One of the main activities in highway construction is earthwork, which is a complex process involving excavation, transportation, and filling of large quantities of different earth material types. Earthwork operations are costly and undergo several constraints because of the fact that they have large environmental and social effects on the areas surrounding the construction site. Using mathematical models to produce a minimum-cost earthwork plan that satisfies all constraints is thus of great significance for enhancing the productivity of the overall construction project. This paper presents an earthwork optimization system on the basis of the use of linear programming that operates in a novel two-phase approach. In the first phase, an aggregate model determines the feasibility of the overall project, whereas in the second phase, disaggregate models determine the actual flows of each material. The two-phase quantitative method for earthwork optimization developed in this paper includes all features derived from the everyday activity of one of the major European companies in construction. It involves classical decisions, such as excavations, fillings, use of quarries and dump sites, and the temporary rent of depots, but it also accounts for several novelties, including the use of recycling facilities and the explicit integration with the existing public road network. Extensive computational results are obtained by running the models on a set of realistic instances, and show the efficiency of the proposed approach in solving complex earthwork problems. (C) 2015 American Society of Civil Engineers.

Worldwide interest in the delivery of computing and storage capacity as a service continues to grow at a rapid pace. The complexities of such cloud computing centers require advanced resource management solutions that are capable of dynamically adapting the cloud platform while providing continuous service and performance guarantees. The goal of this paper is to devise resource allocation policies for virtualized cloud environments that satisfy performance and availability guarantees and minimize energy costs in very large cloud service centers. We present a scalable distributed hierarchical framework based on a mixed-integer nonlinear optimization of resource management acting at multiple timescales. Extensive experiments across a wide variety of configurations demonstrate the efficiency and effectiveness of our approach.

This paper concerns developing two hybrid proximal point methods (PPMs) for finding a common solution of some optimization-related problems. First we construct an algorithm to solve simultaneously an equilibrium problem and a variational inequality problem, combing the extragradient method for variational inequalities with an approximate PPM for equilibrium problems. Next we develop another algorithm based on an alternate approximate PPM for finding a common solution of two different equilibrium problems. We prove the global convergence of both algorithms under pseudomonotonicity assumptions.

In Cloud computing systems, resource management is one of the main issues. Indeed, in any time instant resources have to be allocated to handle effectively workload fluctuations, while providing Quality of Service (QoS) guarantees to the end users. In such systems, workload prediction-based autonomic computing techniques have been developed. In this paper we propose capacity allocation techniques able to coordinate multiple distributed resource controllers working in geographically distributed cloud sites. Furthermore, capacity allocation solutions are integrated with a load redirection mechanism which forwards incoming requests between different domains. The overall goal is to minimize the costs of the allocated virtual machine instances, while guaranteeing QoS constraints expressed as a threshold on the average response time. We compare multiple heuristics which integrate workload prediction and distributed non-linear optimization techniques. Experimental results show how our solutions significantly improve other heuristics proposed in the literature (5-35% on average), without introducing significant QoS violations.

The most advanced solutions that are currently adopted in ports and terminals use technologies based on radio frequency identification (RFID) and the Global Positioning System (GPS) to identify and localize shipping containers in the yard. Nevertheless, because of the limitations of these solutions, the position of containers is still affected by errors, and it cannot be determined in real time. In this paper, a nonconventional approach is presented: Each container is equipped with nodes that use wireless communication to detect neighbor containers and to send proximity information to a base station. At the base station, geometrical constraints and proximity data are combined to determine the positions of containers. Missing information due to faulty nodes is tolerated by modeling geometrical constraints as an integer linear programming problem. Numerical simulations show that most of the containers can be localized, even when the number of nodes that are affected by faults is on the order of 30%.

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.

Cloud computing is an emerging paradigm which allows the on-demand delivering of software, hardware, and data as services. As cloud-based services are more numerous and dynamic, the development of efficient service provisioning policies become increasingly challenging. Game theoretic approaches have shown to gain a thorough analytical understanding of the service provisioning problem. In this paper we take the perspective of Software as a Service (SaaS) providers which host their applications at an Infrastructure as a Service (IaaS) provider. Each SaaS needs to comply with quality of service requirements, specified in Service Level Agreement (SLA) contracts with the end-users, which determine the revenues and penalties on the basis of the achieved performance level. SaaS providers want to maximize their revenues from SLAs, while minimizing the cost of use of resources supplied by the IaaS provider. Moreover, SaaS providers compete and bid for the use of infrastructural resources. On the other hand, the IaaS wants to maximize the revenues obtained providing virtualized resources. In this paper we model the service provisioning problem as a Generalized Nash game, and we propose an efficient algorithm for the run time management and allocation of IaaS resources to competing SaaSs.

