Single Board Computer Clusters Research Articles

Optimization of Distributed RSA Encryption and Decription Processing Using Process Scheduling Method In Single Board Computer Cluster Architecture (SBC)

Data security is still a major issue regarding the need for data confidentiality. The encryption process using the RSA algorithm is still the most popular method used in securing data because the complexity of the mathematical equations used in this algorithm makes it difficult to hack. However, the complexity of the RSA algorithm is still a major problem that hinders its application in a more complex application. Optimization is needed in the processing of this RSA algorithm, one of which is by running it on a distributed system. In this paper, we propose an approach with a FIFO process scheduling algorithm that runs on a single board computer cluster. The test results show that the allocation of resources in a system that uses a FIFO process scheduling algorithm is more efficient and shows a decrease in the overall processing time of RSA encryption.

Optimalisasi Pemrosesan Distributed RSA Algorithm dengan Metode Penjadwalan Proses Pada Single Board Computer Cluster

Data security is still a major issue regarding the need for data confidentiality. The encryption process using the RSA algorithm is still the most popular method used in securing data because the complexity of the mathematical equations used in this algorithm makes it difficult to hack. However, the complexity of the RSA algorithm is still a major problem that hinders its application in a more complex application. Optimization is needed in the processing of this RSA algorithm, one of which is by running it on a distributed system. In this paper, we propose an approach with a FIFO process scheduling algorithm that runs on a single board computer cluster. The test results show that the allocation of resources in a system that uses a FIFO process scheduling algorithm is more efficient and shows a decrease in the overall processing time of RSA encryption

Clustered Single-Board Devices with Docker Container Big Stream Processing燗rchitecture

The expanding amounts of information created by Internet of Things (IoT) devices places a strain on cloud computing, which is often used for data analysis and storage. This paper investigates a different approach based on edge cloud applications, which involves data filtering and processing before being delivered to a backup cloud environment. This Paper suggest designing and implementing a low cost, low power cluster of Single Board Computers (SBC) for this purpose, reducing the amount of data that must be transmitted elsewhere, using Big Data ideas and technology. An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker. To obtain system data and analyze the setup’s performance a Prometheus-based stack monitoring and alerting solution in the cloud based market is employed. This Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease, allowing the suggested solution to be used in industry. An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture, taking into consideration the suggested solution’s resource use in respect to device restrictions.

Cluster of Single-Board Computers at the Edge for Smart Grids Applications

Increases in power demand and consumption are very noticeable. This increase presents a number of challenges to the traditional grid systems. Thus, there is the need to come up with a new solution that copes with the stringent demand on energy and provides better power quality, which gives a better experience to the end users. This is how the concept of smart grids (SG) came to light. SGs have been introduced to better monitor and control the power produced and consumed. In addition to this, SGs help with reducing the electricity bill through the integration of renewable energy sources. The underlying smartness of the SGs resides in the flow of information in addition to the flow of energy. Information/data flowing implies the use of smart sensors and smart meters that sense and send data about the power produced and consumed, and the data about the environment where they are deployed. This makes SGs a direct application of IoT. In this paper, we are implementing an edge platform that is based on single-board computers (SBCs) to process data stemming from SG. The use of SBCs is driven by the energy efficiency and cost effectiveness concepts that the SG is trying to apply. The platform in question is tested against a distributed job that averages random numbers using Hadoop’s MapReduce programming model. The SBC that we are using in this implementation is the NVIDIA Jetson Developer Kit. The results of this work show that a cluster of SBCs is low-cost, easy to maintain, and simple to deploy, which makes it a great candidate for providing edge computing. Although it revealed a performance that beat the one of the remote cloud servers, it could not outperform the single-computer edge platform.

An Energy-Friendly Scheduler for Edge Computing Systems.

The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster.

