Smart Resource Management for Data Streaming using an Online Bin-packing Strategy

A Parallel Genetic Algorithm for Three Dimensional Bin Packing with Heterogeneous Bins

This paper presents a parallel genetic algorithm for three dimensional bin packing with heterogeneous bins using Hadoop Map-Reduce framework. The most common three dimensional bin packing problem which packs given set of boxes into minimum number of equal sized bins is proven to be NP Hard. The variation of three dimensional bin packing problem that allows heterogeneous bin sizes and rotation of boxes is computationally more harder than common three dimensional bin packing problem. The proposed Map-Reduce implementation helps to run the genetic algorithm for three dimensional bin packing with …

Heuristics for the Variable Sized Bin Packing Problem Using a Hybrid P-System and CUDA Architecture

The Variable Sized Bin Packing Problem has a wide range of application areas including packing, scheduling, and manufacturing. Given a list of items and variable sized bin types, the objective is to minimize the total size of the used bins. This problem is known to be NP-hard. In this article, we present two new heuristics for solving the problem using a new variation of P systems with active membranes, which we call a hybrid P system, implemented in CUDA. Our hybrid P-system model allows using the polarity and labels of membranes to represent object properties which results in reducing the co…

Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing

In mobile edge computing, edge servers are geographically distributed around base stations placed near end-users to provide highly accessible and efficient computing capacities and services. In the mobile edge computing environment, a service provider can deploy its service on hired edge servers to reduce end-to-end service delays experienced by its end-users allocated to those edge servers. An optimal deployment must maximize the number of allocated end-users and minimize the number of hired edge servers while ensuring the required quality of service for end-users. In this paper, we model the…

Adaptive Large Neighborhood Search for Circle Bin Packing Problem

We address a new variant of packing problem called the circle bin packing problem (CBPP), which is to find a dense packing of circle items to multiple square bins so as to minimize the number of used bins. To this end, we propose an adaptive large neighborhood search (ALNS) algorithm, which uses our Greedy Algorithm with Corner Occupying Action (GACOA) to construct an initial layout. The greedy solution is usually in a local optimum trap, and ALNS enables multiple neighborhood search that depends on the stochastic annealing schedule to avoid getting stuck in local minimum traps. Specifically, …

动态向量装箱评估用于虚拟机放置

Evaluation of Dynamic Vector Bin Packing for Virtual Machine Placement

本文评估了动态向量装箱算法在虚拟机放置中的应用，探讨了不同设置下的算法性能及改进方法。

Evolutionary Bin Packing for Memory-Efficient Dataflow Inference Acceleration on FPGA

Convolutional neural network (CNN) dataflow inference accelerators implemented in Field Programmable Gate Arrays (FPGAs) have demonstrated increased energy efficiency and lower latency compared to CNN execution on CPUs or GPUs. However, the complex shapes of CNN parameter memories do not typically map well to FPGA on-chip memories (OCM), which results in poor OCM utilization and ultimately limits the size and types of CNNs which can be effectively accelerated on FPGAs. In this work, we present a design methodology that improves the mapping efficiency of CNN parameters to FPGA OCM. We frame the…