PSO-GA Based Resource AllocationStrategy for Cloud-Based SoftwareServices with Workload-Time Windows

Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make run time decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services.The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods.publishedVersio

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Enhancing Dynamic Production Scheduling And Resource Allocation Through Adaptive Control Systems With Deep Reinforcement Learning

Traditional production scheduling and resource allocation methods often struggle to adapt to changing conditions in manufacturing environments. To address this challenge, this study leverages an adaptive control system integrated with a Deep Deterministic Policy Gradient (DDPG) alongside a particle swarm optimization algorithm to enable real-time production scheduling and allocation of resources. The system continuously learns from generated production data and adjusts production schedules with resource allocations based on evolving conditions such as demand fluctuations and resource availability. By harnessing the capabilities of Deep Reinforcement learning, the proposed approach of applying the DDPG algorithm to simulate the environment improves production efficiency, minimizes delays, and optimizes resource utilization. Through conducted experiments, the effectiveness of the DDPG-Particle Swarm Optimization technique (DRPO) was demonstrated in enhancing dynamic production scheduling and resource allocation in simulated manufacturing settings. This study presents a significant step towards intelligent, self-improving production control systems that can navigate complex and dynamic manufacturing environments

Adaptive Dispatching of Tasks in the Cloud

The increasingly wide application of Cloud Computing enables the consolidation of tens of thousands of applications in shared infrastructures. Thus, meeting the quality of service requirements of so many diverse applications in such shared resource environments has become a real challenge, especially since the characteristics and workload of applications differ widely and may change over time. This paper presents an experimental system that can exploit a variety of online quality of service aware adaptive task allocation schemes, and three such schemes are designed and compared. These are a measurement driven algorithm that uses reinforcement learning, secondly a "sensible" allocation algorithm that assigns jobs to sub-systems that are observed to provide a lower response time, and then an algorithm that splits the job arrival stream into sub-streams at rates computed from the hosts' processing capabilities. All of these schemes are compared via measurements among themselves and with a simple round-robin scheduler, on two experimental test-beds with homogeneous and heterogeneous hosts having different processing capacities.Comment: 10 pages, 9 figure

A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique

This To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Swarm based Optimization Algorithms for Task Allocation in Multi Robot Systems: A Comprehensive Review

Multi-robot systems (MRS) have gained significant attention due to their potential applications in various domains such as search and rescue, surveillance, and exploration. An essential aspect of MRS is task allocation, which involves distributing tasks among robots efficiently to achieve collective objectives. Swarm-based optimization algorithms have emerged as effective approaches for task allocation in MRS, leveraging principles inspired by natural swarms to coordinate the actions of multiple robots. This paper provides a comprehensive review of swarm-based optimization algorithms for task allocation in MRS, highlighting their principles, advantages, challenges, and applications. The discussion encompasses key algorithmic approaches, including ant colony optimization, particle swarm optimization, and artificial bee colony optimization, along with recent advancements and future research directions in this field