Abstract

The energy consumption in telecommunication networks is expected to grow considerably, especially in core networks. In this chapter, optimization of energy consumption is approached from two directions. In a first study, multilayer traffic engineering (MLTE) is used to assign energy-efficient paths and logical topology to IP traffic. The relation with traditional capacity optimization is explained, and the MLTE strategy is applied for daily traffic variations. A second study considers the core network below the IP layer, giving a detailed power consumption model. Optical bypass is evaluated as a technique to achieve considerable power savings over per-hop optical–electronic–optical regeneration.

Abstract

Electric mobility is assuming a central role in the new world energy scenario where Renewable Energy Sources (RES) have increased their contribution to the electricity production, mainly due to the fact they are characterized by a reduced environmental impact; in the aforesaid context, Electric Vehicles (EVs) can play a significant role to facilitate the integration of RES into the power grid, also within smart grid infrastructures. Public administration represents one of the sectors that can mostly benefit from the use of the EV technology in its fleets; in many countries, governments have introduced incentive policies to support the spread of the electric mobility and car manufacturers are developing EVs for urban transportation users. In this paper, the attention is focused on a smart microgrid project developed at the University of Genoa and based on the integration of RES, EVs and storage systems. In particular, the attention is here pointed on the use of the EVs to highlight the different habits of recharge adopted at the Genoa University. The paper describes the smart microgrid test-bed facility at the Savona Campus and reports the most significant results relative to an experimental campaign conducted on EVs.

Abstract

For cloud enterprise customers that require services on demand, data centers must allocate and partition data center resources in a dynamic fashion. We consider the problem in which a request from an enterprise customer is mapped to a virtual network (VN) that is allocated requiring both bandwidth and compute resources by connecting it from an entry point of a data center to one or more servers, should this data center be selected from multiple geographically distributed data centers. We present a dynamic traffic engineering framework, for which we develop an optimization model based on a mixed-integer linear programming (MILP) formulation that a data center operator can use at each review point to optimally assign VN customers. Through a series of studies, we then present results on how different VN customers are treated in terms of request acceptance when each VN class has a different resource requirement. We found that a VN class with a low resource requirement has a low blocking even in heavy traffic, while the VN class with a high resource requirement faces a high service denial. On the other hand, cost for the VN with the highest resource requirement is not always the highest in the heavy traffic because of the significantly high service denial faced by this VN class.

Abstract

Prep is a high-performance tool for preparing sequence alignment/map files for variant calling in sequencing pipelines. It can be used as a replacement for SAMtools and Picard for preparation steps such as filtering, sorting, marking duplicates, reordering contigs, and so on, while producing identical results. What sets elPrep apart is its software architecture that allows executing preparation pipelines by making only a single pass through the data, no matter how many preparation steps are used in the pipeline. elPrep is designed as a multithreaded application that runs entirely in memory, avoids repeated file I/O, and merges the computation of several preparation steps to significantly speed up the execution time. For example, for a preparation pipeline of five steps on a whole-exome BAM file (NA12878), we reduce the execution time from about 1: 40 hours, when using a combination of SAMtools and Picard, to about 15 minutes when using elPrep, while utilising the same server resources, here 48 threads and 23GB of RAM. For the same pipeline on whole-genome data (NA12878), elPrep reduces the runtime from 24 hours to less than 5 hours. As a typical clinical study may contain sequencing data for hundreds of patients, elPrep can remove several hundreds of hours of computing time, and thus substantially reduce analysis time and cost. Document type: Article