Cloud-Based Big Data Solutions
Template:Infobox Server Configuration
Technical Documentation: Server Configuration Template:Stub
This document provides a comprehensive technical analysis of the Template:Stub reference configuration. This configuration is designed to serve as a standardized, baseline hardware specification against which more advanced or specialized server builds are measured. While the "Stub" designation implies a minimal viable product, its components are selected for stability, broad compatibility, and cost-effectiveness in standardized data center environments.
1. Hardware Specifications
The Template:Stub configuration prioritizes proven, readily available components that offer a balanced performance-to-cost ratio. It is designed to fit within standard 2U rackmount chassis dimensions, although specific chassis models may vary.
1.1. Central Processing Units (CPUs)
The configuration mandates a dual-socket (2P) architecture to ensure sufficient core density and memory channel bandwidth for general-purpose workloads.
| Specification | Detail (Minimum Requirement) | Detail (Recommended Baseline) |
|---|---|---|
| Architecture | Intel Xeon Scalable (Cascade Lake or newer preferred) or AMD EPYC (Rome or newer preferred) | Intel Xeon Scalable Gen 3 (Ice Lake) or AMD EPYC Gen 3 (Milan) |
| Socket Count | 2 | 2 |
| Base TDP Range | 95W – 135W per socket | 120W – 150W per socket |
| Minimum Cores per Socket | 12 Physical Cores | 16 Physical Cores |
| Minimum Frequency (All-Core Turbo) | 2.8 GHz | 3.1 GHz |
| L3 Cache (Total) | 36 MB Minimum | 64 MB Minimum |
| Supported Memory Channels | 6 or 8 Channels per socket | 8 Channels per socket (for optimal I/O) |
The selection of the CPU generation is crucial; while older generations may fit the "stub" moniker, modern stability and feature sets (such as AVX-512 or PCIe 4.0 support) are mandatory for baseline compatibility with contemporary operating systems and hypervisors.
1.2. Random Access Memory (RAM)
Memory capacity and speed are provisioned to support moderate virtualization density or large in-memory datasets typical of database caching layers. The configuration specifies DDR4 ECC Registered DIMMs (RDIMMs) or Load-Reduced DIMMs (LRDIMMs) depending on the required density ceiling.
| Specification | Detail | |
|---|---|---|
| Type | DDR4 ECC RDIMM/LRDIMM (DDR5 requirement for future revisions) | |
| Total Capacity (Minimum) | 128 GB | |
| Total Capacity (Recommended) | 256 GB | |
| Configuration Strategy | Fully populated memory channels (e.g., 8 DIMMs per CPU or 16 total) | |
| Speed Rating (Minimum) | 2933 MT/s | |
| Speed Rating (Recommended) | 3200 MT/s (or fastest supported by CPU/Motherboard combination) | |
| Maximum Supported DIMM Rank | Dual Rank (2R) preferred for stability |
It is critical that the BIOS/UEFI is configured to utilize the maximum supported memory speed profile (e.g., XMP or JEDEC profiles) while maintaining stability under full load, adhering strictly to the Memory Interleaving guidelines for the specific motherboard chipset.
1.3. Storage Subsystem
The storage configuration emphasizes a tiered approach: a high-speed boot/OS volume and a larger, redundant capacity volume for application data. Direct Attached Storage (DAS) is the standard implementation.
| Tier | Component Type | Quantity | Capacity (per unit) | Interface/Protocol |
|---|---|---|---|---|
| Boot/OS | NVMe M.2 or U.2 SSD | 2 (Mirrored) | 480 GB Minimum | PCIe 3.0/4.0 x4 |
| Data/Application | SATA or SAS SSD (Enterprise Grade) | 4 to 6 | 1.92 TB Minimum | SAS 12Gb/s (Preferred) or SATA III |
| RAID Controller | Hardware RAID (e.g., Broadcom MegaRAID) | 1 | N/A | PCIe 3.0/4.0 x8 interface required |
The data drives must be configured in a RAID 5 or RAID 6 array for redundancy. The use of NVMe for the OS tier significantly reduces boot times and metadata access latency, a key improvement over older SATA-based stub configurations. Refer to RAID Levels documentation for specific array geometry recommendations.
