Implementing Virtualization on Single-Board Computers: A Case Study on Edge Computing
by
, , and
Georgios Lambropoulos
1,*,†,
Sarandis Mitropoulos
2,†
,
Christos Douligeris
1,†
and
Leandros Maglaras
3,*,† 1
Department of Informatics, University of Piraeus, 18534 Piraeus, Greece
2
Regional Development Department, Ionian University, 31100 Lefkada, Greece
3
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Computers 2024, 13(2), 54; https://doi.org/10.3390/computers13020054
Submission received: 28 December 2023
/
Revised: 10 February 2024
/
Accepted: 15 February 2024
/
Published: 18 February 2024
(This article belongs to the Topic Innovation, Communication and Engineering)
Abstract
:The widespread adoption of cloud computing has resulted in centralized datacenter structures; however, there is a requirement for smaller-scale distributed infrastructures to meet the demands for speed, responsiveness, and security for critical applications. Single-Board Computers (SBCs) present numerous advantages such as low power consumption, low cost, minimal heat emission, and high processing power, making them suitable for applications such as the Internet of Things (IoT), experimentation, and other advanced projects. This paper investigates the possibility of adopting virtualization technology on Single-Board Computers (SBCs) for the implementation of reliable and cost-efficient edge-computing environments.The results of this study are based on experimental implementations and testing conducted in the course of a case study performed on the edge infrastructure of a financial organization, where workload migration was achieved from a traditional to an SBC-based edge infrastructure. The performance of the two infrastructures was studied and compared during this process, providing important insights into the power efficiency gains, resource utilization, and overall suitability for the organization’s operational needs.
1. Introduction
The rapid adoption of cloud computing by most modern corporations leads to centralized and consolidated datacenter structures. Nevertheless, in both public and private implementations, cloud computing may not always meet the necessary requirements in terms of speed, responsiveness, and security to cover the needs of several critical applications. To address the above shortcomings, the implementation of smaller-scale distributed infrastructures at the edges of corporate networks and specifically near endpoints that feature intense data transactions is recommended. This practice is often referred to as edge computing. The edge-computing model features distributed micro datacenter infrastructures closer to the data generation sites to allow faster networking response, local data storage, and enhanced security. Specifically, by creating decentralized datacenters near the data creation source, edge computing reduces exposure concerns since data processing takes place on-premise by utilizing local resources, thus minimizing the potential attack risks that arise by the continuous data transmission to remote infrastructures. Furthermore, edge computing facilitates the adoption of traditional security policies and tools that cannot otherwise be implemented in complex cloud-oriented environments [1].
Despite the advantages of edge computing, there are a few concerns that are mostly due to the servicing needs, power consumption and remote administration of the infrastructures that need to be implemented. Especially in cases of small office branches or shop-in-a-shop scenarios, a dedicated and controlled environment for hosting sensitive hardware equipment is very difficult to allocate. Power consumption and air conditioning needs are also limiting factors. A possible solution that addresses these concerns is the usage of Single-Board Computers (SBCs).
Over the last decade, SBCs have become increasingly relevant due to their low power consumption, low purchasing cost and minimal heat generation. Additionally, the rapid development of power-efficient processors, mostly based on the Aarch64 (ARM64) architecture, makes SBCs ideal for numerous applications such as Internet of Things (IoT), experimentation, prototy** and robotics. The increased demand for more powerful and scalable SBC platforms drives hardware manufacturing companies to produce several different boards either for general-purpose development or optimized for specific tasks (i.e., sensor control, image processing and data analytics) [2]. In the same context, modern SBCs also feature powerful specifications, such as more physical memory (RAM), and are equipped with faster embedded hardware, such as USB3 ports, gigabit Ethernet controllers, Bluetooth radios and Wi-Fi adapters. Indicative examples of such SBCs are Raspberry Pi (by Raspberry Foundation), NVIDIA Jetson (by NVIDIA Corporation), Layerscape Design Board (by NXP Semiconductors) and Quartz64 (by Pine64).
