Bin Packing Algorithm Research Articles

Developing a Maximum Inscribed Rectangle Heuristic to Satisfy Rush Orders for Heavy Plate Steel

Steel service centers receive rush orders that must be fulfilled on very short notice. Each order only consumes a portion of one steel plate, so plate selection and job placement are the critical factors that affect the service center’s primary performance metric: plate yield. In conjunction with a steel service center, Artco Steel, we model this problem as a two-dimensional online bin-packing algorithm. Unique in the online bin-packing literature, we calculate the maximum inscribed rectangle (MIR) before and after job placement as the basis for heuristics that assign each job to a plate and position the job on the plate. Our work is the first paper to extend the online two-dimensional bin-packing problem to incorporate scrap, rectangular bin sizes, and a finite number of bins. The MIR procedure significantly outperformed Artco’s existing practice of giving priority to the most recently used plate, and the heuristic’s straightforward nature allowed easy adoption in 2010.

Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds

Cloud computing provides unprecedented advantages of using computing resources with very less efforts and cost. The energy utilization in cloud data centers has forced the cloud service providers to raise the expense of using its services and has increased the carbon footprints in the environment. Many static bin-packing algorithms exist which can reduce energy by some percentage, but with new era of digitization, advanced and dynamic techniques are required which can serve heterogeneous users and random users’ requests. Thus, in this paper, two new dynamic best-fit decreasing-based bin-packing algorithms are proposed wherein the first technique is for service providers and focuses on increasing server utilization and the second approach acts as a switcher to harness best results among all algorithms. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. The simulations are performed using Java. The results exhibited that DEE-BFD can escalate resource utilization by 96% and EM switcher can reduce total energy consumption by 49% and reduce makespan by 56%.

Partitioned Real-Time Scheduling for Preventing Information Leakage

Recent real-time systems have been gradually connected externally through the Internet. As each subsystem constituting a system has been developed from different vendors, the number of cases of security vulnerabilities in communication between subsystems has increased. In particular, real-time systems are vulnerable to timing inference attacks, such as cache-based side-channel attacks. Among the various methods for handling security problems of these systems, the flush task (FT) method is a simple and effective method to reduce the probability of information leakage by conditionally initializing the state of resources shared by tasks before a given scheduling. However, existing FT methods are for global scheduling only, and techniques for partitioned scheduling have not yet been studied. In this paper, we propose an FT-aware bin-packing (FT-bin-packing) algorithm that effectively allocates real-time tasks to identical multiprocessors to reduce the number of FTs invoked during the schedule of tasks in each processor. Based on the experimental results, we analyze the properties and performance of the FT-bin-packing algorithm. These results indicate that it can improve the performance of existing heuristic algorithms by up to 96.8% depending on the experimental setting.

SP-ant: An ant colony optimization based operator scheduler for high performance distributed stream processing on heterogeneous clusters

A key feature of distributed stream processing (DSP) systems is the scheduling of operators on clustered computers. In scheduling, the assignment plan of operators to nodes of the cluster, requirements of operators, and the computational power of each worker node must be considered with the goal of finding a tradeoff between the communication latency of operators and the utilization of worker nodes to minimize the overall system response time. To reach this goal is quite challenging, especially in heterogeneous clusters, because there are no accurate estimations about the loads of worker nodes at run time. To address this challenge, we propose a novel stream processing scheduling using ant colony algorithm (SP-Ant). SP-Ant finds the best operator assignment plan considering the inter-node communication latencies of operators by initially collocating highly communicative operators on the same worker nodes using the bin-packing algorithm and iteratively (re-)scheduling only the less communicative operators using the exploration and exploitation phases of the evolutionary ant colony optimization (ACO) algorithm in order to reduce its convergence time. SP-Ant is implemented on the standard Apache Storm. Using several standard benchmark topologies of Storm, it is shown that SP-Ant outperforms the R-Storm and Storm default schedulers by at least 50% in reducing the response time.

Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud

Smart Home Architecture is suitable for progressive and symmetric urbanization. Data being generated in smart home appliances using internet of things should be stored in cloud where computing resources can analyze the data and generate the decisive pattern within no time. This additional requirement of storage, majorly, comprising of unfiltered data escalates requirement of host machines which carries with itself extra overhead of energy consumption; thus, extra cost has to be beard by service providers. Various static algorithms are already proposed to improve energy management of cloud data centers by reducing number of active bins. These algorithms are not able to cater to the needs of present heterogeneous requests generated in cloud machines by people of diversified work environment with adhering to the requirements of quality parameters. Therefore, the paper has proposed and implemented dynamic bin-packing approaches for smart architecture that can significantly reduce energy consumption without compromising upon makespan, resource utilization and Quality of Service (QoS) parameters. The novelty of the proposed dynamic approaches in comparison to the existing static approaches is that the proposed approach dynamically creates and dissolves virtual machines as per incoming and completed requests which is a dire need of present computing paradigms via attachment of time-frame with each virtual machine. The simulations have been performed on JAVA platform and dynamic energy utilized-best fit decreasing bin packing technique has produced better results in maximum runs.

