Public clouds provide Infrastructure as a Service (IaaS) to users who do not own sufficient compute resources. IaaS achieves the economy of scale by multiplexing, and therefore faces the challenge of scheduling tasks to meet the peak demand while preserving Quality-of-Service (QoS). Previous studies proposed proactive machine purchasing or cloud federation to resolve this problem. However, the former is not economic and the latter for now is hardly feasible in practice. In this paper, we propose a resource allocation framework in which an IaaS provider can outsource its tasks to External Clouds (ECs) when its own resources are not sufficient to meet the demand. This architecture does not require any formal inter-cloud agreement that is necessary for the cloud federation. The key issue is how to allocate users' tasks to maximize the profit of IaaS provider while guaranteeing QoS. This problem is formulated as an integer programming (IP) model, and solved by a self-adaptive learning particle swarm optimization (SLPSO)-based scheduling approach. In SLPSO, four updating strategies are used to adaptively update the velocity of each particle to ensure its diversity and robustness. Experiments show that, SLPSO can improve a cloud provider's profit by 0.25%-11.56% compared with standard PSO; and by 2.37%-16.71% for problems of nontrivial size compared with CPLEX under reasonable computation time.

Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud

SUMMARY

Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP-complete). Most existing task-scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth-aware algorithm for divisible task scheduling in cloud-computing environments. A nonlinear programming model for the divisible task-scheduling problem under the bounded multi-port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth-aware task-scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair-based task-scheduling algorithm, the bandwidth-only task-scheduling algorithm, and the computation-only task-scheduling algorithm, the proposed algorithm (BATS) has better performance. Copyright © 2012 John Wiley & Sons, Ltd.

Bandwidth‐aware divisible task scheduling for cloud computing

Cloud computing is known as a provider of dynamic services using very large scalable and virtualized resources over the Internet. Due to novelty of cloud computing field, there is no many standard task scheduling algorithm used in cloud environment. Especially that in cloud, there is a high communication cost that prevents well known task schedulers to be applied in large scale distributed environment. Today, researchers attempt to build job scheduling algorithms that are compatible and applicable in Cloud Computing environment Job scheduling is most important task in cloud computing environment because user have to pay for resources used based upon time. Hence efficient utilization of resources must be important and for that scheduling plays a vital role to get maximum benefit from the resources. In this paper we are studying various scheduling algorithm and issues related to them in cloud computing.

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A survey of various scheduling algorithm in cloud computing environment

It is possible for IT service providers to provide computing resources in an pay-per-use way in Cloud Computing environments. At the same time, terminal users can also get satisfying services conveniently. But if we take only execution time into consideration when scheduling the cloud resources, it may occur serious load imbalance problem between Virtual Machines (VMs) in Cloud Computing environments. In addition to solve this problem, a new task scheduling model is proposed in this paper. In the model, we optimize the task execution time in view of both the task running time and the system resource utilization. Based on the model, a Particle Swarm Optimization (PSO) --- based algorithm is proposed. In our algorithm, we improved the standard PSO, and introduce a simple mutation mechanism and a self-adapting inertia weight method by classifying the fitness values. In the end of this paper, the global search performance and convergence rate of our adaptive algorithm are validated by the results of the comparative experiments.

A PSO-Based algorithm for load balancing in virtual machines of cloud computing environment

Cloud computing aims to maximize the benefit of distributed resources and aggregate them to achieve higher throughput to solve large scale computation problems. In this technology, the customers rent the resources and only pay per use. Job scheduling is one of the biggest issues in cloud computing. Scheduling of users’ requests means how to allocate resources to these requests to finish the tasks in minimum time. The main task of job scheduling system is to find the best resources for user’s jobs, taking into consideration some statistics and dynamic parameters restrictions of users’ jobs. In this research, we introduce cloud computing, genetic algorithm and artificial neural networks, and then review the literature of cloud job scheduling. Many researchers in the literature tried to solve the cloud job scheduling using different techniques. Most of them use artificial intelligence techniques such as genetic algorithm and ant colony to solve the problem of job scheduling and to find the optimal distribution of resources. Unfortunately, there are still some problems in this research area. Therefore, we propose implementing artificial neural networks to optimize the job scheduling results in cloud as it can find new set of classifications not only search within the available set.

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Job Scheduling for Cloud Computing Using Neural Networks

Job scheduling system problem is a core and challenging issue in Cloud Computing. How to use Cloud computing resources efficiently and gain the maximum profits with job scheduling system is one of the Cloud computing service providers’ ultimate goals. In this paper, firstly, by analysis the differentiated QoS requirements of Cloud computing resources users’ jobs, we build the corresponding non-preemptive priority M/G/1 queuing model for the jobs. Then, considering Cloud computing service providers’ destination which is to gain the maximum profits by offering Cloud computing resources, we built the system cost function for this queuing model. After that, based on the queuing model and system cost function, considering the goals of both the Cloud Computing service users and providers, we gave the corresponding strategy and algorithm to get the approximate optimistic value of service for each job in the corresponding no-preemptive priority M/G/1 queuing model. Finally, we also provide corresponding simulations and numeral results. Analysis and number results show that our approach for job scheduling system can not only guarantee the QoS requirements of the users’ jobs, but also can make the maximum profits for the Cloud computing service providers.

An Optimistic Differentiated Service Job Scheduling System for Cloud Computing Service Users and Providers

The Industrial Internet of Things with positioning service has been applied in many fields, such as: intelligent Internet of Things warehousing system, which realizes the identification, positioning, statistics, and management of cargo information, as well as on-site personnel safety monitoring. This kind of network usually needs to complete the requirements of positioning, information transmission, and emergency message processing, and the priority of these different information transmissions is different. How to optimize resource allocation has become one of the urgent problems to be solved in related fields. This paper realizes the resource allocation of a specific M/M/1/N model by controlling the M/M/1 service resource models with different priorities, so that the relationship between the allocation of different priority service resources and the buffer size can be easily and clearly obtained. The research model provides a theoretical basis for the resource design of the MAC layer or the transport layer in the relevant network.

Luqun Li

Papers

An Optimistic Differentiated Service Job Scheduling System for Cloud Computing Service Users and Providers

Modelling on resource management and allocation for location-based IoT network