Related papers: A Survey on Dynamic Job Scheduling in Grid Environ…
We use ideas from distributed computing to study dynamic environments in which computational nodes, or decision makers, follow adaptive heuristics (Hart 2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly "best…
We consider a stochastic, dynamic job scheduling problem, formulated as a queueing control problem, in which a single server processes jobs of different types that arrive according to independent Poisson processes. The problem is defined on…
Resource management and scheduling plays a crucial role in achieving high utilization of resources in grid computing environments. Due to heterogeneity of resources, scheduling an application is significantly complicated and challenging…
Cloud computing is a newly emerging distributed computing which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes so that the tasks can…
A fundamental problem in distributed computing is the task of cooperatively executing a given set of $t$ tasks by $p$ processors where the communication medium is dynamic and subject to failures. The dynamics of the communication medium…
Grid computing is a distributed computing paradigm which aims to aggregate several heterogeneous and distributed resources, belonging to different and independent organizations, in a dynamic, transparent and coordinated way. Since its…
Grid computing (GC) systems are large-scale virtual machines, built upon a massive pool of resources (processing time, storage, software) that often span multiple distributed domains. Concurrent users interact with the grid by adding new…
Many emerging Artificial Intelligence (AI) applications require on-demand provisioning of large-scale computing, which can only be enabled by leveraging distributed computing services interconnected through networking. To address such…
Traditionally, on-demand, rigid, and malleable applications have been scheduled and executed on separate systems. The ever-growing workload demands and rapidly developing HPC infrastructure trigger the interest of converging these…
Today scheduling problems have an immense effect on various areas of human lives, be it from their application in manufacturing and production industry, transportation, or workforce allocation. The unrelated parallel machines scheduling…
Clusters of computers have emerged as mainstream parallel and distributed platforms for high-performance, high-throughput and high-availability computing. To enable effective resource management on clusters, numerous cluster managements…
This paper presents a scheduling framework that is configured for, and used in physic systems. Our work addresses the problem of scheduling various computationally intensive and data intensive applications that are required for extracting…
Network integration studies try to assess the impact of future developments, such as the increase of Renewable Energy Sources or the introduction of Smart Grid Technologies, on large-scale network areas. Goals can be to support strategic…
Existing research on single-machine scheduling is largely focused on exact algorithms, which perform well on typical instances but can significantly deteriorate on certain regions of the problem space. In contrast, data-driven approaches…
Recent years have witnessed a rapid growth of distributed machine learning (ML) frameworks, which exploit the massive parallelism of computing clusters to expedite ML training. However, the proliferation of distributed ML frameworks also…
The proliferation of multi-core and multiprocessor-based computer systems has led to explosive development of parallel applications and hence the need for efficient schedulers. In this paper, we study hierarchical scheduling for malleable…
Load management is being recognized as an important option for active user participation in the energy market. Traditional load management methods usually require a centralized powerful control center and a two-way communication network…
Virtualization technology has enabled applications to be decoupled from the underlying hardware providing the benefits of portability, better control over execution environment and isolation. It has been widely adopted in scientific grids…
We investigate the global scheduling of sporadic, implicit deadline, real-time task systems on multiprocessor platforms. We provide a task model which integrates job parallelism. We prove that the time-complexity of the feasibility problem…
Remote job submission and execution is fundamental requirement of distributed computing done using Cluster computing. However, Cluster computing limits usage within a single organization. Grid computing environment can allow use of…