Aggregation of clans to speed-up solving linear systems on parallel architectures

The paper further refines the clan composition technique that is considered a way of matrix partitioning into a union of block-diagonal and block-column matrices. This enables solving the individual systems for each horizontal block on a separate computing node, followed by solving the composition system. The size of minimal clans, obtained as a result of matrix decomposition, varies considerably. For load balancing, early versions of ParAd software were using dynamic scheduling of jobs. The present paper studies a task of static balancing the clan size. Rather good results are obtained using a fast bin packing algorithm with the first fit on a sorted array which are considerably improved applying a multi-objective graph partitioning with software package METIS. Aggregation of clans allows us to obtain up to three times extra speed-up, including systems over fields of real numbers, on matrices from Model Checking Contest and Matrix Market.

An efficient computational framework for generating realistic 3D mesoscale concrete models using micro X-ray computed tomography images and dynamic physics engine

This study develops a computational framework for highly efficient generation of realistic mesoscale concrete models by exploiting micro XCT 3D images, a bin packing algorithm and an open-source dynamic physics engine (“Bullet”). The aggregates extracted from an XCT image dataset are first smoothed by the HC-Laplacian algorithm and then surface-meshed with optimisation. A library of aggregates characterised with five shape indices is then built and subsequently used with a “greedy search” bin packing algorithm to generate meso-models of concrete according to specified volume fraction and size gradation. To build concrete models with high compactness, for example, with 40–60% contents of aggregates, the Bullet physics engine, based on a hard-contact discrete element method, is further applied to simulate the complicated compaction and vibration process in real casting procedures. The developed framework provides a promising basis for further mesoscale studies, such as 3D printing, multiscale homogenization, damage and fracture mechanisms, and environmental factors-induced degradation.

Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations

Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box) focus on box volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at the warehouses of the Alibaba Group deviate from algorithmic prescriptions for 5.8% of packages, and these deviations increase packing time and reduce operational efficiency. We posit two mechanisms and demonstrate that they result in two types of deviations: (1) information deviations stem from workers having more information and in turn better solutions than the algorithm; and (2) complexity deviations result from workers’ aversion, inability, or discretion to precisely implement algorithmic prescriptions. We propose a new “human-centric bin packing algorithm” that anticipates and incorporates human deviations to reduce deviations and improve performance. It predicts when workers are more likely to switch to larger boxes using machine learning techniques and then proactively adjusts the algorithmic prescriptions of those “targeted packages.” We conducted a large-scale randomized field experiment with the Alibaba Group. Orders were randomly assigned to either the new algorithm (treatment group) or Alibaba’s original algorithm (control group). Our field experiment results show that our new algorithm lowers the rate of switching to larger boxes from 29.5% to 23.8% for targeted packages and reduces the average packing time of targeted packages by 4.5%. This idea of incorporating human deviations to improve optimization algorithms could also be generalized to other processes in logistics and operations. This paper was accepted by Charles Corbett, operations management.

Distributed cloud resources allocation for fair utilization using multi-objective bin packing algorithm

PurposeCloud computing services gained huge attention in recent years and many organizations started moving their business data traditional server to the cloud storage providers. However, increased data storage introduces challenges like inefficient usage of resources in the cloud storage, in order to meet the demands of users and maintain the service level agreement with the clients, the cloud server has to allocate the physical machine to the virtual machines as requested, but the random resource allocations procedures lead to inefficient utilization of resources.Design/methodology/approachThis thesis focuses on resource allocation for reasonable utilization of resources. The overall framework comprises of cloudlets, broker, cloud information system, virtual machines, virtual machine manager, and data center. Existing first fit and best fit algorithms consider the minimization of the number of bins but do not consider leftover bins.FindingsThe proposed algorithm effectively utilizes the resources compared to first, best and worst fit algorithms. The effect of this utilization efficiency can be seen in metrics where central processing unit (CPU), bandwidth (BW), random access memory (RAM) and power consumption outperformed very well than other algorithms by saving 15 kHz of CPU, 92.6kbps of BW, 6GB of RAM and saved 3kW of power compared to first and best fit algorithms.Originality/valueThe proposed multi-objective bin packing algorithm is better for packing VMs on physical servers in order to better utilize different parameters such as memory availability, CPU speed, power and bandwidth availability in the physical machine.

