Efficient Scheduling Approach Research Articles

Extended water wave optimization (EWWO) technique: a proposed approach for task scheduling in IoMT and healthcare applications

AbstractThe Internet of Medical Things (IoMT) is a version of the Internet of Things. It is getting the attention of researchers because it can be used in a wide range of smart healthcare systems. One of the main advancements employed recently is the IoMT-cloud, which allows users to access cloud services remotely over the internet. These cloud services require an efficient task scheduling approach that satisfies the Quality of Service parameters with a low energy consumption. This paper presents an overview of the integration of IoMT and cloud computing technologies. Besides,this work proposes an efficient Extended Water Wave Optimization (EWWO) task scheduling in the IoMT Cloud for healthcare applications. EWWO algorithm performs based on its operations propagation, refraction and breaking. The proposed EWWO scheduling technique minimizes the energy consumption, makespan time, execution time and increases the resource utilization. Cloudsim simulator is used to simulate the IoMT-Cloud environment to verify the effectiveness of EWWO technique. The performance has been evaluated based on various parameters such as energy consumption, makespan time and execution time.

ETFC: Energy-efficient and deadline-aware task scheduling in fog computing

The Internet of Things (IoT) is constantly evolving and expanding. However, due to the limited IoT resources, it is intertwined with fog computing to use their resources to compensate for the limitations of IoT resources. On the other hand, fog devices face challenges, such as resource heterogeneity, high distribution, dynamism, and limitations, so an efficient task scheduling approach is needed to deploy fog computing resources effectively and improve the quality of service (QoS). This work mathematically formulates the task scheduling problem to minimize energy consumption and cost and improve QoS by reducing response time and deadline violation times of IoT tasks. Then, it proposes an Energy-efficient and deadline-Aware Task scheduling in Fog Computing (ETFC) method that predicts the traffic of fog nodes by a Support Vector Machine (SVM) and divides them into low-traffic and high-traffic groups. Next, the ETFC method schedules the low-traffic part with an algorithm based on reinforcement learning using the proposed ICLA-SOA, which is an algorithm based on irregular cellular learning automata and schedules the tasks of the high-traffic part with a metaheuristic algorithm using the proposed Non-dominated Sorting Genetic Algorithm (NSGA-III). The simulation results demonstrate that the ETFC method exhibits up to an 84 % enhancement in response time, up to a 33 % reduction in energy consumption, up to a 30 % decrease in costs, and up to a 28 % advancement in meeting task deadlines compared to other methods.

Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people&#x2019;s lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we propose <i>LEVID</i>, a <u>LE</u>arning-based cooperative <u>V</u>eh<u>I</u>cle-roa<u>D</u> scheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines.

A double-adaptive general variable neighborhood search for an unmanned electric vehicle routing and scheduling problem in green manufacturing systems

In recent years, more and more manufacturing factories have employed unmanned electric vehicles (UEVs) to perform the materials delivery, and efficient routing and scheduling approaches are required to improve the delivery efficiency. However, most of existing papers studied the routing problem of fuel vehicles or the drones. This paper investigates the optimization of UEVs delivery routes and charging strategies. Due to the limited payload capacity and battery capacity, the UEV must return to the depot multiple times to load the materials and go to the recharging station during the route to charge, and thus the problem is modeled as a multi-trip UEV routing and scheduling problem, which assigns the recharging station to the UEV’s route and optimizes a set of UEV’s trips. The investigated problem is firstly formulated as a mixed integer programming model. Then, considering the routing and scheduling challenges, we develop an efficient double-adaptive variable neighborhood search (DA-GVNS), which incorporates the general variable neighborhood search with a new adaptive mechanism, for this problem. The experimental results on the test and benchmark instances highlight that the DA-GVNS outperforms other existing algorithms. Furthermore, sensitivity analyses are conducted to study the impact of model’s characteristics on the optimality and complexity of solutions. Finally, a case study is proposed to present how the developed approach works for a manufacturing factory. This paper can offer decision-makers a valuable approach to construct a high-quality routing and scheduling scheme for the UEV delivery while providing potential managerial insights for implementing efficient green manufacturing systems.

