid by P.Raghu, K. Sriram (2011); Automatic Distribution of Vision-Tasks on Computing Clusters by Thomas Muller, Binh An Tran and Alois Knoll (2011); Terminology And Taxonomy Parallel Computing Architecture by Amardeep Singh, Satinder Pal Singh, Vandana, Sukhnandan Kaur (2011); Research of Distributed Algorithm based on Parallel Computer Cluster System by Xu He-li, Liu Yan (2010); Cluster Computing Using Orders Based Transparent Parallelizing by Vitaliy D. Pavlenko, Victor V. Burdejnyj (2007) and VCE: A New Personated Virtual Cluster Engine for Cluster Computing by Mohsen Sharifi, Masoud Hassani, Ehsan Mousavi Khaneghah, Seyedeh Leili Mirtaheri (2008). Keywords:Cluster computing, Cluster Architectures, Dynamic and Static Load Balancing, Distributed Systems, Homogeneous and Non-Homogeneous Processors, Multicore clusters, Parallel computing, Parallel Computer Vision, Task parallelism, Terminology and taxonomy, Virtualization, Virtual Cluster

Analytical Modeling of High Performance Reconfigurable Computers: Prediction and Analysis of System Performance.

The use of a network of shared, heterogeneous workstations each harboring a Reconfigurable Computing (RC) system offers high performance users an inexpensive platform for a wide range of computationally demanding problems. However, effectively using the full potential of these systems can be challenging without the knowledge of the system’s performance characteristics. While some performance models exist for shared, heterogeneous workstations, none thus far account for the addition of Reconfigurable Computing systems. This dissertation develops and validates an analytic performance modeling methodology for a class of fork-join algorithms executing on a High Performance Reconfigurable Computing (HPRC) platform. The model includes the effects of the reconfigurable device, application load imbalance, background user load, basic message passing communication, and processor heterogeneity. Three fork-join class of applications, a Boolean Satisfiability Solver, a Matrix-Vector Multiplication algorithm, and an Advanced Encryption Standard algorithm are used to validate the model with homogeneous and simulated heterogeneous workstations. A synthetic load is used to validate the model under various loading conditions including simulating heterogeneity by making some workstations appear slower than others by the use of background loading. The performance modeling methodology proves to be accurate in characterizing the effects of reconfigurable devices, application load imbalance, background user load and heterogeneity for applications running on shared, homogeneous and heterogeneous HPRC resources. The model error in all cases was found to be less than five percent for application runtimes greater than thirty seconds and less than fifteen percent for runtimes less than thirty seconds. The performance modeling methodology enables us to characterize applications running on shared HPRC resources. Cost functions are used to impose system usage policies and the results of vii the modeling methodology are utilized to find the optimal (or near-optimal) set of workstations to use for a given application. The usage policies investigated include determining the computational costs for the workstations and balancing the priority of the background user load with the parallel application. The applications studied fall within the Master-Worker paradigm and are well suited for a grid computing approach. A method for using NetSolve, a grid middleware, with the model and cost functions is introduced whereby users can produce optimal workstation sets and schedules for Master-Worker applications running on shared HPRC resources

A survey of techniques and technologies for web-based real-time interactive rendering

When exploring a virtual environment, realism depends mainly on two factors: realistic images and real-time feedback (motions, behaviour etc.). In this context, photo realism and physical validity of computer generated images required by emerging applications, such as advanced e-commerce, still impose major challenges in the area of rendering research whereas the complexity of lighting phenomena further requires powerful and predictable computing if time constraints must be attained. In this technical report we address the state-of-the-art on rendering, trying to put the focus on approaches, techniques and technologies that might enable real-time interactive web-based clientserver rendering systems. The focus is on the end-systems and not the networking technologies used to interconnect client(s) and server(s).Siemens; Bertelsmann mediaSystems GmbH; Eptron Multimedia; Instituto Politécnico do Porto - ISEP-IPP; Institute Laboratory for Mixed Realities at the Academy of Media Arts Cologne, LMR; Mälardalen Real-Time Research Centre (MRTC) at Mälardalen University in Västerås; Q-Systems

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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Designing SSI clusters with hierarchical checkpointing and single I/O space

Adopting a new hierarchical checkpointing architecture, the authors develop a single I/O address space for building highly available clusters of computers. They propose a systematic approach to achieving a single system image by integrating existing middleware support with the newly developed features.published_or_final_versio

A Simple Method for Dynamic Scheduling in a Heterogeneous Computing System

A simple method for the dynamic scheduling on a heterogeneous computing system is proposed in this paper. It was implemented to minimize the parallel program execution time. The proposed method decomposes the program workload into computationally homogeneous subtasks, which may be of the different size, depending on the current load of each machine in a heterogeneous computing system