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Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk's position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively. [ABSTRACT FROM AUTHOR]

A mechanism of fog computing environment is employed in order to enhance the cloud computing services toward the edge devices in a range of locations with low latency. A fog computing environment is effective when compared to cloud computing for providing communication between various edge devices such as smart devices and mobile devices used by users in the same location. Even though fog servicing extends the best services of cloud computing, it also suffers from a set of security threats like authentication, key management, data privacy and trust management. Authentication with effective key management between edge devices is the most pressing security issue in fog computing. This paper proposes an effective two-way authentication between edge devices with key management in fog computing environments (TAKM-FC). The edge nodes are the user's mobile devices and set of smart devices controlled by the fog server. To improve the proposed authentication system, we have made use of techniques like fuzzy extractor and one-way hash with cryptographic primitives. The proposed TAKM-FC scheme is validated mathematically based on the ROR model and then verified using the ProVerif tool. The TAKM-FC scheme has been evaluated using iFogSim to measure the performance parameters like throughput, end-to-end delay, packet loss, energy consumption and network usage. The overhead analysis of the proposed scheme is carried out and shows that the computation cost, communication cost and storage cost are improved by 11–21%, 8–19% and 6–13%, respectively, compared to existing schemes. [ABSTRACT FROM AUTHOR]

As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network's total energy consumption. [ABSTRACT FROM AUTHOR]

The cloud-computing paradigm based on containers has progressively grown in recent years as a flexible strategy that has proven to be energy efficient. The increasing usage of the container as a service technology in data centers (DCs) among cloud providers highlights the necessity of the container installation design phase in cloud environments. Cloud providers attempt to enhance resource utilization and reduce energy consumption by employing various VM selection and placement policies. This procedure for placement acquires a new aspect, with containers now being deployed on virtual machines (VMs) and those guest VMs being installed on physical machines (PMs). The intricacy of this issue increases when the variety of the containers, VMs, and PMs is taken into account. In this paper, an optimal placement strategy for containers is proposed based on the bio-inspired algorithms. The firefly algorithm has been modified to use discretization strategy (Discrete Firefly Algorithm, DFF) and has also used local search mechanism (Discrete Firefly with Local Search Mechanism, DFFLSM). The proposed versions of firefly algorithm are compared with first fit, first fit decreasing, random algorithm and ant colony algorithm. The comparison is done based on average energy consumption, average active VM, average active PM and average overall service-level agreement violations in the DC. The results show that DFFLSM performs better than all pre-existing container placement algorithms in terms of energy efficiency. It reduces average energy consumption of DC by 9.32% and 40.85% and average active PM by 18.30% and 21.89% in homogenous and heterogeneous environment, respectively. [ABSTRACT FROM AUTHOR]

Problems with task distribution in cloud data centers persist despite earlier research in cloud computing (CC). Particularly in the infrastructure-as-a-service (IaaS) cloud paradigm. In cloud data centers, effective task allocation is essential due to the restricted availability of resources and virtual machines (VMs). IaaS is one of the main CC models since it controls the backend, which includes VMs and data centers. Cloud service providers can ensure satisfactory service delivery performance in these models by preventing situations of host underutilization or overloading. This is because both results increase network execution time and lead to VM failure. To solve these problems, an improved load balancing approaches was proposed in this work. Therefore, this paper suggested an enhanced load balancing approaches to address these issues. The Artificial Bee Colony (ABC) method and the Bat algorithm are combined to create the balancing technique known as the Hybrid BAT and ABC (HBABC) algorithm, which dynamically distributes resources. The suggested HBABC method was assessed using CloudSim and standard workload format (SWF) data sets, which had file sizes of 200 KB and 400 KB. The evaluation was conducted on even workloads ranging from 200 to 20,000, and the performance of the HBABC method was compared with other state-of-the-art algorithms. The implementation of the suggested HBABC method resulted in a reduction of the Makespan (energy level) within the data center and showed improved accuracy in task allocation for VMs in a cloud data center. The ANOVA comparison test revealed a 1.98 percent enhancement in VM accuracy and task distribution, as well as a 0.98 percent decrease in the Makespan or energy level of the cloud data center. The outcomes are in line with various services broker rules that are employed during process of simulating the suggested algorithm in a cloud datacenter. The suggested method will be employed in subsequent studies as a prediction strategy for the resource management system in cloud datacenters. [ABSTRACT FROM AUTHOR]

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]