Scheduling Algorithm Research Articles

Descriptive analysis of wide area network flow control internet traffic on Metro-E 100 Mbps campus network

QoS in computer networking is the capability to provide better service to network traffic over various technologies such as ethernet and IP networks. This paper presents a descriptive analysis of WAN flow control and internet traffic on a Metro-E campus network. Issues on network congestion and delay in network QoS where internet traffic is gradually increasing, resulting in bursts of network capacity that affect network QoS. The method implies 12 months data collection and analysis on protocol, bytes and packets inbound and correlation between parameters on the Metro-E 100 Mbps campus network. The result presents heavy-tailed distributions on an inbound packet kurtosis value of 347 and an outbound packet kurtosis value of 780. Bytes outbound and inbound are skewed at 122 and right at 17 respectively. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB. Protocol 6 TCP presents the highest amount of -traffic and a weak positive correlation at 0.104 exists between the inbound and outbound packets and bytes on the network. The correlation coefficient's 95% confidence interval ranges between 0.096 and 0.111. This research is significant in the future deployment of traffic scheduling, policing, and shaping algorithms for QoS bandwidth management on the WAN Metro-E campus network.

Power allocation in NOMA using sum rate-based dwarf mongoose optimization

The increasing number of consumers with diverse data rate needs is leading to increased heterogeneity in traditional cellular networks. Nonorthogonal multiple access (NOMA) has emerged as a promising method to serve a large number of users, but research shows that weak users (WU) and strong users (SU) have different throughputs. Intra-group interference reduces WUs throughput due to the superposition of signals. Improper power distribution impacts NOMA performance and lowers the total system rate. The multi-objective sum rate dwarf mongoose optimization algorithm (M-SRDMOA) is implemented as a solution to the NOMA network power allocation problems. The DMOA approach distributes adequate power to all NOMA users to increase the large sum rate. The effectiveness of the M-SRDMOA approach is supported by existing studies on fair NOMA scheduler (FANS) and multi-objective sum rate-based butterfly optimization algorithm (M-SRBOA). The M-SRDMOA’s potential sum rate with an SNR of 9dB and a noise variation=2 is 14.06 bps/Hz, which is high compared to M-SRBOA and FANS.

Multi-priority scheduling algorithm for scientific workflows in cloud

The public cloud environment has emerged as a promising platform for exe-cuting scientific workflows. These executions involve leasing virtual machines (VMs) from public services for the duration of the workflow. The structure of the workflows significantly impacts the performance of any proposed scheduling approach. A task within a workflow cannot begin its execution before receiving all the required data from its preceding tasks. In this paper, we introduce a multi-priority scheduling approach for executing workflow tasks in the cloud. The key component of the proposed approach is a mechanism that logically or-ders and groups workflow tasks based on their data dependencies and locality. Using the proposed approach, the number of available VMs influences the num-ber of groups (partitions) obtained. Based on the locality of each group’s tasks, the priority of each group is determined to reduce the overall execution delay and improve VM utilization. As the results demonstrate, the proposed approach achieves a significant reduction in both execution costs and time in most scenar-ios

AutoTQA: Towards Autonomous Tabular Question Answering through Multi-Agent Large Language Models

With the growing significance of data analysis, several studies aim to provide precise answers to users' natural language questions from tables, a task referred to as tabular question answering (TQA). The state-of-the-art TQA approaches are limited to handling only single-table questions. However, real-world TQA problems are inherently complex and frequently involve multiple tables, which poses challenges in directly extending single-table TQA designs to handle multiple tables, primarily due to the limited extensibility of the majority of single-table TQA methods. This paper proposes AutoTQA, a novel Auto nomous T abular Q uestion A nswering framework that employs multi-agent large language models (LLMs) across multiple tables from various systems (e.g., TiDB, BigQuery). AutoTQA comprises five agents: the User , responsible for receiving the user's natural language inquiry; the Planner , tasked with creating an execution plan for the user's inquiry; the Engineer , responsible for executing the plan step-by-step; the Executor , provides various execution environments (e.g., text-to-SQL) to fulfill specific tasks assigned by the Engineer ; and the Critic , responsible for judging whether to complete the user's natural language inquiry and identifying gaps between the current results and initial tasks. To facilitate the interaction between different agents, we have also devised agent scheduling algorithms. Furthermore, we have developed LinguFlow, an open-source, low-code visual programming tool, to quickly build and debug LLM-based applications, and to accelerate the creation of various external tools and execution environments. We also implemented a series of data connectors, which allows AutoTQA to access various tables from multiple systems. Extensive experiments show that AutoTQA delivers outstanding performance on four representative datasets.

