First-come-first-served Research Articles

Order Batching Optimization for Warehouses with Cluster-Picking

This paper focuses on optimizing the static order batching problem (OBP) with multiple pickers. This problem involves optimal assignment of a set of customer orders to batches, given capacity constraints, in order to minimize the total distance travelled. The order picking strategy considered is cluster picking. We assume that each picker is assigned one batch and all pickers begin the pick tour simultaneously. The aim is to minimize the total distance travelled by all pickers, which is calculated based on the S-shape routing strategy. A mathematical model is developed to solve the OBP for small-scale instances. For larger instances, a simplified version of an order batching heuristic proposed by DHL Supply Chain is utilized and experimented with for different-sized problems. Both approaches are compared to the conventional first come first serve (FCFS) method of order batching.

Hierarchically distributed Peer-to-Peer architecture for load balancing and effective dynamic group scheduling in Grid computing

The local or remote grid computing resources have been normally assigned the task of executing user jobs in a distributed environment. An important factor affecting grid processing is the network bandwidth that differs from network to network. In order to overcome this problem, a few typical algorithms are suggested keeping in view of computing grid resources based on load balancing across the grid system. These algorithms help in improving the performance to some extent, yet none of them has been found experimentally suitable to overcome the major short falls experienced in the grid computing system. A hierarchically distributed peer-to-peer (HDP2P) architecture is proposed to overcome all major problems encountered due to virtuality and heterogeneity. Results suggested that HDP2P architecture was of great use in balancing the load and allocating to the resources to the groups of jobs keeping MIPS accountability using grouping-based dynamic job scheduling algorithm (GBDJS). The proposed algorithm is compared with first come first served (FCFS) job scheduling algorithm and ant colony optimisation (ACO). There is a considerable improvement achieved in performance by using this novel architecture.

Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing

In this article, the authors propose a novel backfilling-based task scheduling algorithm to schedule deadline-based tasks. The existing backfilling algorithm has some performance issues in comparison with the number of task scheduling in OpenNebula cloud platform. A lease could not be scheduled if it is not sorted with respect to its start time. In backfilling, a lease is selected in First Come First Serve (FCFS) to be backfilled from the queue in which some ideal resources can be found out and allocated to other leases. However, the scheduling performance is not better if there are similar types of leases to backfill. It requires a decision maker to resolve conflicts. The proposed approach schedules the number of tasks without any decision maker. An additional queue and the current time of the system is implemented to improve the scheduling performance. It performs quite satisfactorily in terms of number of a leases scheduling, and resource utilization. The performance result is compared with the existing backfilling algorithms.

A Novel Amended Dynamic Round Robin Scheduling Algorithm for Timeshared Systems

Central Processing Unit (CPU) is the most significant resource and its scheduling is one of the main functions of an operating system. In timeshared systems, Round Robin (RR) is most widely used scheduling algorithm. The efficiency of RR algorithm is influenced by the quantum time, if quantum is small, there will be overheads of more context switches and if quantum time is large, then given algorithm will perform as First Come First Served (FCFS) in which there is more risk of starvation. In this paper, a new CPU scheduling algorithm is proposed named as Amended Dynamic Round Robin (ADRR) based on CPU burst time. The primary goal of ADRR is to improve the conventional RR scheduling algorithm using the active quantum time notion. Quantum time is cyclically adjusted based on CPU burst time. We evaluate and compare the performance of our proposed ADRR algorithm based on certain parameters such as, waiting time, turnaround time etc. and compare the performance of our proposed algorithm. Our numerical analysis and simulation results in MATLAB reveals that ADRR outperforms other well-known algorithms such as conventional Round Robin, Improved Round Robin (IRR), Optimum Multilevel Dynamic Round Robin (OMDRR) and Priority Based Round Robin (PRR)

Pre-Flight Conflict Detection and Resolution for UAV Integration in Shared Airspace: Sendai 2030 Model Case

The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs) requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In particular, Pre-Flight Conflict Detection and Resolution (CDR) methods need to scale to future demand levels and generate conflict-free paths for a potentially large number of UAVs before actual takeoff. However, few studies have examined realistic scenarios and the requirements for the UTM system. In this paper, we focus on the Sendai 2030 model case, a realistic projection of UAV usage for deliveries in one area in Japan. This model case considers up to 21,000 requests for Unmanned Aircraft Systems (UAS) operations over a 13 hour service time, and thus poses a challenge for the Pre-Flight CDR methods. Therefore, we propose an airspace reservation method based on 4DT (3D plus time Trajectories) and map the Pre-Flight CDR problem to a Multi-Agent Path Finding (MAPF) problem. We study first-come first-served (FCFS) and “batch” processing of UAS operation requests, and compare the throughput of those methods. We analyze the air traffic topology of deliveries by UAVs, and discuss several metrics to better understand the complexity of air traffic in the Sendai model case.

