57,412 research outputs found
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
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
Window-based Streaming Graph Partitioning Algorithm
In the recent years, the scale of graph datasets has increased to such a
degree that a single machine is not capable of efficiently processing large
graphs. Thereby, efficient graph partitioning is necessary for those large
graph applications. Traditional graph partitioning generally loads the whole
graph data into the memory before performing partitioning; this is not only a
time consuming task but it also creates memory bottlenecks. These issues of
memory limitation and enormous time complexity can be resolved using
stream-based graph partitioning. A streaming graph partitioning algorithm reads
vertices once and assigns that vertex to a partition accordingly. This is also
called an one-pass algorithm. This paper proposes an efficient window-based
streaming graph partitioning algorithm called WStream. The WStream algorithm is
an edge-cut partitioning algorithm, which distributes a vertex among the
partitions. Our results suggest that the WStream algorithm is able to partition
large graph data efficiently while keeping the load balanced across different
partitions, and communication to a minimum. Evaluation results with real
workloads also prove the effectiveness of our proposed algorithm, and it
achieves a significant reduction in load imbalance and edge-cut with different
ranges of dataset
A Simulated Annealing Method to Cover Dynamic Load Balancing in Grid Environment
High-performance scheduling is critical to the achievement of application performance on the computational grid. New scheduling algorithms are in demand for addressing new concerns arising in the grid environment. One of the main phases of scheduling on a grid is related to the load balancing problem therefore having a high-performance method to deal with the load balancing problem is essential to obtain a satisfactory high-performance scheduling. This paper presents SAGE, a new high-performance method to cover the dynamic load balancing problem by means of a simulated annealing algorithm. Even though this problem has been addressed with several different approaches only one of these methods is related with simulated annealing algorithm. Preliminary results show that SAGE not only makes it possible to find a good solution to the problem (effectiveness) but also in a reasonable amount of time (efficiency)
A Three-Level Parallelisation Scheme and Application to the Nelder-Mead Algorithm
We consider a three-level parallelisation scheme. The second and third levels
define a classical two-level parallelisation scheme and some load balancing
algorithm is used to distribute tasks among processes. It is well-known that
for many applications the efficiency of parallel algorithms of the second and
third level starts to drop down after some critical parallelisation degree is
reached. This weakness of the two-level template is addressed by introduction
of one additional parallelisation level. As an alternative to the basic solver
some new or modified algorithms are considered on this level. The idea of the
proposed methodology is to increase the parallelisation degree by using less
efficient algorithms in comparison with the basic solver. As an example we
investigate two modified Nelder-Mead methods. For the selected application, a
few partial differential equations are solved numerically on the second level,
and on the third level the parallel Wang's algorithm is used to solve systems
of linear equations with tridiagonal matrices. A greedy workload balancing
heuristic is proposed, which is oriented to the case of a large number of
available processors. The complexity estimates of the computational tasks are
model-based, i.e. they use empirical computational data
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
Addressing the Challenges in Federating Edge Resources
This book chapter considers how Edge deployments can be brought to bear in a
global context by federating them across multiple geographic regions to create
a global Edge-based fabric that decentralizes data center computation. This is
currently impractical, not only because of technical challenges, but is also
shrouded by social, legal and geopolitical issues. In this chapter, we discuss
two key challenges - networking and management in federating Edge deployments.
Additionally, we consider resource and modeling challenges that will need to be
addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and
Paradigms; Editors Buyya, Sriram
Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster
The widespread use of GPS-enabled smartphones along with the popularity of
micro-blogging and social networking applications, e.g., Twitter and Facebook,
has resulted in the generation of huge streams of geo-tagged textual data. Many
applications require real-time processing of these streams. For example,
location-based e-coupon and ad-targeting systems enable advertisers to register
millions of ads to millions of users. The number of users is typically very
high and they are continuously moving, and the ads change frequently as well.
Hence sending the right ad to the matching users is very challenging. Existing
streaming systems are either centralized or are not spatial-keyword aware, and
cannot efficiently support the processing of rapidly arriving spatial-keyword
data streams. This paper presents Tornado, a distributed spatial-keyword stream
processing system. Tornado features routing units to fairly distribute the
workload, and furthermore, co-locate the data objects and the corresponding
queries at the same processing units. The routing units use the Augmented-Grid,
a novel structure that is equipped with an efficient search algorithm for
distributing the data objects and queries. Tornado uses evaluators to process
the data objects against the queries. The routing units minimize the redundant
communication by not sending data updates for processing when these updates do
not match any query. By applying dynamically evaluated cost formulae that
continuously represent the processing overhead at each evaluator, Tornado is
adaptive to changes in the workload. Extensive experimental evaluation using
spatio-textual range queries over real Twitter data indicates that Tornado
outperforms the non-spatio-textually aware approaches by up to two orders of
magnitude in terms of the overall system throughput
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