Chonglin Gu

Cloud-Fog computing has emerged as an essential paradigm to support the growing demand for real-time data processing driven by the Internet of Things. By integrating the extensive computing capabilities of cloud data centres with the low-latency benefits of fog nodes, this architecture increases resource utilisation and improves quality of service. However, the dynamic and heterogeneous nature of cloud fog environments poses significant workflow scheduling challenges, especially when optimising multiple trade-offs such as latency, cost, energy consumption, and resource utilisation. This paper investigates the many-objective dynamic workflow scheduling problem under deadline constraints in container-based cloud-fog computing environments (MDWS-CoCF). Unlike existing studies that primarily focus on horizontal scaling, this work considers both vertical and horizontal scaling of containers, allowing for real-time adjustments of container configurations based on task-specific requirements. To address this complex problem, we first develop a dynamic workflow scheduling simulator that models real-world scenarios, including a variety of task categories and container scalability. Based on this simulator, we propose a Cooperative Coevolution Genetic Programming (CCGP) approach that evolves specialised heuristics for task selection, resource allocation, and container deployment to facilitate adaptive and efficient scheduling in MDWS-CoCF. Extensive simulations using real-world data traces show that the proposed CCGP approach significantly outperforms existing baseline algorithms, achieving superior performance as measured by the HyperVolume and Inverted Generational Distance metrics. The results show that the evolved heuristics are robust and effective under different dynamic scenarios, ensuring balanced optimisation of many objectives.

In this paper, we aim to save the total energy consumption of servers through elastic scaling of CPU resources in container cloud. To be practical, we propose an online scheduling method, which consists of three parts: container placement, vertical scaling and migration. 1) For container placement, we design an algorithm based on dynamic threshold, resource balancing and delayed running. When there are PMs (Physical Machines) turned on, the CPU threshold increases so that the containers can be placed onto fewest possible PMs. To make full use of multi-dimensional resources of PM, we put forward a resource balancing strategy. Since the number of CPU cores can be scaled dynamically in containers’ run time, the start time of containers can be delayed without violating deadlines. 2) For vertical scaling, a collaborative multi-agent reinforcement learning (MARL) algorithm is proposed to adjust the container’s CPU, so that the containers on the same PM can finish simultaneously if possible. Then, the PM can be turned off to save energy. 3) To further reduce total energy consumption, we consider migrating the containers from underloaded PMs and overloaded PMs. Experiment results show the superior performance of our method to that of the state-of-the-art.

Multi-cloud is a promising paradigm due to its advantages such as avoiding vendor lock-in and optimising costs. This paper focuses on dynamic flexible workflow scheduling with minimum total monetary cost in multi-clouds, considering multiple categories of services for each cloud with different configurations and billing methods. Existing studies generally ignore the characteristics and states of each individual cloud when making schedules, which may be ineffective regarding cost savings and quality of service. To address this issue, we propose to introduce a cloud selection decision on top of the existing task selection and resource selection decisions to help us select appropriate resource for task in an overall cost-effective cloud. To automatically learn the task, cloud and resource selection rules simultaneously, we propose a new genetic programming with multi-tree representation based on a customised discrete event-driven dynamic workflow scheduling simulator. Simulation results based on two real-world data traces show that the proposed algorithm performs significantly better than the state-of-the-art algorithms in terms of reducing the rental costs and deadline deviation, and improving the success rate. The results also show that the superiority of the proposed algorithm lies in the ability to select an appropriate cloud resource for a task.

In virtual machine (VM) deployment, the physical machine (PM) usually first retrieves VM image files from the central image server through block transfer, and the VM image retrieval and communication are the two main factors that consume network bandwidth resources, In this paper, we propose a heuristic-based algorithm to minimize both image retrieval cost and communication cost for VM placement in a fat-tree network. It consists of three phases: PM clustering, VM partitioning, 1) We first cluster the PMs based on the possible longest communication distance, which is estimated by a pre 2) To reduce the traffic between PM clusters, a semidefinite programming algorithm is used to place the coarsened VMs to PM Here coarsening means packing the resources of smaller VMs as a whole, so as to accelerate the solving process. 3) In each PM cluster, the VMs are mapped to PMs one by one, and the VMs with common blocks and communication traffic between each other are more likely to be placed together. Extensive simulations show that our algorithm is more effective and efficient than the state-of-the-art.