Virtual Network Embedding Algorithms Based on Best-Fit Subgraph Detection

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.Comment: arXiv admin note: substantial text overlap with arXiv:1502.0235

Genetic Algorithm-based Mapper to Support Multiple Concurrent Users on Wireless Testbeds

Communication and networking research introduces new protocols and standards with an increasing number of researchers relying on real experiments rather than simulations to evaluate the performance of their new protocols. A number of testbeds are currently available for this purpose and a growing number of users are requesting access to those testbeds. This motivates the need for better utilization of the testbeds by allowing concurrent experimentations. In this work, we introduce a novel mapping algorithm that aims to maximize wireless testbed utilization using frequency slicing of the spectrum resources. The mapper employs genetic algorithm to find the best combination of requests that can be served concurrently, after getting all possible mappings of each request via an induced sub-graph isomorphism stage. The proposed mapper is tested on grid testbeds and randomly generated topologies. The solution of our mapper is compared to the optimal one, obtained through a brute-force search, and was able to serve the same number of requests in 82.96% of testing scenarios. Furthermore, we show the effect of the careful design of testbed topology on enhancing the testbed utilization by applying our mapper on a carefully positioned 8-nodes testbed. In addition, our proposed approach for testbed slicing and requests mapping has shown an improved performance in terms of total served requests, about five folds, compared to the simple allocation policy with no slicing.Comment: IEEE Wireless Communications and Networking Conference (WCNC) 201

Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding

In cloud infrastructure, accommodating multiple virtual networks on a single physical network reduces power consumed by physical resources and minimizes cost of operating cloud data centers. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be NP-hard. With considering energy efficiency, the problem becomes more complicated. In this paper, we model energy-aware virtual network embedding, devise metrics for evaluating performance of energy aware virtual network-embedding algorithms, and propose an energy aware virtual network-embedding algorithm based on multi-objective particle swarm optimization augmented with local search to speed up convergence of the proposed algorithm and improve solutions quality. 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 revenue and decreasing energy consumption.Comment: arXiv admin note: text overlap with arXiv:1504.0684

Multi-capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP) problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem , and we evaluate its efficiency using simulations on various application workloads, and network models.This work was done while author was at Boston University. It was partially supported by NSF CISE awards #1430145, #1414119, #1239021 and #1012798. (1430145 - NSF CISE; 1414119 - NSF CISE; 1239021 - NSF CISE; 1012798 - NSF CISE