Even though SBCs seem to be a viable and appealing option for edge computing, it is essential to take into account a number of important factors in order to implement reliable, expandable and efficient infrastructures. Specifically, one of the most important prerequisites is that these edge infrastructures shall feature enterprise-level functionalities, such as flexible administration, failover clustering capabilities, and disaster recovery tools. Additionally, all hosted services should be hardware-independent and easily migratable among different types of hosts. Based on the above facts, the underlying technology on which these infrastructures should be based on is virtualization.
This paper aims to investigate the possibility of adopting virtualization technology on Single-Board Computers (SBCs) for the implementation of reliable and cost-efficient edge-computing environments. The scope of this investigation is to study the current technological advances and capabilities in both hardware and software bases in order to examine the viability of such implementations.
The structure of the paper is as follows. Section 2 provides an overview of works in the field of Single-Board Computer, edge computing and virtualization technology implementations along with relevant studies that combine these technologies. Section 3 presents the overall research structure and roadmap, featuring a detailed analysis of the phases and the involved steps followed. Section 4 provides a description of the experimentation process concerning the installation of type-1 hypervisors on the selected SBC along with the hardware environment analysis and limitations. Section 5 features the testing environment preparation and the hypervisor platforms testing, and identifies the possible technical limitations involved. Section 6 presents a case study for replacing a traditional edge-computing infrastructure of a financial organization with an SBC base. The performance testing and results analysis are provided. Section 7 presents the conclusions and proposes future work.
2. Related Work
This section provides an overview of works in the field of Single-Board Computer, edge computing, serverless computing and virtualization technology implementations along with relevant studies that combine these technologies. This analysis is crucial in order to define the enabling technologies that could be used in edge SBC computing virtualized scenarios.
The idea of employing a small, reasonably priced, linked computer in various scientific and educational setups was made more popular by the founding of the Raspberry Pi foundation, a nonprofit organization promoting the educational value of its devices. Single-Board Computer research is mainly focused on studying their employment in sectors such as science, engineering and education [3,4], the implementation of Software-Defined Radio (SDR) systems [5], as well as their usage for creating clustered computing environments that leverage their cost efficiency compared to traditional computer systems [6]. Other works study their energy efficiency on edge-computing implementations [7] and their ability to integrate sensor technologies for specific IoT applications [8]. It should be noted that Single-Board Computers have both benefits and drawbacks. On the one hand, vendors can speed up time to market by needing less development time, and a wide range of sizes, functions, and prices are offered by several providers. However, they are not always economically viable for high volumes of computation or data.
As far as edge computing is concerned, the relevant research is mainly focused on the enhancement of cloud provided services due to the incremental growth of utilization and connected devices mostly in the field of IoT [9]. Researchers have identified key areas such as network performance, availability, power consumption and security, where edge computing may considerably contribute [10]. International Data Corporation (IDC) in co-operation with VMware, identifies edge computing as the next step for the transformation and evolution of the cloud industry [11]. Investments on edge computing are expected to increase mainly in the fields of customer service, transportation, tourism and logistics [12]. This is further validated by a forecast by IDC Corporation that predicts an average of USD 176 billion on edge-computing investments by the end of 2022. The same forecast predicts that total investments on edge computing are expected to reach USD 274 billion by the end of 2025. These investments include hardware, software and service procurement costs [13].
Virtualization technology has been employed for more than one decade in most enterprise datacenter implementations. Specifically, virtualization features a variety of benefits, such as significant cost reduction, higher performance, and availability as well as easier maintenance and administrative flexibility [14]. Additionally, virtualization facilitates the deployment and migration of applications while ensuring high availability for operational and application areas. Particularly in terms of energy efficiency and the lowering of an organization’s CO2 footprint, virtualization is an excellent technique for minimizing the environmental effect of datacenters. Additionally, it aids in enhancing flexibility and decreasing maintenance expenses [15]. As compared to traditional virtualization solutions (VMware, KVM), Docker is a high-level container engine technology that is based on LXC (Linux Container), the widely used method for virtualization processes. Lightweight virtualization for resource and process separation is provided by the kernel virtualization technology LXC. Docker containers are the mainstream solution in the current virtualization field [16].
