Best Fit Decreasing Research Articles

Multi-search-routes-based methods for minimizing makespan of homogeneous and heterogeneous resources in Cloud computing

Cloud computing, as a large-scale distributed computing system dynamically providing elastic services, is designed to meet the requirement of delivering computing services to users as subscription-oriented services. In general, the problems of resource scheduling in Cloud computing like minimizing makespan are usually NP-Hard problems. Various common algorithms including heuristic, meta-heuristic and machine learning are applied in resource scheduling of Cloud computing to obtain the solutions, which however are still probable and imperative to be optimized. Through innovatively applying heuristic algorithms namely LPT (Longest Processing Time) and BFD (Best Fit Decreasing) as the basic search routes and integrating these with neighborhood search algorithm namely OneStep, this paper proposes multi-search-routes-based algorithms containing LPT-OneStep, BFD-OneStep and their combinations for the sake of enhancing theoretical performance and improving solutions of scheduling schemes especially for problems of minimizing makespan for homogeneous and heterogeneous resources. Theoretical derivations prove that the proposed algorithms possess better theoretical approximation ratios for P||Cmax. Extensive experiments on simulation environment demonstrate the proposed algorithms outperform than corresponding compared algorithms for minimizing makespan problems in both homogenous resources and heterogeneous resources, which validates the superiority of the proposed algorithms.

Two-Stage Metaheuristic Algorithms for Order-Batching and Routing Problems

Because of time and cost constraints, item picking plays a major role in warehouse operations. Considering diversified orders and a constant warehouse design, deciding how to combine each batch and picker route effectively is a challenge in warehouse management. In this study, we focus on the evaluation of order-batching strategies for a single picker facing multiple orders with the objective of minimizing the total traveling distance. We propose two-stage simulated annealing and variable neighborhood search algorithms to solve the combined problem. The orders are first merged into batches, followed by determining the sequence in each batch. The computational analysis revealed that the best-fit-decreasing (BFD) batch ordering strategy in the two-stage algorithms, the variable neighborhood search algorithm, obtained superior solutions to those of the simulated annealing algorithm.

A Layer & Request Priority-based Framework for Dynamic Resource Allocation in Cloud- Fog - Edge Hybrid Computing Environment

One of the most promising frameworks is the fog computing paradigm for time-sensitive applications such as IoT (Internet of Things). Though it is an extended type of computing paradigm, which is mainly used to support cloud computing for executing deadline-based user requirements in IoT applications. However, there are certain challenges related to the hybrid IoT -cloud environment such as poor latency, increased execution time, computational burden and overload on the computing nodes. This paper offers A Layer & Request priority-based framework for Dynamic Resource Allocation Method (LP-DRAM), a new approach based on layer priority for ensuring effective resource allocation in a fog-cloud architecture. By performing load balancing across the computer nodes, the suggested method achieves an effective resource allocation. Unlike conventional resource allocation techniques, the proposed work assumes that the node type and the location are not fixed. The tasks are allocated based on two constrain, duration and layer priority basis i.e, the tasks are initially assigned to edge computing nodes and based on the resource availability in edge nodes, the tasks are further allocated to fog and cloud computing nodes. The proposed approach's performance was analyzed by comparing it to existing methodologies such as First Fit (FF), Best Fit (BF), First Fit Decreasing (FFD), Best Fit Decreasing (BFD), and DRAM techniques to validate the effectiveness of the proposed LP-DRAM.

Energy Efficient End Device Aware Solution Through SDN in Edge-Cloud Platform

Recently, the networking industries have gone through tremendous changes. It demands high-speed operations and complex problem-solving abilities. To manage these evolutions Internet-of-Things (IoT) is a proposed solution from several technical corners. Numerous researchers and government organizations showing their interest to provide solutions with IoT implementation. Handling a huge amount of network data, its privacy and security, Quality of Service (QoS) requirements and heterogeneity of underlying networking components are the various challenges in IoT implementations. To provide the solution, Software Defined Networking (SDN) is becoming a bliss in managing such complex networking problems. The allocation of the Virtual Machines (VMs) into the end device is an NP-Hard combinatorial optimization problem. We formulate the problem by using simple Additive Weighing (SAW) or Weighted Sum Method (WSM) to allocate the VMs asymmetrically based on CPU Utilization and Memory usage to optimize the energy. The proposed algorithm ServerCons minimizes the number of live migrations and the number of nodes used as well as the energy usage is at par with the state of art algorithms such as First-Fit-Decreasing(FFD), Best-Fit-Decreasing (BFD), and Modified-Best-Fit-Decreasing (MBFD).

