Algorithms for the Resource Management of Large Scale Infrastructures

Dear Colleagues,

The growing demand for fast, powerful, scalable and reliable computing and communication infrastructure has driven the evolution of the computation paradigm from in-house solutions towards shared infrastructure, such as the cloud computing paradigm (that in the long run can provide a reduction of the total cost of ownership of up to 66%), and up to the most innovative distributed infrastructure proposed for edge/fog computing. The result is the advent of large distributed infrastructure, composed of multiple data centers where virtualization is applied to the level of both computing and networking, to the point where we can describe the infrastructure, as composed by software-defined data centers.

This infrastructure is becoming more and more popular, especially to support modern applications, which are characterized by a huge volume of data (on the order of peta or exhabytes—so-called big data), possibly collected from thousands of sensors (in the Internet of Things scenario) and may be deployed on tens of thousands of VMs, possibly interacting through a virtualized network spread over a geographic area. For example, more than 60% of Apache Spark installations are deployed on the cloud. However, the management of this large and complex infrastructure represents a major problem. On the one hand, there is the need to maximize resource utilization and minimize energy consumption of the infrastructure, in order to reduce costs and for environmental reasons. On the other hand, there is the need to guarantee the resources needed by applications hosted on the infrastructure, in terms of computing power, storage and communication. These requirements may be expressed directly or may be directed by a Service Level Agreement at the level of application behavior.

The challenge of infrastructure management in this scenario is clear. The simple monitoring of such infrastructure is difficult, but we must consider that complexity of the problem is further exacerbated by the need to react in a timely and unsupervised manner with an ever-changing workload, characterized by unpredictable oscillations in the demands of each application and by the joining and leaving of new applications that are deployed or dropped. The goal is to devise models, techniques and algorithms that can support a self-management system able to adapt, manage and cope with changes, without the need for human supervision.

We invite researchers to submit innovative and original proposals that can advance the state-of-the-art in the field of management of autonomic infrastructure.

A non-exhaustive list of topics of interest includes:

• Models and algorithms for resource management
• Economic models for the management of data centers
• Models and algorithms for fault tolerance in Cloud Computing scenarios
• Algorithms for VM placement/migration in virtualized datacenters
• Management of Multi-data center scenarios
• Energy model for data centers
• Resource allocation solutions for micro-service based and container based systems
• Management of distributed infrastructures in presence of Software-Defined Networks, network function virtualization and/or Virtual Routers
• Solutions for the management of Fog computing scenarios

Prof. Danilo Ardagna
Dr. Claudia Canali
Dr. Riccardo Lancellotti
Guest Editors

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