With the massive use of edge computing, new possibilities have arisen for IoT and IIoT. These come along with new problems related to storage and computing power. Efficient resource utilization became an urgent need, and virtualization technology came to partially solve this issue. It can solve these issues but at the cost of duplicate resource configuration and provision delays in some instances [17]. To overcome these problems, a new model called serverless computing has recently been introduced [18,19]. Serverless computing can autoscale the service offered following the customers’ demand and also charge the customers fairly only for the service offered, independently of the underlying infrastructure [20]. Moreover, other scholars focused on solving resource allocation problems through the use of optimization methods [21]. Finally, distributed intelligence sharing is handled efficiently in [22]. The latter method can be the solution to the overfitting of learning algorithms that work in edge environments where the data samples can be limited.
Based on the analysis of the related work, it is evident that the technology has progressed to such a state where the transition to Single-Board Computers could be feasible for some applications and processes. This study looks at the idea of using Single-Board Computers (SBCs) with virtualization technologies to develop secure and economical edge-computing environments. The goal of this analysis is to investigate the plausibility of such implementations both now and in the near future by studying current hardware and software technology advancements and capabilities.
3. Research Structure and Roadmap
This section provides an overview of the research structure and roadmap followed in this paper. Specifically, the scope of work for this research is to provide a comprehensive evaluation of virtualization on Single-Board Computers (SBCs) for edge-computing purposes. It is important to note that all tests, both hardware- and software-related, may utilize trial, unsupported, and testing versions of various tools, operating systems and applications. The primary objective is to demonstrate and assess the current technological advancements in the integration of virtualization technology on SBCs, without targeting solutions with long-term support or final software versions at this stage. The focus is on exploring the capabilities and potential of SBCs as virtualization hosts for edge-computing scenarios, understanding the limitations and challenges that may arise, and identifying possible remedies. By using a variety of trial and testing versions, this research aims to gain insights into the feasibility and performance of SBCs in virtualized environments, providing an overview for future optimizations and enhancements. The research flow, which is structured in three main phases, is presented in Figure 1:
In the first phase, we present the process of hardware selection and analysis and the identification of hardware limitations, and conclude by defining the hypervisors to be tested.
In the second phase, we describe the establishment of a common hardware testing environment to ensure a consistent evaluation of the selected hypervisors. The testing environment creation should be reproducible and should remain constant throughout the entire testing process. The final step of the second phase is concerned with the results of the hypervisors’ distributions testing and the final hypervisor selection, which shall be used afterwards during the third phase
In the third and final phase, we present a case study that took place in a real-world production environment of a financial organization. Specifically, the first step of this phase is the presentation an analysis of the existing IT environment in order to define the provided IT services and integration requirements to lead to the definition of the study. In the second step, we analyze the design, implementation and integration of the examined solution, and the third and final step describes the solution-testing process and performance analysis, and delivers the conclusions of the case study.
4. Phase 1: Hardware Selection and Testing Process
This section presents and analyzes all the necessary steps involved during the phase of the SBC selection, the identification of its hardware features, and the limitations that derive from its architectural design. Specifically, this section highlights features such as memory capacity and processing power, taking into account factors like network connectivity, compatibility with different operating systems, and the potential for deploying virtualization technology. Moreover, this section presents the systematic steps for analyzing the hardware environment by considering aspects such as storage, firmware installation and power supply considerations. Additionally, noteworthy limitations such as performance constraints, reliability issues and the absence of essential hardware components are identified and analyzed.