1.4. Networking and I/O
Standardization on 10 Gigabit Ethernet (10GbE) is required for the management and primary data interfaces.
| Component | Specification | Purpose |
|---|---|---|
| Primary Network Interface (Data) | 2 x 10GbE SFP+ or Base-T (Configured in LACP/Active-Passive) | Application Traffic, VM Networking |
| Management Interface (Dedicated) | 1 x 1GbE (IPMI/iDRAC/iLO) | Out-of-Band Management |
| PCIe Slots Utilization | At least 2 x PCIe 4.0 x16 slots populated (for future expansion or high-speed adapters) | Expansion for SAN connectivity or specialized accelerators |
The onboard Baseboard Management Controller (BMC) must support modern standards, including HTML5 console redirection and secure firmware updates.
1.5. Power and Form Factor
The configuration is designed for high-density rack deployment.
- **Form Factor:** 2U Rackmount Chassis (Standard 19-inch width).
- **Power Supplies (PSUs):** Dual Redundant, Hot-Swappable, Platinum or Titanium Efficiency Rating (>= 92% efficiency at 50% load).
- **Total Rated Power Draw (Peak):** Approximately 850W – 1100W (dependent on CPU TDP and storage configuration).
- **Input Voltage:** 200-240V AC (Recommended for efficiency, though 110V support must be validated).
2. Performance Characteristics
The performance profile of the Template:Stub is defined by its balanced memory bandwidth and core count, making it a suitable platform for I/O-bound tasks that require moderate computational throughput.
2.1. Synthetic Benchmarks (Estimated)
The following benchmarks reflect expected performance based on the recommended component specifications (Ice Lake/Milan generation CPUs, 3200MT/s RAM).
| Benchmark Area | Metric | Expected Result Range | Notes |
|---|---|---|---|
| CPU Compute (Integer/Floating Point) | SPECrate 2017 Integer (Base) | 450 – 550 | Reflects multi-threaded efficiency. |
| Memory Bandwidth (Aggregate) | Read/Write (GB/s) | 180 – 220 GB/s | Dependent on DIMM population and CPU memory controller quality. |
| Storage IOPS (Random 4K Read) | Sustained IOPS (from RAID 5 Array) | 150,000 – 220,000 IOPS | Heavily influenced by RAID controller cache and drive type. |
| Network Throughput | TCP/IP Throughput (iperf3) | 19.0 – 19.8 Gbps (Full Duplex) | Testing 2x 10GbE bonded link. |
The key performance bottleneck in the Stub configuration, particularly when running high-vCPU density workloads, is often the memory subsystem's latency profile rather than raw core count, especially when the operating system or application attempts to access data across the Non-Uniform Memory Access boundary between the two sockets.
2.2. Real-World Performance Analysis
The Stub configuration excels in scenarios demanding high I/O consistency rather than peak computational burst capacity.
- **Database Workloads (OLTP):** Handles transactional loads requiring moderate connections (up to 500 concurrent active users) effectively, provided the working set fits within the 256GB RAM allocation. Performance degradation begins when the workload triggers significant page faults requiring reliance on the SSD tier.
- **Web Serving (Apache/Nginx):** Capable of serving tens of thousands of concurrent requests per second (RPS) for static or moderately dynamic content, limited primarily by network saturation or CPU instruction pipeline efficiency under heavy SSL/TLS termination loads.
- **Container Orchestration (Kubernetes Node):** Functions optimally as a worker node supporting 40-60 standard microservices containers, where the CPU cores provide sufficient scheduling capacity, and the 10GbE networking allows for rapid service mesh communication.
3. Recommended Use Cases
The Template:Stub configuration is not intended for high-performance computing (HPC) or extreme data analytics but serves as an excellent foundation for robust, general-purpose infrastructure.
3.1. Virtualization Host (Mid-Density)
This configuration is ideal for hosting a consolidated environment where stability and resource isolation are paramount.
- **Target Density:** 8 to 15 Virtual Machines (VMs) depending on the VM profile (e.g., 8 powerful Windows Server VMs or 15 lightweight Linux application servers).
