Advances in Cloud and Edge Computing

Dear Colleagues,

The origin of cloud computing dates back to 2006, when the term was introduced for the first time in the context of an industrial conference. Academia, big IT players, and international standardization organizations have since made huge efforts to transform a revolutionary computing paradigm into a mature and reliable technology. Today, Cloud computing can provide flexible and scalable resources capable of meeting any application need in terms of computing power. However, with the continuous and uncontrolled increase of the number of “Things” (~20 billion, as of today) becoming connected to the Internet, large amounts of data are being generated and injected in the Cloud in an unprecedented volume, variety, and velocity. The availability of such devices, ranging from smart personal devices up to small dumb connected sensors, enables a whole new set of applications that rely on collecting, aggregating, and analyzing a huge amount of data. Examples range from the most common crowdsensing application up to the management of industrial processes or support for autonomous driving. When dealing with these novel applications, the Cloud can practically supply unlimited computing resources to scale any data size. On the other hand, the geographical distance between Things and Cloud premises makes the data transfer extremely expensive and may impact computing performance indices, e.g., computation timeliness. Very recently, the Edge computing paradigm has emerged to support the elaboration of data flows along the IoT-to-Cloud path by exploiting (less powerful) resources which are alternative to those supplied by the Cloud. By bringing computation closer to data sources, Edge computing enforces a paradigm shift that promises to meet the strict real-time requirements of the most demanding emerging applications (e.g., critical industrial control tasks, autonomous driving) that the Cloud model is not able to serve. Novel Edge computing can be used alone, creating a distributed mesh computing platform, or can be integrated with existing cloud computing infrastructures, creating a continuum computing scenario. 

The novel challenges of infrastructure management in this scenario are related to several aspects. The efficient monitoring of such infrastructure already represents a non-trivial task, but the complexity of the problem is exacerbated by the need to react in a timely and unsupervised manner to ever-changing workloads, characterized by unpredictable oscillations in the demands of each application and by new applications being deployed or dropped by the system. Finally, a further dimension to consider is the presence of virtualization, which is applied both at the level of computational elements and of networking functions. The goal of this Special Issue is to devise models, techniques, and algorithms that can support the management of cloud and edge computing infrastructures. The nature of these applications may introduce new challenges because coordinating large data processing tasks in a widely heterogeneous and distributed environment may pose new issues that need to be addressed. Furthermore, the management of data that may include critical, personal, or sensitive information introduces a new dimension to the problem that cannot be handled by relying on the high security standards that characterize cloud computing.

We invite researchers to submit innovative and original proposals that can advance the state of the art in the field of management of cloud and edge computing infrastructures.

Dr. Claudia Canali
Dr. Giuseppe Di Modica
Dr. Riccardo Lancellotti
Guest Editors

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