Task Rejection Ratio Research Articles

Experimental performance analysis of cloud resource allocation framework using spider monkey optimization algorithm

SummaryThe cloud services demand has increased exponentially in the last decade due to its plethora of services. It becomes a significant platform to compute large and diverse applications over the internet. On the contrary, on‐demand resource allocation to a variety of applications becomes a serious issue due to dynamic workload conditions and uncertainty in the cloud environment. Several existing state of art techniques often fails to allocate the optimal resources to forthcoming demands, leading to an imbalance workload over cloud platform, degrading the performance. This article introduces a secure and self‐adaptive resource allocation framework that addressed the mentioned issues and allocates the most suitable resources to users' applications while ensuring the deadline constraints. Further, the proposed framework is integrated with a metaheuristic algorithm named enhanced spider monkey optimization algorithm that is based on the intelligent foraging behavior of spider monkeys. The proposed algorithm finds an optimal resource for the user's application using the fission‐fusion approach and improves multiple influential parameters like time, cost, degree of load balancing, energy consumption, task rejection ratio and so on. The experimental CloudSim based results verified that the proposed framework performs superior to state of art approaches like PSO, GSA, ABC, and IMMLB.

An Enhanced Load Balancing Technique for Big-data Cloud Computing Environments

The need for cloud computing load balancing is a peculiar area of interest for researchers because it affects both the quality of service provided to users and resource utilisation on the part of cloud service providers. Due to the requirement to minimise processing costs, enhance throughput, improve resource efficiency, and optimise cloud node arrangement, existing cloud computing load balancing methods have been found to be restricted in their capacity to manage big-data cloud system load distribution. This research developed a novel Central-Regional Architecture Based Load Balancing Technique (CRLBT) different from the known central, distributive, and hierarchical cloud architectures. The proposed technique was developed by combining a formulated throughput maximisation algorithm with the algorithms; Throughput Maximised-Particle Swarm Optimisation (TM-PSO) and Throughput Maximised-Firefly optimisation (TM-Firefly). The developed technique was implemented using the MATLAB R2018 software package. The performance of the CRLBT in comparison to the already-in-use PSO and Firefly algorithms was evaluated using response time, throughput, job rejection ratio, and CPU utilisation rate. The significance of the improvement in load balancing brought about by the new approach was further assessed using a statistical T-Test. The results showed that the proposed CRLBT significantly outperformed the PSO and Firefly techniques regarding response time, throughput CPU utilisation rate, and task rejection ratio. Finally, significant improvements in response time, tax rejection ratio, CPU utilisation rate, and network throughput proved the ability of the proposed technique to handle task-resource distribution of big-data cloud centres superiorly.

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

In recent times, the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features. The IoT has shown wide adoption in various applications including smart cities, healthcare, trade, business, etc. Among these applications, fitness applications have been widely considered for smart fitness systems. The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities. Thus, scheduling such a huge number of requests for fitness exercise is a big challenge. Secondly, the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging. To overcome these issues, this work proposed a blockchain-based load-balanced task scheduling approach. A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches. The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’ requests and improve resource utilization. The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio. The obtained results confirm the efficiency of the proposed scheduling approach. For investigating the performance of the blockchain, various experiments are performed using the Hyperledger Caliper concerning latency, throughput, resource utilization. The Solo approach has shown an improvement of 32% and 26% in throughput as compared to Raft and Solo-Raft approaches respectively. The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real‐time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology‐enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES‐ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC‐PSO, and PSO‐COGENT algorithms, the created HES‐ACO algorithm was simulated at CloudSim and found to optimize all parameters.

Optimization model for QoS based task scheduling in cloud computing environment

Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

An enhanced deadline constraint based task scheduling mechanism for cloud environment

In this work, we present a scheduling mechanism for deadline sensitive task or lease. It is very impelling and challenging to allocate on-demand resources within the deadline rather than rejecting the lease. The work studies the insight of existing backfilling algorithm that is used to schedule deadline sensitive lease in the Open Nebula cloud platform. In backfilling mechanism a lease is selected in First Come First Serve (FCFS) to be backfilled in which some ideal resources can be found out and allocated to other leases. Mainly, the scheduling performance of backfilling algorithm could be better when there are conflicts among the similar leases to backfill. Moreover, backfilling algorithm doesn’t allow to schedule a new deadline sensitive task during execution. The proposed work addresses such types of issues and improves the scheduling performances with respect to maximize the task acceptance ratio and reduce the task rejection ratio. Finally, the performances are compared with the existing mechanisms that are quite good.