Critical review on resource scheduling in IaaS clouds: Taxonomy, issues, challenges, and future directions

AbstractIn a cloud computing environment, the primary goal of resource scheduling is to reduce the economic expenditures for cloud users and grow fiscal achievement for cloud providers. In this article, the study of numerous forms of resource scheduling algorithms is presented that has been applied in IaaS clouds. The selected research articles are classified into six categories, according to the nature of the algorithm used. Further, the authors pointed out several issues and challenges with the help of the resource scheduling algorithms, the comparative performance metrics, and the simulation tools used to validate the several algorithms. The enhancement illustrates better performance concerns decreasing the cost and time while improving the competence and utilization of resources for IaaS clouds. These algorithms are executed in numerous simulation tools and real environments similar to CloudSim, SimGrid, MATLAB, and test‐bed environments (practical implementation). This critical review and classification will serve as a foundation for further research in IaaS clouds in the Internet of Things (IoT) environments.

Towards Millions of Database Transmission Services in the Cloud

Alibaba relies on its robust database infrastructure to facilitate realtime data access and ensure business continuity despite regional disruptions. To address these operational imperatives, Alibaba developed the Data Transmission Service (DTS), which has become critical for internal applications and public cloud services alike. This paper presents a comprehensive study of the architectural innovations, resource scheduling mechanisms, and performance optimization strategies that have been implemented within DTS to tackle the significant challenges of cross-network, heterogeneous data transmission in a cost-effective manner. We explore the novel Any-to-Any (A2A) architecture, which simplifies the complexity of data paths between diverse databases and mitigates network connectivity issues, thereby significantly reducing development overhead. Additionally, we examine a dynamic network bandwidth scheduling algorithm that effectively maintains Service-Level Objectives (SLOs), complemented by a serverless mechanism that ensures efficient resource utilization. Furthermore, DTS utilizes advanced strategies such as transaction dependency tracking, hot data consolidation, and batching to enhance synchronization performance and efficiency. DTS has distilled the lessons learned from years of serving our customer base and currently supports nearly 1 million public cloud instances annually. Our evaluation results show that DTS can effectively and efficiently handle real-time data transmission in both experimental and production environments.

Research on Distributed Scheduling Algorithm for Virtual City Power Plants Based on Blockchain Technology

Research on Distributed Scheduling Algorithm for Virtual City Power Plants Based on Blockchain Technology

A new Channel and QoS Aware Scheduler algorithm for real time and non real time traffic in 5G heterogeneous networks

A new Channel and QoS Aware Scheduler algorithm for real time and non real time traffic in 5G heterogeneous networks

Polling Schedule Algorithms for Data Aggregation with Sensor Phase Control in In-Vehicle UWB Networks

Polling Schedule Algorithms for Data Aggregation with Sensor Phase Control in In-Vehicle UWB Networks

Various Variants of Round Robin Scheduling Algorithm

Various Variants of Round Robin Scheduling Algorithm

On the Optimization of Robot Machining: A Simulation-Based Process Planning Approach

The use of industrial robots for machining operations is pursued by industry lately, since they can increase the flexibility of the production system and reduce production costs. However, their industrial adoption is still limited, mainly due to their insufficient structural stiffness and posture-dependent dynamic behavior, leading to limited machining process accuracy. For this purpose, the Digital-Model of a machining robot has been developed, providing a tool for virtual commissioning of the process that can be used during the process planning stage. The Multi-Body Simulation method combined with a Component Mode Synthesis have been adopted, considering flexibility of both the joints and links. On top of that, and motivated from robotic-based machining systems’ flexibility and versatility, two optimization algorithms have been developed, attempting to increase the process accuracy. A workpiece placement optimization algorithm, attempting to maximize the robot stiffness during the process acquiring knowledge from the robot stiffness maps, and a feed-rate scheduling algorithm, attempting to constrain the contour error by regulating the generated cutting forces. The capabilities and functionality of the developed model and optimization algorithms are showcased in two different case studies, with the results proving the improvements on the process accuracy after the application of the optimization algorithms. Finally, an experimental validation of the Digital-Model has been performed, to confirm the consistency between model outputs and real experimental data.

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

A dynamic programming algorithm for order picking in robotic mobile fulfillment systems

AbstractThe order scheduling and rack sequencing problem deals with the order picking process in robotic mobile fulfillment systems: automated guided vehicles lift and transport movable storage racks to picking stations to supply items requested by customer orders which are put together in cardboard boxes on a workbench of limited capacity. To efficiently operate the station, the sequence in which racks visit the station one after another and the intervals at which customer orders are scheduled must be coordinated. We present a dynamic programming algorithm for the order scheduling and rack sequencing problem at a single picking station minimizing the number of rack visits. Despite monolithic mixed integer linear programming formulations, our approach appears to be the first combinatorial solution method for the problem in literature. A computational study demonstrates the effectiveness of the approach.