4.1. Hardware Selection and Technical Specifications
For the purposes of this research, the Raspberry Pi 4B was chosen as the subject of investigation due to its hardware attributes and features. The first and most important consideration for selecting the Raspberry Pi 4B is that it features an edition equipped with 8GB LPDDR4-3200 SDRAM. At the time this research was conducted, other popular SBCs, such as Nvidia Jetson, Quartz64, and Layerscape Design Board, were equipped with lower RAM configurations and specifically with a maximum of 4 GB or less. By having more RAM available, the Raspberry Pi 4B is able to handle more demanding application workloads, providing greater scalability. More importantly, since this research focuses on virtualization technology, RAM is a determining factor, mostly regarding the amount and load of virtual machines (VMs) that it may facilitate.
Additionally, Raspberry Pi 4B embeds a Gigabit Network adapter, enabling it to integrate with existing networking infrastructures and a dual USB3.0 controller, allowing the interconnection of hi-speed peripherals, such as external storage devices. Raspberry Pi 4B supports a variety of operating systems, such as Raspberry Pi OS, Ubuntu, FreeBSD, Microsoft Windows and other Linux distributions [23], providing the ability to select among multiple software environments for testing and experimentation. The technical specifications of Raspberry PI 4B are summarized in Table 1:
4.2. Hardware Environment Analysis and Limitations
Before proceeding with the installation process and the testing of the hypervisor platforms, it is essential to further explain a number of preparatory steps concerning the Raspberry Pi 4B hardware environment. These steps include the interfacing of the main storage unit, the firmware and booting configuration, the power supply selection and thermal protection. During the analysis of these steps, hardware limitations that may impact the overall reliability and performance of the system are identified and addressed.
4.2.1. Storage Devices
The Raspberry Pi 4B is equipped with a micro SD card slot, which serves as a default primary storage device. However, micro SD cards are not reliable or fast enough to be used as the primary storage for a server operating system and therefore, not recommended for hosting virtual machines. Another significant concern is that the Raspberry Pi 4B does not embed an onboard storage controller, such as SAS or SATA, nor does it feature an expansion bus such as a PCI-e interface for installing an external one. Because of these constraints, the best available option for installing an external hard disk that performs adequately and reliably is to utilize the provided 2-port USB3.0 controller. To connect a SATA3 hard drive, a third-party SATA3 to USB3 adapter is required. Nevertheless, the USB3.0 bus supports a maximum theoretical transfer speed of up to 5 Gbit/s that practically limits the overall throughput of the drive. Additionally, another limitation is the absence of hardware RAID options, which would allow for redundancy and improved performance by creating and utilizing drive arrays.
4.2.2. Firmware and Booting
Raspberry Pi 4B does not include a built-in Unified Extensible Firmware Interface (UEFI). For booting the majority of modern operating systems such as Microsoft Windows or linux-based distributions, UEFI firmware is required. For the purposes of this research, a custom community UEFI firmware [29] based on the QEMU Tianocore EDK2 image [30] was employed. The utilization of the custom UEFI is achieved by storing it in a FAT16 or FAT32 formatted micro SD card inserted to the Raspberry Pi at all times. During the initial boot process, the custom ROM code is executed and loads the UEFI firmware while initializing the hardware components and establishing the operating system booting environment. Once the hardware and boot environment are prepared, the UEFI firmware loads the operating system from the predefined storage volume, which in this case is a USB3-attached hard drive. Finally, the loaded operating system takes control of the hardware, initializes further components and launches the user interface.
4.2.3. Power Supply and Thermal Protection
According to the manufacturer’s specifications, the Raspberry Pi 4B requires a power supply of 5V DC capable of delivering a minimum current of 3A. During the course of this research, the official Raspberry 15.3W AC adapter (model SC0217) was used in order to provide sufficient power for the operational needs of the Raspberry Pi and to also cover the needs of the USB3 external drive [31]. Additionally, to ensure the safety of the board components during extensive operation and to mitigate the risk of high temperatures that could potentially damage it, a passive aluminum heatsink was installed that covers the CPU, chipset and memory modules.