- **Hypervisor Support:** Full compatibility with VMware vSphere, Microsoft Hyper-V, and Kernel-based Virtual Machine.
- **Benefit:** The dual-socket architecture ensures sufficient PCIe lanes for multiple virtual network interface cards (vNICs) and provides ample physical memory for guest allocation.
3.2. Application and Web Servers
For standard three-tier application architectures, the Stub serves well as the application or web tier.
- **Backend API Tier:** Suitable for hosting RESTful services written in languages like Java (Spring Boot), Python (Django/Flask), or Go, provided the application memory footprint remains within the physical RAM limits.
- **Load Balancing Target:** Excellent as a target for Network Load Balancing (NLB) clusters, offering predictable latency and throughput.
3.3. Jump Box / Bastion Host and Management Server
Due to its robust, standardized hardware, the Stub is highly reliable for critical management functions.
- **Configuration Management:** Running Ansible Tower, Puppet Master, or Chef Server. The storage subsystem provides fast configuration deployment and log aggregation.
- **Monitoring Infrastructure:** Hosting Prometheus/Grafana or ELK stack components (excluding large-scale indexing nodes).
3.4. File and Backup Target
When configured with a higher count of high-capacity SATA/SAS drives (exceeding the 6-drive minimum), the Stub becomes a capable, high-throughput Network Attached Storage (NAS) target utilizing technologies like ZFS or Windows Storage Spaces.
4. Comparison with Similar Configurations
To contextualize the Template:Stub, it is useful to compare it against its immediate predecessors (Template:Legacy) and its successors (Template:HighDensity).
4.1. Configuration Matrix Comparison
| Feature | Template:Stub (Baseline) | Template:Legacy (10/12 Gen Xeon) | Template:HighDensity (1S/HPC Focus) |
|---|---|---|---|
| CPU Sockets | 2P | 2P | 1S (or 2P with extreme core density) |
| Max RAM (Typical) | 256 GB | 128 GB | 768 GB+ |
| Primary Storage Interface | PCIe 4.0 NVMe (OS) + SAS/SATA SSDs | PCIe 3.0 SATA SSDs only | All NVMe U.2/AIC |
| Network Speed | 10GbE Standard | 1GbE Standard | 25GbE or 100GbE Mandatory |
| Power Efficiency Rating | Platinum/Titanium | Gold | Titanium (Extreme Density Optimization) |
| Cost Index (Relative) | 1.0x | 0.6x | 2.5x+ |
The Stub configuration represents the optimal point for balancing current I/O requirements (10GbE, PCIe 4.0) against legacy infrastructure compatibility, whereas the Template:Legacy is constrained by slower interconnects and less efficient power delivery.
4.2. Performance Trade-offs
The primary trade-off when moving from the Stub to the Template:HighDensity configuration involves the shift from balanced I/O to raw compute.
- **Stub Advantage:** Superior I/O consistency due to the dedicated RAID controller and dual-socket memory architecture providing high aggregate bandwidth.
- **HighDensity Disadvantage (in this context):** Single-socket (1S) high-density configurations, while offering more cores per watt, often suffer from reduced memory channel access (e.g., 6 channels vs. 8 channels per CPU), leading to lower sustained memory bandwidth under full virtualization load.
5. Maintenance Considerations
Maintaining the Template:Stub requires adherence to standard enterprise server practices, with specific attention paid to thermal management due to the dual-socket high-TDP components.
5.1. Thermal Management and Cooling
The dual-socket design generates significant heat, necessitating robust cooling infrastructure.
- **Airflow Requirements:** Must maintain a minimum front-to-back differential pressure of 0.4 inches of water column (in H2O) across the server intake area.
- **Component Specifics:** CPUs rated above 150W TDP require high-static pressure fans integrated into the chassis, often exceeding the performance of standard cooling solutions designed for single-socket, low-TDP hardware.
- **Hot Aisle Containment:** Deployment within a hot-aisle/cold-aisle containment strategy is highly recommended to maximize chiller efficiency and prevent thermal throttling, especially during peak operation when all turbo frequencies are engaged.
