Robust Energy Management for Green and Survivable IP Networks

Despite the growing necessity to make Internet greener, it is worth pointing out that energy-aware strategies to minimize network energy consumption must not undermine the normal network operation. In particular, two very important issues that may limit the application of green networking techniques concern, respectively, network survivability, i.e. the network capability to react to device failures, and robustness to traffic variations. We propose novel modelling techniques to minimize the daily energy consumption of IP networks, while explicitly guaranteeing, in addition to typical QoS requirements, both network survivability and robustness to traffic variations. The impact of such limitations on final network consumption is exhaustively investigated. Daily traffic variations are modelled by dividing a single day into multiple time intervals (multi-period problem), and network consumption is reduced by putting to sleep idle line cards and chassis. To preserve network resiliency we consider two different protection schemes, i.e. dedicated and shared protection, according to which a backup path is assigned to each demand and a certain amount of spare capacity has to be available on each link. Robustness to traffic variations is provided by means of a specific modelling framework that allows to tune the conservatism degree of the solutions and to take into account load variations of different magnitude. Furthermore, we impose some inter-period constraints necessary to guarantee network stability and preserve the device lifetime. Both exact and heuristic methods are proposed. Experimentations carried out with realistic networks operated with flow-based routing protocols (i.e. MPLS) show that significant savings, up to 30%, can be achieved also when both survivability and robustness are fully guaranteed

Energy management in communication networks: a journey through modelling and optimization glasses

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed

Chemotherapy planning and multi-appointment scheduling: formulations, heuristics and bounds

The number of new cancer cases is expected to increase by about 50% in the next 20 years, and the need for chemotherapy treatments will increase accordingly. Chemotherapy treatments are usually performed in outpatient cancer centers where patients affected by different types of tumors are treated. The treatment delivery must be carefully planned to optimize the use of limited resources, such as drugs, medical and nursing staff, consultation and exam rooms, and chairs and beds for the drug infusion. Planning and scheduling chemotherapy treatments involve different problems at different decision levels. In this work, we focus on the patient chemotherapy multi-appointment planning and scheduling problem at an operational level, namely the problem of determining the day and starting time of the oncologist visit and drug infusion for a set of patients to be scheduled along a short-term planning horizon. We use a per-pathology paradigm, where the days of the week in which patients can be treated, depending on their pathology, are known. We consider different metrics and formulate the problem as a multi-objective optimization problem tackled by sequentially solving three problems in a lexicographic multi-objective fashion. The ultimate aim is to minimize the patient's discomfort. The problems turn out to be computationally challenging, thus we propose bounds and ad-hoc approaches, exploiting alternative problem formulations, decomposition, and $k$-opt search. The approaches are tested on real data from an Italian outpatient cancer center and outperform state-of-the-art solvers.Comment: 28 pages, 3 figure

On Optimal Infrastructure Sharing Strategies in Mobile Radio Networks

partially_open5noopenCano, Lorela; Capone, Antonio; Carello, Giuliana; Cesana, Matteo; Passacantando, MauroCano, Lorela; Capone, Antonio; Carello, Giuliana; Cesana, Matteo; Passacantando, Maur

Cooperative Infrastructure and Spectrum Sharing in Heterogeneous Mobile Networks

To accommodate the ever-growing traffic load and bandwidth demand generated by mobile users, mobile network operators (MNOs) need to frequently invest in high spectral efficiency technologies and increase their hold of spectrum resources; MNOs have then to weigh between building individual networks or entering into network and spectrum sharing agreements. We address here the problem of radio access network and spectrum sharing in 4G mobile networks by focusing on a case when multiple MNOs plan to deploy small cell base stations in a geographical area in order to upgrade their existing network infrastructure. We propose two cooperative game models (with and without transferable utility) to address the proposed problem: for given network (user throughput, MNO market, and spectrum shares) and economic (coalition cost and mobile data pricing model) settings, the proposed models output a cost division policy that guarantees coalition (sharing agreement) stability

A non-cooperative game approach for RAN and spectrum sharing in mobile radio networks

