Scheduling of directed acyclic graphs by a genetic algorithm with a repairing mechanism

Amirjanov, Adil; Соболев, Константин

doi:10.1002/cpe.3954

Cited by 5 publications

(3 citation statements)

References 17 publications

(36 reference statements)

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“…The models are equivalent if all delays are machine delays, so our machine delay results hold in the alternate model. The models differ in the presence of general job delays but are equivalent in several special cases, for instance in the setting where the job DAG is transitively closed, which has been extensively studied and proved useful in several important applications [34,4,93].…”

Section: Job Delays and Symmetric Machine Delaysmentioning

confidence: 99%

“…). There exists a polynomial time algorithm to compute a valid machine delays schedule with makespan O((log n) 4 (opt + ρ max )).…”

Section: Theorem 3 (Machine Delays Approximationmentioning

confidence: 99%

“…For special cases in which any one parameter is uniform across all machines, we have K = (log n) 2 groups, yielding an O((log n) 7 • (∆ σ + ρ σ )) approximation for any schedule σ. For special cases in which any two parameters are uniform across all machines, we have K = (log n) groups, yielding an O((log n) 4 • (∆ σ + ρ σ )) approximation for any schedule σ.…”

Section: Deriving An Rounded Solution To the Linear Programmentioning

confidence: 99%

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Scheduling under network communication constraints

Stalfa

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We study problems in job scheduling in the presence of communication constraints incurred as a result of the underlying machine network. We consider types of communication constraints: delay constraints and bandwidth constraints.In the delay constraints model, jobs represent precedence-constrained tasks to be executed on machines. Communication delays lower bound the time between completiing of a job and executing its successors on different machines.Our objective is to minimize makespan. Duplication of jobs is permitted. When scheduling on related machines with uniform delay, we provide an asymptotic polylogarithmic approximation. We also consider non-uniform delays where the delay between completing a predecessor job on one machine and executing its successor on another machine is given as an summation of the out-delays of the predecessor job and machine, and the in-delays of the successor job and maching.In this setting, we provide polylogarithmic asymptotic approximations for unit size jobs and related machines. For the uniform delay setting and the symmetric in/out-delay setting we convert our asymptotic approximations into true approximations when duplication is not allowed. We also demonstrate lower bounds on the solutions attainable using our LP-based methods, as well as a general lower bound when delays can arbitrarily depend on the job and machine being communicated to.When scheduling with bandwidth constraints, jobs or flows consist of pairs of machine nodes and represent communication requests between machines. Flows have release times and demands, and nodes have a capacity on how much demand can be satisfied from adjacent flows in a given round. We provide algorithms for several different models. Online and offline settings are considered, as well as settings where jobs may be fractionally executed across multiple rounds or must be scheduled all at once. We also consider optimizing maximum and average response times, and the simultaneous optimization of both. We present algorithms for various models, each using resource augmentation in the form of augmenting the capacities of the machine nodes. We also present lower bounds for offline and online approximations.

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Section: Job Delays and Symmetric Machine Delaysmentioning

confidence: 99%

“…). There exists a polynomial time algorithm to compute a valid machine delays schedule with makespan O((log n) 4 (opt + ρ max )).…”

Section: Theorem 3 (Machine Delays Approximationmentioning

confidence: 99%

Section: Deriving An Rounded Solution To the Linear Programmentioning

confidence: 99%

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Scheduling under network communication constraints

Stalfa

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Multi‐objective GA to schedule task graphs on heterogeneous voltage frequency islands

Sanchit,

Singh,

Singh

2024

Concurrency and Computation

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SummaryEnergy consumption of multiprocessor's system is increasing day by day. The capability of multiprocessor systems and high compute‐intensive tasks play a major role in increasing energy consumption. Voltage frequency island (VFI) architecture partitioned the cores into groups for which voltage/frequency can be controlled by a single switch. VFI plays a major role in optimizing the energy consumption. We have generated the initial population by using the slot technique to VFI architecture. The genetic algorithm studied by many researchers to solve scheduling problems. So we combined the genetic algorithm with the VFI‐enabled architecture and slot approach called VFIGen. Then apply the VFIGen algorithm to optimize the energy consumption. When comparing the results of the proposed one with the existing state‐of‐art we achieved the performance gain by to .

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A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

Silva

Gabriel

2020

Computation

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The multiprocessor task scheduling problem has received considerable attention over the last three decades. In this context, a wide range of studies focuses on the design of evolutionary algorithms. These papers deal with many topics, such as task characteristics, environmental heterogeneity, and optimization criteria. To classify the academic production in this research field, we present here a systematic literature review for the directed acyclic graph (DAG) scheduling, that is, when tasks are modeled through a directed acyclic graph. Based on the survey of 56 works, we provide a panorama about the last 30 years of research in this field. From the analyzes of the selected studies, we found a diversity of application domains and mapped their main contributions.

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Scheduling of directed acyclic graphs by a genetic algorithm with a repairing mechanism

Cited by 5 publications

References 17 publications

Scheduling under network communication constraints

Scheduling under network communication constraints

Multi‐objective GA to schedule task graphs on heterogeneous voltage frequency islands

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

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