Integer Programming for Telecommunications

Lee, Eva K.; Lewis, D. S.

doi:10.1007/978-0-387-30165-5_3

Cited by 3 publications

(2 citation statements)

References 148 publications

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“…In the remainder of this paper, we denote by P(c, A, b, , u, I) a MIP of form (1) in dependence of the provided data. This allows to model many real-world optimization problems from various fields like production planning [28], scheduling [20], transportation [13], or telecommunication networks [24]. On the other hand, the strict specifications for the problem statement make it possible to solve arising optimization problems for all these applications using the same algorithm.…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Primal Heuristics for Mixed Integer Programming

Gamrath

Berthold

Stefan

et al. 2015

Optimization in the Real World

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Primal heuristics play an important role in the solving of mixed integer programs (MIPs). They help to reach optimality faster and provide good feasible solutions early in the solving process. In this paper, we present two new primal heuristics which take into account global structures available within MIP solvers to construct feasible solutions at the beginning of the solving process. These heuristics follow a large neighborhood search (LNS) approach and use global structures to define a neighborhood that is with high probability significantly easier to process while (hopefully) still containing good feasible solutions. The definition of the neighborhood is done by iteratively fixing variables and propagating these fixings. Thereby, fixings are determined based on the predicted impact they have on the subsequent domain propagation. The neighborhood is solved as a sub-MIP and solutions are transferred back to the original problem. Our computational experiments on standard MIP test sets show that the proposed heuristics find solutions for about every third instance and therewith help to improve the average solving time.

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Section: Introductionmentioning

confidence: 99%

Structure-Based Primal Heuristics for Mixed Integer Programming

Gamrath

Berthold

Stefan

et al. 2015

Optimization in the Real World

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“…Optimization algorithms have become a very useful and frequently used solution for this paradigm. Mixed integer programming (MIP) has become a useful tool for resource allocation problems, addressing network synthesis and allocation issues [21]. In other words, they opted for greedy heuristic solutions [22][23][24] to solve the task offloading problem.…”

Section: Introductionmentioning

confidence: 99%

Task Offloading in Edge Computing Using GNNs and DQN

Garmendia-Orbegozo,

Nunez-Gonzalez,

Anton

2024

CMES

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In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions. At the same time, establishing an optimal offloading policy, which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy. This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms, Graph Neural Networks (GNN) and Deep Q-Network (DQN). It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSim and compares them with the two default methods, Trade-Off and Round Robin. Experiments showed that variants offer a slight improvement in task success rate and workload distribution. In terms of energy efficiency, they provided similar results. Finally, the success rates of different computing centers are tested, and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated. These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively, considering the quality of its connections and constant updates. The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed. Simultaneously, the suitability of Reinforcement Learning (RL) techniques is demonstrated due to the dynamism of the network environment, considering all the key factors that affect the decision to offload a given task, including the actual state of all devices.

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Service Operations: Behavioral Operational Research in British Telecommunications

Malpass

2016

Behavioral Operational Research

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Integer Programming for Telecommunications

Cited by 3 publications

References 148 publications

Structure-Based Primal Heuristics for Mixed Integer Programming

Structure-Based Primal Heuristics for Mixed Integer Programming

Task Offloading in Edge Computing Using GNNs and DQN

Service Operations: Behavioral Operational Research in British Telecommunications

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