Runtime application observability is crucial not only for system provenance but also for the orchestration of deployed microservices in dynamic sandboxed distributed computing environments. Also, log extraction and aggregation in highly distributed and sandboxed environments pose significant challenges, especially when preserving the causal order of the events triggered by different asynchronous microservices running over multiple hosts. However, ensuring causally consistent logging of application events is challenging, although it is vital for continuously tracing and profiling the underlying platform. This paper proposesXPLOG, a scalable, pluggable, easily deployable, and dynamic runtime observability framework for distributed sandboxed computing platforms that leverages the capability of extended Berkeley Packet Filters (eBPF) to intercept system-level events within the host while capturing and amalgamating relevant application and system logs to produce globally causally-consistent log streams. Through qualitative and quantitative analysis, we observe thatXPLOGsignificantly improves log richness with minimum system overhead while preserving the causality of log-generating events across multiple microservices.

The shift from monolithic services to microservices brings modularity and elasticity, but detecting faults and anomalies is challenging due to diverse data and evolving technology. The heterogeneous nature of this data complicates the analysis of anomaly signatures across various dimensions. Given the continuous evolution of this technology, exhaustively learning from historical data poses difficulties. To address these challenges, we present URCD, a solution designed to identify and localize faults or anomalies at the application and service level. Remarkably, URCD achieves this without explicit training on faulty data. Our approach integrates heterogeneous microservice data into a bidirectional weighted graph, leveraging a sophisticated Hyper Graph Attention Network (HGAN) model to process heterogeneous data logs generated by microservices. Our evaluation shows the optimal performance of URCD while detecting root cause of anomalies.

Provenance tracking has been widely used in the recent literature to debug system vulnerabilities and find the root causes behind faults, errors, or crashes over a running system. However, the existing approaches primarily developed graph-based models for provenance tracking over monolithic applications running directly over the operating system kernel. In contrast, the modern DevOps-based service-oriented architecture relies on distributed platforms, like serverless computing that uses container-based sandboxing over the kernel. Provenance tracking over such a distributed micro-service architecture is challenging, as the application and system logs are generated asynchronously and follow heterogeneous nomenclature and logging formats. This paper develops a novel approach to combining system and micro-services logs together to generate a Universal Provenance Graph (UPG) that can be used for provenance tracking over serverless architecture. We develop a Loadable Kernel Module (LKM) for runtime unit identification over the logs by intercepting the system calls with the help from the control flow graphs over the static application binaries. Finally, we design a regular expression-based log optimization method for reverse query parsing over the generated UPG. A thorough evaluation of the proposed UPG model with different benchmarked serverless applications shows the system’s effectiveness.

Low maintenance of the roads is one of the extensive cause of increasing road accidents and vehicle breakage. Mostly roads contain potholes, wreckage or detritus which is best to be bypassed however if not sometimes lead to severe road causalities. Many car or bus accident happens over a wrecked bridge, as it slips and overturns. To avoid this kind of mishap and improve the safety of the road we have proposed a machine learning based model, MagTrack, to detect these road conditions and inform apriori to the responsible authority for fast reparation as well as other passers-by to drive carefully or take an alternative route. We have collected various road surface conditions such as smooth roads, uneven roads, potholes, speed breakers, and rumble strips data using magnetometer and accelerometer sensor embedded in our Smartphone and analyzed using various classification algorithms like Random Forest (RF), Random Tree (RT) and Support Vector Machine (SVM). The classification has done after performing the feature selection using GreedyStepwise, Ranker, and BestFirst Algorithms considering minimum, maximum, median and standard deviation as statistical features. 92% of accuracy to detect the road surface condition has been achieved by MagTrack.

Blockchain is the backbone technology behind crypto-currency and Bitcoin. By concept, Blockchain is a distributed database where transactions are recorded in an incorruptible and non-modifiable manner. Currently, Blockchain technology is envisioned as a powerful framework for open-access networks, decentralized information processing and sharing systems, etc. This review is motivated due to the lack of an extensive survey on the existing decentralized consensus mechanisms in Blockchain technology. So in this paper, an in-depth review of the distributed consensus mechanisms has been presented. In addition to this, a comparative analysis of the consensus protocols based on the type of Blockchain is also demonstrated.