Efficient Scheduling Algorithm Research Articles

Designing heuristic driven hybrid optimization algorithms for efficient workflow scheduling in IaaS cloud

<p class="Abstract">Each one of the real resources or hardware like workstations, work ranges, joins, switches, switches, server ranches, and capacity contraptions are fundamental for the establishment. In disseminated computing, all the establishment is virtualized and given to infrastructure as a service. Usually called IaaS. IaaS quickly increments or down with ask and avoids the got to procure genuine servers and distinctive server cultivate establishment; each resource is displayed as a specific help portion. A conveyed computing pro-organization bargains with the establishment, whereas the client presents, plans, and manages programming, counting applications, middleware, and working systems. IaaS dispersed computing offers clients induction to figuring resources like servers, stockpiling, and frameworks organization. Affiliations utilize their possess establishment and applications interior a master organization’s system. For cloud service providers, cloud schedulers automate IT procedures. Schedulers are used by end users to automate jobs, or tasks, that support everything from big data pipelines to machine learning processes to cloud infrastructure. Infrastructure as a service, or IaaS, is a type of cloud computing that uses the internet to provide virtualized computing resources. IaaS is one of the three main types of cloud computing services, along with Platform as a Service (PaaS) and Software as a Service (SaaS). The precise task is assigned to the CPU, the network, and the storage by resource scheduling. The point behind this is the outrageous use of assets. However, both cloud providers and users require well-organized scheduling.</p>

A Robust RF-Based Wireless Charging System for Dockless Bike-Sharing

In the past few years, dockless bike-sharing has become a popular means of public transportation and brought significant convenience to millions of citizens. As one of the key components of a shared bike, the smart locking/unlocking module has proposed a new challenge of how to provide robust power supplement for them. Current charging solutions for shared bikes are mainly based on mechanical power and solar power, and rarely take user experience and charging delay into consideration. In this paper, we design a robust RF-based wireless charging system for dockless bike-sharing. Our system utilizes radio frequency (RF) power to provide stable charging service while preserving the quality of service. In our system, an RF wireless charging sensing node is integrated on the bike's basket, so that the mutual interference during charging process and space occupation can be reduced. In order to reduce charging delay, we first design an efficient charging direction scheduling algorithm for a single charger. Then, we extend the solution to multiple-charger scenarios via dynamic programming. Our system has been successfully implemented on a dockless bike-sharing system. The experimental results verify that our design can satisfy the charging demands of shared-bikes and achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$85\%$</tex-math></inline-formula> of the optimal solution.

Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks

&lt;div align="center"&gt;&lt;span&gt;Efficient scheduling algorithms are necessary in the cloud paradigm to optimize service provision to clients while minimizing time duration, energy consumption, and violations of service level agreements (SLAs). Disregarding task appropriateness in resource scheduling can have a detrimental effect on the quality of service provided by cloud providers. Moreover, the utilization of resources in an ineffective manner will necessitate a substantial expenditure of energy to execute activities, leading to prolonged processing duration that adversely affect the temporal duration. Many research projects have focused on employment scheduling problems, and the algorithms used in these studies have offered answers that were deemed nearly flawless. This study presents a chaos bird swarm algorithm (Chaos BSA) approach that use machine learning to consider task priority while allocating tasks to the cloud platform. The method calculates the priorities of task virtual machines and incorporates these values into the scheduler. The scheduler will select tasks that align with the specified priorities and are compatible with the virtual machines. The implementation of the system utilized the openstack cloud platform and the cloudsim tool. The results and comparison with the baseline approach genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) clearly demonstrate that our Chaos BSA outperforms them by 18% in terms of efficiency.&lt;/span&gt;&lt;/div&gt;

Data Freshness in Information-update Systems: Modeling, Scheduling, and Tradeoffs

Brief Biography: Zhongdong Liu is a final-year Ph.D. student in the Department of Computer Science at Virginia Tech. He received his B.S. degree in Mathematics and Applied Mathematics with honors from Northeast Forestry University in 2016. His research interests are in the modeling, analysis, control, and optimization of complex network systems. His specific area of focus lies in analyzing and improving data freshness within real-time applications and services through the development of efficient scheduling algorithms. Zhongdong's research has been featured in notable publications, including IEEE INFOCOM, Proceedings of ICCCN (as an invited paper), IEEE Internet of Things Journal, and IEEE/ACM Transactions on Networking.

