Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing

By H. Topcuoglu, Salim Hariri, Minyou Wu. IEEE Trans. Parallel Distributed Syst., 2002

3,602 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/2730606a9d29bb52bcc42124393460503f736d74

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Citations in the corpus

A Survey of Data-Intensive Scientific Workflow Management (2015, methodology): “The static task scheduling algorithms have two kinds of processor selection methods [128]: heuristic-based and guided random search based.”
A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues (2014, methodology): “In this section, we first address the heuristic task-to-processor mapping approach based on the HEFT algorithm for DAG applications with precedence constraints in Section 4.4.1.”
Domain-specific Genetic Algorithm for Multi-tenant DNNAccelerator Scheduling (2021, methodology): “We also consider a joint method, Heterogeneous Earliest-Finish-Time (HEFT) [89, 110, 111], as a baseline.”
Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms (2006, methodology): “The HEFT algorithm is a list scheduling algorithm which attempts to schedule interdependent tasks at minimum execution time on a heterogeneous environment.”
HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds (2011, methodology): “The Heterogeneous Earliest Finish Time (HEFT) [32] is a well known heuristic to schedule DAGs in heterogeneous systems.”
A hybrid heuristic for DAG scheduling on heterogeneous systems (2004, methodology): “In a recent study [16], it was observed that the performance of a commonly cited list scheduling heuristic, HEFT [ 14 ], is affected significantly by the approach followed to assign weights to the nodes and edges of the graph.”
An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements (2009, background): “The most popular approaches are the static or dynamic list scheduling algorithms based on different heuristics, such as opportunistic load balancing (OLB), minimum execution time (MET), minimum completion time (MCT) [8], min–min [8]–[12], max– min [8]–[12], duplex [8], sufferage [10], [11], random [”
Scheduling Algorithms for Grid Computing: State of the Art and Open Problems (2006, methodology): “HEFT: Topcuoglu et al [107] present a heuristic called Heterogeneous Earliest-Finish-Time (HEFT) algorithm.”
WorkflowSim: A toolkit for simulating scientific workflows in distributed environments (2012, methodology): “For example, scheduling algorithms, such as HEFT [3], Min-Min [1], Max-Min [2], etc., have used simulators to evaluate their effectiveness.”
Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions (2011, methodology): “Since these applications in scientific and engineering fields are the most typical application model, the problem of scheduling these applications (task scheduling) has been studied extensively over the past few decades (e.g., [9], [10], [11], [12]).”
StarPU: a unified platform for task scheduling on heterogeneous multicore architectures (2011, methodology): “greedy Greedy policy with support for priorities no-prio Greedy policy without support for priorities ws Greedy policy based on Work Stealing w-rand Random weighted by processor speeds heft-tm Heterogeneous Earliest Finish Time [14]”
A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing (2015, background): “The focus is on reducing the time associated with scheduling performance, such as the response time, optimal span and completion time [6]–[9].”
Evolutionary Multi-Objective Workflow Scheduling in Cloud (2016, neutral): “F”