Towards the Exploration of Task and Workflow Scheduling Methods and Mechanisms in Cloud Computing Environment

Cloud computing sets a domain and application-specific distributed environment to distribute the services and resources among users. There are numerous heterogeneous VMs available in the environment to handle user requests. The user requests are defined with a specific deadline. The scheduling methods are defined to set up the order of request execution in the cloud environment. The scheduling methods in a cloud environment are divided into two main categories called Task and Workflow Scheduling. This paper, is a study of work performed on task and workflow scheduling. Various feature processing, constraints-restricted, and priority-driven methods are discussed in this research. The paper also discussed various optimization methods to improve scheduling performance and reliability in the cloud environment. Various constraints and performance parameters are discussed in this research

A Budget-constrained Time and Reliability Optimization BAT Algorithm for Scheduling Workflow Applications in Clouds

AbstractEffective scheduling is one of the key concerns while executing workflows in the cloud environment. Workflow scheduling in clouds refers to the mapping of workflow tasks to the cloud resources to optimize some objective function. In this paper, we apply a recently developed meta-heuristic method called the BAT algorithm to solve the multi-objective problem of workflow scheduling in clouds that minimizes the execution time and maximizes the reliability by keeping the budget within user specified limit. Comparison of the results is made with basic, randomized, evolutionary algorithm (BREA) that uses greedy approach to allocate resources to the workflow tasks on the basis of low cost, high reliability and improved execution time machines. It is clear from the experimental results that the BAT algorithm performs better than the basic randomized evolutionary algorithm

Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

SecFlow: Adaptive Security-Aware Workflow Management System in Multi-Cloud Environments

In this paper, we propose an architecture for a security-aware workflow management system (WfMS) we call SecFlow in answer to the recent developments of combining workflow management systems with Cloud environments and the still lacking abilities of such systems to ensure the security and privacy of cloud-based workflows. The SecFlow architecture focuses on full workflow life cycle coverage as, in addition to the existing approaches to design security-aware processes, there is a need to fill in the gap of maintaining security properties of workflows during their execution phase. To address this gap, we derive the requirements for such a security-aware WfMS and design a system architecture that meets these requirements. SecFlow integrates key functional components such as secure model construction, security-aware service selection, security violation detection, and adaptive response mechanisms while considering all potential malicious parties in multi-tenant and cloud-based WfMS.Comment: 16 pages, 6 figure

Resource provisioning and scheduling algorithms for hybrid workflows in edge cloud computing

In recent years, Internet of Things (IoT) technology has been involved in a wide range of application domains to provide real-time monitoring, tracking and analysis services. The worldwide number of IoT-connected devices is projected to increase to 43 billion by 2023, and IoT technologies are expected to engaged in 25% of business sector. Latency-sensitive applications in scope of intelligent video surveillance, smart home, autonomous vehicle, augmented reality, are all emergent research directions in industry and academia. These applications are required connecting large number of sensing devices to attain the desired level of service quality for decision accuracy in a sensitive timely manner. Moreover, continuous data stream imposes processing large amounts of data, which adds a huge overhead on computing and network resources. Thus, latency-sensitive and resource-intensive applications introduce new challenges for current computing models, i.e, batch and stream. In this thesis, we refer to the integrated application model of stream and batch applications as a hybrid work ow model. The main challenge of the hybrid model is achieving the quality of service (QoS) requirements of the two computation systems. This thesis provides a systemic and detailed modeling for hybrid workflows which describes the internal structure of each application type for purposes of resource estimation, model systems tuning, and cost modeling. For optimizing the execution of hybrid workflows, this thesis proposes algorithms, techniques and frameworks to serve resource provisioning and task scheduling on various computing systems including cloud, edge cloud and cooperative edge cloud. Overall, experimental results provided in this thesis demonstrated strong evidences on the responsibility of proposing different understanding and vision on the applications of integrating stream and batch applications, and how edge computing and other emergent technologies like 5G networks and IoT will contribute on more sophisticated and intelligent solutions in many life disciplines for more safe, secure, healthy, smart and sustainable society