5. Phase 2: Testing Environment Creation and Hypervisor Selection
This section focuses on the establishment of the testing environment utilizing Raspberry Pi 4B, involving both hardware specifications and the evaluation of hypervisors. Specifically, it provides an analysis of the necessary steps employed to configure the testing environment, detailing the hardware components and their specific configurations. Furthermore, this section analyzes the selection process of hypervisors for evaluation, presenting the outcomes of installation tests. Concluding, the main goal in this phase is to strategically choose a hypervisor for the upcoming research stages. This decision was reached by analyzing and evaluating factors such as compatibility and overall suitability for virtualization tasks on the Raspberry Pi 4B platform.
5.1. Hardware Environment Overview
Based on the observations and limitations described in Section 4, the selected hardware configuration to be used for the hypervisors’ installation testing is summarized in Table 2:
A graphical representation of the hardware environment is presented in Figure 2.
5.2. Hypervisor Installation Testing
The scope of this research is focused on Type 1 Hypervisors, also known as a “Bare-Metal” Hypervisors. Specifically, Type 1 Hypervisors are designed to directly operate on the host’s hardware, leveraging direct access and control to physical resources and allocate them to virtual machines without the need for an underlying operating system layer [32]. Type 1 Hypervisors commonly feature mechanisms for hardware isolation between hosted virtual machines and the ability to dynamically assign physical resources such as processors, memory and storage, as well as tools for live migration that enable administrators to move virtual machines between different hosts without service disruption.
Gartner, a prominent research and advisory company, regularly publishes its “Magic Quadrant for Server Virtualization Hypervisors”, which provides a ranking of the top Type 1 Hypervisors [33]. As per the latest Magic Quadrant, the top two Type 1 Hypervisors are as follows:
- VMware vSphere: VMware vSphere is a market-leading hypervisor that provides a wide range of virtualization and cloud management tools, making it the most commonly used hypervisor in enterprise environments.
- Microsoft Hyper-V: Microsoft Hyper-V is a mature and highly dependable hypervisor that integrates smoothly with the Microsoft products ecosystem, offering substantial security and automation features.
The aforementioned hypervisors are considered the best due to their performance, security, overall reliability, scalability and compatibility. They also provide a wide variety of tools and features that enable efficient management and monitoring for large-scale virtualized environments. Additionally, they are currently holding the largest market share in enterprise environments.
In order to evaluate the installation of Microsoft Hyper-V and VMware ES**ing by another machine on the same network.
The results of CPU utilization and storage latency for the Raspberry-based infrastructure are illustrated in Figure 10 and Figure 11, respectively, and summarized in Table 7:
The results of CPU utilization and storage latency for the workstation-based infrastructure are illustrated in Figure 12 and Figure 13, respectively, and summarized in Table 8:
Based on the above results, the success of vMotion in an ARM64-based environment, exemplified by the Raspberry Pi 4B, showcases its adaptability to diverse hardware configurations. While the migration duration was significantly longer, and CPU utilization was higher on the Raspberry Pi, this test clearly demonstrates the feasibility of vMotion in ARM64-based systems, potentially opening doors for specific use cases. However, it is crucial to contextualize the elevated datastore latency observed in the Raspberry Pi environment. This latency can be once again attributed to the Raspberry Pi’s storage, connected via a USB3 to the SATA adapter, introducing additional latency compared to direct SATA controller connections.
6.4. Discussion on Results
This subsection focuses on the examination of performance comparisons between the Raspberry Pi 4B and the workstation-based ES** host, with a specific focus on CPU utilization, power consumption, datastore latency, and vMotion performance.
Specifically, CPU utilization, a critical consideration for hypervisors, was directly influenced by both systems, with significant utilization observed in both instances. As presented on Table 4, the Raspberry Pi-based infrastructure featured increased utilization, with an average of 41.34% and a significant peak value of 99.66%, whereas the workstation-based infrastructure demonstrated an average of 30.11% with a lower peak value of 78.63%. The higher CPU utilization in the Raspberry Pi environment suggests potential differences in processing efficiency between ARM64 and x86 architectures, which is crucial for resource management and performance optimization. Nevertheless, this disparity can be partially justified since the Intel i5 650 processor, though older, is considered more powerful than the Broadcom BCM2711.
