Fog Computing over Challenged Networks: a Real Case Evaluation

Fog computing enables a multitude of resource-constrained end devices (e.g., sensors and actuators) to benefit from the presence of fog nodes in their close vicinity, which can provide the required computing and storage facilities instead of relying on a distant Cloud infrastructure. However, guaranteeing stable communication between end devices and fog nodes is often not trivial. Indeed, in some application scenarios such as mining operations, building sites, precision agriculture, and more, communication occurs over Challenged Networks e.g., because of the absence of a fixed and reliable network infrastructure. This paper analyzes the applicability of Fog Computing in a real Industrial Internet of Things (IIoT) environment, providing an architecture that enables disruption-tolerant communication over Challenged Networks and evaluating the achieved performance on an open-source prototype implementation

Enabling Fog Computing over Delay/Disruption-Tolerant Networks

Fog computing enables a multitude of resource-constrained end devices (e.g., sensors and actuators) to benefit from the presence of fog nodes in their close vicinity, which can provide the required computing and storage facilities instead of relying on a distant cloud infrastructure. However, guaranteeing stable communication between end devices and fog nodes is often not trivial. Indeed, in some application scenarios such as mining operations, building sites, precision agriculture, and more, communication occurs over Challenged Networks e.g., because of the absence of a fixed and reliable network infrastructure. This paper analyzes the applicability of Fog Computing in a real Industrial Internet of Things (IIoT) environment, providing an architecture that enables disruption-tolerant communication over challenged networks and evaluating the achieved performance on an open-source prototype implementation

A Service-Defined Approach for Orchestration of Heterogeneous Applications in Cloud/Edge Platforms

Edge Computing is moving resources toward the network borders, thus enabling the deployment of a pool of new applications that benefit from the new distributed infrastructure. However, due to the heterogeneity of such applications, specific orchestration strategies need to be adopted for each deployment request. Each application can potentially require different optimization criteria and may prefer particular reactions upon the occurrence of the same event. This paper presents a Service- Defined approach for orchestrating cloud/edge services in a distributed fashion, where each application can define its own orchestration strategy by means of declarative statements, which are parsed into a Service-Defined Orchestrator (SDO). Moreover, to coordinate the coexistence of a variety of SDOs on the same infrastructure while preserving the resource assignment optimality, we present DRAGON, a Distributed Resource AssiGnment and OrchestratioN algorithm that seeks optimal partitioning of shared resources between different actors. We evaluate the advantages of our novel Service-Defined orchestration approach over some representative edge use cases, as well as measure convergence and performance of DRAGON on a prototype implementation, assessing the benefits compared to conventional orchestration approaches

A Distributed Orchestration Algorithm for Edge Computing Resources with Guarantees

Edge Computing brings flexibility and scalability of virtualization technologies at the edge of the network, enabling service providers to deploy new applications over a richer network infrastructure. However, the coexistence of such variety of applications on the same infrastructure exacerbates the already challenging problem of coordinating resource allocation while preserving the resource assignment optimality. In fact, (i) each application can potentially require different optimization criteria due to their heterogeneous requirements, and (ii) we may not count on a centralized orchestrator due to the highly dynamic nature of edge networks. To solve this problem, we present DRAGON, a Distributed Resource AssiGnment and OrchestratioN algorithm that seeks optimal partitioning of shared resources between different applications running over a common edge infrastructure.We designed DRAGON to guarantee both a bound on convergence time and an optimal (1-1/e)-approximation with respect to the Pareto optimal resource assignment. We evaluate convergence and performance of DRAGON on a prototype implementation, assessing the benefits compared to traditional orchestration approaches

A Disaggregated MEC Architecture Enabling Open Services and Novel Business Models

Network and Service Providers are exploring different exploitation strategies for the Multi-access Edge Computing (MEC), mainly motivated by the opportunities for saving costs and generating new revenues (e.g., through new business models). On the other hand, the overall standardization picture is still very fragmented, delaying or even jeopardizing the real exploitation of MEC; furthermore, current standardization efforts are mainly envisioning a traditional monolithic architecture, with many technological partners but a single administrative domain. This paper argues that a clear separation of IaaS, PaaS and SaaS levels for MEC, together with standardized interfaces, will help accelerating the development of new business roles (e.g., IaaS, PaaS and SaaS providers) and models, possibly replacing the current competition-oriented practices in the telco domain with new forms of cooperation, which are already starting to appear in the IT sector. In this direction, this paper proposes a disaggregated MEC architecture and presents two use cases that show how different categories of resources and services could be provided by infrastructure, platform and software providers in an evolutionary scenario towards 5G

A Model-Based Abstraction Layer for Heterogeneous SDN Applications

Modern controllers for software-defined networks (SDN) enable the execution of arbitrary SDN applications (eg, Network Address Translation (NAT), traffic monitors) that may be exploited by an overarching set of services (eg, application-layer orchestrators) to build even richer services. To this purpose, the above overarching services require a mechanism that allows reading the run-time state and writing the configuration of arbitrary SDN applications, possibly through a uniform API. Unfortunately, most SDN applications are not designed/implemented by taking into account the possibility to be used as part of higher level service workflows (eg, a complex intrusion prevention system that leverages multiple elementary services as individual components), hence they may not provide an adequate interface that would allow overarching services to exploit their features. This paper addresses this problem by proposing an approach to represent the run-time state of arbitrary applications, where data are exported according to high-level model-based structures. Furthermore, the mapping from the high-level data model to the actual data representation within the SDN application is enabled by a suite of algorithms that are generic enough to operate independently of the actual source code of the application, thus avoiding undesired and invasive modifications to existing applications. The paper also presents a software framework and a prototype implementing the proposed approach, characterizes the resulting performance, and discusses pros and cons of the proposed approach

