Aware Task Research Articles

VESBELT: An energy-efficient and low-latency aware task offloading in Maritime Internet-of-Things networks using ensemble neural networks

Due to increasing maritime activities, the number of Maritime Internet-of-things (MIoT) devices requiring real-time marine data processing is growing exponentially. To offload maritime tasks and address the limited computational capabilities of heterogeneous MIoT devices, edge and cloud computing networks are employed. However, these networks introduce several challenges, including increased energy consumption and service latency within the complex marine network environment. Current state-of-the-art solutions address these issues by focusing exclusively on real-time offloading data, neglecting the relationship with past offloading tasks. In this work, we develop an optimization framework, named VESBELT, for offloading tasks from Vessel users to nearby Edge Servers or the cloud server, aiming to reduce Energy consumption and service Latency Trade-off through a multi-objective linear programming problem. However, finding optimal solutions from this formulation is considered an NP-hard problem. To address this, we introduce VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems that leverage ensemble convolutional, artificial neural networks and Long-short-term memory, respectively, to achieve solutions in polynomial time. The developed ensemble models integrate multiple combinations of deep learning models and exploit the pre-trained models to provide real-time solutions with better prediction accuracy. The experimental findings, obtained using Python programming version 3.10.2, indicate that the proposed VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems outperform existing approaches in terms of user Quality of Experience (QoE) in the timeliness domain and energy savings.

Optimizing Spatial Crowdsourcing: A Quality- Aware Task Assignment Approach for Mobile Communication

Optimizing Spatial Crowdsourcing: A Quality- Aware Task Assignment Approach for Mobile Communication

Genetic algorithm with skew mutation for heterogeneous resource-aware task offloading in edge-cloud computing

Recent years, edge-cloud computing has attracted more and more attention due to benefits from the combination of edge and cloud computing. Task scheduling is still one of the major challenges for improving service quality and resource efficiency of edge-clouds. Though several researches have studied on the scheduling problem, there remains issues needed to be addressed for their applications, e.g., ignoring resource heterogeneity, focusing on only one kind of requests. Therefore, in this paper, we aim at providing a heterogeneity aware task scheduling algorithm to improve task completion rate and resource utilization for edge-clouds with deadline constraints. Due to NP-hardness of the scheduling problem, we exploit genetic algorithm (GA), one of the most representative and widely used meta-heuristic algorithms, to solve the problem considering task completion rate and resource utilization as major and minor optimization objectives, respectively. In our GA-based scheduling algorithm, a gene indicates which resource that its corresponding task is processed by. To improve the performance of GA, we propose to exploit a skew mutation operator where genes are associated to resource heterogeneity during the population evolution. We conduct extensive experiments to evaluate the performance of our algorithm, and results verify the performance superiority of our algorithm in task completion rate, compared with other thirteen classical and up-to-date scheduling algorithms.

LBATSM: Load Balancing Aware Task Selection and Migration Approach in Fog Computing Environment

LBATSM: Load Balancing Aware Task Selection and Migration Approach in Fog Computing Environment

Benders decomposition for the energy aware task scheduling of constellations of nanosatellites

This paper describes a novel method for optimizing nanosatellite constellation scheduling based on Benders decomposition. The optimization of nanosatellite constellations is challenging due to the large number of variables and constraints involved, which increases the computational work required to get optimal solutions as the number of participating objects increases. Our strategy overcomes this obstacle by decomposing the complex problem into smaller, more manageable subproblems, rendering the original formulation scalable. The outcomes demonstrate that the Benders decomposition achieves the same optimal solutions obtained with the Gurobi solver applied to the baseline formulation while being significantly faster and more efficient, especially for larger constellation sizes. This method provides a valuable tool for decision-makers in the nanosatellite job scheduling process, highlighting the potential benefits of using decomposition methods such as the Benders decomposition for large-scale or challenging optimization problems in Offline Nanosatellite Task Scheduling (ONTS).

Quality of Service Aware Task Replanning Algorithm for Web-Service Provisioning in Cloud Environment

Quality of Service Aware Task Replanning Algorithm for Web-Service Provisioning in Cloud Environment

SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing

Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.