Cuckoo search optimization-based energy efficient job scheduling approach for IoT-edge environment

Recently developed IoT devices are capable of gathering, storing, and processing more data than ever before. This calls for the need for scalability. Through the use of edge computing, more processing functions can be relocated closer to where the data is gathered through the IoT devices. Here, processing tasks may be placed in the edge computing units (ECUs). Each ECU may host a cloudlet consisting of a number of virtual machines, where tasks could be executed. Due to the need to ensure near real-time response of the jobs, efficient job scheduling is required. Few recent works addressed this issue of job scheduling at the edges. However, many important constraints such as job dependency, job conflict along with heterogeneous edge infrastructure are not found to be considered. Accordingly, in this paper, we have proposed an optimal job scheduling approach based on the cuckoo search algorithm to handle these challenges subject to energy efficiency and resource utilization. The proposed algorithm is simulated and found to work notably well as compared to state-of-the-art edge-computing-based job scheduling techniques, especially when the jobs have conflict or dependency among them. The work is reported to achieve above 85% resource utilization even in the presence of job conflicts and dependencies.

Assessing prioritization measures for a private land conservation program in the U.S. Prairie Pothole Region

AbstractPrivate land conservation has become an important tool for protecting biodiversity and habitat, but methods for prioritizing and scheduling conservation on private land are still being developed. While return on investment methods have been suggested as a potential path forward, the different processes linking private landscapes to the socioeconomic systems in which they are embedded create unique challenges for scheduling conservation with this approach. We investigated a range of scheduling approaches within a return on investment framework for breeding waterfowl and broods in the Prairie Pothole Region of North Dakota, South Dakota, and Montana. Current conservation targeting for waterfowl in the region focuses mostly on the distribution and abundance of breeding waterfowl. We tested whether MaxGain approaches for waterfowl conservation differed from MinLoss approaches in terms of return on investment and which approach performed best in avoiding loss of waterfowl and broods separately. We also examined variation in results based upon the temporal scale of the abundance layers used for input and compared the region's current scheduling approach with results from our simulations. Our results suggested that MinLoss was the most efficient scheduling approach for both breeding waterfowl and broods and that using just breeding waterfowl to target areas for conservation programs might cause organizations to overlook important areas for broods, particularly over shorter timespans. The higher efficiency of MinLoss approaches in our simulations also indicated that incorporating probability of wetland drainage into decision‐making improved the overall return on investment. We recommend that future conservation scheduling for easements in the region and for private land conservation in general include some form of return on investment or cost‐effective analysis to make conservation more transparent.

An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications

The use of smartphones and mobile devices has increased significantly, as have Mobile Cloud Applications based on cloud computing. These applications are used in various fields, including Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. While existing cloud-based frameworks provide such services on Virtual Machines, they incur problems such as overhead, lengthy boot time, and high costs. To address these issues, the paper proposes a Dynamic Decision-Based Task Scheduling Approach for Microservice-based Mobile Cloud Computing Applications (MSCMCC) that can run delay-sensitive applications and mobility with less cost than existing approaches. The study focuses on Task Offloading problems on heterogeneous Mobile Cloud servers. It proposes a Task Offloading and Microservices based Computational Offloading (TSMCO) framework to solve Task Scheduling in steps such as Resource Matching, Task Sequencing, and Task Offloading. Experimental results show that the proposed MSCMCC and TSMCO enhance Mobile Server Utilization while minimizing costs and improving boot time, resource utilization, and task arrival time for various applications. Specifically, the proposed system effectively reduces the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

HGPSO: An efficient scientific workflow scheduling in cloud environment using a hybrid optimization algorithm

Cloud technology has raised significant prominence providing a unique market economic approach for resolving large-scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on-demand to meet a variety of user QoS standards. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. One of the most important and difficult non-deterministic polynomial-hard challenges in cloud technology is task scheduling. Therefore, in this paper, an efficient task scheduling approach is developed. To achieve this objective, a hybrid genetic algorithm with particle swarm optimization (HGPSO) algorithm is presented. The scheduling is performed based on the multi-objective function; the function is designed based on three parameters such as makespan, cost, and resource utilization. The proper scheduling system should minimize the makespan and cost while maximizing resource utilization. The proposed algorithm is implemented using WorkflowSim and tested with arbitrary task graphs in a simulated setting. The results obtained reveal that the proposed HGPSO algorithm outperformed all available scheduling algorithms that are compared across a range of experimental setups.