Regularized Bottleneck with Early Labeling

International audienceSmall IoT devices, such as drones and lightweight battery-powered robots, are emerging as a major platform for the deployment of AI/ML capabilities. Autonomous and semiautonomous device operation relies on the systematic use of deep neural network models for solving complex tasks, such as image classification. The challenging restrictions of these devices in terms of computing capabilities, network connectivity, and power consumption are the main limits to the accuracy of latencysensitive inferences. This paper presents ReBEL, a split computing architecture enabling the dynamic remote offload of partial computations or, in alternative, a local approximate labeling based on a jointly-trained classifier. Our approach combines elements of head network distillation, early exit classification, and bottleneck injection with the goal of reducing the average endto-end latency of AI/ML inference on constrained IoT devices

A unifying orchestration operating platform for 5G

5G will revolutionize the way ICT and Telecommunications infrastructures work. Indeed, businesses can greatly benefit from innovation introduced by 5G and exploit the new deep integration between ICT and networking capabilities to generate new value-added services. Although a plethora of solutions for virtual resources and infrastructures management and orchestration already exists (e.g., OpenDaylight, ONOS, OpenStack, Apache Mesos, Open Source MANO, Docker Swarm, LXD/LXC, etc.), they are still not properly integrated to match the 5G requirements. In this paper, we present the 5G Operating Platform (5G-OP) which has been conceived to fill in this gap and integrate management, control and orchestration of computing, storage and networking resources down to the end-user devices and terminals (e.g., smart phone, machines, robots, drones, autonomous vehicles, etc.). The 5G-OP is an overarching framework capable to provide agnostic interfaces and a universal set of abstractions in order to implement seamless 5G infrastructure control and orchestration. The functional structure of the 5G-OP, including the horizontal and vertical interworking of functions in it, has been designed to allow Network Operators and Service Providers to exploit diverse roles and business strategies. Moreover, the functional decoupling of the 5G-OP from the underneath management, control and orchestration solutions allows pursuing faster innovation cycles, being ready for the emergence of new service models

Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review

Purpose: The extent of resection (EOR) is an independent prognostic factor for overall survival (OS) in adult patients with Glioma Grade 4 (GG4). The aim of the neuro-oncology section of the Italian Society of Neurosurgery (SINch®) was to provide a general overview of the current trends and technical tools to reach this goal. Methods: A systematic review was performed. The results were divided and ordered, by an expert team of surgeons, to assess the Class of Evidence (CE) and Strength of Recommendation (SR) of perioperative drugs management, imaging, surgery, intraoperative imaging, estimation of EOR, surgery at tumor progression and surgery in elderly patients. Results: A total of 352 studies were identified, including 299 retrospective studies and 53 reviews/meta-analysis. The use of Dexamethasone and the avoidance of prophylaxis with anti-seizure medications reached a CE I and SR A. A preoperative imaging standard protocol was defined with CE II and SR B and usefulness of an early postoperative MRI, with CE II and SR B. The EOR was defined the strongest independent risk factor for both OS and tumor recurrence with CE II and SR B. For intraoperative imaging only the use of 5-ALA reached a CE II and SR B. The estimation of EOR was established to be fundamental in planning postoperative adjuvant treatments with CE II and SR B and the stereotactic image-guided brain biopsy to be the procedure of choice when an extensive surgical resection is not feasible (CE II and SR B). Conclusions: A growing number of evidences evidence support the role of maximal safe resection as primary OS predictor in GG4 patients. The ongoing development of intraoperative techniques for a precise real-time identification of peritumoral functional pathways enables surgeons to maximize EOR minimizing the post-operative morbidity

Protein Kinase C Delta (PKCδ) Affects Proliferation of Insulin-Secreting Cells by Promoting Nuclear Extrusion of the Cell Cycle Inhibitor p21Cip1/WAF1

BACKGROUND:High fat diet-induced hyperglycemia and palmitate-stimulated apoptosis was prevented by specific inhibition of protein kinase C delta (PKCδ) in β-cells. To understand the role of PKCδ in more detail the impact of changes in PKCδ activity on proliferation and survival of insulin-secreting cells was analyzed under stress-free conditions. METHODOLOGY AND PRINCIPAL FINDINGS:Using genetic and pharmacological approaches, the effect of reduced and increased PKCδ activity on proliferation, apoptosis and cell cycle regulation of insulin secreting cells was examined. Proteins were analyzed by Western blotting and by confocal laser scanning microscopy. Increased expression of wild type PKCδ (PKCδWT) significantly stimulated proliferation of INS-1E cells with concomitant reduced expression and cytosolic retraction of the cell cycle inhibitor p21(Cip1/WAF1). This nuclear extrusion was mediated by PKCδ-dependent phosphorylation of p21(Cip1/WAF1) at Ser146. In kinase dead PKCδ (PKCδKN) overexpressing cells and after inhibition of endogenous PKCδ activity by rottlerin or RNA interference phosphorylation of p21(Cip1/WAF1) was reduced, which favored its nuclear accumulation and apoptotic cell death of INS-1E cells. Human and mouse islet cells express p21(Cip1/WAF1) with strong nuclear accumulation, while in islet cells of PKCδWT transgenic mice the inhibitor resides cytosolic. CONCLUSIONS AND SIGNIFICANCE:These observations disclose PKCδ as negative regulator of p21(Cip1/WAF1), which facilitates proliferation of insulin secreting cells under stress-free conditions and suggest that additional stress-induced changes push PKCδ into its known pro-apoptotic role