Highly efficient scheduling algorithms for identical parallel machines with sufficient conditions for optimality of the solutions

Highly efficient scheduling algorithms for identical parallel machines with sufficient conditions for optimality of the solutions

The effect of vehicle-to-grid integration on power grid stability: A review

This article explores the promising potential of vehicle-to-grid (V2G) technology, which has attracted considerable interest due to its capacity to enhance power grid stability, decrease carbon emissions, and boost renewable energy utilization. The study begins by examining recent developments in V2G technology, then proceeds to discuss the challenges associated with integrating V2G into the power grid, and finally presents strategies to improve grid stability. Despite the potential of V2G technology, certain challenges must be addressed, including grid stress, complexities in load management, and grid imbalances. The article outlines various strategies to improve power grid stability, such as smart energy metering, time-of-use (TOU) schemes, rolling prediction decision frameworks, and an array of control approaches. Additionally, the research highlights the application of machine learning algorithms for efficient EV scheduling and power control units, voltage stabilization systems, and energy management units to ensure grid reliability. Future research should focus on developing scalable infrastructure solutions, establishing universal standards, designing optimization algorithms, and devising strategies to enhance battery life, to facilitate efficient V2G integration.

GPARS: Graph predictive algorithm for efficient resource scheduling in heterogeneous GPU clusters

Efficient resource scheduling in heterogeneous graphics processing unit (GPU) clusters are critical for maximizing system performance and optimizing resource utilization. However, prior research in resource scheduling algorithms typically employed machine learning (ML) algorithms to estimate job durations or GPU utilization in the cluster based on training progress and task speed. Regrettably, these studies often overlooked the performance variations among different GPU types within these clusters, as well as the presence of spatiotemporal correlations among jobs. To address these limitations, this paper introduces the graph predictive algorithm for efficient resource scheduling (GPARS) designed specifically for heterogeneous clusters. GPARS leverages spatiotemporal correlations among jobs and utilizes graph attention networks (GANs) for precise job duration prediction. Building upon the prediction results, we develop a dynamic objective function to allocate suitable GPU types for newly submitted jobs. By conducting a comprehensive analysis of Alibaba’s heterogeneous GPU cluster, we delve into the impact of GPU capacity and type on job completion time (JCT) and resource utilization. Our evaluation, using real traces from Alibaba and Philly, substantiates the effectiveness of GPARS. It achieves a remarkable 10.29% reduction in waiting time and an average improvement of 7.47% in resource utilization compared to the original scheduling method. These findings underscore GPARS’s superior performance in enhancing resource scheduling within heterogeneous GPU clusters.

Efficient algorithms for driver scheduling with interdependent routes

Efficient algorithms for driver scheduling with interdependent routes

A Secure Key Expansion Algorithm for Present

The efficiency of a cryptographic algorithm in terms of security depends on the resistance against cryptanalytic attacks. Besides the complexity of the encryption algorithm, the key plays an essential role in the security against cryptanalytic attacks. The strength and complexity of the encryption algorithm do not suffice and serve the fundamental purpose of security if the key is compromised at any stage. So, apart from the cryptanalytically robust encryption algorithm, a strong key schedule is also essential to thwart possible attacks against a particular algorithm. PRESENT, a lightweight encryption technique with a simple design, is resistant to linear and differential attacks but has a weak key schedule and is susceptible to cryptanalytic attacks. This paper proposes an efficient key scheduling algorithm for PRESENT lightweight encryption technique resistant against cryptanalytic attacks. Statistical tests examine the proposed key schedule’s cryptographic strength on the properties of subkeys produced in the key generation mechanism. The tests prove the efficiency of the proposed key schedule in terms of cryptanalytic attacks. Implementation and comparison of the efficacy of the proposed key schedule in terms of security and implementation costs are illustrated with the PRESENT-80 key schedule and the PRESENT-128 key schedule. The key scheduling algorithm is implemented on the ARM Cortex M3-based NXP LPC 1857 and 1768 hardware development platforms. The results indicate the efficiency of the proposed algorithm in terms of security, performance, and power consumption across the two hardware platforms.

