Efficient Task Scheduling Research Articles

Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds

The federated cloud is the future generation of cloud computing, allowing sharing of computing and storage resources, and servicing of user tasks among cloud providers through a centralized control mechanism. However, a great challenge lies in the efficient management of such federated clouds and fair distribution of the load among heterogeneous cloud providers. In our proposed approach, called QPFS_MASG, at the federated cloud level, the incoming tasks queue are partitioned in order to achieve a fair distribution of the load among all cloud providers of the federated cloud. Then, at the cloud level, task scheduling using the Modified Activity Selection by Greedy (MASG) technique assigns the tasks to different virtual machines (VMs), considering the task deadline as the key factor in achieving good quality of service (QoS). The proposed approach takes care of servicing tasks within their deadline, reducing service level agreement (SLA) violations, improving the response time of user tasks as well as achieving fair distribution of the load among all participating cloud providers. The QPFS_MASG was implemented using CloudSim and the evaluation result revealed a guaranteed degree of fairness in service distribution among the cloud providers with reduced response time and SLA violations compared to existing approaches. Also, the evaluation results showed that the proposed approach serviced the user tasks with minimum number of VMs.

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing.

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.

A Computational Offloading Method for Edge Server Computing and Resource Allocation Management

The emerging technology of mobile cloud is introduced to overcome the constraints of mobile devices. We can achieve that by offloading resource intensive applications to remote cloud-based data centers. For the remote computing solution, mobile devices (MDs) experience higher response time and delay of the network, which negatively affects the real-time mobile user applications. In this study, we proposed a model to evaluate the efficiency of the close-end network computation offloading in MEC. This model helps in choosing the adjacent edge server from the surrounding edge servers. This helps to minimize the latency and increase the response time. To do so, we use a decision rule based Heuristic Virtual Value (HVV). The HVV is a mapping function based on the features of the edge server like the workload and performance. Furthermore, we propose availability of a virtual machine resource algorithm (AVM) based on the availability of VM in edge cloud servers for efficient resource allocation and task scheduling. The results of experiment simulation show that the proposed model can meet the response time requirements of different real-time services, improve the performance, and minimize the consumption of MD energy and the resource utilization.

Application of a Task Stalling Buffer in Distributed Hybrid Cloud Computing

The purpose of the research is to create a hybrid cloud platform that performs distributed computing tasks using high-performance servers and volunteer computing resources. The proposed platform uses a new task scheduling method, which is also presented in this paper. It uses a task stalling buffer to manage workload among the two grids without any additional information about the tasks. Since efficient task scheduling in these distributed systems is the actual problem, the system reliability issue is solved using a hybrid cloud architecture when both high-performance servers and volunteer computing resources are combined. The results of the experiment showed that the proposed solution solves the problem of balancing workload between two grids better than the standard scheduling algorithm. Computer study and experiments also showed that the proposed hybrid cloud tasks scheduling method with a task stalling buffer reduces up to 47.3 % of total task execution time. The outcome of this paper provides a background for future research on a task stalling buffer in hybrid cloud computing.

Energy efficient routing and task scheduling for autonomous transfer vehicles in intra logistics

Autonomous Transfer Vehicles (ATVs) are becoming increasingly prevalent in intra logistics. Industry 4.0 is bringing us closer to the efficient routing and scheduling of autonomous multi robot systems which perform transportation tasks. In this study, an energy efficient routing and scheduling system is proposed to minimize the total energy that the vehicles spend. Not only travelled distance but also the load of the vehicle is considered between two points. The routes of vehicles are obtained by using the proposed Hybrid Simulated Annealing Algorithm. An algorithm for the initial solution is also proposed for determining of the minimum number of vehicles for pickup and delivery requests. The performance of the algorithm is compared with the best solutions of the test problems in the literature. Besides, the proposed energy efficient routing and task scheduling model is compared with the classical distance model for routing and scheduling with backhauls. An analysis of trade-offs between energy and distance is proposed for intra logistics.

A Learning Automata-Based Scheduling for Deadline Sensitive Task in The Cloud

The evolution of cloud computing facilitates applications with varying demands to operate in a virtualized environment. For instance, applications like the healthcare system, video streaming, Internet of Things (IoT) that are moving to the cloud, demand responses within a particular time limit, i.e., deadline. However, the cloud computing system consumes a considerable amount of electric energy while providing services to these type of applications, which in turn contribute to the high operational cost. Specifically, it becomes cumbersome to offer services to deadline sensitive task while minimizing energy consumption. In this regard, efficient task scheduling is an attractive way to cut down energy usage while ensuring satisfactory services for cloud users. In this paper, the task scheduling problem is considered as a bi-objective minimization problem which includes minimization of energy consumption and makespan. First, we proposed a novel learning automata-based scheduling framework for deadline sensitive tasks in the cloud. Learning automata (LA) is an adaptive decision-making unit that helps the scheduler to select the best responses. Later, the LA-based Scheduling (LAS) algorithm is introduced which exploits the heterogeneity of tasks and virtual machines (VMs) while ensuring the timing requirements of the tasks. Extensive simulation is carried out to designate the effectiveness and applicability of LAS for deadline sensitive task scheduling in the heterogeneous cloud environment.