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.

Hybrid integer-coded Wolf Pack Algorithm for multiple-type flatcars loading problem

This paper presents an efficient hybrid integer-coded Wolf Pack Algorithm (HICWPA) for multiple-type flatcars loading problem. The multiple-type flatcars loading problem aims at loading a given set of multi-size vehicles on the multiple-type railway flatcars so that the length utilization ratio of flatcars is maximized. The algorithm introduced in this paper is based on three important operations. First, an integer coding method of individual sequence is designed to express the usage order of flatcars. The integer-coded Wolf Pack Algorithm (ICWPA) is responsible for searching the optimal individual coding sequence wildly to improve the probability of achieving the approximated optimal solution. Second, the Best Fit Decreasing (BFD) algorithm is used to decode the individual coding sequences and load the vehicles on flatcars to generate initial loading plan. Third, a moving operator is designed to repair the infeasible loading sequences by reassigning the individual coding bits which can improve the convergence speed of the proposed algorithm. The effectiveness and advancement of HICWPA are verified by a practical instance of multiple-type flatcars loading problem. The computational results indicate that the flatcars length utilization ratio of our algorithm outperforms current excellent algorithms for the considered problem.

Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers

The fast growth in demand for utility-based IT services has lead to the formation of large scale Cloud data centers. The electrical energy consumption of these data centers results in high operational costs and carbon dioxide emissions. Cloud data centers benefit from the use of virtualization technology to reduce their energy consumption. This technology enables a Cloud data center to allocate its physical resources (CPU, memory, hard disk, network bandwidth) on demand and balance loads between their physical hosts by live migration of Virtual Machines (VMs). However, the migration of VMs can result in Service Level Agreement Violations (SLAVs) and consequently low Quality of Service (QoS). Hence, in this paper, we propose an energy aware VM consolidation algorithm that minimizes SLAVs.Dynamic VM consolidation has three stages: a) Detecting over- and under-utilized hosts; b) Selecting one or more VMs for migration from those hosts; c) Finding destination hosts for the selected VMs. Therefore, the proposed VM consolidation algorithm contains different models for each stage. For the first stage, we developed different fine-tuned Machine Learning (ML) prediction models for individual VMs to predict the best time to trigger migrations from hosts. For the second stage, we lexicographically consider migration time and host CPU usage when selecting VMs to migrate. Finally, a new method based on the Best Fit Decreasing (BFD) algorithm was developed to select a destination host for the VMs being migrated. Our algorithm was compared to a baseline VM consolidation algorithm that used Local Regression for detecting over-utilized hosts, minimum migration time for the VM selection stage and power-aware best fit for the host selection stage. The comparison demonstrated that our VM consolidation algorithm improved energy consumption and SLAVs by 26% and 50%, respectively.

Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation

The virtual machine (VM) workload of a datacenter is dynamic, where the reallocation of a subset of active VMs can result in better VM allocation by avoiding over-loaded/under-loaded physical machines (PMs). Over-loaded PMs lead to customer dissatisfaction, whereas under-loaded PMs result in increased energy consumption. In this work, we propose a multi-objective best-fit-decreasing (BFD) approach to the VM reallocation problem. Our multi-objective formulation considers power costs and resource utilization. We use the expressive power of fuzzy algebra to combine both objectives into a single-objective function. Extensive simulations, using CloudSim, show that our fuzzy-based multi-objective implementation of BFD leads to significantly better solutions with respect to energy and resource utilization. Indeed, the results show an improvement of as much as 30% to 40% of energy consumption and 30% of resource utilization when compared with reported heuristics which minimize energy only, using five real workloads provided as a part of the coMon project, which is a monitoring infrastructure for PlanetLab.

Chemical reaction optimization for virtual machine placement in cloud computing

With the development of virtualization technologies, cloud data centers are faced with more and more virtual machines (VMs) requests. How to realize an efficient virtual machine placement (VMP) becomes a hot research topic. The optimal resource consumption and the utilization rate of a physical machine in the whole cloud data center can be realized by optimizing the process from the virtual machine to the physical machine. VMP is a combinatorial optimization problem which was demonstrated to be NP-hard. In this paper, a new formulation of VMP problem is presented by taking into consideration the optimization of the two following objectives: (i) minimize the energy consumption, and (ii) maximize the resource utilization. In order to achieve these targets, we propose two algorithms based on chemical reaction optimization (CRO) algorithm, namely CVP and CVV, with two types of solution representation. The proposed algorithms are compared with other optimal placement strategies, namely Cuckoo Search Optimization (CSO), Reordered Grouping Genetic Algorithm (RGGA), First Fit Decreasing (FFD) and Best Fit Decreasing (BFD). Experimental results show that the proposed CVP and CVV give better performance comparing with the other compared algorithms in terms of resource consumption and resource utilization. In term of scalability, the proposed CVV algorithm benefits from the high computational speed and performs well when there are a large number of virtual machine scheduling requests in the cloud data center.

