A WOA-based optimization approach for task scheduling in cloud Computing systems

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks

Resource management in a containerized cloud : status and challenges

Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research

Value-Based Manufacturing Optimisation in Serverless Clouds for Industry 4.0

There is increasing impetus towards Industry 4.0, a recently proposed roadmap for process automation across a broad spectrum of manufacturing industries. The proposed approach uses Evolutionary Computation to optimise real-world metrics. Features of the proposed approach are that it is generic (i.e. applicable across multiple problem domains) and decentralised, i.e. hosted remotely from the physical system upon which it operates. In particular, by virtue of being serverless, the project goal is that computation can be performed `just in time' in a scalable fashion. We describe a case study for value-based optimisation, applicable to a wide range of manufacturing processes. In particular, value is expressed in terms of Overall Equipment Effectiveness (OEE), grounded in monetary units. We propose a novel online stopping condition that takes into account the predicted utility of further computational effort. We apply this method to scheduling problems in the (max,+) algebra, and compare against a baseline stopping criterion with no prediction mechanism. Near optimal profit is obtained by the proposed approach, across multiple problem instances