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The increasing number of cloud computing infrastructure and the users’ demands for services has made the cloud resource management an impossible task to be manually performed by human operators. In this paper, we surveyed the state of the art of cloud resource management for infrastructure as a service. We provided an overview of the recent researc...
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... Traditionally, monitoring tools have focused on hostlevel services. Nevertheless, with the new digitalisation era, rises a new challenge that lies in understanding the behaviour and performance of resources at deeper and wider levels [3]. The fundamental issue is clear: ensuring service reliability, performance and security in softwarised and virtualised environments requires new approaches that go beyond the traditional techniques. ...
Modern computing infrastructures are evolving due to virtualisation, especially with the advent of 5G and future technologies. While this transition offers numerous benefits, it also presents challenges. Consequently, understanding these complex systems, including networks, services, and their interconnections, is crucial. This paper introduces ServiceNet, a groundbreaking architecture that accurately performs the important task of providing understanding of a multi-tenant architecture by discovering the complete topology, crucial in the realm of high-performance distributed computing. Experimental results have been carried out in different scenarios in order to validate our approach, demonstrating the effectiveness of our approach in comprehensive multi-tenant topology discovery. The experiments, involving up to forty tenant, highlight the adaptability of ServiceNet as a valuable tool for real-time monitoring in topology discovery purposes, even in challenging scenarios.
... Cloud computing is a currently growing technology which intelligently connects different virtual machines, servers, load balancers and data centers in a cooperative way for establishing a distributed environment [1]. The cloud environment facilitates the strategy of lending the associated resources based on the pay-as-you-go model, sharing multimedia, storing and retrieving of documents [2]. This field of cloud computing has visualized much advancement in the era of computers and Internet of Things (IoT) in terms of flexibility, reliability, and responsiveness [3]. ...
... Initially, this SHOA algorithm is used for scheduling the incoming tasks to the suitable VMs. In this cloud environment, the complete set of incoming tasks tsk (1) , tsk (2) , ...., tsk (m) needs to be scheduled on the available VMs. VM (i) = {VM (0) , VM (1) , VM (2) , … , VM (k) } in a well balanced manner. This SHOA algorithm plays an anchor role in tasks assignment to suitable VMs as significant as feasible. ...
... Initially, this SHOA algorithm is used for scheduling the incoming tasks to the suitable VMs. In this cloud environment, the complete set of incoming tasks tsk (1) , tsk (2) , ...., tsk (m) needs to be scheduled on the available VMs. VM (i) = {VM (0) , VM (1) , VM (2) , … , VM (k) } in a well balanced manner. This SHOA algorithm plays an anchor role in tasks assignment to suitable VMs as significant as feasible. ...
Cloud Computing is the significant paradigm responsible for the massive migration of enterprise applications in the information world. The resource allocation and scheduling in data centers of the cloud is one of the predominant optimization issues in clouds. Diversified numbers of static and dynamic allocation schemes were propounded for handling the problem of resource allocation. However, the conventional resource management schemes are not adequate to handle resource allocation based on the demands received from the tasks of the client in a reliable and intelligent manner. In this paper, a Hybrid Simulated Annealing and Spotted Hyena Optimization Algorithm (HSA-SHOA) is proposed as a significant bio-inspired resource management and task scheduling technique that plays an anchor role in the cloud environment. This HSA-SHOA-based resource management technique facilitates the option of task allocation to the individual virtual machines in an effective way based on the benefits of Spotted Hyena Optimization Algorithm (SHOA). It is propounded for maintaining the balance between exploitation and exploration involved in the task of optimizing resources, such that virtual machines are never underloaded or overloaded. Further, the management of resources that includes memory and CPU is handled based on the demands introduced by the tasks based on the incorporation of Simulated Annealing (SA) with SHOA. Simulation experiments conducted through CloudSim demonstrated the superior performance of the proposed HSA-SHOA-based resource management scheme over the benchmarked bio-inspired scheme evaluated based on enhanced reliability, makespan, energy consumption, minimized mean response time and cloud resources utilization rate.
... Using the dataset of 11 human photos, the accuracy of the system is 95%. Nzanywayingoma et al. [9] study reviews cloud computing resource management in detail, highlighting the difficulties brought on by the growing complexity of cloud infrastructure and user needs. With an emphasis on Infrastructure as a Service (IaaS), it offers a thorough review of methods including resource provisioning, monitoring, allocation, and migration strategies. ...