Deadline‐cost aware task scheduling algorithm in fog computing networks

SummaryThe Internet of Things (IoT) has provided us with various applications that need processing in real‐time. Fog computing has arisen as a highly effective computing paradigm that provides services closer to the IoT devices and hence fulfills the need for rapid response time and delay requirements of IoT applications. However, the heterogeneity of IoT tasks and the limited resources of fog nodes pose considerable challenges to task scheduling. Therefore, the effective utilization of fog resources is highly significant and necessitates an optimal and intelligent approach to task scheduling. This paper presents the “deadline‐cost aware task scheduling” (DCaTS) algorithm, which is designed for fog‐based IoT applications. The primary objective of this algorithm is to minimize the monetary cost while taking into account the deadline requirements and priority of tasks. The efficacy of DCaTS is assessed by analyzing various parameters, including completion time, monetary cost, response time, and waiting time. The experimental results show that in comparison with traditional policies, the proposed algorithm shows superiority in all the parameters. The proposed algorithm reduces the monetary cost up to 11.07% compared with the second best result. The results also indicate that DCaTS achieves a reduction of 10.05% in completion time, 10% in response time, and 10.12% in waiting time compared with the second best result.

Replication-Based Resource Provisioning and Constrained Aware Task Scheduling Framework for Cloud Workflows

Replication-Based Resource Provisioning and Constrained Aware Task Scheduling Framework for Cloud Workflows

Reliability and Trust Aware Task Scheduler for Cloud-Fog Computing Using Advantage Actor Critic (A2C) Algorithm

Reliability and Trust Aware Task Scheduler for Cloud-Fog Computing Using Advantage Actor Critic (A2C) Algorithm

MPPT aware task scheduling for nanosatellites using MIP-based ReLU proxy models

MPPT aware task scheduling for nanosatellites using MIP-based ReLU proxy models

Bivariate Correlative Modest Adaptive Boost Resource Aware Task Scheduling in Cloud Environment

Cloud computing is a rapidly evolving paradigm that provides accessible and virtualized resources through Internet technology. In this model, Cloud Service Providers (CSPs) offer online access to computing resources for users to execute their application tasks. Task scheduling in cloud computing involves the allocation of computational tasks to available resources within the cloud environment. The primary objectives of task scheduling are to optimize resource utilization, minimize task completion time, and enhance overall system performance. Task scheduling plays a vital role in cloud resource management as it directly impacts the efficiency of cloud data centers. With the increasing number of cloud computing users, task scheduling has become more challenging, requiring the use of suitable scheduling algorithms. To improve task scheduling efficiency, a novel method called Bivariate Correlative Modest Adaptive Boost Resource Optimized Task Scheduling (BCMABROTS) is developed for efficient service provisioning in the cloud environment. The BCMABROTS method begins by collecting the number of incoming user-requested tasks. After that, the resources of virtual machines, such as energy, bandwidth, memory, and CPU, are measured. The ensemble classifier constructs the set of weak learners as the nearest prototype centroid classifier. The weak classifier uses Bivariate Correlation to classify the resource-optimal virtual machine based on the available resources. The results of the weak learners are combined to provide strong classification results. Once the optimal virtual machine is categorized, the task is scheduled for that particular virtual machine by the task assigner. This approach ensures efficient cloud service provisioning with minimal time consumption. Experimental evaluation is carried out to assess factors such as task scheduling efficiency, makespan, throughput and average response time in relation to the number of cloud-requested tasks. The observed performance results confirm that the BCMABROTS method improves the task scheduling efficiency, throughput and minimizes the makespan as well as the average response time than the conventional machine learning methods.

Thermal aware task assignment for multicore processors using genetic algorithm

Microprocessor power and thermal density are increasing exponentially. The reliability of the processor declined, cooling costs rose, and the processor's lifespan was shortened due to an overheated processor and poor thermal management like thermally unbalanced processors. Thus, the thermal management and balancing of multi-core processors are extremely crucial. This work mostly focuses on a compact temperature model of multicore processors. In this paper, a novel task assignment is proposed using a genetic algorithm to maintain the thermal balance of the cores, by considering the energy expended by each task that the core performs. And expecting the cores’ temperature using the hotspot simulator. The algorithm assigns tasks to the processors depending on the task parameters and current cores’ temperature in such a way that none of the tasks’ deadlines are lost for the earliest deadline first (EDF) scheduling algorithm. The mathematical model was derived, and the simulation results showed that the highest temperature difference between the cores is 8 °C for approximately 14 seconds of simulation. These results validate the effectiveness of the proposed algorithm in managing the hotspot and reducing both temperature and energy consumption in multicore processors.

AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm

In IoT concepts, efficient approaches like cloud-fog computing are emerging, enhancing system benefits. The performance and output of such frameworks can be greatly improved by optimized scheduling of Internet of Things (IoT) task requests. This study presents a novel technique for scheduling Internet of Things requests in a cloud-fog environment, based on an adaption of ant grey wolf optimisation (AGWO). By combining the operators of ant colony optimisation (ACO) and grey wolf optimisation (GWO), AGWO aims to improve the speed and quality of ACO's solution discovery. The suggested AGWO approach is evaluated using numerous datasets of varying sizes, both synthetic and real-world. The effectiveness of AGWO is further investigated by comparing it to standard metaheuristic methods. The experimental results demonstrate that AGWO is superior to competing strategies in terms of makespan time and cost while resolving the task scheduling problem.

Joint time and energy-optimal approach to allocate task to actors in wireless sensor actor networks

In most applications of wireless sensor actor networks (WSANs) in harsh environments, minimizing the make-span and maximizing the residual energy are of paramount importance. The majority of existing task allocation approaches is typically concerned with one of the energy savings or time constraints. These approaches do not consider the types and various features of tasks WSANs may need to perform. Moreover, the limitation on the capacity of task queues has been overlooked by these approaches, and thus may not be applicable to some types of real applications such as search and rescue missions. To this end, a novel queue length aware task allocation (QLATA) approach is proposed that considers the energy consumption as well as the make-span. QLATA is aware of task queues constraint, types of tasks, and the distribution necessities of WSANs with hybrid architecture. QLATA comprises of two protocols, namely a Make-span Calculation Protocol (MsCP) and an Energy Consumption Calculation Protocol (ECCP). Through considering both time and energy, QLATA makes a tradeoff between minimizing make-span and maximizing the residual energies of actors by solving the joint optimization for make-span and energy consumption, simultaneously. A series of extensive simulation results on typical scenarios show shorter make-span and higher remaining energy in comparison to when opportunistic load balancing (OLB), minimum make-span task allocation (MMTa), stochastic task allocation (STA), and task assignment algorithm based on quasi-newton interior point (TA-QNIP) approaches is used. It is also shown that the proposed approach performs significantly better than the four compared algorithms in terms of network lifetime.

IAware: Interaction Aware Task Scheduling for Reducing Resource Contention in Mobile Systems

To ensure the user experience of mobile systems, the foreground application can be differentiated to minimize the impact of background applications. However, this article observes that system services in the kernel and framework layer, instead of background applications, are now the major resource competitors. Specifically, these service tasks tend to be quiet when people rarely interact with the foreground application and active when interactions become frequent, and this high overlap of busy times leads to contention for resources. This article proposes iAware, an interaction-aware task scheduling framework in mobile systems. The key insight is to make use of the previously ignored idle period and schedule service tasks to run at that period. iAware quantify the interaction characteristic based on the screen touch event, and successfully stagger the periods of frequent user interactions. With iAware, service tasks tend to run when few interactions occur, for example, when the device’s screen is turned off, instead of when the user is frequently interacting with it. iAware is implemented on real smartphones. Experimental results show that the user experience is significantly improved with iAware. Compared to the state-of-the-art, the application launching speed and frame rate are enhanced by 38.89% and 7.97% separately, with no more than 1% additional battery consumption.

Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

AbstractEnergy reduction is a key issue of virtualized cloud computing (CC) schemes, because it provide significant benefits, like lower operating costs, improving system effectiveness, securing the environment. Energy‐efficient task scheduling is a feasible mode to attain these objectives. Nevertheless, it is difficult to match cloud resources to user requests in a way that improves performance while meeting a user‐defined deadline for energy consumption reduction. Therefore, hybrid Pelican and Archimedes optimization algorithm fostered energy‐aware task scheduling in heterogeneous virtualized cloud computing (TS‐HVCC‐Hyb‐POA‐AOA) is proposed in this article. A hybrid Pelican and Archimedes optimization algorithm (Hyb‐POA‐AOA) is used to lessen the task duration and the consumption of power in the cloud. For evaluation, the cloud environment uses the provided environment hybrid Pelican and Archimedes optimization algorithm for execute a few common workloads at the simulated data center. The performance metrics, like makespan right skewed analysis, makespan left skewed analysis, energy consumption right skewed analysis, energy consumption left skewed analysis, availability and resource utilization is considered. The performance of the proposed TS‐HVCC‐Hyb‐POA‐AOA method provides 23.69%, 29.50%, and 36.78% higher resource utilization; 38.23%, 31.35%, and 26.19% lower consumption of energy left skewed analysis and 34.52%, 30.28%, and 23.54% lower makespan for right skewed analysis compared with existing methods such as; task scheduling in heterogeneous cloud environment with gray wolf optimization algorithm (TS‐HVCC‐GWOA), hybrid whale optimization approach and differential evolution optimization for multiple objective virtual machine programming at cloud services (TS‐HVCC‐WOA), energy and performance planning in TasterkHeterek virtualized cloud computing using particle swarm optimization algorithm (TS‐HVCC‐PSOA).

An effective fault tolerance aware scheduling using hybrid horse herd optimisation‐reptile search optimisation approach for a cloud computing environment

AbstractIT services can be requested via cloud computing, a model based on services as well as the Internet. It includes all computer systems resources from hardware components to software platforms and software applications in a distributed environment. Scheduling is a key step in processing tasks using remote resources. Serious issues have been brought forward, including the ineffective use of resources and task execution failure. The concurrent provision of fault tolerance and resource optimisation is achallenging task. In the context of cloud computing, this research offers a brand‐new job scheduling and fault‐tolerant system. Tasks submitted by users are taken as an input for the proposed method. Several virtual machines (VM) are initially arranged for scheduling work and execution process. Initially, Horse Herd Optimisation is employed here to allocate the job based on key factors such as deadline and user budget. Once the jobs are assigned to each VM, then each job's deadline is confirmed and transferred to VM which has sufficient capacity. Here, the Reptile Search Optimisation technique is applied to identify the VM error. Any VM that does not have enough capacity is the one that has a problem. When a fault is found, a fault‐tolerant process is instantly started. A replication‐based fault‐tolerant mechanism is used in this manuscript. The proposed approach is tested with several metrics which attains better performance like 80 s response time, turnaround time of 32 s, 17% resource utilisation and a success rate of 92%. Thus the designed model is the best choice for fault‐tolerant aware task scheduling.

Fault aware task scheduling in cloud using min-min and DBSCAN

Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of resource allocation failure that can maximize the delay in task execution. Such adverse impact in the cloud environment also raises questions on quality of service (QoS). Resource management for cloud application and service have bigger challenges and many researchers have proposed several solutions but there is room for improvement. Clustering the resources clustering and mapping them according to task can also be an option to deal with such task failure or mismanaged resource allocation. Density-based spatial clustering of applications with noise (DBSCAN) is a stochastic approach-based algorithm which has the capability to cluster the resources in a cloud environment. The proposed algorithm considers high execution enabled powerful data centers with least fault probability during resource allocation which reduces the probability of fault and increases the tolerance. The simulation is cone using CloudsSim 5.0 tool kit. The results show 25% average improve in execution time, 6.5% improvement in number of task completed and 3.48% improvement in count of task failed as compared to ACO, PSO, BB-BC (Bib ​= ​g bang Big Crunch) and WHO(Whale optimization algorithm).

Cloud service analysis using round-robin algorithm for quality-of-service aware task placement for internet of things services

Round-robin (RR) is a process approach to sharing resources that requires each user to get a turn using them in an agreed order in cloud computing. It is suited for time-sharing systems since it automatically reduces the problem of priority inversion, which are low-priority tasks delayed. The time quantum is limited, and only a one-time quantum process is allowed in round-robin scheduling. The objective of this research is to improve the functionality of the current RR method for scheduling actions in the cloud by lowering the average waiting, turnaround, and response time. CloudAnalyst tool was used to enhance the RR technique by changing the parameter value in optimizing the high accuracy and low cost. The result presents the achieved overall min and max response times are 36.69 and 650.30 ms for running 300 min RR. The cost for the virtual machines (VMs) is identified from $0.5 to $3. The longer the time used, the higher the cost of the data transfer. This research is significant in improving communication and the quality of relationships within groups.