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User allocation-aware edge cloud placement in mobile edge computing

SOFTWARE-PRACTICE & EXPERIENCE 2020年第5期50卷 489-502页

作者： Guo, Yan Wang, Shangguang Zhou, Ao Xu, Jinliang Yuan, Jie Hsu, Ching-Hsien Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Chung Hua Univ Dept Comp Sci & Informat Hsinchu Taiwan

Mobile edge computing is emerging as a novel ubiquitous computing platform to overcome the limit resources of mobile devices and bandwidth bottleneck of the core network in mobile cloud computing. In mobile edge computing, it is a significant issue for cost reduction and QoS improvement to place edge clouds at the edge network as a small data center to serve users. In this paper, we study the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. Specifically, we formulate it as a multiobjective optimization problem with objective to balance the workload between edge clouds and minimize the service communication delay of mobile users. To this end, we propose an approximate approach that adopted the K-means and mixed-integer quadratic programming. Furthermore, we conduct experiments based on Shanghai Telecom's base station data set and compare our approach with other representative approaches. The results show that our approach performs better to some extent in terms of workload balance and communication delay and validate the proposed approach.

关键词： edge cloud placement mixed-integer quadratic programming mobile edge computing user allocation workload balance

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Optimal Battery Sizing & Dispatch Model for Transformer Loss Reduction: A New Local Service 4

Optimal Battery Sizing & Dispatch Model for Transformer Loss...

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4th IEEE Global Power, Energy and Communication Conference (IEEE GPECOM)

作者： Sancar, Semanur Kartci, Emin Poyrazoglu, Gokturk Ozyegin Univ Elect & Elect Engn Istanbul Turkey

ISBN: (纸本)9781665469258

The possible services of batteries are expanding within the scope of transforming electricity networks. In this study, the potential of batteries to increase transformer efficiency is revealed. It is aimed to maximize the transformer efficiency depending on the demand and the installed power of the transformer. Based on this aim, optimization algorithms that can decide on the optimal battery size are developed. The daily charge-discharge cycle of the battery within the scope of transformer efficiency is explained. According to the pilot site studies carried out, up to 22% efficiency increase potential is discovered in a transformer with the battery. Considering this potential, the use of batteries to support transformer efficiency is recommended as a new local service concept.

关键词： optimal battery sizing transformer losses grid-size battery usage mixed-integer quadratic programming

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Joint arrival-departure taxiway scheduling considering departure uncertainty 26

Joint arrival-departure taxiway scheduling considering depar...

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IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

作者： Zhang, Yi Zhang, Yicheng Zhang, Sheng Huang, Liping Yin, Yifang ASTAR I2R 1 Fusionopolis Way21-01 Connexis Singapore 138632 Singapore

ISBN: (纸本)9798350399462

The increasing demand for air travel combined with uncertainties has put additional strain on airport infrastructure and ground handling resources. To improve the efficiency of airport operations, in this paper, We firstly perform an in-depth analysis of the A-SMGCS dataset for Singapore Changi Airport, and then propose taxiing routing solutions for both landing and departure aircraft with involved departure uncertainty, formulating the problem as a mixed-integer quadratic programming (MIQP) problem. The proposed model considers the waypoint-based conflict checking, as well as incorporates anti-overtaking constraints, and head-on constraints exclusive for bidirected graph, aiming to minimize the taxiing time as well as the gap with the scheduled in-block time for landing aircraft and scheduled take-off time for departure aircraft. The presence of departure uncertainty prompts us to build a stochastic model, where constraints with stochastic variables are converted into corresponding chance constraints under designated confidence levels. Incorporating stochastic factors enhances the resilience and dependability of our solution. To evaluate the efficiency of our proposed method, we have elaborately investigated the computational complexity under various test scales, and analyzed how changes in uncertainty and confidence levels impact routing and scheduling solutions on a simplified Singapore Changi Airport network, which could provide significant reference for other work.

关键词： airport surface operation taxiway routing/scheduling departure uncertainty stochastic optimization mixed-integer quadratic programming

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Solving mixed-integer quadratic Programs via Nonnegative Least Squares

Solving Mixed-Integer Quadratic Programs via Nonnegative Lea...