An enhanced deadline constraint based task scheduling mechanism for cloud environment

In this work, we present a scheduling mechanism for deadline sensitive task or lease. It is very impelling and challenging to allocate on-demand resources within the deadline rather than rejecting the lease. The work studies the insight of existing backfilling algorithm that is used to schedule deadline sensitive lease in the Open Nebula cloud platform. In backfilling mechanism a lease is selected in First Come First Serve (FCFS) to be backfilled in which some ideal resources can be found out and allocated to other leases. Mainly, the scheduling performance of backfilling algorithm could be better when there are conflicts among the similar leases to backfill. Moreover, backfilling algorithm doesn’t allow to schedule a new deadline sensitive task during execution. The proposed work addresses such types of issues and improves the scheduling performances with respect to maximize the task acceptance ratio and reduce the task rejection ratio. Finally, the performances are compared with the existing mechanisms that are quite good.

Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes

The influence of Information Communication and Technology (ICT) in power systems necessitates Smart Grid (SG) with monitoring and real-time control of electricity consumption. In SG, huge requests are generated from the smart homes in residential sector. Thus, researchers have proposed cloud based centralized and fog based semi-centralized computing systems for such requests. The cloud, unlike the fog system, has virtually infinite computing resources; however, in the cloud, system delay is the challenge for real-time applications. The prominent features of fog are; awareness of location, low latency, wired and wireless connectivity. In this paper, the impact of longer delay of cloud in SG applications is addressed. We proposed a cloud-fog based system for efficient processing of requests coming from the smart homes, their quick response and ultimately reduced cost. Each smart home is provided with a 5G based Home Energy Management Controller (HEMC). Then, the 5G-HEMC communicates with the High Performance Fog (HPF). The HPFs are capable of processing energy consumers’ huge requests. Virtual Machines (VMs) are installed on physical systems (HPFs) to entertain the requests using First Come First Service (FCFS) and Ant Colony Optimization (ACO) algorithms along with Optimized Response Time Policy (ORTP) for the selection of potential HPF for efficient processing of the requests with maximum resource utilization. It is analysed that size and number of virtual resources affect the performance of the computing system. In the proposed system model, micro grids are introduced in the vicinity of energy consumers for uninterrupted and cost optimized power supply. The impact of the number of VMs on the performance of HPFs is analysed with extensive simulations with three scenarios.

Approximating the First-Come, First-Served Stochastic Matching Model with Ohm’s Law

The first-come, first-served (FCFS) stochastic matching model, where each server in an infinite sequence is matched to the first eligible customer from a second infinite sequence, developed from queueing problems addressed by Kaplan (1984) in the context of public housing assignments. The goal of this model is to determine the matching rates between eligible customer types and server types, that is, the fraction of all matches that occur between type-i customers and type-j servers. This model was solved in a beautiful paper by Adan and Weiss, but the resulting equation for the matching rates is quite complicated, involving the sum of permutation-specific terms over all permutations of the server types. Here, we develop an approximation for the matching rates based on Ohm’s Law that in some cases reduces to exact results, and via analytical, numerical, and simulation examples is shown to be highly accurate. As our approximation only requires solving a system of linear equations, it provides an accurate and tractable alternative to the exact solution.

MTTDFR-SJFR: A Combinatorial Rule Approach to Balance Tradeoff Between Flowtime, Makespan, Delayed Jobs, Total Tardiness and Utilization

In order to fully utilize the Grid resources, implementing a good scheduling algorithm is really important. Currently, some of the well-known researches and enterprise schedulers have applied Priority Rule (PR) schedulers to manage the Grid jobs because they are simple and easy to implement. To date, there is no strong performance justification or proof given on why these specific PR algorithms are preferred compared to other PR algorithms. For example, five PR algorithms; First Come First Serve (FCFS), Longest Job First (LJF), Shortest Job First (SJF), Earliest Deadline First (EDF) and Minimum Time To Deadline (MTTD) have been proposed. However, none of these algorithms perform well in every metrics of performance. An attempt to improve their performance has been made using Combinatorial Rule (CR) which is the combination of more than one PR. Unfortunately, the current implementation of CR only focuses on two performance metrics which are flowtime and makespan, while sacrificing the others. In order to tackle the aforementioned problem, this paper introduces Minimum Time To Deadline to Fastest Resource - Shortest Job to Fastest Resource (MTTDFR-SJFR), a new CR scheduler that performs better than other CR algorithms tested within the scope. To achieve high performance, a combination of PR with Fastest Resource (FR) resource selection scheme that further improve the performance is proposed. Graphical results obtained from experimental simulation showed the superiority of the proposed CR algorithm in term of overall performance, compared to other CR as well as the original PR.