Fairness-Aware Radio Resource Management in OFDMA-Based Wimax Networks with Comparison Between Three Downlink Scheduling Algorithms

Fairness-Aware Radio Resource Management in OFDMA-Based Wimax Networks with Comparison Between Three Downlink Scheduling Algorithms

A Q-learning-based biology migration algorithm for energy-saving flexible job shop scheduling with speed adjustable machines and transporters

Due to the increasing demand for green manufacturing, energy-saving scheduling problems have garnered significant attention. These problems aim to reduce energy consumption at the production system level within workshops. To simulate a realistic production environment, this study addresses an energy-saving flexible job shop scheduling problem that considers two types of speed-adjustable resources, namely machines and transporters. The optimization objective is to minimize the comprehensive energy consumption of the workshop. A novel mathematical model is initially constructed based on the specific characteristics of the problem at hand. Given its NP-hard nature, a new Q-learning-based biology migration algorithm (QBMA) is proposed, which encompasses diverse search strategies and employs a Q-learning algorithm to dynamically select search strategies, thereby preventing blind search during the evolutionary process. The experimental results of our study demonstrate the promising efficacy of QBMA in effectively addressing the aforementioned problem, while also highlighting the positive impact of considering resources with adjustable speed.

Dynamic computation scheduling for hybrid energy mobile edge computing networks

The rapid expansion of Mobile Edge Computing (MEC) is driven by the growing demand for resource-intensive applications within the Internet of Things. Computation offloading allows these applications to be executed at the network edge, but it leads to significant electricity expenses for network operators. To mitigate these costs, one promising solution is to power base stations (BSs) with a hybrid energy supply that combines unpredictable harvested energy with stable energy from the smart grid. This paper investigates joint computation scheduling for mobile devices (MDs) and resource allocation in a MEC network incorporating hybrid energy sources. Our objective is to maximize long-term time-averaged service utility by optimizing parameters such as BS battery supply, harvestable energy, CPU frequency, transmission power, task-partition factor, and MD-BS associations. To tackle this complex problem, we exploit the Lyapunov optimization framework to decompose it into deterministic subproblems for each time slot and propose an online network service utility maximization scheduling (NSUMS) algorithm. Experimental results show that our algorithm outperforms benchmark schemes in service utility and energy expenditure, improving the completion ratio by 32%, reducing the failure rate by 80%, and decreasing MD energy consumption by 28%.

Resource Utilization Based on Hybrid WOA-LOA Optimization with Credit Based Resource Aware Load Balancing and Scheduling Algorithm for Cloud Computing

Resource Utilization Based on Hybrid WOA-LOA Optimization with Credit Based Resource Aware Load Balancing and Scheduling Algorithm for Cloud Computing

A Multi-Objective Non-Dominated Sorting Gravitational Search Algorithm for Assembly Flow-Shop Scheduling of Marine Prefabricated Cabins

Prefabricated cabin modular units (PMCUs) are a widespread type of intermediate products used during ship or offshore platform construction. This paper focuses on the scheduling problem of PMCU assembly flow shops, which is summarized as a multi-objective, fuzzy-blocking hybrid flow-shop-scheduling problem based on learning and fatigue effects (FB-HFSP-LF) to minimize the maximum fuzzy makespan and maximize the average fuzzy due-date agreement index. This paper proposes a multi-objective non-dominated sorting gravitational search algorithm (MONSGSA) to solve it. In the proposed MONSGSA, the ranked-order value is used to convert continuous solutions to discrete solutions. Multi-dimensional Latin hypercube sampling is used to enhance initial population diversity. Setting up an external archive to maintain non-dominated solutions while introducing an adaptive inertia factor and a trap avoidance operator to guide individual positional updates. The results of multiple sets of experiments show that Pareto solutions of MONSGSA have better distribution and convergence compared to other competitors. Finally, the instance of PMCU manufacturer is used for validation, and the results show that MONSGSA has better applicability to practical problems.

Dynamic variable analysis guided adaptive evolutionary multi-objective scheduling for large-scale workflows in cloud computing

Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm, namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential resources, thereby alleviating the negative impact of redundant cloud resources on evolutionary search efficiency. Then, a variable analysis strategy is designed to dynamically measure the contribution of each decision variable in pushing the population towards Pareto-optimal fronts. Moreover, AESA embraces an adaptive strategy to reward more evolutionary opportunities for decision variables with higher contributions to handle large-scale decision variables in a targeted manner, further improving the efficiency of evolutionary search. Finally, extensive experiments are performed based on real-world cloud platforms and workflow traces to verify the effectiveness of the proposed AESA. The comparison results validate its superior performance by significantly outperforming five representative baselines in optimizing makespan and energy consumption. Also, the results of ablation experiments demonstrate that all three components contribute to AESA’s overall performance, with the adaptive reward mechanism being the most significant.

New Partitioning Techniques and Faster Algorithms for Approximate Interval Scheduling

New Partitioning Techniques and Faster Algorithms for Approximate Interval Scheduling