One of the primary challenges associated with network functions virtualization (NFV) is the automated management of the service lifecycle. In this paper, we present a full software-based management and orchestration (MANO) stack which operates with OpenStack and OpenDaylight controllers and has the in-built functionality to automate the key phases of the NFV service lifecycle, namely resource discovery and matching, service mapping, service deployment, and monitoring. The MANO stack is being implemented by the EU FP7 project T-NOVA, with the components being released as open-source software. Service mapping and service deployment solutions developed in the scope of T-NOVA are presented in detail. As a proof-of-concept, we evaluate the performance of a virtualized traffic classifier network function, demonstrating the gains of virtualized hardware acceleration.

In these last years, we have assisted to a skyrocketing diffusion of Massively Multi-player Online Role-Playing Games (MMORPGs) and Massively Multiplayer Online Games (MMOs or MMOGs). In these environments, which are complex socio-technical distributed systems, a huge amount of players interact to have fun and develop their characters. Looting Systems (LSs) help allocating valuable in- game objects, as fairly as possible, among the participating players. The medium/long term effects of the adoption of different Looting Systems on players have not yet been adequately investigated, in spite of the fact that they could impact heavily on players' satisfaction and hence on their "fidelity" to a specific game service. In the present work, we simulate the impact of several LSs on different players base, offering hints for reflection and further investigation to MMOs developers.

With the increase of energy consumption associated with IT infrastructures, energy management is becoming a priority in the design and operation of complex service-based systems. At the same time, service providers need to comply with Service Level Agreement (SLA) contracts which determine the revenues and penalties on the basis of the achieved performance level. This paper focuses on the resource allocation problem in multitier virtualized systems with the goal of maximizing the SLAs revenue while minimizing energy costs. The main novelty of our approach is to address—in a unifying framework—service centers resource management by exploiting as actuation mechanisms allocation of virtual machines (VMs) to servers, load balancing, capacity allocation, server power state tuning, and dynamic voltage/frequency scaling. Resource management is modeled as an NP-hard mixed integer nonlinear programming problem, and solved by a local search procedure. To validate its effectiveness, the proposed model is compared to top-performing state-of-the-art techniques. The evaluation is based on simulation and on real experiments performed in a prototype environment. Synthetic as well as realistic workloads and a number of different scenarios of interest are considered. Results show that we are able to yield significant revenue gains for the provider when compared to alternative methods (up to 45 percent). Moreover, solutions are robust to service time and workload variations.

Multiple combinations of hardware and network components can be selected to design an information technology (IT) infrastructure that satisfies requirements. The professional criterion to deal with these degrees of freedom is cost minimization. However, a scientific approach has been rarely applied to cost minimization, particularly for the joint optimization of hardware and network systems. This paper provides an overall methodology for combining hardware and network designs in a single cost minimization problem for multisite computer systems. Costs are minimized by applying a heuristic optimization approach to a sound decomposition of the problem. We consider most of the design alternatives that are enabled by current hardware and network technologies, including server sizing, localization of mutitier applications, and reuse of legacy systems. The methodology is empirically verified with a database of costs that has also been built as part of this paper. Verifications consider several test cases with different computing and communication requirements. Cost reductions are evaluated by comparing the cost of methodological results with those of architectural solutions that are obtained by applying professional design guidelines. The quality of heuristic optimization results is evaluated through comparison with lower bounds.

Abstract The present work tackles a recent problem in the class of cardinality constrained combinatorial optimization problems for the planar graph case: the minimum k‐cardinality cut problem. Given an undirected edge‐weighted connected graph the min k‐cardinality cut problem consists in finding a partition of the vertex set V in two sets V1, V2 such that the number of the edges between V1 and V2 is exactly k and the sum of the weights of these edges is minimal. Although for general graphs the problem is already strongly 𝒩𝒫‐hard, we have found a pseudopolynomial algorithm for the planar graph case. This algorithm is based on the fact that the min k‐cardinality cut problem in the original graph is equivalent to a bi‐weighted exact perfect matching problem in a suitable transformation of the geometric dual graph. Because the Lagrangian relaxation of cardinality constraint yields a max cut problem and max cut is polynomially solvable in planar graphs, we also develop a Lagrangian heuristic for the min k‐cardinality cut in planar graphs. We compare the performance of this heuristic with the performance of a more general heuristic based on a Semidefinite Programming relaxation and on the Goemans and Williamson's random hyperplane technique. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(4), 195–208 2006

Nowadays, large service centers provide computational capacity to many customers by sharing a pool of IT resources. The service providers and their customers negotiate utility based service level agreement (SLA) to determine the costs and penalties on the base of the achieved performance level. The system is often based on a multi-tier architecture to service requests. The service provider would like to maximize the SLA revenues, while minimizing its operating costs. The system we consider is based on a centralized network dispatcher which controls the allocation of applications to servers, the request volumes at various servers and the scheduling policy at each server. The dispatcher can also decide to turn ON or OFF servers depending on the system load. This paper designs a resource allocation scheduler for such multi-tier environments so as to maximize the profits associated with multiple class SLAs. The overall problem is NP-hard. We develop heuristic solutions by implementing a local-search algorithm. Results are presented to demonstrate the benefits of our approach

We consider an extension of the 0–1 multidimensional knapsack problem in which there are greater-than-or-equal-to inequalities, called demand constraints, in addition to the standard less-than-or-equal-to constraints. Moreover, the objective function coefficients are not constrained in sign. This problem is worth considering because it is embedded in models of practical application, it has an intriguing combinatorial structure, and it appears to be a challenging problem for commercial ILP solvers. Our approach is based on a nested tabu-search algorithm in which neighborhoods with different structures are exploited. First, a tabu-search procedure is carried out in which mainly the infeasible region is explored. Once feasibility has been established, a second tabu-search procedure, which analyzes only feasible solutions, is applied. The algorithm has been tested on a wide set of instances. Computational results are discussed.