An Improved Round Robin CPU Scheduling Algorithm based on Priority of Process

The most important and integral part of a computer system is its operating system. Scheduling various resources is one of the most critical tasks an operating system needs to perform. Process scheduling being one of those tasks, involves various techniques that define how more than one processes can be executed simultaneously. The primary aim here is to the system more efficient and faster. The fundamental scheduling algorithms are: First Come First Serve (FCFS), Round Robin, Priority Based Scheduling, and Shortest Job First (SJF). This paper focuses on Round Robin Scheduling algorithm and various issues related to it. One major issue in RR scheduling is determining the length of Time Quantum. If the Time Quantum is too large RR scheduling behaves as FCFS. On the other hand, if it is too small it forces considerable increase in the number of context switches. Our main objective is to overcome this limitation of traditional RR scheduling algorithm and maximize CPU utilization, further, leading to more efficient and faster system. Here we propose an algorithm that categorizes available processes into High Priority processes and Low Priority process. The proposed algorithm reduces the average waiting time of High Priority processes in all cases and of Low Priority processes in not all but some cases. The overall waiting time changes on the basis of set of processes considered. The simulation results justify that the proposed schemes reduces the overall average waiting time when compared to the existing schemes.

A Comparative Analysis of Job Scheduling for Optimum Performance of Parallel Machines by Considering the Energy Consumption

In this study, a comparative analysis of scheduling of machines and jobs was conducted by proposing a method, which aims to optimize the performance of the job-shop environment. A wide range of objective functions including make-span, maximum tardiness, total flow time, total tardiness, total weighted flow time, and total weighted tardiness, and energy consumption. The decision variables of a manufacturing company including the job’s weight, stating and completion time, due dates, releasing date, and processing time were considered as inputs of the optimization model. Then, subject to the defined technical constraints of the system, a comparative analysis on the basis of eight scheduling methods was conducted to assess the performance indicators of the job-shop system. Results of the analysis revealed variations of different objective functions based on the proposed methods. The optimum make-span varied from minimum of 210 days estimated by the Earliest Due Date (EDD) method, up to 222 days estimated by the Critical Ratio (CR) method. Further recommendations were also made for optimum values of the involved decision variables in the considered job-shop environment. Results revealed that the maximum tardiness was best optimized using the EDD method, whilst the Shortest Processing Time (SPT) led to the best optimum value for the total flow time. Analysis of the energy evaluation of the machines showed that the optimum overall energy consumption has been observed with the value of 7,934 kWh for the first operating machine under the effect of the Minimum Slack method, whilst the optimum energy consumption for the second machine was observed with the value of 7,968 kWh using the First Come First Served (FCFS). Last stage of study recommended the optimum planning schedules and resource allocations of the jobs in the machines considering each scheduling method during the operation of the designed job-shop.

A Comparative Analysis of Job Scheduling for Optimum Performance of Parallel Machines by Considering the Energy Consumption

In this study, a comparative analysis of scheduling of machines and jobs was conducted by proposing a method, which aims to optimize the performance of the job-shop environment. A wide range of objective functions including make-span, maximum tardiness, total flow time, total tardiness, total weighted flow time, and total weighted tardiness, and energy consumption. The decision variables of a manufacturing company including the job’s weight, stating and completion time, due dates, releasing date, and processing time were considered as inputs of the optimization model. Then, subject to the defined technical constraints of the system, a comparative analysis on the basis of eight scheduling methods was conducted to assess the performance indicators of the job-shop system. Results of the analysis revealed variations of different objective functions based on the proposed methods. The optimum make-span varied from minimum of 210 days estimated by the Earliest Due Date (EDD) method, up to 222 days estimated by the Critical Ratio (CR) method. Further recommendations were also made for optimum values of the involved decision variables in the considered job-shop environment. Results revealed that the maximum tardiness was best optimized using the EDD method, whilst the Shortest Processing Time (SPT) led to the best optimum value for the total flow time. Analysis of the energy evaluation of the machines showed that the optimum overall energy consumption has been observed with the value of 7,934 kWh for the first operating machine under the effect of the Minimum Slack method, whilst the optimum energy consumption for the second machine was observed with the value of 7,968 kWh using the First Come First Served (FCFS). Last stage of study recommended the optimum planning schedules and resource allocations of the jobs in the machines considering each scheduling method during the operation of the designed job-shop.

