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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

Resource Allocation Optimization Study of Cloud-Edge-End Collaboration Based on QUBO Model

Liangyu Qin

Published Jul 26, 2024• 0 citations
Publisher

Abstract

The core of this article is to construct an effective QUBO model to solve the problem of cloud edge end computing resource allocation, including two key scenarios: optimization of server maximum computing demand coverage and optimization of cloud edge server computing network layout. Each scenario provides a detailed description of the model's process from analysis, establishment to solution, overcoming several major difficulties in establishing a QUBO model, including using binary variables to express the objective function, transforming the objective function into QUBO expressions, and transforming inequality constraints. A scheme for the number of constraint relaxation factors is introduced, which effectively reduces the number of quantum bits and computational complexity. Finally, statistical analysis of the solution results, non parametric testing based on data characteristics, and detailed analysis are conducted, demonstrating the potential application of the QUBO model and quantum computing in dealing with complex combinatorial optimization problems. During the solving process, China's first optical quantum computing power cloud platform and corresponding quantum computing library Kaiwu SDK were utilized for research. Multiple solvers were used and their performance was compared to select the best performing taboo search solver, and then detailed results were calculated. The final results were fully visualized to make them more intuitive. The results indicate that quantum computing can provide an efficient, accurate, and stable solution for cloud edge end computing resource allocation. Through these studies, this article not only confirms the efficiency of quantum computing in solving specific optimization problems, but also provides some experience and reference for exploring the application of quantum computing in a wider field.

DOI
10.1109/icpics62053.2024.10796207

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The core of this article is to construct an effective QUBO model to solve the problem of cloud edge end computing resource allocation, including two key scenarios: optimization of server maximum computing demand coverage and optimization of cloud edge server computing network layout. Each scenario provides a detailed description of the model's process from analysis, establishment to solution, overcoming several major difficulties in establishing a QUBO model, including using binary variables to express the objective function, transforming the objective function into QUBO expressions, and transforming inequality constraints. A scheme for the number of constraint relaxation factors is introduced, which effectively reduces the number of quantum bits and computational complexity. Finally, statistical analysis of the solution results, non parametric testing based on data characteristics, and detailed analysis are conducted, demonstrating the potential application of the QUBO model and quantum computing in dealing with complex combinatorial optimization problems. During the solving process, China's first optical quantum computing power cloud platform and corresponding quantum computing library Kaiwu SDK were utilized for research. Multiple solvers were used and their performance was compared to select the best performing taboo search solver, and then detailed results were calculated. The final results were fully visualized to make them more intuitive. The results indicate that quantum computing can provide an efficient, accurate, and stable solution for cloud edge end computing resource allocation. Through these studies, this article not only confirms the efficiency of quantum computing in solving specific optimization problems, but also provides some experience and reference for exploring the application of quantum computing in a wider field.
To address the problem, we need to deploy two edge servers in a specific area to maximize the computational demand of the coverage. The problem can be transformed into a maximum coverage problem where the goal is to select a set of locations to deploy edge servers such that the total computational demand being covered is maximized. To solve this problem, we use a greedy algorithm. For quantum computing, the problem can be modeled as a quadratic unconstrained binary optimization (QUBO) problem. The solution process involves calculating the coverage area and computational requirements of possible server locations, constructing a QUBO model, solving the model using a solver and simulator, and parsing the results to obtain the optimal server deployment locations. The final result is the coordinate locations of the edge servers with optimized coverage of computational demands and the corresponding amount of coverage computational demands.
Edge computing is a promising technology that offers a superior user experience and enables various innovative Internet of Things applications. In this paper, we present a mixed-integer linear programming (MILP) model for optimal edge server placement and workload allocation, which is known to be NP-hard. To this end, we explore the possibility of addressing this computationally challenging problem using quantum computing. However, existing quantum solvers are limited to solving unconstrained binary programming problems. To overcome this obstacle, we propose a hybrid quantum-classical solution that decomposes the original problem into a quadratic unconstrained binary optimization (QUBO) problem and a linear program (LP) subproblem. The QUBO problem can be solved by a quantum solver, while the LP subproblem can be solved using traditional LP solvers. Our numerical experiments demonstrate the practicality of leveraging quantum supremacy to solve complex optimization problems in edge computing.
This paper discusses the solution method of the computational resource allocation problem based on the QUBO model, and analyzes the application in combination with the simulated annealing algorithm and the CIM simulator. The reasons for choosing the QUBO model and its advantages in logical reasoning are first introduced, and then the core principles of the QUBO model are analyzed, including its relationship with quadratic unconstrained optimization problems. Then the principles of simulated annealing algorithm and CIM simulator and their applications in problem solving are discussed in detail. In terms of specific problem solving, taking the optimal position of base station setting and the optimal path of overhauling as examples, the problem definition, decision variables, the establishment of QUBO model, and the solving process are described respectively, and the solving process is demonstrated through flowcharts. Finally, the solution results of the base station setup and maintenance path problems are given and the model is evaluated.
Edge computing is a promising technology that offers a superior user experience and enables various innovative Internet of Things applications. In this paper, we present a mixed-integer linear programming (MILP) model for optimal edge server placement and workload allocation, which is known to be NP-hard. To this end, we explore the possibility of addressing this computationally challenging problem using quantum computing. However, existing quantum solvers are limited to solving unconstrained binary programming problems. To overcome this obstacle, we propose a hybrid quantum-classical solution that decomposes the original problem into a quadratic unconstrained binary optimization (QUBO) problem and a linear program (LP) subproblem. The QUBO problem can be solved by a quantum solver, while the LP subproblem can be solved using traditional LP solvers. Our numerical experiments demonstrate the practicality of leveraging quantum supremacy to solve complex optimization problems in edge computing.
Optimization is a crucial challenge across various domains, including finance, resource allocation, and mobility. Quantum computing has the potential to redefine the way we handle complex problems by reducing computational complexity and enhancing solution quality. Optimization, particularly of objective functions, stands to benefit significantly from quantum solvers, which leverage principles of quantum mechanics like superposition, entanglement, and tunneling. The Ising and Quadratic Unconstrained Binary Optimization (QUBO) models are the most suitable formulations for these solvers, involving binary variables and constraints treated as penalties within the overall objective function. To harness quantum approaches for optimization, two primary strategies are employed: exploiting quantum annealers—special-purpose optimization devices—and designing algorithms based on quantum circuits. This review provides a comprehensive overview of quantum optimization methods, examining their advantages, challenges, and limitations. It demonstrates their application to real-world scenarios and outlines the steps to convert generic optimization problems into quantum-compliant models. Lastly, it discusses available tools and frameworks that facilitate the exploration of quantum solutions for optimization tasks.
This article explores the use of QUBO as a practical and educational framework for solving complex optimization problems. QUBO models are widely applicable across fields such as scheduling, routing, and resource allocation, and are compatible with classical, hybrid, and quantum solvers. We discuss the core principles behind QUBO, its connection to physical systems like the Ising model, and its growing relevance in research and education. To demonstrate the practical side, we apply QUBO to a real-world-inspired Multi-agent Route Planning problem, where the goal is to maximize the network coverage while minimizing the total overlap of vehicle-routes. This use-case helps illustrate how QUBO formulations can model trade-offs, encode constraints, and scale to larger problem sizes. In addition, we evaluate midsized instances of the problem on D-Wave Systems’ Advantage2 quantum annealer. The study aims to bridge theoretical optimization with hands-on experimentation, making it suitable for both teaching and applied research.