Essential elements, conceptual foundations and workflow design in crowd-powered projects

Crowdsourcing arose as a problem-solving strategy that uses a large number of workers to achieve tasks and solve specific problems. Although there are many studies that explore crowdsourcing platforms and systems, little attention has been paid to define what a crowd-powered project is. To address this issue, this article introduces a general-purpose conceptual model that represents the essential elements involved in this kind of project and how they relate to each other. We consider that the workflow in crowdsourcing projects is context-oriented and should represent the planning and coordination by the crowdsourcer in the project, instead of only facilitating decomposing a complex task into subtask sets. Since structural models are limited to cannot properly represent the execution flow, we also introduce the use of behavioural conceptual models, specifically Unified Modeling Language (UML) activity diagrams, to represent the user, tasks, assets, control activities and products involved in a specific project.

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.

Novel energy-aware approach to resource allocation in cloud computing

In this paper, we address the issue of resource allocation in a Cloud Computing environment. Since the need for cloud resources has led to the rapid growth of data centers and the waste of idle resources, high-power consumption has emerged. Therefore, we develop an approach that reduces energy consumption. Decision-making for adequate tasks and virtual machines (VMs) with their consolidation minimizes this latter. The aim of the proposed approach is energy efficiency. It consists of two processes; the first one allows the mapping of user tasks to VMs. Whereas, the second process consists of mapping virtual machines to the best location (physical machines). This paper focuses on this latter to develop a model by using a deep neural network and the ELECTRE methods supported by the K-nearest neighbor classifier. The experiments show that our model can produce promising results compared to other works of literature. This model also presents good scalability to improve the learning, allowing, thus, to achieve our objectives.

Locust Inspired Algorithm for Cloudlet Scheduling in Cloud Computing Environments

Cloud computing is an emerging paradigm that offers flexible and seamless services for users based on their needs, including user budget savings. However, the involvement of a vast number of cloud users has made the scheduling of users’ tasks (i.e., cloudlets) a challenging issue in selecting suitable data centres, servers (hosts), and virtual machines (VMs). Cloudlet scheduling is an NP-complete problem that can be solved using various meta-heuristic algorithms, which are quite popular due to their effectiveness. Massive user tasks and rapid growth in cloud resources have become increasingly complex challenges; therefore, an efficient algorithm is necessary for allocating cloudlets efficiently to attain better execution times, resource utilisation, and waiting times. This paper proposes a cloudlet scheduling, locust inspired algorithm to reduce the average makespan and waiting time and to boost VM and server utilisation. The CloudSim toolkit was used to evaluate our algorithm’s efficiency, and the obtained results revealed that our algorithm outperforms other state-of-the-art nature-inspired algorithms, improving the average makespan, waiting time, and resource utilisation.

Dynamic Task Scheduling based on Burst Time Requirement for Cloud Environment

Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.

A Relevant SNC Application for Data Computation using Python Programming

Multiple applications of cloud servicing can be seen in the field of logical programming as well as IT industries. Complex computations over local machines may demand for plenty of system resources thereby delaying the data processing operations. In order to achieve speed in processing one must opt for cloud computing techniques. Extensive maneuver of cloud services is desirable for scientific computation of user data and application. This will require a platform designed in a way to meet the specific requirements of individual users, providing an ease for moving their data and applications over different devices. Symbolic-Numeric Computation using cloud service platform is presented in the paper. In this approach user tasks are presented in the form of symbolic expressions using languages like Java, C/C++, APIs etc. Proposed work employs Python programming for carrying out compilation process.

Notes Fields in Metadata Records Generated by the Children’s and Young Adults’ Cataloguing Program: Exploration of Support for General Specific Needs of School Library Users

Notes fields in metadata records used in school library catalogs provide important value added and facilitate resource discovery for students and teachers. Variety of notes are intended to support general user tasks, as well as specific user tasks of school library users. The study reported in this paper examined levels and patterns of application of summary notes, audience notes, grade level notes, reading interest level notes, study program information notes, table of contents notes etc. in the bibliographic records created by the United States Library of Congress Children’s and Young Adults’ Cataloguing Program for fiction books between 2014 and 2020.

Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches.

An Efficient Technique for Virtual Machine Clustering and Communications Using Task-Based Scheduling in Cloud Computing

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.