Biblio

The Cloud Native paradigm has quickly emerged as a new trend in Web Services architectures. Applications are now developed as a network of microservices and functions that can be quickly re-deployed anywhere, decoupled from their state. In this scenario, workloads are usually packaged as container images that can be quickly provisioned anywhere in a provider web service. To enforce security, traditional Docker container runtime mechanisms are now being enhanced by stronger isolation techniques such as lightweight hardware level virtualization. Such sandboxing inserts a strong boundary - the guest space - and therefore security containers do not share filesystem semantics with the host Operating System. However, the existing container storage drivers are designed and optimized to run directly on the host. In this paper we bridge the gap between traditional containers and virtualized containers. We present Deverlay, a container storage driver that prepares a block-based container root filesystem view, targeting lightweight Virtual Machines and keeping host native execution compatibility. We show that, in contrast to other block-based drivers, Deverlay can boot 80 micro VM containers in less than 4s by efficiently sharing host cache buffers among containers and reducing I/O disk access by 97.51 %.

Nowadays, microservice architecture is known as a successful and promising architecture for smart city applications. Applying microservices in the designing and implementation of systems has many advantages such as autonomy, loosely coupled, composability, scalability, fault tolerance. However, the complexity of calling between microservices leads to problems in security, accessibility, and data management in the execution of systems. In order to address these challenges, in recent years, various researchers and developers have focused on the use of microservice patterns in the implementation of microservice-based systems. Microservice patterns are the result of developers’ successful experiences in addressing common challenges in microservicebased systems. However, hitherto no guideline has been provided for an in-depth understanding of microservice patterns and how to apply them to real systems. The purpose of this paper is to investigate in detail the most widely used and important microservice patterns in order to analyze the function of each pattern, extract the behavioral signatures and construct a service dependency graph for them so that researchers and enthusiasts use the provided guideline to create a microservice-based system equipped with design patterns. To construct the proposed guideline, five real open source projects have been carefully investigated and analyzed and the results obtained have been used in the process of making the guideline.

Nowadays Osmotic Computing is emerging as one of the paradigms used to guarantee the Cloud Continuum, and this popularity is strictly related to the capacity to embrace inside it some hot topics like containers, microservices, orchestration and Function as a Service (FaaS). The Osmotic principle is quite simple, it aims to create a federated heterogeneous infrastructure, where an application's components can smoothly move following a concentration rule. In this work, we aim to solve two big constraints of Osmotic Computing related to the incapacity to manage dynamic access rules for accessing the applications inside the Osmotic Infrastructure and the incapacity to keep alive and secure the access to these applications even in presence of network disconnections. For overcoming these limits we designed and implemented a new Osmotic component, that acts as an eventually consistent distributed peer to peer access management system. This new component is used to keep a local Identity and Access Manager (IAM) that permits at any time to access the resource available in an Osmotic node and to update the access rules that allow or deny access to hosted applications. This component has been already integrated inside a Kubernetes based Osmotic Infrastructure and we presented two typical use cases where it can be exploited.

Distributed computation and AI processing at the edge has been identified as an efficient solution to deliver real-time IoT services and applications compared to cloud-based paradigms. These solutions are expected to support the delay-sensitive IoT applications, autonomic decision making, and smart service creation at the edge in comparison to traditional IoT solutions. However, existing solutions have limitations concerning distributed and simultaneous resource management for AI computation and data processing at the edge; concurrent and real-time application execution; and platform-independent deployment. Hence, first, we propose a novel three-layer architecture that facilitates the above service requirements. Then we have developed a novel platform and relevant modules with integrated AI processing and edge computer paradigms considering issues related to scalability, heterogeneity, security, and interoperability of IoT services. Further, each component is designed to handle the control signals, data flows, microservice orchestration, and resource composition to match with the IoT application requirements. Finally, the effectiveness of the proposed platform is tested and have been verified.

