Task Scheduling is a critical design issue of distributed computing. The emerging Grid computing infrastructure consists of heterogeneous resources in widely distributed autonomous domains and makes task scheduling even more challenging. Grid considers both static, unmovable hardware and moveable, replicable data as computing resources. While intensive research has been done on task scheduling ...

Loop scheduling techniques aim to achieve load-balanced executions of scientific applications. Dynamic loop self-scheduling (DLS) libraries for distributed-memory systems are typically MPI-based and employ a centralized chunk calculation approach (CCA) assign variably-sized chunks iterations. We present distributed (DCA) that supports various types DLS techniques. Using both CCA DCA, twelve imp...

The increasing acceptance of the Earliest Deadline First (EDF) scheduling algorithm in industrial environments, together with the continued usage of Fixed Priority (FP) scheduling is leading to heterogeneous systems with different scheduling policies in the same distributed system. Schedulability analysis techniques usually consider the entire system as a whole (holistic approach), with only on...

Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. It merges a lot of physical resources and offers them to users as services according to service level agreement. Therefore, resource management alongside with task scheduling has direct influence on cloud networks’ performance and efficiency. Presenting a proper scheduling ...

Cloud computing is a model for convenient on-demand user’s access to changeable and configurable computing resources such as networks, servers, storage, applications, and services with minimal management of resources and service provider interaction. Task scheduling is regarded as a fundamental issue in cloud computing which aims at distributing the load on the different resources of a distribu...

We consider the problem of minimizing the energy needed for data fusion in a large scale sensor network by varying the transmission times assigned to different sensor nodes. The optimal scheduling protocol is derived, based on which, we develop a low-complexity inverse-log scheduling algorithm that achieves near-optimal energy efficiency. To eliminate the communication overhead required by cent...

in this paper, a stochastic two-stage model is offered for optimization of the day-ahead scheduling of the microgrid. system uncertainties including dispatchable distributed generation and energy storage contingencies are considered in the stochastic model. for handling uncertainties, monte carlo simulation is employed for generation several scenarios and then a reduction method is used to d...

To an increasing extent, large-scale information processing is a distributed phenomenon. As the trend in computing moves further towards distributed networks of powerful workstations and information servers, we see the growing importance of solutions to dynamic distributed scheduling problems. In these domains, resource providers are distributed both geographically and bureaucratically, so that...

With the emergence of Cloud computing and Grid Computing, Distributed Scheduling (DS) problems have attracted attention by researchers in recent years. Distributed scheduling requires an uneven distribution of tasks on individual processors. Different heuristic based algorithms to perform the task scheduling have been proposed by the various researchers. This paper offers a new strategy for tas...

A distributed constraint satisfaction problem (DisCSP) is a CSP in which variables and constraints are distributed among multiple automated agents. Many researchers have developed techniques for solving DisCSPs. They assume for simplicity that each agent has exactly one variable. For real planning and scheduling problems, these techniques require a large number of messages passing among agents,...