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5th IFAC Conference on Nonlinear-Model-Predictive-Control (NMPC)

作者： Bemporad, Alberto IMT Inst Adv Studies Lucca Lucca Italy

'Phis paper proposes a new algorithm for solving mixed-integer quadratic Program =r=ing (MIQP) problems The algorithm is particularly tailored to solving small-scale \1IQPs such as those that arise in embedded hybrid Model Predictive Control (A/1PC) applications. The approach combines branch and bound (B&B) with nonnegative least squares (NNLS), that are used to solve quadratic programming (QP) relaxations. The QP algorithm extends a method recently proposed by the author for solvingstrictly convex. QP's, by (i) equality and bilateral inequality constraints, (ii,) warm starting, and (ii) exploiting easy-to-compute lower hounds 011 the optimal cost to reduce the 1'1 ibe r of QP iterations required to solve the relaxed problems. The proposed MIQP algorithm has a speed of execution that is comparable to stateof-the-art commercial MIQP solvers and is relatively simple to code, as it requires only basic arithmetic operations to solve least-square problems.

关键词： mixed-integer quadratic programming quadratic programming Active set methods Nonnegative least squares Model predictive control Hybrid systems

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Embedded mixed-integer quadratic Optimization using Accelerated Dual Gradient Projection

Embedded Mixed-Integer Quadratic Optimization using Accelera...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Naik, Vihangkumar V. Bemporad, Alberto IMT Sch Adv Studies Lucca Lucca Italy

The execution of a hybrid model predictive controller (MPC) on an embedded platform requires solving a mixed-integer quadratic programming (MIQP) in real time. The MIQP problem is NP-hard, which poses a major challenge in an environment where computational and memory resources are limited. To address this issue, we propose the use of accelerated dual gradient projection (GPAD) to find both the exact and an approximate solution of the MIQP problem. In particular, an existing GPAD algorithm is specialized to solve the relaxed quadratic programming (QP) subproblems that arise in a Branch and Bound (B&B) method for solving the MIQP to optimality. Furthermore, we present an approach to find a suboptimal integer feasible solution of a MIQP problem without using B&B. The GPAD algorithm is very simple to code and requires only basic arithmetic operations which makes it well suited for an embedded implementation. The performance of the proposed approaches is comparable with the state of the art MIQP solvers for small-scale problems. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： mixed-integer quadratic programming quadratic programming Accelerated gradient projection model predictive control hybrid systems

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Optimal Sizing of Energy Station in the Multi-energy System Integrated with Data Center 56

Optimal Sizing of Energy Station in the Multi-energy System ...

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IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference

作者： Lyu, Jiawei Zhang, Shenxi Cheng, Haozhong Yuan, Kai Song, Yi Fang, Sidun Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ 800 Dongchuan Rd Shanghai 200240 Peoples R China State Grid Econ & Technol Res Inst CO LTD 18 Riverside Ave Beijing 102209 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9781728171951

With the springing up of data centers (DCs) worldwide, their huge energy consumption presents a daunting challenge to the energy system. This paper considers DC's integration, especially DC's dynamic voltage frequency scaling (DVFS) and thermal inertia, proposing a novel optimal sizing method for the energy station in the multi-energy system (MES), which can realize the co-optimization of energy flow and information flow. Firstly, the MES integrated with DC is modeled. Based on DVFS and thermal inertia, the deep coupling relationship of DC's computing workload, electricity load and cooling load is delved. Then, the optimal sizing model aimed to minimize the total cost is established. Constraints include power balance, DC's computing workload balance and thermal balance. The problem is simplified to a mixed-integer quadratic programming one. Case studies based on a test system illustrate that the co-optimization considering DC's integration can reduce the total cost of the energy station by 7.2%.

关键词： optimal sizing of energy station data center dynamic voltage frequency scaling thermal inertia mixed-integer quadratic programming

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Optimal Feature Selection for Support Vector Machine Classifiers

Optimal Feature Selection for Support Vector Machine Classif...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Strub, O. Univ Bern Dept Business Adm Bern Switzerland

ISBN: (纸本)9781538672204

Binary classification is a fundamental task in machine learning. It consists of learning a relationship between observable features of a set of training objects and their observable membership to either of two classes to predict as accurately as possible the class membership of new test objects whose features are observable but whose class membership is unknown. One of the most successful methods for binary classification is the support vector machine classifier that aims at finding a hyperplane in the feature space separating the training objects of the two classes. However, the accuracy of this classifier in predicting the correct classes strongly depends on the features selected for determining the hyperplane. In this paper, we propose the first exact approach, which is based on mixed-integer quadratic programming and delayed constraint generation, to identify an optimal set of relevant features for determining the hyperplane. The results of a computational experiment demonstrate that the proposed approach is able to successfully select an optimal set of relevant features in a short running time even for classification tasks with over 10,000 objects and 100 features.