An efficient appliance scheduling approach for cost and peak minimization in a smart home

An efficient appliance scheduling approach for cost and peak minimization in a smart home

An efficient scheduling approach for multi-level industrial chain flows in time-sensitive networking

Time-sensitive networking (TSN) is a promising real-time network technology that has deterministic delay and jitter guarantee capabilities. However, TSN flow scheduling faces challenges in the multi-level topology due to the cooperative scheduling requirements of multiple data flows, which are abstracted as TSN chain flows. In this paper, we present an efficient scheduling approach, namely TSN Chained Flow Scheduling (TCFS), which is utilized to globally schedule chain flows in the multi-level topology. To obtain optimal TCFS scheduling results, we design the scheduling constraints and propose an offline algorithm to efficiently schedule chain flows in the multi-level topology. Based on offline scheduling results, we further propose a heuristic method that includes time-tabling and sequencing algorithms to improve the dynamic scheduling performance. Experimental results show that the TCFS approach efficiently improves the schedulability for chain flows, as compared with benchmarks.

Reinforcement Learning Assisted Bandwidth Aware Virtual Network Resource Allocation

Space-air-ground integration to support seamless coverage of ground, satellite, airborne, and marine communications, is likely to be a key trend in the 6G era. One of several key challenges in such space-air-ground integration networks (SAGINs) is to design efficient scheduling approaches for multi-dimension network resources. Due to the inherent heterogeneity characteristics, we demonstrate how can transform the network resource allocation problem in SAGINs into a multi-domain virtual network resource allocation problem, as well as proposing a reinforcement learning assisted bandwidth aware virtual network resource allocation algorithm (RL-BA-VNA). Specifically, RL-BA-VNA leverages reinforcement learning and uses a policy network as an agent to perform the node embedding. In order to support users’ exacting bandwidth requirements, we prefer to select virtual network requests with large bandwidth for embedding. Experiment findings show that the proposed algorithm RL-BA-VNA outperforms respectively the other three conventional virtual network resource allocation algorithms RL, DRL and BASELINE by an average of 2.06%, 4.93%, 11.07% in terms of long-term average reward, acceptance rate, and long term reward/cost.

GA-IRACE: Genetic Algorithm-Based Improved Resource Aware Cost-Efficient Scheduler for Cloud Fog Computing Environment

The ever-growing number of Internet of Things (IoT) devices increases the amount of data produced on daily basis. To handle such a massive amount of data, cloud computing provides storage, processing, and analytical services. Besides this, real-time applications, i.e., online gaming, smart traffic management, and smart healthcare, cannot tolerate the high latency and bandwidth consumption. The fog computing paradigm brings the cloud services closer to the network edge to provide quality of service (QoS) to such applications. However, efficient task scheduling becomes critical for improving the performance due to the heterogeneous nature, resource-constrained, and distributed environment of fog resources. With an efficient task scheduling algorithm, the response time to application requests can be reduced along with bandwidth and cloud resource costs. This paper presents a genetic algorithm-based solution to find an efficient scheduling approach for mapping application modules in a cloud fog computing environment. Our proposed solution is based on the execution time as a fitness function to determine an efficient module scheduling on the available fog devices. The proposed approach has been evaluated and compared against baseline algorithms in terms of execution time, monetary cost, and bandwidth. Comprehensive simulation results show that the proposed approach offers a better scheduling strategy than the existing scheduler.

Optimization of Performance and Scalability Measures across Cloud Based IoT Applications with Efficient Scheduling Approach

Optimization of Performance and Scalability Measures across Cloud Based IoT Applications with Efficient Scheduling Approach

Toward Efficient Time-Sensitive Network Scheduling

Time-sensitive networks (TSNs) provide reliable, deterministic message delivery, ideal for networked control systems. Such networks are increasingly implemented in dynamic, resource-constrained avionics environments. This places pressure on the TSN scheduler to compute network flow schedules faster with slower processors, less memory, and limited power consumption. Avionics full-duplex switched Ethernet and IEEE 802.1 Time-Sensitive Networking are briefly compared and found to be largely similar in concept except for mechanisms enforcing determinism (BAG versus IEEE 802.1Qbv gate control) and the added requirement for time synchronization in time-sensitive networking. A new form of reliability known as polymorphic reconfiguration is introduced and contrasted with static, preconfigured redundancy. Scalability issues impacting TSN scheduling for polymorphic reconfiguration are derived. Preconfiguring and storing the TSN schedule for every possible network failure is infeasible in terms of memory storage. IEEE 802.1CB and other redundancy mechanisms exacerbate the problem by further increasing the number of flows that have to be reconfigured and scheduled. The computational complexity of TSN scheduling is reviewed and the notion of a processing-storage tradeoff via a schedule seed is introduced. The resource constrained scheduling challenge is addressed by analyzing tradeoffs in efficient scheduling approaches where qualitative performance metrics are inferred, high-level computational methods are compared, and suggestions for future approaches are outlined. This article contributes to time-sensitive networking scheduling capable of operating in a resource-constrained, dynamically changing network environment rather than in fixed avionics network environments to which they are limited today. Finally, the need for standards for TSN scheduling performance profiling and benchmarks as well as partial schedules and distributed TSN scheduling protocol standards is highlighted.