Power consumption is a crucial factor concerning computing infrastructures due to its major impact on operational costs, sustainability and overall efficiency of the systems involved. As presented in Table 5, the Raspberry PI-based infrastructure exhibited an average consumption of 5.8 watts, ranging from 4.14 to 7.97 watts. On the contrary, the workstation-based infrastructure exhibited considerably higher values, with an average power consumption of 50.95 watts, ranging from 36.57 to 71.07 watts. This significant disparity in power consumption further validates the arguments regarding the power efficiency of ARM-based SBCs.
Datastore latency, another critical metric for virtualization infrastructures, also features significant disparities between the Raspberry Pi- and workstation-based infrastructures. Specifically, the Raspberry Pi infrastructure exhibited notably higher latency, with average read and write latencies of 12.1 ms and 5.31 ms, respectively, and peak latency reaching 22 ms and 8 ms. On the contrary, the workstation-based infrastructure demonstrated considerably lower latency, with average read and write latencies of 1.42 ms and 1.05 ms, and peak latency of 3 ms and 2 ms. These findings reveal the substantial performance gap in latency between the two infrastructures. The considerable disparity in latency performance between the Raspberry Pi- and workstation-based infrastructures reveals the major challenges and concerns in storage efficiency on the Raspberry Pi platform, which are directly attributed to the lack of a dedicated storage controller and the implementation of a USB3 to SATA adapter setup.
Live migration (vMotion) is an essential feature for enterprise-level virtualized environments, allowing the direct transfer of running virtual machines between hosts without disruption. The evaluation process was based on the migration of a Windows 10 ARM64 VM between two Raspberry Pi 4B hosts and a Windows 10 x64 VM between two workstation-based hosts. Despite the longer migration duration and higher CPU utilization observed in the Raspberry Pi environment, the successful execution of vMotion in the ARM64-based environment highlights its adaptability to diverse hardware configurations. The feasibility of vMotion in the ARM64-based Raspberry Pi environment, despite the significantly higher resource utilization and longer migration duration, suggests the potential versatility in ARM64 systems for specific use cases, while emphasizing the importance of optimizing for hardware variations, particularly storage latency. In conclusion, these results emphasize the potential utility of vMotion in ARM64-based systems, such as the Raspberry Pi 4B, for specific use cases. However, the significant disparities in CPU utilization and storage latency underscore the need for strategic hardware selection and optimization to maximize performance and ensure seamless VM migration in virtualized environments.
7. Conclusions and Future Work
The primary focus of this research was to examine the possibility of adopting SBCs on edge-computing scenarios by employing virtualization technology. During the course of hardware investigation, the Raspberry PI 4B SBC was used as a reference to create a testing environment used to conduct an evaluation of Microsoft Hyper V and VMware ES** 7 on ARM64 Type 1 Hypervisors. This evaluation revealed that while Microsoft Hyper V was unable to operate on Raspberry PI 4B, the VMWare ES** on ARM is fully operational and exhibited adequate performance and features compatibility to be considered the base platform for the rest of the research. During the course of both hardware and hypervisor investigations, a number of limitations were revealed that could potentially lead to performance degradation or disruption of the provided services. These limitations were mostly concerning hardware and software compatibility, storage performance and other reliability issues.
The transition from traditional x86-based edge infrastructure to an ARM64-based Single-Board Computer (SBC) setup brings both opportunities and challenges, notably in the context of virtualization technology. ARM64-based SBCs, such as the Raspberry Pi 4B, excel in power efficiency, making them ideal for energy-conscious edge deployments, crucial in resource-constrained environments. Their compact form factor and minimal heat generation address space limitations in edge deployments. However, it is important to acknowledge that the ARM64 architecture, while gaining traction, is still less mature than x86, leading to issues mostly concerning software compatibility, driver support and system stability, especially regarding virtualization solutions.