Automotive Industry Line Board Optimization Through Operations Research Techniques

The automotive industry has suffering a lot with the current economic crisis that affects the country and, at this critical moment, more than never, they have been looking for methods to optimize their production, reducing wastes as much as possible. This paper concerns about optimization of a physical arrangement in a production line board from an automotive industry. This line board has 17 workstations (WS), each of them has been divided into 4 clusters of racks containing the parts to assembly. The goal is to get an optimized layout for these racks, which can be of different sizes and purposes, into these clusters, to maximize the area use. Those racks should accommodate assembly parts (put in boxes) in an optimized way too. This problem was treated as a Cutting and Packing Problems (CPP). In order to optimize the clusters (1st. problem), it was used a two-dimensional CPP and we proposed the use of Wang Algorithm to solve it, taking into account the demand constraints for each cluster, the racks dimensions to be assigned into them and the distances among them in order to allow the operator passage to pick up the parts to assembly. Doing these definitions, the algorithm was executed separately for each cluster and it is proposed a new layout. For the optimization of the boxes layout which are in the racks (2nd. Problem), it was a one-dimensional CPP and we proposed the algorithms First Fit (FF); First Fit Decreasing (FFD); Best Fit (BF) e Best Fit Decreasing (BFD) to solve it, in a comparatively way, in order to maximize the quantity of boxes in the racks. For that, it was considered the boxes sizes, the required quantity of each of them and the process constraints.

Optimum Software Aging Prediction and Rejuvenation Model for Virtualized Environment

<p><em>Advancement in electronics and hardware has resulted in multiple softwares running on the same hardware. The result is multiuser, multitasking and virtualized environments. However, reliability of such high performance computing systems depends both on hardware and software. For hardware, aging can be dealt with replacement. But, software aging needs to be dealt with software only. For aging detection, a new approach using machine learning framework has been proposed in this paper. For rejuvenation, Adaptive Genetic Algorithm (A-GA) has been developed to perform live migration to avoid downtime and SLA violation. The proposed A-GA based rejuvenation controller (A-GARC) has outperformed other heuristic techniques such as Ant Colony Optimization (ACO) and best fit decreasing (BFD) for migration. Results reveal that the proposed aging forecasting method and A-GA based rejuvenation outperforms other approaches to ensure optimal system availability, minimum task migration, performance degradation and SLA violation.</em></p>

Best-Harmonically-Fit Periodic Task Assignment Algorithm on Multiple Periodic Resources

The periodic task set assignment problem in the context of multiple processors has been studied for decades. Different heuristic approaches have been proposed, such as the Best-Fit (BF), the First-Fit (FF), and the Worst-Fit (WF) task assignment algorithms. However, when processors are not dedicated but only periodically available to the task set, whether existing approaches still provide good performance or if there is a better task assignment approach in the new context are research problems which, to our best knowledge, have not been studied by the real-time research community. In this paper, we present the Best-Harmonically-Fit (BHF) task assignment algorithm to assign periodic tasks on multiple periodic resources. By periodic resource we mean that for every fixed time interval, i.e., the period, the resource always provides the same amount of processing capacity to a given task set. Our formal analysis indicates that if a harmonic task set is also harmonic with a resource's period, the resource capacity can be fully utilized by the task set. Based on this analysis, we present the Best-Harmonically-Fit task assignment algorithm. The experimental results show that, on average, the BHF algorithm results in $53.26$ , $42.54$ , and $27.79$ percent higher resource utilization rate than the Best-Fit Decreasing (BFD), the First-Fit Decreasing (FFD), and the Worst-Fit Decreasing (WFD) task assignment algorithms, respectively; but comparing to the optimal resource utilization rate found by exhaustive search, it is about $11.63$ percent lower.