PSO-COGENT: Cost and energy efficient scheduling in cloud environment with deadline constraint

The main goal of cloud service provider is to maximize the profit from cloud infrastructure, while cloud users want to execute their applications in minimum execution cost and time. The rapid growth in demand of computational power invites the massive growth in cloud data centers and requirement of large amount of energy consumption in cloud data centers, becomes a serious threat to the environment. To reduce the energy consumption and gain the maximum profit in cloud environment is a challenging problem due to incompatibility between workstation (physical machine) and unpredictable user demand. In this paper, we have proposed a resource allocation model for processing the applications efficiently and Particle Swarm Optimization based scheduling (PSO) algorithm named as PSO-COGENT algorithm that not only optimize execution cost and time but also reduces the energy consumption of cloud data centers, considering deadline as constraint. The developed PSO-COGENT algorithm has been simulated at cloudsim and observed that it reduces the execution time, execution cost, task rejection ratio, energy consumption and increase the throughput in comparison to PSO, honey bee and min-min algorithm.

Energy-Aware Fault-Tolerant Dynamic Task Scheduling Scheme for Virtualized Cloud Data Centers

As clouds have been implemented and widely used in various fields, both the size and the number of cloud data centers (CDCs) are growing rapidly. Serious problems have been raised, such as the inefficient use of resources, high energy consumption, and failure of heterogeneous task execution. The existing studies have aimed to solve these challenging problems separately, but it is difficult to optimize resources and energy efficiency while simultaneously providing fault-tolerance. In this study, a dynamic task assignment and scheduling scheme, namely, the energy-aware fault-tolerant dynamic scheduling scheme (EFDTS), is developed to coordinately optimize resource utilization and energy consumption with a fault tolerant mechanism. In the task assignment scheme, a task classification method is developed to partition the coming tasks into different classes and then allocate them to the most suitable virtual machines based on their classes to reduce the mean response time while considering energy consumption. Replication is used for the fault tolerance to minimize the task rejection ratio caused by machine failure and delay. An elastic resource provisioning mechanism is designed in the context of fault-tolerance to improve resource utilization and energy efficiency. Furthermore, a migration policy is developed that can simultaneously improve resource utilization and energy efficiency. The experimental results show that compared with existing techniques, EFDTS significantly improves the overall scheduling performance, achieves a higher degree of fault tolerance with high CDC resource utilization, minimizes the mean response time and task rejection ratio, and reduces energy consumption.

Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment

The most challenging problem for a cloud service provider is maintaining the quality of service parameters like reliability, elasticity, keeping the deadline and minimizing the makespan time as also the task rejection ratio. Therefore, the cloud service provider needs a dynamic task scheduling algorithm that reduces the makespan time while increasing the utilization ratio of cloud resources and meeting the user defined QoS parameters. In this paper, we have developed a dynamic scheduling algorithm that balances the workload among all the virtual machines with elastic resource provisioning and deprovisioning based on the last optimal k-interval. Further, the algorithm has been tested on variable number of tasks (10 to 30) to achieve better scalability. The computational results (Figs. 5–10) show that the developed algorithm decreases the makespan time and increases the task to meet the deadline ratio compared with the min-min, the first come-first-serve and the shortest-job-first algorithms in all conditions.

Dynamic Scheduling Real-Time Task Using Primary-Backup Overloading Strategy for Multiprocessor Systems

The scheduling of real-time tasks with fault-tolerant requirements has been an important problem in multiprocessor systems. The primary-backup (PB) approach is often used as a fault-tolerant technique to guarantee the deadlines of tasks despite the presence of faults. In this paper we propose a dynamic PB-based task scheduling approach, wherein an allocation parameter is used to search the available time slots for a newly arriving task, and the previously scheduled tasks can be re-scheduled when there is no available time slot for the newly arriving task. In order to improve the schedulability we also propose an overloading strategy for PB-overloading and Backup-backup (BB) overloading. Our proposed task scheduling algorithm is compared with some existing scheduling algorithms in the literature through simulation studies. The results have shown that the task rejection ratio of our real-time task scheduling algorithm is almost 50% lower than the compared algorithms.