Dynamic Scheduling Stochastic Multiworkflows With Deadline Constraints in Clouds

Nowadays, more and more workflows with different computing requirements are migrated to clouds and executed with cloud resources. In this work, we study the problem of stochastic multi-workflows scheduling in clouds and formalize this problem as an optimization problem that is NP-hard. To solve this problem, an efficient stochastic multi-workflows dynamic scheduling algorithm called SMWDSA is designed to schedule multi-workflows with deadline constraints for optimizing multi-workflows scheduling cost. The proposed SMWDSA consists of three stages including multi-workflows preprocessing, multi-workflow scheduling and scheduling feedback. In SMWDSA, a novel task sub-deadlines assignment stretagy is design to assign the task sub-deadlines to each task of multi-workflows for meeting workflow deadline constraints. Then, we propose a task scheduling method based on the minimal time slot availability to execution task for minimizing workflow scheduling cost while meetingt workflow deadlines. Finally, a scheduling feedback strategy is adopted to update the priorities and sub-deadlines of unscheduled tasks, for further minimizing workflow scheduling cost. We conduct the experiments using both synthetic data and real-world data to evaluate SMWDSA. The results demonstrate the superiority of SMWDSA as compared with the state-of-the-art algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Workflow scheduling in clouds is significantly challenging due to not only the large scale of workflows but also the elasticity and heterogeneity of cloud resources. Moreover, minimizing workflow scheduling cost and satisfying workflow deadlines are two critical issues in scheduling with cloud resources, especially the uncertainty of workflow arrive time and task execution time are considered. To meet workflow deadlines, it is an effective strategy to decompose workflow deadline constraints into task sub-deadline constraints. To minimize the workflow scheduling cost, each task in a workflow needs to be assigned to their most suitable VMs for execution. This article presents a novel workflow scheduling algorithm to schedule stochastic multi-workflows in clouds for optimizing multi-workflows scheduling cost and meeting workflows deadlines. This algorithm obtains the task sub-deadline constraints based on the characteristics of workflows for meeting the worklfow deadline constraint. Under the premise of meeting task deadlines, it schedules tasks to a VM with minimum the slot time, for minimizing the cost. Case studies based on well-known real-world workflows data sets suggest that it outperforms traditional ones in terms of success and cost of multi-workflows scheduling. It can thus aid the design and optimization of multi-workflows scheduling in a cloud environment. It can help practitioners better manage the scheduling cost and performance of real-world applications built upon cloud services.

Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment

The task scheduling has a potential impact on overall system performance and resource utilization in the domain of Cloud Computing. Cloud computing adopts a cloud service provider (CSP) for facilitating access and delivering services to the shared resources. Task scheduling in clouds can be attained some symmetry form which helps in achieving predominant resource optimization that includes energy efficiency and load balancing. This task scheduling process of cloud computing pertains to the problem of Non-deterministic Polynomial (NP) which can be significantly solved using the techniques of metaheuristic optimization for enhancing the job scheduling effectiveness. In this paper, an Improved Coati Optimization Algorithm-based Task Scheduling (ICOATS) is presented for addressing the issues of lengthier scheduling time, high consumptions of cost and maximized load on Virtual Machine (VM) in cloud computing environment. In this proposed ICOATS, a model for distribution and scheduling of tasks is constructed using the factors of VMs, cost and time. It further included a multi-objective fitness function which targets on minimizing makespan, and at the same time maximizing the rate of resource utilization. It established possible plan for every coati with respect to task scheduling process that aids in determining the best solution (optimal assignment of incoming tasks to VMs). It is proposed with the capability of handing the problem of premature convergence by incorporating an exploitation strategy which improving the local search potential with well-balanced trade-off amid exploration and exploitation. The simulation results of this ICOATS approach under its evaluation with the existing metaheuristic task scheduling approaches confirmed better improvement in reducing makespan, and simultaneously enhances the turnaround efficiency, success rate and availability.

Ranking routes in semiconductor wafer fabs

We develop a method to estimate the quality of processing routes in a wafer fabrication process. Ranking such routes can be useful for identifying the “best” and “worst” routes when making adjustments to recipes. Route categorization is also useful in developing efficient scheduling algorithms. In particular, we propose a method for ranking routes based on count-based metrics such as the number of defects on a wafer. We start with a statistical model to produce a “local” ranking of a tool and then build a “global” ranking via a heuristic procedure. Creating a fully statistical procedure for ranking routes in semiconductor fabrication plants is virtually impossible, given the number of possible routes and the limited data available. Nonetheless, our discussions with working engineers indicate that even approximate rankings are useful for making better operational decisions.

Scheduling aerial resource operations for the extinction of large-scale wildfires

The significant increase in large-scale wildfire events in recent decades, caused primarily by climate change, has resulted in a growing number of aerial resources being used in suppression efforts. Present-day management lacks efficient and scalable algorithms for complex aerial resource allocation and scheduling for the extinction of such fires, which is crucial to ensuring safety while maximizing the efficiency of operations. In this work, we present a Mixed Integer Linear Programming (MILP) optimization model tailored to large-scale wildfires for the daily scheduling of aerial operations. The main objective is to achieve a prioritized target water flow over all areas of operation and all time periods. Minimal target completion across individual areas and time periods and total water output are also maximized as secondary and ternary objectives, respectively. An efficient and scalable multi-start heuristic, combining a randomized greedy approach with simulated annealing employing large neighborhood search techniques, is proposed for larger instances. A diverse set of problem instances is generated with varying sizes and extinction strategies to test the approaches. Results indicate that the heuristic can achieve (near)-optimal solutions for smaller instances solvable by the MILP, and gives solutions approaching target water flows for larger problem sizes. The algorithm is parallelizable and has been shown to give promising results in a small number of iterations, making it applicable for both night-before planning and, more time-sensitive, early-morning scheduling.

Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of internet of things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as required. This article proposes hybrid meta-heuristic optimization algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combined modified particle swarm optimization (MPSO) meta-heuristic and deterministic spanning tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the MPSO was used to schedule user task requests among fog devices, while hybrid MPSO-SPT was used to perform resource allocation and resource management in the fog computing environment. The study implemented the proposed algorithm using iFogSim; the performance of the algorithm was evaluated, assessed, and compared with other state-of-the-art task scheduling and resource management algorithms, the proposed method performs better in terms of energy consumption, resource utilization and response time, and the study proposed future research on evaluating the execution time using the hybrid algorithm.

An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users’ jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits.

Automatic Migration-Enabled Dynamic Resource Management for Containerized Workload

Containerized workloads are gaining traction due to microservices architecture adaptation in many fields, including healthcare, finance, Internet of Things, and smart cities. Modern data centers are containerized to facilitate this growing demand. Most of the existing resource allocation methods for data centers used efficient scheduling algorithms to place the containers using static computing resources. These static resource allocation techniques are not energy efficient and do not help maximize data center utilization. Dynamic resource allocation and a migration-enabled placement method can reduce energy utilization while improving the utilization of the available computing infrastructure. This article presents and evaluates a novel dynamic resource management system that uses active migrations to minimize energy utilization to serve containerized workloads and improve data center utilization. Our approach uses a deep learning method to estimate the job execution time and then employs an unsupervised learning method to identify similar jobs. Similar jobs are placed and migrated to achieve energy efficiency and better utilization of the available data center infrastructure. Our proposed system is evaluated and compared with the existing state-of-the-art baseline methods. The proposed solution reduces the energy consumption from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 1.18$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 2.35$</tex-math></inline-formula> compared to the baseline methods while maintaining similar performance.

Transaction Scheduling: From Conflicts to Runtime Conflicts

This paper studies how to improve the performance of main memory multicore OLTP systems for executing transactions with conflicts. A promising approach is to partition transaction workloads into mutually conflict-free clusters, and distribute the clusters to different cores for concurrent execution. We show that if transactions in each cluster are properly scheduled, transactions that are traditionally considered conflicting can be executed without conflicts at runtime. In light of this, we propose to schedule transactions and reduce runtime conflicts, instead of partitioning based on the conventional notion of conflicts. We formulate the transaction scheduling problem to minimize runtime conflicts, and show that the problem is NP-complete. This said, we develop an efficient scheduling algorithm to improve parallelism. Moreover, for transactions that are not packed in batches, we show that runtime conflict analysis also helps reduce conflict penalties, by proposing a proactive deferring method. Using standard and enhanced benchmarks, we show that on average our scheduling and proactive deferring methods improve the throughput of existing partitioners and concurrency control protocols by 131% and 109%, respectively, up to 294% and 152%.

Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm

The cloud computing has emerged as a novel distributed computing system in past few years. It provides computation and resources over the Internet via dynamic provisioning of services. There are quite a few challenges and issues connected with implementation of cloud computing. This paper considers one of its major problems, i.e. task scheduling. The function of an efficient task scheduling algorithm is that it concentrates not only on attaining the requirements of the user but also in enhancing the efficiency of the cloud computing system. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. This paper proposes a modified Penguins Search Optimization Algorithm (MPeSOA) for efficient cloud task scheduling. The main contribution of our work is to schedule all tasks to available virtual machines so that the makespan is minimized, resource utilization is increased and the degree of imbalance is reduced. The proposed scheduling algorithm was simulated using the CloudSim 4.0 simulator. Experimental results showed that the proposed MPeSOA outperformed three existing meta-heuristics, namely Penguins Search Optimization Algorithm (PeSOA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

A novel improved hybrid optimization algorithm for efficient dynamic medical data scheduling in cloud-based systems for biomedical applications

A novel improved hybrid optimization algorithm for efficient dynamic medical data scheduling in cloud-based systems for biomedical applications

A New Algorithm for Real-Time Scheduling and Resource Mapping for Robot Operating Systems (ROS)

A new version of a robot operating system (ROS-2) has been developed to address the real-time and fault constraints of distributed robotics applications. However, current implementations lack strong real-time scheduling and the optimization of response time for various tasks and applications. This may lead to inconsistent system behavior and may affect system performance. This article presents a new and efficient heuristic scheduling algorithm for the ROS-2 framework to improve resource mapping and system scheduling for robotics applications. The proposed scheduling design includes grouping callbacks into executors, assigning executor priority, and sequencing callbacks inside executors. The timing constraints and functional properties are expressed in the formal design model, and real-time scheduling is performed with respect to the timing constraints. The benefits of the proposed solution are demonstrated by a set of experimental results. Using this algorithm, it is shown that the number of executors needed to schedule a set of callbacks is minimized and the system schedulability is maximized for loaded and overloaded cases.