Experimental Evaluation of Energy Saving Task Scheduling (ESTS) in Cloud Computing

Cloud Computing provides services, on-demand access, infrastructure, storage of data and application. It possesses the reliability, availability and the scalability. One of the issues in cloud computing is Energy Saving. In this paper, the proposed work is Energy Saving Task Scheduling (ESTS) methodology. The aim of this methodology is to show the performance comparison of all the task scheduling types. Task scheduling or Job scheduling is referred to as policies that control the work order to be performed by a computer system. Types of Task Scheduling are Shortest Job First (SJF), First Come First Serve (FCFS), Round Robin (RR) and Priority Scheduling. In each type of schedule, the processes used by the parameters were calculated. Finally, the performance comparison is made in scheduling algorithms and shows better results. This method is implemented in net beans toolkit.

A High-Efficiency Data Collection Method Based on Maximum Recharging Benefit in Sensor Networks.

To reduce time delays during data collection and prolong the network lifetime in Wireless Rechargeable Sensor Networks (WRSNs), a type of high-efficiency data collection method based on Maximum Recharging Benefit (DCMRB) is proposed in this paper. According to the minimum number of the Mobile Data Collectors (MDCs), the network is firstly divided into several regions with the help of the Virtual Scan Line (VSL). Then, the MDCs and the Wireless Charging Vehicles (WCVs) are employed in each region for high efficient data collection and energy replenishment. In order to ensure the integrity of data collection and reduce the rate of packet loss, a speed adjustment scheme for MDC is also proposed. In addition, by calculating the adaptive threshold of the recharging request, those nodes with different energy consumption rates are recharged in a timely way that avoids their premature death. Finally, the limited battery capacity of WCVs and their energy consumption while moving are also taken into account, and an adaptive recharging scheme based on maximum benefit is proposed. Experimental results show that the energy consumption is effectively balanced in DCMRB. Furthermore, this can not only enhance the efficiency of data collection, but also prolong the network lifetime compared with the Energy Starvation Avoidance Online Charging scheme (ESAOC), Greedy Mobile Scheme based on Maximum Recharging Benefit (GMS-MRB) and First-Come First-Served (FCFS) methods.

Economically Efficient Hepatitis C Virus Treatment Prioritization Improves Health Outcomes.

The total cost of treating the 3 million Americans chronically infected with hepatitis C virus (HCV) represents a substantial affordability challenge requiring treatment prioritization. This study compares the health and economic outcomes of alternative treatment prioritization schedules. We developed a multiyear HCV treatment budget allocation model to evaluate the tradeoffs of 7 prioritization strategies. We used optimization to identify the priority schedule that maximizes population net monetary benefit (NMB). We compared prioritization schedules in terms of the number of individuals treated, the number of individuals who progress to end-stage liver disease (ESLD), and population total quality-adjusted life years (QALYs). We applied the model to the population of treatment-naive patients with a total annual HCV treatment budget of US$8.6 billion. First-come, first-served (FCFS) treats the fewest people with advanced fibrosis, prevents the fewest cases of ESLD, and gains the fewest QALYs. A schedule developed from optimizing population NMB prioritizes treatment in the first year to patients with moderate to severe fibrosis who are younger than 65 years, followed by older individuals with moderate to severe fibrosis. While this strategy yields the greatest population QALYs, prioritization by disease severity alone prevents more cases of ESLD. Sensitivity analysis indicated that the differences between prioritization schedules are greater when the budget is smaller. A 10% annual treatment price reduction enabled treatment 1 year sooner to several patient subgroups, specifically older patients and those with less severe liver fibrosis. In the absence of a sufficient budget to treat all patients, explicit prioritization targeting younger people with more severe disease first provides the greatest health benefits. We provide our spreadsheet model so that decision makers can compare health tradeoffs of different budget levels and various prioritization strategies with inputs tailored to their population.

A cross-layer design: energy efficient multilevel dynamic feedback scheduling in wireless sensor networks using deadline aware active time quantum for environmental monitoring

ABSTRACTRecently, Packet scheduling plays a vital role in Wireless Sensor Networks (WSNs). The major key challenges include delay, packet dropping, energy consumption and lifetime due to constraints in energy and computing resources. All the research works on packet scheduling scheme in WSN uses only First Come First Served (FCFS) and Dynamic Multilevel Priority (DMP) schemes. FCFS works based on packet arrival time, it leads to starvation and high processing overhead for real-time packets. DMP works in multilevel with dynamic priority reduces the transmission overhead and bandwidth; it consumes more resources for real-time task leads to deadlock. To solve these problems, this work presents Multilevel Dynamic Feedback Scheduling (MDFS) algorithm. The sensor node classifies the emergency and normal data into three different ready queues named as high, medium and low priority, respectively. The queues are connected with a feedback mechanism; each packet from the sensor node has its own time quantum value based on the deadline. The updated time quantum value is compared with the boundary value of the queues, depends on the updated value the data packets are moved between queues with help of feedback mechanism. The simulation result proves that the projected MDFS outperforms in WSN environment.

