Bernardetta Addis

Autonomic Management of Cloud Service Centers with Availability Guarantees

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.

A Hierarchical Approach for the Resource Management of Very Large Cloud Platforms

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.

Local optima smoothing for global optimization

It is widely believed that in order to solve large-scale global optimization problems, an appropriate mixture of local approximation and global exploration is necessary. Local approximation, if first-order information on the objective function is available, is efficiently performed by means of local optimization methods. Unfortunately, global exploration, in absence of some kind of global information on the problem, is a ‘blind’ procedure, aimed at placing observations as evenly as possible in the search domain. Often, this procedure reduces to uniform random sampling (like in Multistart algorithms or in techniques based on clustering). In this paper, we propose a new framework for global exploration which tries to guide random exploration towards the region of attraction of low-level local optima. The main idea originated by the use of smoothing techniques (based on Gaussian convolutions): the possibility of applying a smoothing transformation not to the objective function but to the result of local searches seems to have never been explored yet. Although an exact smoothing of the results of local searches is impossible to implement, in this paper we propose a computational approximation scheme which has proven to be very efficient and (maybe more important) extremely robust in solving large-scale global optimization problems with huge numbers of local optima and, in particular, for problems displaying a ‘funnel’ structure.

Energy Management Through Optimized Routing and Device Powering for Greener Communication Networks

Recent data confirm that the power consumption of the information and communications technologies (ICT) and of the Internet itself can no longer be ignored, considering the increasing pervasiveness and the importance of the sector on productivity and economic growth. Although the traffic load of communication networks varies greatly over time and rarely reaches capacity limits, its energy consumption is almost constant. Based on this observation, energy management strategies are being considered with the goal of minimizing the energy consumption, so that consumption becomes proportional to the traffic load either at the individual-device level or for the whole network. The focus of this paper is to minimize the energy consumption of the network through a management strategy that selectively switches off devices according to the traffic level. We consider a set of traffic scenarios and jointly optimize their energy consumption assuming a per-flow routing. We propose a traffic engineering mathematical programming formulation based on integer linear programming that includes constraints on the changes of the device states and routing paths to limit the impact on quality of service and the signaling overhead. We show a set of numerical results obtained using the energy consumption of real routers and study the impact of the different parameters and constraints on the optimal energy management strategy. We also present heuristic results to compare the optimal operational planning with online energy management operation .

Disk Packing in a Square: A New Global Optimization Approach

We present a new computational approach to the problem of placing n identical nonoverlapping disks in the unit square in such a way that their radii are maximized. The problem has been studied in a large number of papers, from both a theoretical and a computational point of view. In this paper, we conjecture that the problem possesses a so-called funneling landscape, a feature that is commonly found in molecular conformation problems. Based on this conjecture, we develop a stochastic search algorithm that displays excellent numerical performance. Thanks to this algorithm, we could improve over previously known putative optima in the range n ≤ 130 in as many as 32 instances, the smallest of which is n = 53.

Efficiently packing unequal disks in a circle

Placing non-overlapping circles in a smallest container is a hard task. In this paper we present our strategy for optimally placing circles in a smallest circle which led us to win an international competition by properly mixing local and global optimization strategies with random search and local moves.

Identifying critical nodes in undirected graphs: Complexity results and polynomial algorithms for the case of bounded treewidth

We consider the problem of deleting a limited number of nodes from a graph in order to minimize a connectivity measure of the surviving nodes. We prove that the problem isNP-complete even on quite particular types of graphs, and define a dynamic programming recursion that solves the problem in polynomial time when the graph has bounded treewidth. We extend this polynomial algorithm to several variants of the problem.

A new class of test functions for global optimization

In this paper we propose a new class of test functions for unconstrained global optimization problems. The class depends on some parameters through which the difficulty of the test problems can be controlled. As a basis for future comparison, we propose a selected set of these functions, with increasing difficulty, and some computational experiments with two simple global optimization algorithms.

A Trust-Region Algorithm for Global Optimization

We consider the global minimization of a bound-constrained function with a so-called funnel structure. We develop a two-phase procedure that uses sampling, local optimization, and Gaussian smoothing to construct a smooth model of the underlying funnel. The procedure is embedded in a trust-region framework that avoids the pitfalls of a fixed sampling radius. We present a numerical comparison to three popular methods and show that the new algorithm is robust and uses up to 20 times fewer local minimizations steps.

A Robust Optimization Approach for the Operating Room Planning Problem with Uncertain Surgery Duration

This paper deals with the Surgical Case Assignment Problem (SCAP) taking into account the variability pertaining patient surgery duration. In particular, given a surgery waiting list, a set of Operating Room (OR) blocks and a planning horizon, the decision herein addressed is to determine the subset of patients to be scheduled in the considered time horizon and their assignment to the available OR block times. The aim is to minimize a penalty associated to waiting time, urgency and tardiness of patients. We propose a robust optimization approach for the SCAP with uncertain surgery duration, which allows to exploit the potentialities of a mathematical programming model without the necessity of generating scenarios. Tests on a set of real-based instances are carried on in order to evaluate the solutions obtained solving different versions of the problem. Besides the value of the penalty objective function, the solution quality is also 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 proposed solutions in terms of OR utilization rate and number of cancelled patients.