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).

Development of a Hybrid Algorithm for efficient Task Scheduling in Cloud Computing environment using Artificial Intelligence

Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms.

A Bio-Inspired and Heuristic-Based Hybrid Algorithm for Effective Performance With Load Balancing in Cloud Environment

In a cloud computing environment, effective scheduling policies and load balancing have always been the aim. An efficient task scheduler must be proficient in a dynamically distributed environment and to the policy of efficient scheduling of jobs based upon the workload. In this research, a novel hybrid heuristic algorithm is developed for balancing the load among cloud nodes. This is achieved by hybridizing the existing ant colony optimization (ACO), artificial bee colony algorithm (ABC), and AHP (analytical hierarchy process) algorithm. The AHP algorithm and the artificial bee colony (ABC) algorithm is used for figuring out the best servers suitable for a particular job, and the ant colony algorithm is used to find the most efficient path to that particular server. The proposed algorithm is better in resource utilization. It also performs better load balancing, which keeps on improving with time. The result analysis shows better average response time and better average makespan time compared to other two existing algorithms.

EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.

Novel efficient on-chip task scheduler for multi-core hard real-time systems

Novel efficient on-chip task scheduler for multi-core hard real-time systems

An efficient task scheduling for weather forecasting suites in high performance computing

An efficient task scheduling for weather forecasting suites in high performance computing

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs.

A novel multi-objective CR-PSO task scheduling algorithm with deadline constraint in cloud computing

In cloud computing, efficient task scheduling espouses many challenges. To schedule the multiple cloudlets with deadline constraints on hybrid cloud resources while meeting the various quality requirements is a challenging issue. The purpose of this research work is to address the task scheduling problem of cloud computing. A novel hybrid task scheduling algorithm named Chemical Reaction Partial Swarm Optimization has been proposed for the allotment of multiple independent tasks on the available virtual machines. It enhances the classical chemical reaction optimization and partial swarm optimization and does hybridization by combining the features for the optimal schedule sequence where tasks can be processed based upon the demand and deadline simultaneously to improve the quality in terms of factors like cost, energy, and makespan. We present the comprehensive simulation experiment using the CloudSim toolkit, which shows the effectiveness of the proposed algorithms. To analyse average execution time, comparative experiments have been carried out using various combinations of virtual machines and the number of tasks. The results bring out a significant reduction in execution time of the order of 1–6 percent, which further improves even more than 10 percent in some cases. The results of the makespan reflect the effectiveness of the algorithm in order of 5–12 percent, and the outcome of total cost 2–10 percent and energy consumption rate shows the 1–9 percent improvement.

A Queuing Based Technique for Efficient Task Scheduling in Fog

In recent years, with the rapid growth of the IoT devices, Fog computing also known as Edge computing has gained a lot of attention and is emerging as a popular computing technology for performing the IoT applications which requires immediate response in real-time. Fog as a virtualized layer of distributed network present between the Cloud and the end-users aims to process a large amount of data locally and send only the necessary information to the Cloud with a goal of reducing delay and cost. Fog having a limited amount of resources requires an efficient and systematic approach for scheduling of tasks. The sequence of scheduling the resources plays a crucial role in satisfying the deadlines of the requests. Hence, a Queuing based Technique (QBT) for efficient task scheduling in Fog has been proposed in this paper to support the rapidly increasing amount of data, enhance the performance and decrease the response time. Regarding this, an overall architecture is presented along with the flow of activities. Then corresponding node reservation and queuing mechanism with the algorithms are described. Experimental evaluation and results comparison is conducted to test the efficiency of the proposed QBT model..

Non-Invasive Wearable Optical Sensors for Full Gait Analysis in E-Health Architecture

With the current sedentary lifestyle we are living, along with an aging population, there is a noticeable rise in the number of impaired and physically debilitated citizens. Such debilitations require new solutions to aid in the rehabilitation process and lift the burden on healthcare systems, which are already overwhelmed in many developed countries. In this article, we target this problem by introducing the design and implementation of a non-invasive wearable architecture for full gait analysis, enabled by optical fiber Bragg grating sensors. The proposed sensing network provides the necessary tools to simultaneously monitor parameters, including foot plantar pressure, peak muscle activity, and the ankle and knee sagittal range of motion, with high precision, without the need for any synchronization or delay adjustments, between multiple sensors. The integration of the sensor network within an edge-cloud networking architecture is also discussed, which enables remote live monitoring of patients during exercise, in addition to secured storing and further analysis of data, with efficient scheduling of different tasks between the edge and the cloud, based on different levels of privacy and required latency.

Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing

Virtualization technology provides a new way to improve resource utilization and cloud service throughput. However, the randomness of task arrival, tight coupling between resource load imbalance and node heterogeneity, high computing power, and other factors have hindered the energy consumption optimization and cost reduction objectives of the existing technology. Consequently, task scheduling failure cannot be easily eliminated, and cloud computing performance is decreased dramatically. In this study, a strong agile response task scheduling optimization algorithm is proposed on the basis of the peak energy consumption of data centers and the time span of task scheduling. Agile response optimization techniques are also adopted. From the perspective of task failure rate, the proposed algorithm can be used to investigate the strong agile response optimization model, explore the probability density function of the task request queue overflow, and request a timeout to avoid network congestion. Experimental results indicate that the proposed algorithm can achieve the stability and efficiency of task scheduling and effectively improve the throughput of the cloud computing system.

Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment

Cloud computing enables the execution of various applications submitted by the users in the virtualized Cloud environment. However, the Cloud infrastructure consumes a significant amount of electrical energy to provide services to its users that have a detrimental effect on the environment. Many of these applications (tasks), like those belonging to the healthcare system, scientific research, the Internet of Things (IoT), and others, are deadline-sensitive. Hence efficient scheduling of tasks is essential to prevent deadline violation, decrease makespan, and at the same time reduce energy consumption. To address this issue, we have considered the bi-objective optimization problem of minimization of energy and makespan and have proposed two scheduling approaches for independent, deadline-sensitive tasks in a heterogeneous Cloud environment. Our first approach is a greedy heuristic based on the Linear Weighted Sum technique. The second one is based on Ant Colony Optimization and uses a combination of heuristic search and positive feedback of information to improve the solution. Both approaches use a three-tier model where tasks are scheduled by taking into account the properties of three entities- tasks, VMs, and hosts. Moreover, we have proposed a suitable strategy for scaling of Cloud resources to improve energy-efficiency and task schedulability. Extensive simulations using Google Cloud trace-logs and comparison with some state-of-art approaches validate the effectiveness of our proposed scheduling techniques in achieving a proper trade-off between the energy consumption of the virtualized Cloud infrastructure and the average makespan of the tasks.

Probabilistic-QoS-Aware Multi-Workflow Scheduling Upon the Edge Computing Resources

Edge computing is an evolving decentralized computing infrastructure by which end applications are situated near the computing facilities. While the edge servers leverage the close proximity to the end-users for provisioning services at reduced latency and lower energy costs, their capabilities are constrained by limitations in computational and radio resources, which calls for smart, quality-of-service (QoS) guaranteed, and efficient task scheduling methods and algorithms. For addressing the edge-environment-oriented multi-workflow scheduling problem, the authors consider a probabilistic-QoS-aware approach to multi-workflow scheduling upon edge servers and resources. It leverages a probability-mass function-based QoS aggregation model and a discrete firefly algorithm for generating the multi-workflow scheduling plans. This research conducted an experimental case study based on varying types of workflow process models and a real-world dataset for edge server positions. It can be observed the method clearly outperforms its peers in terms of workflow completion time, cost, and deadline violation rate.

An improved list-based task scheduling algorithm for fog computing environment

A high-performance execution of programs predominately depends on the efficient scheduling of tasks. An application consists of a sequence of tasks that can be represented as a directed acyclic graph (DAG). The tasks in the DAG have precedence constraints between them and each task has a different timeline on different processors. In this paper, a new list-based scheduling algorithm is proposed which schedules the tasks which are represented as a DAG structure. The main focus of this algorithm is to schedule the tasks to the suitable processing node in fog environment as the fog nodes have limited processing capacity. The assignment of tasks on the fog node should consider both the computation cost of the node and the execution finishing time of the node. The proposed algorithm has three phases. (1) the level sorting phase, where the independent tasks are identified (2) in the Task prioritization phase the proposed algorithm assigns priority to the task which has more successors so that more tasks in the next level can start their execution and (3) in the task selection phase a balanced combination of local optimal and global optimal approach is considered to assign a task to a suitable processor which further enhances the processor selection phase results in minimizing both the makespan and overall computation cost of the processors. Extensive experiments are carried out using randomly generated graphs and graphs from the real-world to analyze the performance of the proposed algorithm. The results show that the proposed algorithm outperforms all other well-known algorithms like predict earliest finish time, heterogeneous earliest finish time algorithm, minimal optimistic processing time, and SDBBATS in terms of performance matrices like average scheduling length ratio, speedup, and makespan.