3,700 research outputs found
Decentralized load balancing in heterogeneous computational grids
With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research
Dynamic load balancing for the distributed mining of molecular structures
In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of
methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the
past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially
render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to
discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no
reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic
partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated
load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer
Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed
approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable
for large-scale, multi-domain, heterogeneous environments, such as computational grids
A Novel Workload Allocation Strategy for Batch Jobs
The distribution of computational tasks across a diverse set of geographically distributed heterogeneous resources is a critical issue in the realisation of true computational grids. Conventionally, workload allocation algorithms are divided into static and dynamic approaches. Whilst dynamic approaches frequently outperform static schemes, they usually require the collection and processing of detailed system information at frequent intervals - a task that can be both time consuming and unreliable in the real-world. This paper introduces a novel workload allocation algorithm for optimally distributing the workload produced by the arrival of batches of jobs. Results show that, for the arrival of batches of jobs, this workload allocation algorithm outperforms other commonly used algorithms in the static case. A hybrid scheduling approach (using this workload allocation algorithm), where information about the speed of computational resources is inferred from previously completed jobs, is then introduced and the efficiency of this approach demonstrated using a real world computational grid. These results are compared to the same workload allocation algorithm used in the static case and it can be seen that this hybrid approach comprehensively outperforms the static approach
Cluster-Based Load Balancing Algorithms for Grids
E-science applications may require huge amounts of data and high processing
power where grid infrastructures are very suitable for meeting these
requirements. The load distribution in a grid may vary leading to the
bottlenecks and overloaded sites. We describe a hierarchical dynamic load
balancing protocol for Grids. The Grid consists of clusters and each cluster is
represented by a coordinator. Each coordinator first attempts to balance the
load in its cluster and if this fails, communicates with the other coordinators
to perform transfer or reception of load. This process is repeated
periodically. We analyze the correctness, performance and scalability of the
proposed protocol and show from the simulation results that our algorithm
balances the load by decreasing the number of high loaded nodes in a grid
environment.Comment: 17 pages, 11 figures; International Journal of Computer Networks,
volume3, number 5, 201
DECENTRALIZED RESOURCE ORCHESTRATION FOR HETEROGENEOUS GRIDS
Modern desktop machines now use multi-core CPUs to enable improved performance. However, achieving high performance on multi-core machines without optimized software support is still difficult even in a single machine, because contention for shared resources can make it hard to exploit multiple computing resources efficiently. Moreover, more diverse and heterogeneous hardware platforms (e.g. general-purpose GPU and Cell processors) have emerged and begun to impact grid computing. Given that heterogeneity and diversity are now a major trend going forward, grid computing must support these environmental changes.
In this dissertation, I design and evaluate a decentralized resource management scheme to exploit heterogeneous multiple computing resources effectively. I suggest resource management algorithms that can efficiently utilize a diverse computational environment, including multiple symmetric computing entities and heterogeneous multi-computing entities, and achieve good load-balancing and high total system throughput. Moreover, I propose expressive resource description techniques to accommodate more heterogeneous environments, allowing incoming jobs with complex requirements to be matched to available resources.
First, I develop decentralized resource management frameworks and job scheduling schemes to exploit multi-core nodes in peer-to-peer grids. I present two new load-balancing schemes that explicitly account for resource sharing and contention across multiple cores within a single machine, and propose a simple performance prediction model that can represent a continuum of resource sharing among cores of a CPU. Second, I provide scalable resource discovery and load balancing techniques to accommodate nodes with many types of computing elements, such as multi-core CPUs and GPUs, in a peer-to-peer grid architecture. My scheme takes into account diverse aspects of heterogeneous nodes to maximize overall system throughput as well as minimize messaging costs without sacrificing the failure resilience provided by an underlying peer-to-peer overlay network. Finally, I propose an expressive resource discovery method to support multi-attribute, range-based job constraints. The common approach of using simple attribute indexes does not suffice, as range-based constraints may be satisfied by more than a single value. I design a compact ID-based representation for resource characteristics, and integrate this representation into the decentralized resource discovery framework.
By extensive experimental results via simulation, I show that my schemes can match heterogeneous jobs to heterogeneous resources both effectively (good matches are found, load is balanced), and efficiently (the new functionality imposes little overhead)
Resource allocation for query processing in grid systems: A survey
Grid systems are very useful platforms for distributed databases, especially in some situations in which the scale of data sources and user requests is very high. However, the main characteristics of grid systems such as dynamicity, large size and heterogeneity, bring new problems to the query processing domain such as resource discovery and resource allocation. In this paper, we provide a survey related to resource allocation methods for query processing In data grid systems. We provide a classification for existing studies considering their approaches to the resource allocation problem. We provide a synthesis of the studies and propose evaluations and comparisons for the different classes of studies. ©2012 CRL Publishing Ltd
A Case for Cooperative and Incentive-Based Coupling of Distributed Clusters
Research interest in Grid computing has grown significantly over the past
five years. Management of distributed resources is one of the key issues in
Grid computing. Central to management of resources is the effectiveness of
resource allocation as it determines the overall utility of the system. The
current approaches to superscheduling in a grid environment are non-coordinated
since application level schedulers or brokers make scheduling decisions
independently of the others in the system. Clearly, this can exacerbate the
load sharing and utilization problems of distributed resources due to
suboptimal schedules that are likely to occur. To overcome these limitations,
we propose a mechanism for coordinated sharing of distributed clusters based on
computational economy. The resulting environment, called
\emph{Grid-Federation}, allows the transparent use of resources from the
federation when local resources are insufficient to meet its users'
requirements. The use of computational economy methodology in coordinating
resource allocation not only facilitates the QoS based scheduling, but also
enhances utility delivered by resources.Comment: 22 pages, extended version of the conference paper published at IEEE
Cluster'05, Boston, M
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