One of the main objectives of cloud computing providers is increasing the revenue of their cloud datacenters by accommodating virtual network requests as many as possible. However, arrival and departure of virtual network requests fragment physical network's resources and reduce the possibility of accepting more virtual network requests. To increase the number of virtual network requests accommodated by fragmented physical networks, we propose two virtual network embedding algorithms, which coarsen virtual networks using Heavy Edge Matching (HEM) technique and embed coarsened virtual networks on best-fit sub-substrate networks. The performance of the proposed algorithms are evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithms increase the acceptance ratio and the revenue.
In cloud environments, load balancing task scheduling is an important issue that directly affects resource utilization. Unquestionably, load balancing scheduling is a serious aspect that must be considered in the cloud research field due to the significant impact on both the back end and front end. Whenever an effective load balance has been achieved in the cloud then good resource utilization will also be achieved. An effective load balance means distributing the submitted workload over cloud VMs in a balanced way, leading to high resource utilization and high user satisfaction. In this paper, we propose a load balancing algorithm, Binary Load Balancing -Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (Bin-LB-PSOGSA), which is a bio-inspired load balancing scheduling algorithm that efficiently enables the scheduling process to improve load balance level on VMs. The proposed algorithm finds the best Task-to-Virtual machine mapping that is influenced by the length of submitted workload and VM processing speed. Results show that the proposed Bin-LB-PSOGSA achieves better VM load average than the pure Bin-LB-PSO and other benchmark algorithms in terms of load balance level.
Keywords-Gravitational search algorithm; load balancing; particle swarm optimization; task scheduling; task-to-virtual machine mapping; virtual machine loadI.
Network virtualization allows cloud infrastructure providers to accommodate multiple virtual networks on a single physical network. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be non-deterministic polynomial-time hard (NP-hard). Effective virtual network embedding increases the revenue by increasing the number of accepted virtual networks. In this paper, we propose virtual network embedding algorithm, which improves virtual network embedding by coarsening virtual networks. Heavy Clique matching technique is used to coarsen virtual networks. Then, the coarsened virtual networks are enhanced by using a refined Kernighan-Lin algorithm. The performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing the acceptance ratio and the revenue.
Most of current Software-as-a-Service (SaaS) applications are developed as customizable serviceoriented applications that serve a large number of tenants (users) by one application instance. The current rapid evolution of SaaS applications increases the demand to study the commonality and variability in software product lines that produce customizable SaaS applications. During runtime, Customizability is required to achieve different tenants' requirements. During the development process, defining and realizing commonalty and variability in SaaS applications' families is required to develop reusable, flexible, and customizable SaaS applications at lower costs, in shorter time, and with higher quality. In this paper, Orthogonal Variability Model (OVM) is used to model variability in a separated model, which is used to generate simple and understandable customization model. Additionally, Service oriented architecture Modeling Language (SoaML) is extended to define and realize commonalty and variability during the development of SaaS applications.
Assigning virtual network resources to physical network components, called Virtual Network Embedding, is a major challenge in cloud computing platforms. In this paper, we propose a memetic elitist pareto evolutionary algorithm for virtual network embedding problem, which is called MEPE-VNE. MEPE-VNE applies a non-dominated sortingbased multi-objective evolutionary algorithm, called NSGA-II, to reduce computational complexity of constructing a hierarchy of non-dominated Pareto fronts and assign a rank value to each virtual network embedding solution based on its dominance level and crowding distance value. Local search is applied to enhance virtual network embedding solutions and speed up convergence of the proposed algorithm. To reduce loss of good solutions, MEPE-VNE ensures elitism by passing virtual network embedding solutions with best fitness values to next generation. Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing acceptance ratio and revenue while decreasing the cost incurred by substrate network.
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