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In traditional research on task scheduling algorithms based on user satisfaction, all users can only have the same satisfaction function that means system assumes all users have the same scheduling motivation. But in real distributed system, different users usually have different motivations. To solve this problem, in this paper, we propose a genetic scheduling algorithm based on user overall satisfaction,...
Cloud computing has been gaining popularity for quite some time in various areas, on the infrastructure, platform and application level. Recently, the possibility to provide high performance computing (HPC) as a service has been investigated in conjunction with the cloud computing paradigm. While this is a viable solution for applications that do not require HPC in the truest sense -- with supercomputers...
We address the problem of scheduling precedence-constrained scientific applications on a heterogeneous distributed processor system with the twin objectives of minimizing simultaneously energy consumption and schedule length. Previous research efforts on scheduling have focused on the minimization of a quality of service metric based on the completion time of applications (e.g., the schedule length)...
The emergence of grid and cloud computing require load balancers to deal with potential problems, such as high level of scalability and heterogeneity of computing resources. In this paper, we present a generic load balancing framework which separates allocating process and migrating process while preserving a guaranteed level of service. Based on this framework, an intelligent load balancer that is...
The heterogeneous distributed computing system consists of network of heterogeneous computers and the applications to execute on it. The applications may have different deadline criteria. Based on the nature of the deadline, the applications are categorised as mission-critical, firm and soft. In this paper, a load balanced algorithm is proposed for non-pre-emptively scheduling a bag of independent...
The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. The goal of grid task scheduling is to achieve high system throughput and to allocate various computing resources...
In this paper we present a distributed event driven middleware architecture for situational awareness and intelligent decision making for command and control of geographically distributed networked battlefield agents. We tackle the important and challenging issues of distributed agents scheduling, synchronization, load balancing, and terrain database distribution/management/allocation in a distributed...
Constructing Web service workflow faces huge challenges in the volatile, heterogeneous, distributed environment: it is necessary to consider the dynamic changes in web services, but also take into account the rapid method of modeling workflow. Comparing workflow modeling and artificial intelligence planning process, if the Web service as a planned action (or activity), then the modern AI planning...
Cloud computing has been build upon the development of distributed computing, grid computing and virtualization. Since cost of each task in cloud resources is different with one another, scheduling of user tasks in cloud is not the same as in traditional scheduling methods. The objective of this paper is to schedule task groups in cloud computing platform, where resources have different resource costs...
To solve high real-time and complexity calculation problems such as feature extraction and pattern classification when wireless sensor network real-time diagnosis and equipment health record of the mine coal underground equipments monitoring, this paper purpose a optimal algorithm for task scheduling underground wireless monitoring network based on distributed computing, this method use the fast convergence...
Recent advances in parallel and distributed computing have made it very challenging for programmers to reach the performance potential of current systems. In addition, recent advances in numerical algorithms and software optimizations have tremendously increased the number of alternatives for solving a problem, which further complicates the software tuning process. Indeed, no single algorithm can...
This paper presents a framework for developing and executing parallel and distributed applications using the peer-to-peer computing model. The framework - called P2PComp - follows the main philosophy of the pure peer-to-peer model, since there is no hierarchy among the peers, all peers have the same functions and there is no central authority server responsible for the system organization. SPMD parallel...
In this paper, we present an approach to scalable co-scheduling in distributed computing for complex sets of interrelated tasks (jobs). The scalability means that schedules are formed for job models with various levels of task granularity, data replication policies, and the processor resource and memory can be upgraded. The necessity of guaranteed job execution at the required quality of service causes...
In traditional distributed computing the users and owners of the computational resources usually belong to the same administrative domain. Therefore all users are equally entitled to use the resources. The situation is completely different in large-scale emergent distributed computing systems, such as Grid systems, where the roles of the users are asymmetric as regards their access rights and usage...
Scheduling large amounts of tasks in distributed computing platforms composed of millions of nodes is a challenging goal, even more in a fully decentralized way and with low overhead. Thus, we propose a new scalable scheduler for task workflows with deadlines following a completely decentralized architecture. It's built upon a tree-based P2P overlay that supports efficient and fast aggregation of...
Task schedule is a critical issue of distributed computing. Foster et al. (2001) defined "Grid problem", which is defined as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources -what they referred to as virtual organizations (VO). Improving the performance of grid computing relies much on the grid task scheduling algorithm...
The drift towards new challenges in Grid computing including scientific workflow management implies the need for new, robust, multicriteria scheduling algorithms that can be applied by the user in an intuitive way. Currently existing bi-criteria scheduling approaches for scientific workflows are usually restricted to certain criterion pairs and require the user to define his preferences either as...
Cloud computing focuses on delivery of reliable, fault-tolerant and scalable infrastructure for hosting Internet based application services. This paper presents the implementation of an efficient Quality of Service (QoS) based Meta-Scheduler and Backfill strategy based light weight Virtual Machine Scheduler for dispatching jobs. The user centric meta-scheduler deals with selection of proper resources...
Heterogeneity of resources can not be ignored while scheduling application task graphs in grid environment. In this paper, a list-based task scheduling algorithm, called scheduling with heterogeneity using critical path (SHCP) for grid computing system is presented. Some other scheduling algorithms such as HEFT use mean execution time based b-level for deciding task priority and ignore the importance...
Over the past decade, scheduling in distributed computing system has been an active research. However, it is still difficult to find an optimal scheduling algorithm to achieve load balancing for a specific scientific application which is executed in an unpredictable environment. This is due to the complex nature of the application which changes during runtime and due to the dynamic nature and unpredictability...
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