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MapReduce model was designed for distributed large volume of data processing. Time constraints are very important for user productivity but, in a shared cluster, the need for job starvation avoidance also arises. In this paper we propose an extension to the well-known FairScheduler algorithm from Hadoop which takes into consideration soft deadlines for jobs in homogeneous clusters, aiming to improve...
Massive job scheduling problem is an important research area in big data research era. This paper proposed self-adaptive job scheduling mechanism based on Ant-Genetic Algorithm aiming at improving convergence speed and accuracy by mutation strategy based on Ant Algorithm and efficient refinement within Genetic Algorithm. The experimental results show that the proposed algorithm can find the most suitable...
With the increasingly widespread adoption of cloud computing and tenants' growing needs for large-scale data processing, cluster scheduling frameworks (e.g. MapReduce, Spark, etc.) have emerged as important programming models that works for distributed and parallel computing on cloud systems. While several recent researches proposed some solutions to optimize the MapReduce-like scheduler, they hardly...
Today, data grid appears more and more like the future solution of hardware and software offering infinite computing and storage capacity. In order to best exploit the available resources, it seems necessary to design new replication solutions and data movement suited to this type of architecture. It is therefore necessary to adapt the travel policies, positioning and data management based on the...
Distributed computing environment has become a new technology to execute large-scale applications and Cloud computing is one of these technologies. Resource allocation is one of the most important challenges in the Cloud Computing. The optimally assigning of the available resources to the needed cloud applications is known to be a NP complete problem. In this paper, we propose a new task scheduling...
The massive demand for running parallel applications on distributed systems has led to an upsurge in the system power consumption. These systems often consist of thousands or millions of cores, storage disks, interconnection devices and other power-hungry components. To address this power consumption problem, we propose two energy-aware scheduling algorithms, namely, Energy-Efficiency with Duplication...
We present a completely decentralized algorithm for parallel job scheduling and load balancing in distributed peer-to-peer environments. This algorithm is useful for meta-scheduling across known clusters and scheduling on desktop grids. To accomplish this, we build on previous work to route jobs to appropriate resources then use the new algorithm to start parallel jobs and balance load across the...
Cloud Computing propounds a striking option for business to pay only for the resources that were consumed. The prime challenge is to increase the MapReduce clusters to minimize their costs. MapReduce is a widely used parallel computing framework for large scale data processing. The major concern of map reduce programming model are job execution time and cluster throughput. Multiple speculative execution...
In the past decade, more and more attention focuses on job scheduling strategies in a variety of scenarios. Due to the characteristics of clouds, meta-scheduling turns out to be an important scheduling pattern because it is responsible for orchestrating resources managed by independent local schedulers and bridges the gap between participating nodes. Likewise, to overcome issues such as bottleneck,...
In this paper we study the problem of batch scheduling within a homogeneous cluster. In this context, the problem is that the more processors the job requires the more difficult it is to find an idle slot to run it on. As a consequence the resources are often inefficiently used as some of them remain unallocated in the final schedule. To address this issue we propose a technique called job folding...
Cloud computing has recently emerged as a convergence of concepts such as cluster computing, grid computing, utility computing, and virtualization. In hybrid clouds, the user has its private cloud available for use, but she can also request new resources to public clouds in a pay-per-use basis when there is an increase in demand. In this scenario it is important to decide when and how to request these...
The diversity of job characteristics such as unstructured/unorganized arrival of jobs and priorities, could lead to inefficient resource allocation. Therefore, the characterization of jobs is an important aspect worthy of investigation, which enables judicious resource allocation decisions achieving two goals (performance and utilization) and improves resource availability.
The management of resources and scheduling computations is a challenging problem in grid. Load balancing is essential for efficient utilization of resources and enhancing the performance of computational grid. In this paper, we propose a decentralized grid model, as a collection of clusters. We then introduce a dynamic load balancing algorithm (DLBA) which performs intra cluster and inter cluster...
Task scheduling still remains one of the most challenging problems to achieve high performance in heterogeneous computing environments in spite of numerous efforts. This paper presents a novel scheduling algorithm based on learning classifier system for heterogeneous computing environment. In the presented algorithm, XCS classifier system is used to find the optimal task assignment on different processors,...
An essential issue in distributed high-performance computing is how to allocate efficiently the workload among the processors. This is specially important in a computational Grid where its resources are heterogeneous and dynamic. Algorithms like Quadratic Self-Scheduling (QSS) and Exponential Self-Scheduling (ESS) are useful to obtain a good load balance, reducing the communication overhead. Here,...
The problem of scheduling and allocation of tasks to processing nodes in large computational grids (CG) is studied in this paper. Each node of the system is considered as an autonomous stand-alone processing unit, ranging from workstations or small computing devices to computational clusters. For large-scale scheduling on very large CGs, two heuristic algorithms (neighbor and tabu search)are applied...
MPI is the most important parallel programming tool in cluster currently. It implements communication in parallel program by message. Implementing load balance in MPI parallel program is very important. It may reduce running time and improve performance of MPI parallel program, aiming at solving the dynamic balancing problem in homogeneous cluster system. This paper proposes an implementing method...
MPICH is the most important parallel programming tool in cluster currently. It implements communication in parallel program by message. Implementing load balance in MPI parallel program is very important. It may reduce running time and improve performance of MPI parallel program, aiming at solving the dynamic balancing problem in homogeneous cluster system. This paper proposes an implementing method...
In recent years, an increasing amount of computer network research has focused on the problem of cluster system in order to achieve higher performance and lower cost. Memory management becomes a prerequisite when handling applications that require immense volume of data for e.g. satellite images used for remote sensing, defense purposes and scientific applications. The load unbalance is the major...
The paper presents a task allocation technique for multiple applications onto heterogeneous distributed computing system to minimize the overall makespan. An existing critical path based algorithm for scheduling of tasks of single application has been used to allocate tasks of multiple applications onto heterogeneous distributed computing system. The paper discusses how a composite application is...
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