Search

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]

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]

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]