Incast mitigation in a data center storage cluster through a dynamic fair-share buffer policy

Incast is a phenomenon when multiple devices interact with only one device at a given time. Multiple storage senders overflow either the switch buffer or the single-receiver memory. This pattern causes all concurrent-senders to stop and wait for buffer/memory availability, and leads to a packet loss and retransmission—resulting in a huge latency. We present a software-defined technique tackling the many-to-one communication pattern—Incast—in a data center storage cluster. Our proposed method decouples the default TCP windowing mechanism from all storage servers, and delegates it to the software-defined storage controller. The proposed method removes the TCP saw-tooth behavior, provides a global flow awareness, and implements the dynamic fair-share buffer policy for end-to-end I/O path. It considers all I/O stages (applications, device drivers, NICs, switches/routers, file systems, I/O schedulers, main memory, and physical disks) while achieving the maximum I/O throughput. The policy, which is part of the proposed method, allocates fair-share bandwidth utilization for all storage servers. Priority queues are incorporated to handle the most important data flows. In addition, the proposed method provides better manageability and maintainability compared with traditional storage networks, where data plane and control plane reside in the same device

Incremental composition process for the construction of component-based management

Cyber-physical systems (CPS) are composed of software and hardware components. Many such systems (e.g., IoT based systems) are created by composing existing systems together. Some of these systems are of critical nature, e.g., emergency or disaster management systems. In general, component-based development (CBD) is a useful approach for constructing systems by composing pre-built and tested components. However, for critical systems, a development method must provide ways to verify the partial system at different stages of the construction process. In this paper, for system architectures, we propose two styles: rigid architecture and flexible architecture. A system architecture composed of independent components by coordinating exogenous connectors is in flexible architecture style category. For CBD of critical systems, we select EX-MAN from flexible architecture style category. Moreover, we define incremental composition mechanism for this model to construct critical systems from a set of system requirements. Incremental composition is defined to offer preservation of system behaviour and correctness of partial architecture at each incremental step. To evaluate our proposed approach, a case study of weather monitoring system (part of a disaster management) system was built using our EX-MAN tool

Detection and prediction of traffic accidents using deep learning techniques

Road transportation is a statutory organ in a modern society; however it costs the global economy over a million lives and billions of dollars each year due to increase in road accidents. Researchers make use of machine learning to detect and predict road accidents by incorporating the social media which has an enormous corpus of geo-tagged data. Twitter, for example, has become an increasingly vital source of information in many aspects of smart societies. Twitter data mining for detection and prediction of road accidents is one such topic with several applications and immense promise, although there exist challenges related to huge data management. In recent years, various approaches to the issue have been offered, but the techniques and conclusions are still in their infancy. This paper proposes a deep learning accident prediction model that combines information extracted from tweet messages with extended features like sentiment analysis, emotions, weather, geo-coded locations, and time information. The results obtained show that the accuracy is increased by 8% for accident detection, making test accuracy reach 94%. In comparison with the existing state-of-the-art approaches, the proposed algorithm outperformed by achieving an increase in the accuracy by 2% and 3% respectively making the accuracy reach 97.5% and 90%. Our solution also resolved high-performance computing limitations induced by detector-based accident detection which involved huge data computation. The results achieved has further strengthened confidence that using advanced features aid in the better detection and prediction of traffic accidents