An efficient population-based multi-objective task scheduling approach in fog computing systems

With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

AbstractDistributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy‐conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy‐conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption. Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy‐conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real‐world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy‐saving and makespan reducing.

Efficient scheduling approaches to time-constrained single-armed cluster tools with condition-based chamber cleaning operations

In semiconductor manufacturing, an extremely stringent quality control is enforced for wafer fabrication processes. Consequently, cleaning operations that clear residual chemical gases and heat in the chambers are frequently performed. As one kind of cleaning operations, a purge operation which executes a cleaning operation each time a wafer is removed from a chamber is widely adopted in leading semiconductor fabrication plants. Such an operation can definitely improve the quality of wafer. However, it results in lower productivity. To make a tradeoff between quality and productivity, a condition-based chamber cleaning operation that performs a cleaning operation with the consideration of the actual state of a chamber is introduced in practice. Aiming to address the scheduling problem of time-constrained single-armed cluster tools with condition-based chamber cleaning operations, efficient scheduling approaches are proposed in this work for the first time and, algorithms for searching for a feasible schedule are also derived. Two illustrative examples are given to show the power of the approach.

A hybrid approach to Enhancing Process Scheduling in multiple core Systems

Process scheduling within computer systems and with regards to the CPU always encounters bottle necks due to over reliance on single processor scheduling techniques. The presence of multicore processor systems attempts to increase throughput without however operating at full optimum capacity, hence the need for the proposal of a more efficient scheduling approach for use in multiple core systems. This paper conducted a comparative analysis of the rate of efficiency of existing scheduling algorithms with the aid of secondary data. It then employed CPU user benchmark analysis to asses the effectiveness of the proposed approach. Quad-core processor systems are most suitable for the proposed approach which basically implements two approaches one where scheduling decisions are handled by a master processor while other processors execute the user code thus always ensuring that all processors are busy and always utilized. Keywords: Process scheduling, multicore systems, system optimization DOI: 10.7176/JIEA/11-2-04 Publication date: May 30 th 2021

Flowshop scheduling with sequence dependent setup times and batch delivery in supply chain

With the emergence of advanced manufacturing and Industry 4.0 technologies, there is a growing interest in coordinating the production and distribution in supply chain management. This paper addresses the production and distribution problems with sequence dependent setup time for multiple customers in flow shop environments. In this complex decision-making problem, an efficient scheduling approach is required to optimize the trade-off between the total cost of tardiness and batch delivery. To achieve this, three new metaheuristic algorithms such as Differential Evolution with different mutation strategy variation and a Moth Flame Optimization, and Lévy-Flight Moth Flame Optimization algorithm are proposed and presented. In addition, a design-of-experiment method is used to identify the best possible parameters for the proposed approaches for the problem under study. The proposed algorithms are validated on a set of problem instances. The variants of differential evolution performed better than the other compared algorithms and this demonstrates the effectiveness of the proposed approach. The algorithms are also validated using an industrial case study.

Dynamic event-triggered scheduling and control for vehicle active suspension over controller area network

This paper addresses the problem of event-triggered scheduling and control for active suspension of uncertain quarter vehicle suspension systems over resource-constrained controller area network (CAN). The central aim is to develop an efficient dynamic event-triggered scheduling and control co-design approach for improving suspension performance, while significantly alleviating the resource occupancy over CAN in the simultaneous presence of transmission delays and data losses. To this end, two new dynamic event-triggered schedulers (DETSs) are first developed to orchestrate the transmissions of sensor data packets at each sampling time. One salient feature of the DETSs is that the dynamic variables employed in the relevant triggering laws can unremittingly adjust their values over time, which greatly favors the dynamic scheduling of data transmissions over CAN. Then, tractable criteria for stability and suspension performance analysis as well as criteria for co-designing the desired controllers and DETSs are derived. It is shown that the co-design criteria enable comprehensive trade-off analysis between network quality of service and control quality of performance and communication efficiency. Finally, simulation results for a two-degree-of-freedom quarter vehicle model demonstrate possible improvements on ride comfort, good road holding, limited suspension stroke and actuator force, meanwhile saving a large portion of communication resource expenditure.