Modern cloud infrastructures live in an open world, characterized by continuous changes in the environment and in the requirements they have to meet. Continuous changes occur autonomously and unpredictably, and they are out of control of the cloud provider. Therefore, advanced solutions have to be developed able to dynamically adapt the cloud infrastructure, while providing continuous service and performance guarantees. A number of autonomic computing solutions have been developed such that resources are dynamically allocated among running applications on the basis of short-term demand estimates. However, only performance and energy trade-off have been considered so far with a lower emphasis on the infrastructure dependability/availability which has been demonstrated to be the weakest link in the chain for early cloud providers. The aim of this paper is to fill this literature gap devising resource allocation policies for cloud virtualized environments able to identify performance and energy trade-offs, providing a priori availability guarantees for cloud end-users.

Cloud computing represents a new way to deliver and use services on a shared IT infrastructure. Traditionally, IT hardware and software were acquired and provisioned on business premises. Software applications were built, possibly integrating off-the-shelf components, deployed and run on these privately owned resources. With service-oriented computing, applications are offered by service providers to clients, who can simply invoke them through the network. The offer specifies both the functionality and the Quality of Service (QoS). Providers are responsible for deploying and running services on their own resources. Cloud computing moves one step further. Computing facilities can also be delivered on demand in the form of services over a network. In this paper we take the perspective of a Software as a Service (SaaS) provider whose goal is to maximize the revenues from end users who access services on a pay-per-use basis. In turn, the SaaS provider exploits the cloud, which provides an Infrastructure as a Service (IaaS), where the service provider dynamically allocates hardware physical resources.This paper presents a distributed algorithm for run-time management of SaaS cloud systems that jointly addresses the capacity allocation and admission control of multiple classes of applications providing an heuristic solution which closely approximates the global optimal solution.

In this paper we propose a class of differentiable gap functions in order to formulate a generalized variational inequality (GVI) problem, involving a set-valued map with closed and convex graph, as an optimization problem. We also show that under appropriate assumptions on the set-valued map, any stationary point of the equivalent optimization problem is a global optimal solution and solves the GVI. Finally, we describe descent methods for solving the optimization problem equivalent to the GVI and we prove its global convergence.

We propose a descent method via gap functions for solving nonsmooth variational inequalities with a locally Lipschitz operator. Assuming monotone operator (not necessarily strongly monotone) and bounded domain, we show that the method with an Armijo-type line search is globally convergent. Finally, we report some numerical experiments.

This paper deals with the generalized Nash equilibrium problem (GNEP), i.e. a noncooperative game in which the strategy set of each player, as well as his payoff function, depends on the strategies of all players. We consider an equivalent optimization reformulation of GNEP using a regularized Nikaido–Isoda function so that solutions of GNEP coincide with global minima of the optimization problem. We then propose a derivative-free descent type method with inexact line search to solve the equivalent optimization problem and we prove that our algorithm is globally convergent. The convergence analysis is not based on conditions guaranteeing that every stationary point of the optimization problem is a solution of GNEP. Finally, we present the performance of our algorithm on some examples.

The image space approach is applied to the study of vector quasiequilibrium problems. Exploiting separation arguments in the image space, Lagrangian-type optimality conditions and gap functions for vector quasiequilibrium problems are derived.

An algorithm for solving nonsmooth monotone variational inequalities subject to linear constraints is proposed. Combining a cutting plane procedure for strictly monotone variational inequalities with the Tikhonov regularization technique, we devise an algorithm based on successive linear programming. Preliminary numerical results are reported.

In this paper we propose a descent method for solving variational inequality problems where the underlying operator is nonsmooth, locally Lipschitz, and monotone over a closed, convex feasible set. The idea is to combine a descent method for variational inequality problems whose operators are nonsmooth, locally Lipschitz, and strongly monotone, with the Tikonov-Browder regularization technique. Finally, numerical results are presented and discussed.

We show variational models for traffic equilibrium problems and we develop algorithmic schemes, using mathematical programming approach, for solving them. [ DOI : 10.1685 / CSC06124] About DOI