Despite all the challenges mentioned above, during the course of this research, it has been proven that Raspberry Pi 4B may successfully operate as a dependable virtualization host in conjunction with VMware ES** 7 on ARM. Additionally, even though ES** on ARM is still under development and not an enterprise-ready product, the majority of its features are operational and can be adequately utilized. Furthermore, the host can be successfully managed by an existing vCenter Server infrastructure, featuring advanced remote management capabilities such as advanced health monitoring and live migration for virtual machines, enabling it to be fully integrated in an existing x86 enterprise-level infrastructure.
Specifically, during the course of the case study conducted in an actual environment of a financial organization, an edge-computing infrastructure based on a traditional x86 workstation, featuring VMware ES** hosting two virtual machines was successfully replaced by a Raspberry-based host, supporting the same workloads. Even though comparative performance testing between the two edge infrastructures revealed higher CPU utilization and increased storage latency for the Raspberry-based host, we could potentially disqualify the solution due to poor performance, as there was no service downtime reported either for the host or for the hosted VMs.Additionally, the most severe performance issue revealed by the conducted tests is datastore latency, which was approximately ten times higher in comparison with the average latency exhibited by the workstation-based infrastructure. This situation can be directly attributed to the absence of a dedicated storage controller and not due to the lack of processing power. Nevertheless, it is essential to mention that during the execution of identical computing scenarios, Raspberry Pi exhibited a substantially lower power consumption of approximately nine times lower on an average scale compared to the workstation-based infrastructure, revealing the enormous potential for sustainability, energy efficiency and cost effectiveness of the solution.
In conclusion, transitioning from the x86-based edge infrastructure to ARM64-based SBCs, like the Raspberry Pi 4B, has been proven to be feasible and beneficial in terms of power expenditures but is still in its early stage due to the serious performance issues that need to be addressed. These issues derive from the lack of standardization concerning ARM64 SBCs, limited software support and hardware expandability that could potentially assist system engineers in solving critical issues such as storage performance by customizing the hardware according to the needs of each implementation. Nevertheless, the compelling difference in power consumption is a strong motivating factor for working on overcoming the above issues towards the creation of modern, cost-effective and environmentally friendly computing solutions. Even though this research revealed that for the specific edge environment, the poor storage performance could potentially disqualify the solution, it is important to clarify that all integration tests with the organization’s infrastructure were successful and that all necessary services were provided properly and as intended. Because of that, the edge infrastructure developed for the purposes of this research can be used in the context of another case study featuring less disk-intensive VMs than Microsoft Windows. Additionally, as a future research, the possibility of replacing the USB3-based storage with a faster dedicated storage controller on a hardware level should be considered. Such research could involve performing hardware modifications on an SBC that is currently available or by employing another more efficient model that will be produced in the near future.
Author Contributions
Conceptualization, G.L.; data curation, G.L.; formal analysis, S.M., C.D. and L.M.; investigation, G.L.; methodology, G.L. and L.M.; project administration, S.M. and C.D.; resources, G.L. and L.M.; supervision, S.M. and C.D.; validation, S.M. and C.D.; writing—original draft, G.L.; writing—review and editing, S.M., C.D. and L.M. All authors have read and agreed to the published version of the manuscript.
Funding
The work in this paper has been co-financed by Greece and the European Union (European Regional Development Fund-ERDF) through the Regional Operational Program “Attiki” 2014-2020.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors on request.
Acknowledgments
Georgios Lambropoulos acknowledges the use of ChatGTP 3.5 for correcting spelling, punctuation and grammar errors.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Research phases and flow.
Figure 2.
Primary hardware environment overview, including connected peripheral devices (storage, power supply and USB devices).
Figure 2.
Primary hardware environment overview, including connected peripheral devices (storage, power supply and USB devices).
Figure 3.
The ES** on the ARM management web interface.
Figure 4.
Raspberry Pi enrolled to the vCenter vSphere environment.
Figure 5.
Representation of edge branch topology.
Figure 6.
Consolidated CPU Utilization for BCM2711 and i5 650 processors.
Figure 7.
Ammeter connection diagram for PSU current measuring.
Figure 8.
Power consumption for workstation and Raspberry Pi 4.