Big Data Processing on Single Board Computer Clusters: Exploring Challenges and Possibilities

For more than a decade, “big data” has been an industry and academia buzz phrase. Over this time, many companies adopted Apache Hadoop and Spark frameworks for their massive data storage and analysis efforts, using powerful, energy-hungry, general-purpose server as their big data processing platforms. But not all industry or academic fields want, or even need, such large systems. Moreover, capital costs aside, power consumption has also become a primary data center concern. Consequently, lower-cost, lower-power microservers have emerged as viable alternatives in many settings. Now, the latest generation Raspberry Pi (RPi), model 4B, exhibits significant computational performance improvements over its predecessors, and is presently considered a sufficiently powerful single board computer (SBC) to run many mainstream operating systems and accommodate heavy workloads. This paper reexamines SBC cluster big data processing possibilities by integrating the most powerful (presently) RPi model–the RPi 4B with 4 Gigabytes (GB) main memory. We examine external storage’s performance impact on such an SBC cluster’s big data processing performance by employing three different external storage solutions with measurably distinct performance characteristics. Moreover, we discuss challenges we encountered and identify further SBC cluster performance optimizations. We perform several representative big data application benchmarks and measure various key performance metrics such as execution time, power consumption, throughput, performance-per-dollars, etc. Our extensive experiments and comprehensive studies conclude this current, fourth-generation RPi has evolved to become the first generation to effectively run massive (i.e., more than 100GB) workloads in big data processing applications.

Performance analysis of single board computer clusters

The past few years have seen significant developments in Single Board Computer (SBC) hardware capabilities. These advances in SBCs translate directly into improvements in SBC clusters. In 2018 an individual SBC has more than four times the performance of a 64-node SBC cluster from 2013. This increase in performance has been accompanied by increases in energy efficiency (GFLOPS/W) and value for money (GFLOPS/$). We present systematic analysis of these metrics for three different SBC clusters composed of Raspberry Pi 3 Model B, Raspberry Pi 3 Model B+ and Odroid C2 nodes respectively. A 16-node SBC cluster can achieve up to 60GFLOPS, running at 80W. We believe that these improvements open new computational opportunities, whether this derives from a decrease in the physical volume required to provide a fixed amount of computation power for a portable cluster; or the amount of compute power that can be installed given a fixed budget in expendable compute scenarios. We also present a new SBC cluster construction form factor named Pi Stack; this has been designed to support edge compute applications rather than the educational use-cases favoured by previous methods. The improvements in SBC cluster performance and construction techniques mean that these SBC clusters are realising their potential as valuable developmental edge compute devices rather than just educational curiosities.

On Energy Efficiency and Performance Evaluation of Single Board Computer Based Clusters: A Hadoop Case Study

Energy efficiency in a data center is a challenge and has garnered researchers interest. In this study, we addressed the energy efficiency issue of a small scale data center by utilizing Single Board Computer (SBC)-based clusters. A compact layout was designed to build two clusters using 20 nodes each. Extensive testing was carried out to analyze the performance of these clusters using popular performance benchmarks for task execution time, memory/storage utilization, network throughput and energy consumption. Further, we investigated the cost of operating SBC-based clusters by correlating energy utilization for the execution time of various benchmarks using workloads of different sizes. Results show that, although the low-cost benefit of a cluster built with ARM-based SBCs is desirable, these clusters yield low comparable performance and energy efficiency due to limited onboard capabilities. It is possible to tweak Hadoop configuration parameters for an ARM-based SBC cluster to efficiently utilize resources. We present a discussion on the effectiveness of the SBC-based clusters as a testbed for inexpensive and green cloud computing research.

Commodity single board computer clusters and their applications

Current commodity Single Board Computers (SBCs) are sufficiently powerful to run mainstream operating systems and workloads. Many of these boards may be linked together, to create small, low-cost clusters that replicate some features of large data center clusters. The Raspberry Pi Foundation produces a series of SBCs with a price/performance ratio that makes SBC clusters viable, perhaps even expendable. These clusters are an enabler for Edge/Fog Compute, where processing is pushed out towards data sources, reducing bandwidth requirements and decentralizing the architecture. In this paper we investigate use cases driving the growth of SBC clusters, we examine the trends in future hardware developments, and discuss the potential of SBC clusters as a disruptive technology. Compared to traditional clusters, SBC clusters have a reduced footprint, are low-cost, and have low power requirements. This enables different models of deployment—particularly outside traditional data center environments. We discuss the applicability of existing software and management infrastructure to support exotic deployment scenarios and anticipate the next generation of SBC.We conclude that the SBC cluster is a new and distinct computational deployment paradigm, which is applicable to a wider range of scenarios than current clusters. It facilitates Internet of Things and Smart City systems and is potentially a game changer in pushing application logic out towards the network edge.