Mobile Network Operators (MNOs) are nowadays forced to continuously invest in their network infrastructure to keep up with the increasing bandwidth demand and traffic load coming from mobile users. In this context, MNOs have to face the strategic problem of whether to invest on their own or deploy shared networks. We address here the problem of Radio Access Network (RAN) and spectrum sharing in 4G mobile networks. Namely, we consider the case in which multiple MNOs are planning to deploy small cell Base Stations to improve their current network infrastructure; the deployment investment may be shared with other MNOs, thus giving rise to shared RANs. The RAN and spectrum sharing problem is formalized as a Generalized Nash Equilibrium Problem, where the strategy of each MNO in the game is twofold: selecting a coalition (whom to cooperate) and the fraction of the coalition cost to pay, with the goal of maximizing the individual return on investment. The proposed approach is leveraged to characterize the stable coalitions and their respective cost division policies for various network and economic conditions

A robust optimization approach for the Advanced Scheduling Problem with uncertain surgery duration in Operating Room Planning - an extended analysis

We consider the Advanced Scheduling Problem (ASP) in the operating room block scheduling, taking into account stochastic patient surgery duration. A surgery waiting list, a set of Operating Room (OR) blocks, and a planning horizon are given. The problem herein addressed is to determine the subset of patients to be scheduled in the considered time horizon and their assignment to the available OR blocks. The problem aims at minimizing a measure of the waiting time of the patients. To this purpose, we introduce a penalty function associated to waiting time, urgency and tardiness of patients. We propose a robust optimization model to solve the ASP with uncertain surgery durations. The proposed approach does not need to generate a set of scenarios, and guarantees that solutions remain feasible for some variations of the surgery length parameters. We solve the problem on a set of real-based instances to test the behavior of the proposed model. The solution quality is evaluated with regards to the number of patients operated and their tardiness. Furthermore, assuming lognormal distribution for the surgery times, we use a set of randomly generated scenarios in order to assess the performance of the solutions in terms of OR utilization rate and number of cancelled patients

A framework for joint resource allocation of MapReduce and web service applications in a shared cloud cluster

The ongoing uptake of cloud-based solutions by different business domains and the rise of cross-border e-commerce in the EU require for additional public and private cloud solutions. Private clouds are an alternative for e-commerce sites to host not only Web Service (WS) applications but also Business Intelligence ones that consist of batch and/or interactive queries and resort to the MapReduce (MR) programming model. In this study, we take the perspective of an e-commerce site hosting its WS and MR applications on a fixed-size private cloud cluster. We assume Quality of Service (QoS) guarantees must be provided to end-users, represented by upper-bounds on the average response times of WS requests and on the MR jobs execution times, as MR applications can be interactive nowadays. We consider multiple MR and WS user classes with heterogeneous workload intensities and QoS requirements. Being the cluster capacity fixed, some requests may be rejected at heavy load, for which penalty costs are incurred. We propose a framework to jointly optimize resource allocation for WS and MR applications hosted in a private cloud with the aim to increase cluster utilization and reduce its operational and penalty costs. The optimization problem is formulated as a non linear mathematical programming model. Applying the KKT conditions, we derive an equivalent problem that can be solved efficiently by a greedy procedure. The proposed framework increases cluster utilization by up to 18% while cost savings go up to 50% compared to a priori partitioning the cluster resources between the two workload types

A rolling horizon framework for the operating rooms planning under uncertain surgery duration

We consider the Advanced Scheduling Problem (ASP) assuming a block scheduling strategy. A set of patients and the related surgery waiting list are given, together with a set of Operating Room (OR) blocks and a planning horizon. The problem asks to determine the subset of patients to be scheduled and their assignment to the available OR blocks. We consider a so-called rolling horizon approach in order to solve the ASP over a planning horizon of several weeks. The approach is iterative and readjusts the schedule each week: at each iteration the mid-term schedule over the next $n$ weeks is generated by solving an optimization problem, minimizing a penalty function based on patients' delay and tardiness; the first week schedule is then implemented. Unpredictable extensions of surgeries and new arrivals may disrupt the schedule. The schedule is then repaired in the next week iteration, again optimizing over $n$ weeks the penalty function while limiting the number of disruptions from the previously computed plan. The total delay and tardiness minimization problem is formulated as an ILP model and solved with a commercial solver. A deterministic formulation and a robust one are proposed and compared over different stochastic realization of surgery times