Solving a Real-Life Distributor’s Pallet Loading Problem

We consider the distributor’s pallet loading problem where a set of different boxes are packed on the smallest number of pallets by satisfying a given set of constraints. In particular, we refer to a real-life environment where each pallet is loaded with a set of layers made of boxes, and both a stability constraint and a compression constraint must be respected. The stability requirement imposes the following: (a) to load at level k+1 a layer with total area (i.e., the sum of the bottom faces’ area of the boxes present in the layer) not exceeding α times the area of the layer of level k (where α≥1), and (b) to limit with a given threshold the difference between the highest and the lowest box of a layer. The compression constraint defines the maximum weight that each layer k can sustain; hence, the total weight of the layers loaded over k must not exceed that value. Some stability and compression constraints are considered in other works, but to our knowledge, none are defined as faced in a real-life problem. We present a matheuristic approach which works in two phases. In the first, a number of layers are defined using classical 2D bin packing algorithms, applied to a smart selection of boxes. In the second phase, the layers are packed on the minimum number of pallets by means of a specialized MILP model solved with Gurobi. Computational experiments on real-life instances are used to assess the effectiveness of the algorithm.

Virtual network function placement with bounded migrations

With the penetration of Network Function Virtualization (NFV), network functions, traditionally deployed as proprietary physical equipment like firewalls, Network Address Translations (NATs), are gradually being implemented as software and deployed on standardized hardware. One of the crucial challenges in this paradigm is how to place the software implemented network functions to minimize the number of used physical servers. In this paper, we study the problem of how to optimally place Virtual Network Functions (VNFs) in networks where it is allowed to migrate already placed VNFs to decrease used servers. We first formulate the offline problem as an Integer Linear Programming (ILP) problem, and then propose a semi-online algorithm to solve the online variant of the problem. We name the proposed algorithm Semi-onlIne Vnf plAcement (SIVA). In particular, SIVA is based on a bin packing algorithm that solves online bin packing problem while taking care of migrations. According to our theoretical analysis, SIVA migrates at most $$\lambda$$ VNFs each step, and it has Asymptotic Competitive Ratio (ACR) of 3/2 if $$k \rightarrow \infty$$ , where $$\lambda = k \cdot |N|$$ , k is a tunable parameter, and $$|N|$$ is the the number of supported VNF types. We conduct extensive numerical simulations to evaluate the performances of SIVA. The experiment results validate the theoretical analysis and show that SIVA outperforms the state-of-the-art algorithms by achieving near-optimal performance with minor VNF migrations.

A New Lower Bound for Classic Online Bin Packing

We improve the lower bound on the asymptotic competitive ratio of any online algorithm for bin packing to above 1.54278. We demonstrate for the first time the advantage of branching and the applicability of full adaptivity in the design of lower bounds for the classic online bin packing problem. We apply a new method for weight based analysis, which is usually applied only in proofs of upper bounds. The values of previous lower bounds were approximately 1.5401 and 1.5403.

Asymptotic Approximation Ratios for Certain Classes of Online Bin Packing Algorithms

Asymptotic Approximation Ratios for Certain Classes of Online Bin Packing Algorithms

Performance of particle swarm optimization bin packing algorithm for dynamic virtual machine placement for the consolidation of cloud server

Infrastructure as a service offered by the cloud computing is one of the most important service. It allows physical machines to get virtualized by creating many instances of virtual machines. Mapping virtual machines on physical machine has become the major challenge in cloud data centres. The dynamic virtual machine placement methods are used to solve this issue with objectives like maximizing the resource utilization, minimizing the energy consumption and maximizing the scalability of data centres. In this paper a virtual machine placement-based bin packaging algorithm is proposed and analysed with four different fitness strategies to obtain the optimal solution. The unimodal (Sphere, Step) and multimodal (Graywang and Rastridge) benchmark functions are used with proposed algorithm for the analysis and obtain the quantitative measurements. The results show that optimizing the mass of particles using the best fitting strategy reduces the energy consumption, resource utilization and improved the scalability of data centres.

Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

Joint Resource Allocation Based on Traffic Flow Virtualization for Edge Computing

Edge computing can provide network services with low latency and real-time processing by operating cloud services on network edges. Edge computing has numerous advantages such as low latency, local characteristics, and network congestion localization, however, associated resource management is becoming a significant challenge because of its features such as hierarchy, decentralization, and heterogeneity. Thus, a joint resource operation and management scheme for edge computing that can greatly reduce the traffic load in the network by considering the generated traffic load and computing resources is proposed in this paper. It is based on the concept of a virtual service flow (VSF) consisting of clients, data sources, a server instance, and external network entities for a specific service. The VSF consists of several drafts according to the expected server location, and each draft estimates a traffic load that can occur according to its characteristics. The location of the edge server of the VSF is determined for each VSF, and it is performed based on the VSF rejection and reconfiguration algorithm using the weighted vector bin packing algorithm that considers the client node coverage of the VSF. The proposed scheme is evaluated based on simulations that consider the actual characteristics of the network services and devices.