Application of Viral System Algorithm in Load Balancing of Cloud Environment

As the cloud computing technology is gaining popularity with time, more and more users and applications are shifting towards it. This is why clouds are experiencing high load, which demands for load balancing of user tasks submitted to cloud for execution. This makes load balancing of non-preemptive tasks a key issue in cloud computing. Superior task scheduling leads to balanced loads among cloud nodes, which results in faster execution of tasks. Task scheduling in cloud environment is an instance of NP-hard optimization problem. When few nodes in a cloud are overloaded whereas other nodes are under loaded then in such situation the performance of overloaded VMs is diminished. It demands a task scheduling so that the incoming tasks can be distributed uniformly across virtual machines (VMs) for proper utilization of available resources. In this study, we propose a novel load balancing algorithm named Viral System Based Load Balancing (VSB-LB) algorithm, which is based on bio-inspired viral system algorithm that distributes the tasks uniformly among VMs. The proposed algorithm is compared with basic load balancing algorithms such as First Come First Serve (FCFS), Weighted Round Robin (WRR) as well as newer bio-inspired Load balance Aware Genetic Algorithm (LAGA) to show its effectiveness. Simulation results proved that VSBLB outperforms FCFS and WRR and LAGA for performing load balancing.

SOAP

We consider an extremely broad class of M/G/1 scheduling policies called SOAP: Schedule Ordered by Age-based Priority. The SOAP policies include almost all scheduling policies in the literature as well as an infinite number of variants which have never been analyzed, or maybe not even conceived. SOAP policies range from classic policies, like first-come, first-serve (FCFS), foreground-background (FB), class-based priority, and shortest remaining processing time (SRPT); to much more complicated scheduling rules, such as the famously complex Gittins index policy and other policies in which a job's priority changes arbitrarily with its age. While the response time of policies in the former category is well understood, policies in the latter category have resisted response time analysis. We present a universal analysis of all SOAP policies, deriving the mean and Laplace-Stieltjes transform of response time. The full version of this work appears in POMACS [Scully et al., 2018].

Frame Trolley Dispatching Algorithm for the Frame Bridge Based Automated Container Terminal

This paper studies the container handling process for a newly designed container terminal, known as the Frame Bridge based Automated Container Terminal (FB-ACT). The system was shown to be an effective solution to the next generation container terminal, but its efficiency depends on the dispatching of frame trolleys (FTs), which transport containers along the apron. We address the FT dispatching problem to ensure conflict-free movements, while considering the handshakes with other devices in the system. A mixed-integer programming (MIP) model is formulated to minimize the makespan considering FT conflicts and handshakes. An algorithm based on filtered beam search is developed to solve the problem. In this algorithm, two filtering approaches are used to guide the search for beam nodes at each level. The first approach uses a surrogate model to effectively screen out the less promising nodes. Then, the second approach uses a reduced MIP model to further identify the beam nodes for the next level. Numerical experiments show that our proposed algorithm yields near-optimal solutions in small-scale problems. In large-scale problems, our algorithm significantly outperforms two other algorithms based on first come, first served (FCFS) or FCFS with dedicated vehicle assignment. Some interesting insights about the FB-ACT system are also shown in the study.

A Fuzzy Rule-Based Optimisation Model for Efficient Resource Utilisation in a Grid Environment Using Proximity Awareness and Semantic Technology

The performance of computational grids mainly depends on the resource allocation service of a resource management system. Efficient resource allocation is essential for better resource utilisation which could be for both providers and grid users. Resource allocation includes the scheduling of gridlets to the available resources. However, the biggest challenges for grid users are to select the best resources from the available grid resources and to allocate these resources for scheduling of the gridlets. To address these issues and enhance the resource utilisation process, we propose a semantic and proximity-aware fuzzy rule-based model that improves the resource utilisation in a grid environment. The model uses fuzzy techniques with four parameters such as semantic similarity, proximity, number of total machines and number of total processors of each machine. The experimental results provide promising results. Overall, the proposed semantic and proximity-aware fuzzy rule-based decentralised resource discovery model improves the resource utilisation by 23% as compared to non-fuzzy first come first serve (FCFS) technique in a computational grid environment.