The goal of this study is to examine the importance of effective resource management across different fields and the critical role that cloud computing apps have in accomplishing this effectiveness. To show how these various approaches and strategies can be used in real-world scenarios, the article looks at case studies and application usage. Optimizing resource utilization and performance in cloud computing requires effective scheduling, resource allocation, and other algorithms Using Cloud Simulation (cloud sim) Tool and other cloud environment tools. By adjusting to changing workloads, these algorithms improve fault tolerance, cost effectiveness, and load balancing. Furthermore, maintaining cloud service integrity depends heavily on algorithms for data processing, network optimization, and fault tolerance. Sufficient research in this area is essential to fully utilize cloud computing.
... More recent surveys have defined strategies to manage resource allocation while optimizing different resource types, such as energy consumption, storage, network, memory and CPU utilization [26][27][28][29][30][31]. These strategies are divided into specific heuristics, bin packing heuristics, meta-heuristics (evolutionary approaches and machine learning models) and optimization approaches (integer and linear programming). ...
In cloud platforms, a common task is to allocate computational resources, e.g., memory and CPU, requested by applications and users. The allocation of these resources, which is an optimal load-balancing task, is considered an NP-Hard problem, being a challenging research area. There are many works proposed in the literature to address this problem. They use several strategies to deal with this optimization problem such as evolutionary algorithms, exact programming and also heuristics. However, some steps of the allocation process are not considered by these works, which are sometimes treated separately and not in an integrated manner. These steps include the applications and users resource consumption request profile as part of the optimization process and defining adequate metrics to check the optimized allocation. To integrate these steps, this work proposes a framework based on the following strategies, widely used in the literature: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Linear Programming, besides our Heuristic approach. Furthermore, we restricted the resource allocation optimization to a real-time scenario, facilitating its use in an industrial process. In addition, Key Performance Indicators (KPIs) are proposed to carry out a comparative study in a public dataset. Our experiments showed that our proposed framework achieved better results than the baseline one, e.g., using a random allocation. In some cases, we observed an increase of almost 60% in performance compared with the baseline. In addition, when trying to balance memory and CPU consumption in the cluster, the linear approach performed the best, while the GA achieved the best result in allocating different user profiles across the cluster.
... Due to space limitation, we will not discuss them in details. We recommend readers who are interested in this research topic to read the following nice review papers: [218], [219], [220], [221], [222]. Most of these approaches apply ML techniques to forecast future resource consumption and then do resource provisioning or scheduling based on the forecasting results. ...
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities.
... In recent years, wireless and emerging technologies have undergone significant progress, particularly the Internet of Things (IoT) [1,2], artificial intelligence [3], machine learning [4][5][6], smart grids [7], Blockchain [8], 5G connectivity [9], and cloud computing [10], resulting in a number of positive effects on society. Cloud computing offers convenient, flexible, and ubiquitous access to a set of configurable computing resources, such as servers, storage, applications, and services, which are delivered via the web and released instantly [11,12]. It allows users to access computer resources without having to manage them actively [13]. ...
... Usually, these processes are error-prone and have large delays, which prevent the right data from being available in real-time [1]. As a novel technology, cloud computing with internet infrastructure and novel solutions provides considerable advantages in providing medical services electronically [2]. Indeed, the advent of cloud computing has brought a fundamental change in the path of the novel, developed, scaled, and up-to-date services of information technology [3]. ...
... This scalable and flexible approach to resource delivery has attracted many organizations and individuals. Cloud computing has enabled many enterprises to migrate, compute, and host their applications, giving them seamless access to a range of services without hassle [2]. It is reported that approximately 60 percent of organizations use cloud services to meet their resource needs, accounting for nearly 15 percent of global IT spending [3]. ...
... To run applications, the cloud service providers supply a pre-installed operating system. IaaS is a model for deploying consumer applications and systems on shared infrastructure [3]. ...
Data replication is crucial in enhancing data availability and reducing access latency in cloud computing. This paper presents a dynamic duplicate management method for cloud storage systems based on the Inverted Ant Colony Optimization (IACO) algorithm and a fuzzy logic system. The proposed approach optimizes data replication decisions focusing on energy consumption, response time, and cost. Extensive simulations demonstrate that the IACO-based method outperforms existing techniques, achieving a remarkable 25% reduction in energy consumption, a significant 15% improvement in response time, and a substantial 20% cost reduction. By addressing the research gap concerning integrating IACO and fuzzy logic for data replication, our work contributes to advancing cloud computing solutions for large datasets. The proposed method offers a viable and efficient approach to improve resource utilization and system performance, benefiting various scientific fields. © 2023, Science and Information Organization. All Rights Reserved.
... These fog nodes have heterogeneous properties and capabilities assembled in close vicinity to mobile devices [3][4][5][6]. The Fog Server (FS) assigns a resource-efficient fog node to schedule the task placed by a mobile user [7,8]. But as the user device is moving from one network to another, the task needs to be rescheduled every time the device enters a new network [9,10]. ...
In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling.