Smart Virtual Machine Placement Using Learning Automata to Reduce Power Consumption in Cloud Data Centers

Today, cloud computing is one of the most challenging research topics in the field of information technology. It is so important for computer researchers that it was included on a list of top ten technologies in the world. Data centers include reservoirs where processing power can meet the needs of many users computing. The popularity and acceptance of cloud computing has increased the number of these centers in recent years. One of the challenging issues in cloud computing environments is high energy consumption in data centers, which has been ignored in the corporate competition to develop data centers. High energy consumption by data centers leads to increased costs, as well as CO2 emissions. Researchers are now struggling to find an effective approach to decrease energy consumption in data centers. In recent years, many attempts have been made to reduce the power consumption of data centers, and many approaches have been proposed to reduce power consumption, such as hardware and software approaches and approaches using virtualization technology. In fact, placement of a virtual machine (VM) means finding a suitable physical place for the VM. The placement goal can either maximize the usage of available resources or it can save power by being able to shut down some servers. In this paper, we present an approach based on a best-fit decreasing (BFD) algorithm, which uses learning automata to reach a compromise between decreasing energy consumption and violating service level agreements.

An Adaptive Genetic Algorithm based Robust QoS Oriented Green Computing Scheme for VM Consolidation in Large Scale Cloud Infrastructures

Backgrounds: The high pace increase in cloud applications requires an optimal computing platform like, Virtual Machine (VM) Consolidationor virtualization to ensure optimal computational efficiency, energy consumption and minimal SLA violation. Methods: In this paper, an evolutionary computing approach called Adaptive Genetic Algorithm (A-GA) has been proposed for VM placement policy, to be used in VM consolidation. In the proposed model, the modified Robust Local Regression (LRR) and Inter-Quartile Range (IQR) schemes estimate the dynamic CPU utilization for overload detection, which is followed by Maximum Correlation (MC) and Minimum Migration Time (MMT) based VM selectionand A-GA based VM placement. Findings: The comparative performance analysis for the proposed system with Planet Lab cloud benchmark dataset has exhibited that the proposed model exhibits better results as compared to other heuristic approaches such as Best Fit Decreasing (BFD) algorithm and Ant Colony Optimization (ACO). The implementation of the proposed A-GA based consolidation with modified IQR and LRR, and MMT selection policyhas performed better in terms of energy efficiency and SLA violation as compared to the other heuristic approaches for placement such as Best Fit Decreasing (BFD) algorithm with conventional IQR, Local Regression (LR), robust local regression, Static Threshold (THR) and Median Absolute Deviation (MAD) based CPU utilization threshold estimation schemes. Furthermore, the proposed A-GA based scheme has outperformed Ant Colony Optimization (ACO) based consolidation scheme. The performance analysis with two distinct VM selection policies, MC and MMT has revealed that A-GA performs better with MMT selection policy and provides higher host shutdown, minimal VM migration and SLA violation, and minimal energy consumption. Applications: The proposed A-GAbased VM consolidation scheme can be significant for energy aware and QoS oriented virtualization application in large scale cloud infrastructures.

Energy-saving resource scheduling algorithm based on workload characteristic clustering

When infrastructure as a service( Iaa S) providers offer high performance services for users,they must think about how to reduce the energy cost of the cloud platform without violating the service level agreement( SLA). A resource scheduling algorithm to ensure SLA was proposed based on clustering analysis of the load characteristic. Ultimately,the targets of reducing SLA violation rate and saving energy were realized.The resource scheduling algorithm was analyzed based on improved K-means clustering analysis and extraction of workload characteristic according to energy consumption. Physical resources were effectively allocated to ensure the requirement of energy saving of Iaa S platform. Based on the extension of the Cloud Sim simulation platform,the algorithm proposed was compared with the optimized best fit decreasing( BFD) to show lower SLA violation rate and energy consumption.

Solving bin Packing Problem with a Hybrid Genetic Algorithm for VM Placement in Cloud

Abstract The Bin Packing Problem's purpose (BPP) is to find the minimum number of bins needed to pack a given set of objects of known sizes so that they do not exceed the capacity of each bin. This problem is known to be NP- Hard. In this paper, we propose an hybrid genetic algorithm using BFD (Best Fit Decreasing) to deal with infeasible solution due to the bin-used representation. Experimental results showed the effectiveness of our approach for infeasible chromosomes thereby improving the quality of the obtained solution. This will give a good result for the virtual machine placement in Cloud to minimize energy since it looks like a BPP.

Performance evaluation of heuristic algorithms for routing and wavelength assignment in WDM optical networks

We evaluate the average-case performance of eight offline heuristic algorithms to solve the routing and wavelength assignment (RWA) problem and the related throughput maximisation (TM) problem in wavelength division multiplexing optical networks. These algorithms are first-fit-decreasing (FFD), first-fit-increasing (FFI), best-fit-decreasing (BFD), best-fit-increasing (BFI), densest-fit-decreasing (DFD), densest-fit-increasing (DFI), random-fit-decreasing (RFD) and random-fit-increasing (RFI). Our experimental performance evaluation is conducted by extensive simulations on a wide range of WDM optical networks, including a mesh network, four real networks and three types of random networks. We find offline RWA algorithms and TM algorithms which perform better than previously studied online algorithms, namely, first-fit (FF), best-fit (BF), densest-fit (DF) and random-fit (RF). In particular, algorithm FFD (BFD, DFD and RFD, respectively) has better performance than algorithm FF (BF, DF and RF, respectively) for RWA, and algorithm FFI (BFI, DFI and RFI, respectively) has better performance than algorithm FF (BF, DF and RF, respectively) for TM.

