# Special Issue on Algorithms for the Resource Management of Large Scale Infrastructures
## 2 Special Issue  
All submissions to this Special Issue were reviewed by at least three experts. After a strict review, four papers were accepted, as introduced below.  
All the papers focus on distributed systems management, but the considered scenarios range from multi-cloud to fog-like and Peer-to-Peer (P2P) architectures, with two papers proposing solutions that are power- and energy-aware. The proposed techniques are wide ranging, from modeling based on stochastic Petri-networks to heuristics and machine learning algorithms. We believe that this selection highlights the vast heterogeneity that characterizes modern distributed infrastructures, thereby proposing novel solutions for their management and highlighting the broad scope of the research tackling these problems.  
The paper \"Multi-level elasticity for wide-area Data Streaming Systems: A reinforcement learning approach\" by [PERSON] et al. [1] addresses the issue of data stream processing (DSP) in the context of a multi cloud/fog. The reference architecture is hierarchically distributed according to the guidelines of the considered Multi-Level Elastic and Distributed DSP Framework (E2 DF). In particular, the paper focuses on the control module of the framework and it proposes a Reinforcement Learning algorithm, combined with a heuristic approach to define multi-level policies for the placement and the management of the operators in a distributed stream processing system.  
The paper \"SLoPCloud: An Efficient Solution for Locality Problem in Peer-to-Peer Cloud Systems\" by [PERSON] et al. [2] is characterized by a P2P approach to distributed cloud systems. In particular, the paper tackles the problem of mapping the overlay network over the physical networking infrastructure. The mapping problem considers a hypercube-shaped overlay and the metric of interest to be optimized is the communication Round Trip Time (RTT). The final goal is to build a hypercubic overlay network with a locality-aware approach, thereby evaluating how this optimized infrastructure can provide a performance benefit.  
The paper \"Modeling and Evaluation of Power-Aware Software Rejuvenation in Cloud Systems\" by [PERSON] et al. [3] considers the problem of software rejuvenation in cloud systems. The paper focuses on the trade-off between the power consumption related to software ageing (typically due to failures and degradation in the Quality of Service, often reffered to as QoS degradation), and the power consumption related to a frequent rejuvenation process (that requires the shut down and restarting of parts of the system). The paper advocated for coordination between the rejuvenation process and the lifecycle of VMs (including migrations), which may significantly affect the power consumption. The authors propose a model, based on Stochastic Petri Networks, to determine when the rejuvenation should be performed. The experimental results confirm the benefit of this approach in terms of reduced failures and lower power consumption.  
Finally, the paper \"Reducing the Operational Cost of Cloud Data Centers through Renewable Energy\" by [PERSON] et al. [4] proposes a new framework, namely the EcoMultiCloud, that aims at increasing the sustainability of a multi-cloud infrastructure considering not only management techniques aimed at a power reduction, but also taking into account the utilization of renewable energy sources, as well as cooling costs. The considered architecture is scalable for a geographically distributed infrastructure, owing to the proposed hierarchical approach, where the main control mechanism for the management of the infrastructure is based on the decisions about VMs migrations (i.e., when, from where, and to where).  
**Acknowledgments:** The editors would like to thank all the authors who have submitted their work to this special issue. Last but not least, we would like to express our sincere gratitude to all the colleagues who collaborated with us during the review process.  
**Conflicts of Interest:** The authors declare no conflict of interest.