关键词： Delayed Constraint Generation Feature Selection mixed-integer quadratic programming Support Vector Machines

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Moving Horizon Estimation for hybrid systems

Moving Horizon Estimation for hybrid systems

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2000 American Control Conference (ACC 2000)

作者： Ferrari-Trecate, G Mignone, D Morari, M ETH Zurich Swiss Fed Inst Technol Inst Automat CH-8092 Zurich Switzerland

ISBN: (纸本)0780355202

In this paper, by exploiting the equivalence between hybrid systems modeled in the mixed Logic Dynamical form and Piece-Wise Affine systems, we propose a state smoothing algorithm based on Moving Horizon Estimation (MHE). We provide sufficient conditions on the time horizon and the initial penalties to guarantee asymptotic convergence of the MHE scheme. Moreover we propose an algorithm for the computation of the initial penalties that allows to implement MHE by solving mixed-integer quadratic Programs.

关键词： hybrid systems state estimation observability piecewise affine systems mixed-integer quadratic programming

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Subtask-Level Elastic Scheduling 45

Subtask-Level Elastic Scheduling

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45th Real Time Systems Symposium

作者： Sudvarg, Marion Wang, Daisy Buhler, Jeremy Gill, Chris Washington Univ Dept Comp Sci & Engn St Louis MO 14263 USA

ISBN: (纸本)9798331540265;9798331540272

Buttazzo et al.'s elastic scheduling model allows task utilizations to be "compressed" to ensure schedulability atop limited resources. Each task is assigned a range of acceptable utilizations and an "elastic constant" representing the relative adaptability of its utilization. In this paper, we consider federated scheduling, under which each high-utilization parallel task is assigned dedicated processor cores. We propose a new model of elastic workload compression for parallel DAG tasks that assigns each subtask its own elastic constant and continuous range of acceptable workloads. We show that the problem can be solved offline as a mixed-integer quadratic program, or online using a pseudo-polynomial dynamic programming algorithm. We also consider joint core allocation and compression of low-utilization sequential tasks and present a mixed-integer linear program for optimal elastic compression of tasks under partitioned EDF scheduling. We show empirical improvements in schedulability over the prior work and present a case study for the Fast Integrated Mobility Spectrometer (FIMS).

关键词： atmospheric aerosol monitoring elastic scheduling federated scheduling mixed-integer quadratic programming parallel dag tasks real-time systems

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Offset-Free MPC for Resource Sharing on a Nonlinear SCARA Robot

Offset-Free MPC for Resource Sharing on a Nonlinear SCARA Ro...

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6th International-Federation-of-Automatic-Control (IFAC) Conference on Nonlinear-Model-Predictive-Control (NMPC)

作者： Bianchi, M. van der Maas, A. Maljaars, E. Heemels, W. P. M. H. Eindhoven Univ Technol Dept Mech Engn Control Syst Technol Grp NL-5600 MB Eindhoven Netherlands Univ Aquila Dept Informat Engn Comp Sci & Math Ctr Excellence DEWS I-67100 Laquila Italy

High-precision motion industrial systems must satisfy tight performance requirements. Both positioning accuracy and throughput demands are typically achieved through improvements in hardware, thereby raising the bill of materials. A cost saving alternative could be to strive for a reduction in the hardware components needed, in combination with advanced motion control, to still meet the desired specifications. Particularly, in this paper, the possibility is analyzed to allow for resource sharing among several actuators. This results in a switched system, for which we develop a real-time MPC algorithm for optimization of both the input and the switching signals. This implementation applies to a fairly general class of nonlinear systems and uses a novel offset-free formulation in velocity form for LTV prediction models, to realize good tracking performance under the resource sharing constraints. We provide a proof of concept for this MPC solution on a high fidelity model of an industrial SCARA robot, where it is proposed to use a single amplifier to serve two actuators. The MPC solution is compared to heuristically switched LTI controllers, and the potential of the proposed approach is shown in simulations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Control applications switching systems hybrid systems robotics motion control offset-free MPC mixed-integer quadratic programming

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