MIP formulation for robust resource allocation in dynamic real-time systems

Gertphol, Sethavidh; Prasanna, Viktor K.

doi:10.1016/j.jss.2003.12.040

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Task allocation in real-time systems in order to meet certain deadlines is known to be an NP-hard problem (Gertphol and Prasanna 2005). In addition, the highly uncertain weather conditions and the dynamic environment add to the complexity of the problem.…”

Section: Methodsmentioning

confidence: 99%

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

Assimizele¹,

Oppen²,

Bye³

2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

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Oil tanker traffic constitutes a vital part of the maritime operations in the High North and is associated with considerable risk to the environment. As a consequence, the Norwegian Coastal Administration (NCA) administers a number of vessel traffic services (VTS) centers along the Norwegian coast, one of which is located in the town of Vardø, in the extreme northeast part of Norway. The task of the operators at the VTS center in Vardø is to command a fleet of tug vessels patrolling the northern Norwegian coastline such that the risk of oil tanker drifting accidents is reduced. Currently, these operators do not use computer algorithms or mathematical models to solve this dynamic resource allocation problem but rely on their own knowledge and experience when faced with constantly changing weather and traffic conditions. We therefore propose a novel sustainable model called the receding horizon mixed integer programming (RHMIP) model for optimal dynamic allocation of patrol vessels to oil tankers. The model combines features from model predictive control and linear programming. Simulations run with real-world parameters highlight the performance and quality of our method. The developed RHMIP model can be implemented as an operational decision support tool to the NCA.

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Section: Methodsmentioning

confidence: 99%

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

Assimizele¹,

Oppen²,

Bye³

2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

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“…Work has also been done on improving the robustness or flexibility of a schedule in a job-shop execution environment, by minimizing the lateness factor and by using neighborhood-based methods for recovering from a disturbance [13]. A stochastic mixed integer programming (SMIP) approach is used to obtain a robust resource allocation for parallel and distributed systems [14]. As a subsequent work, the authors generate a more robust resource allocation using an iterative integer programming (IIP) approach [15].…”

Section: A Robustness Of Resource Allocation and Application Schedulingmentioning

confidence: 99%

Towards Robust Resource Allocations via Performance Modeling with Stochastic Process Algebra

Srivastava

Banicescu

2015

2015 IEEE 18th International Conference on Computational Science and Engineering

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Recent developments in the field of parallel and distributed computing have led to a proliferation in solving large and computationally intensive mathematical, science, or engineering problems, which consist of several parallelizable parts and several non-parallelizable (sequential) parts. However, such computational environments are often prone to unpredictable variations due to problem, algorithm, and system characteristics. Therefore, a robustness study of resource allocations and application scheduling is required to guarantee a desired level of performance. Given an initial workload, a mapping of applications to resources is considered to be robust if that mapping optimizes the execution performance and guarantees a desired level of performance in the presence of unpredictable perturbations at runtime.In this research, a stochastic process algebra, Performance Evaluation Process Algebra (PEPA), is used for obtaining performance of various resource allocations via a numerical analysis of performance modeling of the parallel execution of applications on parallel computing resources. The PEPA performance model is translated into an underlying mathematical Markov chain model for obtaining performance measures. Further, a robustness analysis of various allocations is performed for finding a robust mapping from a set of initial mapping schemes. The numerical results obtained from this performance modeling of resource allocations have been validated by the simulation results of earlier research which were made available in the existing literature, thus underscoring the significance and benefits of using stochastic process algebra models and the related numerical analysis in providing a cost effective and low overhead analysis of robustness.

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“…As the name suggests, variables in mathematical equations constituting the objective function and constraints of LP must be in linear form. Many techniques, such as pre-selection [8] and variable substitution [7,11], can be used to eliminate non-linearity that may occur in a formulation. …”

Section: Related Workmentioning

confidence: 99%

“…The first MIP-based approach used for comparison is called SMIP [7], which encodes the c min value (a real number) into an MIP formulation and optimizes c min directly. The SMIP formulation derived from the resource allocation problem is solved only once, and is expected to always produce an optimal allocation.…”

Section: Other Approaches For Comparisonmentioning

confidence: 99%

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Iterative integer programming formulation for robust resource allocation in dynamic real-time systems

Gertphol¹,

Prasanna²

18th International Parallel and Distributed Processing Symposium, 2004. Proceedings.

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Dynamic real-time systems often operate in a continuously changing environment, causing workload of the system to fluctuate. An initial resource allocation for these systems should be robust with respect to the variation in workload. Using the amount of additional workload that an allocation can accommodate as a measure of robustness, we develop an iterative integer programming approach, called IIP, to determine a robust resource allocation. IIP guarantees to produce an allocation with the measure of robustness that falls within δ from the optimal value, where δ is a user provided parameter for the IIP algorithm. In addition, trade-off between the quality of the resulting allocation and the execution time of IIP can be achieved by adjusting the parameter δ.

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MIP formulation for robust resource allocation in dynamic real-time systems

Cited by 5 publications

References 15 publications

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

Towards Robust Resource Allocations via Performance Modeling with Stochastic Process Algebra

Iterative integer programming formulation for robust resource allocation in dynamic real-time systems

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