List-scheduling and column-generations for scheduling of n job-groups with set up time and due date through m identical parallel machines to minimize makespan

This paper presents an optimization based heuristic for minimizing makespan on scheduling of n job-groups with qi as the number of identical jobs, pi as the processing time, sui as the setup time required and ddi as the due date, in job-group i through a set of m identical parallel machines. This heuristic, referred as LSCCG, is based on LS (List- Scheduling) heuristic and CG (Column Generation) which are hybrid between the wellknown in scheduling problems such as the LPT (Longest Processing Time) heuristic or in packing problems such as the BFD (Best-Fit Decreasing) heuristic and column generation procedure in a cutting stock problem, respectively. The first algorithm, referred to as LPTCCG which are hybrid between LPT heuristic, is constructed using the initial pattern and adjusted to a lower bound by ALSP (Adjusted List-Scheduling Pattern); while the CG, is modified to CCG (Continuous Column Generation) attempting to strengthen makespan bound and collectively generated patterns to minimize a number of machines required. This model is repeatedly looped by LPT, ALSP and CCG to reach an upper bound of minimum makespan. The average performance of this proposed algorithm is considerably better than that of the LPT heuristic. The better algorithm, referred to as BFDCCG, is based on BFD heuristic and CG. As the same principle, the BFD heuristic is generated the initial pattern and adjusted to a lower bound by ALSP and then attempted to strengthen makespan bound with a number of available machines by CCG. BFDCCG heuristic is repeatedly looped by BFD, ALSP and CCG to reach an upper bound of minimum makespan. The average performance of this proposed algorithm, BFDCCG heuristic is considerably better than that of the LPTCCG heuristic and computational time also. In set up time case, referred to as LPTsuCCG and BFDsuCCG which are similar to LSCCG, the only difference is in the set up time addition after List-scheduling the initial pattern and sub problem formulation in CCG procedure. As expected, the average performance and computational time of the BFDsuCCG heuristic is considerably better than the LPTsuCCG heuristic. In the set up time and due date case, implemented as EDDsuddCCG, EDD (Earliest Due Date) is used to generate the initial pattern. This algorithm is appropriate for the case of a few job-groups.

Bin‐packing problem with concave costs of bin utilization

AbstractWe consider a generalized one‐dimensional bin‐packing model where the cost of a bin is a nondecreasing concave function of the utilization of the bin. Four popular heuristics from the literature of the classical bin‐packing problem are studied: First Fit (FF), Best Fit (BF), First Fit Decreasing (FFD), and Best Fit Decreasing (BFD). We analyze their worst‐case performances when they are applied to our model. The absolute worst‐case performance ratio of FF and BF is shown to be exactly 2, and that of FFD and BFD is shown to be exactly 1.5. Computational experiments are also conducted to test the performance of these heuristics. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006

Minimum and maximum utilization bounds for multiprocessor rate monotonic scheduling

The utilization bound for real-time rate monotonic (RM) scheduling on uniprocessors is extended to multiprocessors with partitioning-based scheduling. This allows fast schedulability tests to be performed on multiprocessors and quantifies the influence of key parameters, such as the number of processors and task sizes on the schedulability of the system. The multiprocessor utilization bound is a function of the allocation algorithm, so among all the allocation algorithms there exists at least one allocation algorithm providing the minimum multiprocessor utilization bound, and one allocation algorithm providing the maximum multiprocessor utilization bound. We prove that the multiprocessor utilization bound associated with the allocation heuristic worst fit (WF) coincides with that minimum if we use Liu and Layland's bound (LLB) as the uniprocessor schedulability condition. In addition, we present a class of allocation algorithms sharing the same multiprocessor utilization bound which coincides with the aforementioned maximum using LLB. The heuristics first fit decreasing (FFD) and best fit decreasing (BFD) belong to this class. Thus, not even an optimal allocation algorithm can guarantee a higher multiprocessor utilization bound than that of FFD and BFD using LLB. Finally, the pessimism of the multiprocessor utilization bounds is estimated through extensive simulations.