Balanced Leader Distribution Algorithm in Kubernetes Clusters

Container-based virtualization is becoming a de facto way to build and deploy applications because of its simplicity and convenience. Kubernetes is a well-known open-source project that provides an orchestration platform for containerized applications. An application in Kubernetes can contain multiple replicas to achieve high scalability and availability. Stateless applications have no requirement for persistent storage; however, stateful applications require persistent storage for each replica. Therefore, stateful applications usually require a strong consistency of data among replicas. To achieve this, the application often relies on a leader, which is responsible for maintaining consistency and coordinating tasks among replicas. This leads to a problem that the leader often has heavy loads due to its inherent design. In a Kubernetes cluster, having the leaders of multiple applications concentrated in a specific node may become a bottleneck within the system. In this paper, we propose a leader election algorithm that overcomes the bottleneck problem by evenly distributing the leaders throughout nodes in the cluster. We also conduct experiments to prove the correctness and effectiveness of our leader election algorithm compared with a default algorithm in Kubernetes.

Design of a Resilient, High-Throughput, Persistent Storage System for the ATLAS Phase-II DAQ System

The ATLAS experiment will undergo a major upgrade to take advantage of the new conditions provided by the upgraded High-Luminosity LHC. The Trigger and Data Acquisition system (TDAQ) will record data at unprecedented rates: the detectors will be read out at 1 MHz generating around 5 TB/s of data. The Dataflow system (DF), component of TDAQ, introduces a novel design: readout data are buffered on persistent storage while the event filtering system analyses them to select 10000 events per second for a total recorded throughput of around 60 GB/s. This approach allows for decoupling the detector activity from the event selection process. New challenges then arise for DF: design and implement a distributed, reliable, persistent storage system supporting several TB/s of aggregated throughput while providing tens of PB of capacity. In this paper we first describe some of the challenges that DF is facing: data safety with persistent storage limitations, indexing of data at high-granularity in a highly-distributed system, and high-performance management of storage capacity. Then the ongoing R&D to address each of the them is presented and the performance achieved with a working prototype is shown.

Browser Forensic Investigations of WhatsApp Web Utilizing IndexedDB Persistent Storage

Digital Evidence is becoming an indispensable factor in most legal cases. However, technological advancements that lead to artifact complexity, are forcing investigators to create sophisticated connections between the findings and the suspects for admissibility of evidence in court. This paper scrutinizes whether IndexedDB, an emerging browser technology, can be a source of digital evidence to provide additional and correlating support for traditional investigation methods. It particularly focuses on the artifacts of the worldwide popular application, WhatsApp. A single case pretest–posttest quasi experiment is applied with WhatsApp Messenger and Web Application to populate and investigate artifacts in IndexedDB storage of Google Chrome. The findings are characterized and presented with their potential to be utilized in forensic investigation verifications. The storage locations of the artifacts are laid out and operations of extraction, conversion and presentation are systematized. Additionally, a proof of concept tool is developed for demonstration. The results show that WhatsApp Web IndexedDB storage can be employed for time frame analysis, demonstrating its value in evidence verification.

An implementation architecture for crowd network simulations

Purpose Crowd network systems have been deemed as a promising mode of modern service industry and future economic society, and taking crowd network as the research object and exploring its operation mechanism and laws is of great significance for realizing the effective governance of the government and the rapid development of economy, avoiding social chaos and mutation. Because crowd network is a large-scale, dynamic and diversified online deep interconnection, its most results cannot be observed in real world, and it cannot be carried out in accordance with traditional way, simulation is of great importance to put forward related research. To solve above problems, this paper aims to propose a simulation architecture based on the characteristics of crowd network and to verify the feasibility of this architecture through a simulation example. Design/methodology/approach This paper adopts a data-driven architecture by deeply analyzing existing large-scale simulation architectures and proposes a novel reflective memory-based architecture for crowd network simulations. In this paper, the architecture is analyzed from three aspects: implementation framework, functional architecture and implementation architecture. The proposed architecture adopts a general structure to decouple related work in a harmonious way and gets support for reflection storage by connecting to different devices via reflection memory card. Several toolkits for system implementation are designed and connected by data-driven files (DDF), and these XML files constitute a persistent storage layer. To improve the credibility of simulations, VV&A (verification, validation and accreditation) is introduced into the architecture to verify the accuracy of simulation system executions. Findings Implementation framework introduces the scenes, methods and toolkits involved in the whole simulation architecture construction process. Functional architecture adopts a general structure to decouple related work in a harmonious way. In the implementation architecture, several toolkits for system implementation are designed, which are connected by DDF, and these XML files constitute a persistent storage layer. Crowd network simulations obtain the support of reflective memory by connecting the reflective memory cards on different devices and connect the interfaces of relevant simulation software to complete the corresponding function call. Meanwhile, to improve the credibility of simulations, VV&A is introduced into the architecture to verify the accuracy of simulation system executions. Originality/value This paper proposes a novel reflective memory-based architecture for crowd network simulations. Reflective memory is adopted as share memory within given simulation execution in this architecture; communication efficiency and capability have greatly improved by this share memory-based architecture. This paper adopts a data-driven architecture; the architecture mainly relies on XML files to drive the entire simulation process, and XML files have strong readability and do not need special software to read.