Figure 9.
Consolidated datastore latency for USB and SATA AHCI controllers.
Figure 10.
Broadcom BCM2711 utilization.
Figure 11.
USB3 storage controller latency.
Figure 12.
Intel i5 650 utilization.
Figure 13.
SATA ACHI controller latency.
Table 1.
Raspberry Pi 4B specifications.
| Category | Specifications |
|---|---|
| Processor | Broadcom BCM2711 Quad-core SoC ARM Cortex-A72 (ARM v8), 1.5 GHz [24] |
| Network Connectivity | Gigabit Ethernet (Microchip LAN7515) [25] Dual-band Wi-Fi (Cypress CYW43455) [26] Bluetooth 5.0 with BLE [27] Four USB ports (2 USB2, 2 USB3.0) [28] |
| Form Factor | Compact (85.6 mm × 56.5 mm) |
| Input Power | 5V DC-3A |
Table 2.
Hardware specifications of the testing system.
| Hardware Attribute | Value |
|---|---|
| Board Model | Raspberry Pi model 4B |
| Processor Count | 1 Physical Processor - 4 Cores |
| Processor Speed | 1.5 GHz |
| Physical Memory | 8 GB LPDDR4-3200 |
| NIC Type | Single-Port 1 Gbps Ethernet Adapter |
| Primary Hard Disk | 1 TB SATA3 SSD Drive |
| Hard Disk Controller | USB3.0 to SATA3 Adapter |
| Controller Speed | Max. 5 Gbit/sec |
Table 3.
Current edge workstation hardware attributes.
| Hardware Attribute | Value |
|---|---|
| Processor Type | Intel Core i5 650 |
| Cores/Threads Count | 2 Cores/4 Threads |
| Processor Speed | 3.20 GHz |
| Physical Memory | 8 GB DDR3-1600 SDRAM |
| NIC Type | Single-Port 1 Gbps Ethernet Adapter |
| Primary Hard Disk | 1 TB SATA3 SSD Drive |
| Controller Speed | Max. 6 Gbit/sec |
Table 4.
CPU utilization comparison.
| CPU Model | Average Utilization (%) | Peak Utilization (%) |
|---|---|---|
| Broadcom BCM2711 | 41.34 | 99.66 |
| Intel i5 650 | 30.11 | 78.63 |
Table 5.
Power consumption summary.
| Metric | Workstation | Raspberry Pi 4B |
|---|---|---|
| Min. Consumption (W) | 36.57 | 4.14 |
| Max. Consumption (W) | 71.07 | 7.97 |
| Average Consumption (W) | 50.95 | 5.80 |
Table 6.
Datastore latency.
| Storage Controller | Operation | Average (ms) | Peak (ms) |
|---|---|---|---|
| USB3 Storage Controller | Read | 12.1 | 22 |
| Write | 5.31 | 8 | |
| SATA AHCI Controller | Read | 1.42 | 3 |
| Write | 1.05 | 2 |
Table 7.
Test results—Raspberry Pi 4B host.
| Metric | Value |
|---|---|
| Migration Duration | 23 min |
| Average CPU Usage | 49.44% |
| Peak CPU Usage | 76.16% |
| Average Read Latency | 15.8 ms |
| Average Write Latency | 4.8 ms |
| Peak Read Latency | 43 ms |
| Peak Write Latency | 21 ms |
| VM Availability | Packet Loss: 1%, Minimum: 0 ms, Maximum: 437 ms, Average: 3 ms |
Table 8.
Test results—Intel i5 650 host.
| Metric | Value |
|---|---|
| Migration Duration | 10 min |
| Average CPU Usage | 18.10% |
| Peak CPU Usage | 39% |
| Average Read Latency | 2.3 ms |
| Average Write Latency | 0.7 ms |
| Peak Read Latency | 4 ms |
| Peak Write Latency | 2 ms |
| VM Availability | Packet Loss: 0%, Minimum: 0 ms, Maximum: 129 ms, Average: 1 ms |
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