A Novel Macro and Micro Scheduling Model for Regulated Safety Inspections Based on Probability Integral Transform and Modified Bin Packing Algorithm

Occupational safety and health (OSH) inspection is an essential element of OSH regulatory enforcement programs. The effectiveness of regulated OSH inspections is dependent on many factors of which proper planning and scheduling of inspections, and optimum utilization of inspectors are extremely important. In spite of this, there is no published scheduling model to solve the problem of regulated safety inspections. Absence of such models negatively affected the performance of safety inspectorates in many countries which is manifested by low portion of firms being inspected over long periods of time. For these, there is urgent need to introduce new scheduling models to solve such problem that has distinctive characteristics compared to the scheduling problems in other fields. The current paper presents a novel hybrid macro and micro scheduling approach that is based on Probability Integral Transform (PIT) and a modified Bin Packing algorithm that address the Inspector Utilization Problem with Firm Inspection Sharing (IUPFISh). The PIT-IUPFISh model consists of uniform long-term macro scheduling of firms for inspection using PIT approach coupled with short-term micro scheduling of firm inspection using the IUPFISh algorithm to determine the daily scheduling of the firms and inspectors so that maximum utilization of inspectors is achieved. A case study was presented to show the application of the proposed model. The results indicate that the application of the proposed model can improve OSH inspectorate performance through improved planning and scheduling as well as maximizing effective utilization of manpower.

Research on semi-partitioned scheduling algorithm in mixed-criticality system

In order to overcome the problem that in a mixed-critical system, once the critical level of the system changes, lower-critical tasks may be abandoned in order to ensure the schedulability of higher-critical tasks. A semi-partitioned scheduling algorithm SPBRC, which is based on a homogeneous multi-processor mixed-criticality platform and integrates the advantages and disadvantages of global scheduling and partitioned scheduling is proposed. First-fit and worst-fit bin-packing algorithms are firstly used in this method to sort high and low critical tasks separately, all high critical tasks as fixed task allocation in different processors in turns, and then distribute the lower-critical tasks. When the criticality of processor changes, lower-cirtical tasks will be allowed to migrate to the processor that is paired with the processor and is in low-critical mode, rather than abandoned. Thus, the overall performance of the system is improved. The simulation experiment verifies the effectiveness of this method in reducing the task loss rate and job loss rate.

Jampacker: An Efficient and Reliable Robotic Bin Packing System for Cuboid Objects

Bin packing using a robotic arm is an important problem in the context of Industry 4.0. In this work, we present a reliable and efficient bin packing system, called Jampacker. We propose a new offline 3D bin packing algorithm, called Jampack that achieves higher packing efficiency in comparison to the state-of-the-art algorithms. Jampack computes placement points, called internal corner points that tries to maximize the utilization of free spaces in-between objects, which are generally ignored by existing algorithms. Additionally, we introduce a fault recovery module (FRM) for the robotic manipulator that helps to achieve a more reliable and efficient packing in a physical container. The FRM module monitors the object placement by the robotic arm, calculates a fault score for the placement, adjusts the placement if required, and also learns & adjusts the offset for the placement procedure on-the-go. We show that this system achieves faster completion of the overall packing process.

Streaming Algorithms for Bin Packing and Vector Scheduling

Problems involving the efficient arrangement of simple objects, as captured by bin packing and makespan scheduling, are fundamental tasks in combinatorial optimization. These are well understood in the traditional online and offline cases, but have been less well-studied when the volume of the input is truly massive, and cannot even be read into memory. This is captured by the streaming model of computation, where the aim is to approximate the cost of the solution in one pass over the data, using small space. As a result, streaming algorithms produce concise input summaries that approximately preserve the optimum value. We design the first efficient streaming algorithms for these fundamental problems in combinatorial optimization. For Bin Packing, we provide a streaming asymptotic (1 + ε)-approximation with widetilde {O}left (frac {1}{varepsilon }right ), where widetilde {{{O}}} hides logarithmic factors. Moreover, such a space bound is essentially optimal. Our algorithm implies a streaming (d + ε)-approximation for Vector Bin Packing in d dimensions, running in space widetilde {{{O}}}left (frac {d}{varepsilon }right ). For the related Vector Scheduling problem, we show how to construct an input summary in space widetilde {{{O}}}(d^{2}cdot m / varepsilon ^{2}) that preserves the optimum value up to a factor of 2 - frac {1}{m} +varepsilon , where m is the number of identical machines.