The service mesh is a dedicated infrastructure layer in a microservice architecture. It manages service-to-service communication within an application between decoupled or loosely coupled microservices (called services) without modifying their implementations. The service mesh includes APIs for security, traffic and policy management, and observability features. These features are enabled using a pre-defined configuration, which can be changed at runtime with human intervention. However, it has no autonomy to self-manage changes to the microservice application’s operational environment. A better configuration is one that can be customized according to environmental conditions during execution to protect the application from potential threats. This customization requires enabling self-protection mechanisms within the service mesh that evaluate the risk of environmental condition changes and enable appropriate configurations to defend the application from impending threats. In this paper, we design an assessment component into a service mesh that includes a security assurance case to define the threat model and dynamically assess the application given environment changes. We experiment with a demo application, Bookinfo, using an open-source service mesh platform, Istio, to enable self-protection. We consider certain parameters extracted from the service request as environmental conditions. We evaluate those parameters against the threat model and determine the risk of violating a security requirement for controlled and authorized information flow.

As Edge deployments move closer towards the end devices, low latency communication among Edge aware applications is one of the key tenants of Edge service offerings. In order to simplify application development, service mesh architectures have emerged as the evolutionary architectural paradigms for taking care of bulk of application communication logic such as health checks, circuit breaking, secure communication, resiliency (among others), thereby decoupling application logic with communication infrastructure. The latency to throughput ratio needs to be measurable for high performant deployments at the Edge. Providing benchmark data for various edge deployments with Bare Metal and virtual machine-based scenarios, this paper digs into architectural complexities of deploying service mesh at edge environment, performance impact across north-south and east-west communications in and out of a service mesh leveraging popular open-source service mesh Istio/Envoy using a simple on-prem Kubernetes cluster. The performance results shared indicate performance impact of Kubernetes network stack with Envoy data plane. Microarchitecture analyses indicate bottlenecks in Linux based stacks from a CPU micro-architecture perspective and quantify the high impact of Linux's Iptables rule matching at scale. We conclude with the challenges in multiple areas of profiling and benchmarking requirement and a call to action for deploying a service mesh, in latency sensitive environments at Edge.

Nowadays, the increasing complexity of digital applications for social and business activities has required more and more advanced mechanisms to prove the identity of subjects like those based on the Two-Factor Authentication (2FA). Such an approach improves the typical authentication paradigm but it has still some weaknesses. Specifically, it has to deal with the disadvantages of a centralized architecture causing several security threats like denial of service (DoS) and man-in-the-middle (MITM). In fact, an attacker who succeeds in violating the central authentication server could be able to impersonate an authorized user or block the whole service. This work advances the state of art of 2FA solutions by proposing a decentralized Microservices and Blockchain Based One Time Password (MBB-OTP) protocol for security-enhanced authentication able to mitigate the aforementioned threats and to fit different application scenarios. Experiments prove the goodness of our MBB-OTP protocol considering both private and public Blockchain configurations.

In the economics environment, Job Matching is always a challenge involving the evolution of knowledge and skills. A good matching of skills and jobs can stimulate the growth of economics. Recommender System (RecSys), as one kind of Job Matching, can help the candidates predict the future job relevant to their preferences. However, RecSys still has the problem of cold start and data sparsity. The content-based filtering in RecSys needs the adaptive data for the specific staffing tasks of Bidirectional Encoder Representations from Transformers (BERT). In this paper, we propose a job RecSys based on skills and locations using a domain-specific Knowledge Graph (KG). This system has three parts: a pipeline of Named Entity Recognition (NER) and Relation Extraction (RE) using BERT; a standardization system for pre-processing, semantic enrichment and semantic similarity measurement; a domain-specific Knowledge Graph (KG). Two different relations in the KG are computed by cosine similarity and Term Frequency-Inverse Document Frequency (TF-IDF) respectively. The raw data used in the staffing RecSys include 3000 descriptions of job offers from Indeed, 126 Curriculum Vitae (CV) in English from Kaggle and 106 CV in French from Linx of Capgemini Engineering. The staffing RecSys is integrated under an architecture of Microservices. The autonomy and effectiveness of the staffing RecSys are verified through the experiment using Discounted Cumulative Gain (DCG). Finally, we propose several potential research directions for this research.