5.2. Power Requirements and Redundancy
The redundant power supplies (N+1 or 2N configuration) must be connected to diverse power paths whenever possible.
- **PDU Load Balancing:** The total calculated power draw (approaching 1.1kW peak) means that servers should be distributed across multiple Power Distribution Units (PDUs) to avoid overloading any single circuit breaker in the rack infrastructure.
- **Firmware Updates:** Regular firmware updates for the BMC, BIOS/UEFI, and RAID controller are mandatory to ensure compatibility with new operating system kernels and security patches (e.g., addressing Spectre variants).
5.3. Operating System and Driver Lifecycle
The longevity of the Stub configuration relies heavily on vendor support for the chosen CPU generation.
- **Driver Validation:** Before deploying any major OS patch or hypervisor upgrade, all hardware drivers (especially storage controller and network card firmware) must be validated against the vendor's Hardware Compatibility List (HCL).
- **Diagnostic Tools:** The BMC must be configured to stream diagnostic logs (e.g., Intelligent Platform Management Interface sensor readings) to a central System Monitoring platform for proactive failure prediction.
The stability of the Template:Stub ensures that maintenance windows are predictable, typically only required for major component replacements (e.g., PSU failure or expected drive rebuilds) rather than frequent stability patches.
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️ Template:Technical documentation
- Cloud-Based Big Data Solutions: Technical Overview
This document details the hardware configuration for our cloud-based Big Data solutions, outlining specifications, performance, use cases, comparisons, and maintenance considerations. This configuration is designed for handling extremely large datasets and complex analytical workloads. It is a fully managed service, leveraging the scalability and redundancy of our cloud infrastructure. This document assumes the reader has a fundamental understanding of server hardware, networking, and Big Data concepts like Hadoop and Spark.
1. Hardware Specifications
The core of our Big Data solution is a cluster of virtualized servers, each built upon high-performance bare-metal hosts. The following specifications represent the standard configuration for each individual node within the cluster. Scaling is achieved by adding or removing nodes as needed, managed by our automated orchestration system. The architecture utilizes a distributed file system, primarily HDFS, across the cluster for data storage.
1.1 Compute (CPU)
We utilize dual Intel Xeon Platinum 8380 processors per node. These processors were selected for their high core count, large cache sizes, and support for advanced instruction sets crucial for Big Data processing.
| Specification | Value |
|---|---|
| Processor Family | Intel Xeon Platinum 8300 Series |
| Model Number | 8380 |
| Cores per Processor | 40 |
| Threads per Core | 2 |
| Total Cores per Node | 80 |
| Base Clock Speed | 2.3 GHz |
| Max Turbo Frequency | 3.4 GHz |
| Cache | 60 MB Intel Smart Cache (30 MB per processor) |
| TDP (Thermal Design Power) | 270W |
| Instruction Set Extensions | AVX-512, VMD, TSX-NI |
1.2 Memory (RAM)
Each node is equipped with 512GB of DDR4 ECC Registered DIMMs (RDIMMs). This provides ample memory for in-memory data processing and caching, critical for performance with frameworks like Spark. The memory is configured in a multi-channel configuration to maximize bandwidth.
| Specification | Value |
|---|---|
| Memory Type | DDR4 ECC Registered DIMM (RDIMM) |
| Capacity per Node | 512 GB |
| Memory Speed | 3200 MHz |
| DIMM Configuration | 16 x 32 GB |
| Channels per Memory Controller | 8 |
| Memory Bandwidth (Theoretical) | 256 GB/s |
1.3 Storage
Storage is a tiered approach, utilizing both NVMe SSDs for fast local caching and high-capacity HDDs for bulk data storage. Each node includes local NVMe storage for operating system and temporary data, plus access to shared storage via our networked file system.
| Specification | Value |
|---|---|
| Local NVMe SSD | 1 TB Samsung PM1733 |
| NVMe Interface | PCIe Gen4 x4 |
| Local HDD (Shared via Networked Filesystem) | 16 TB SAS 7.2K RPM |
| RAID Configuration (HDD) | RAID 6 (for data redundancy) |
| Network Filesystem | Optimized implementation of HDFS and Object Storage |
| Total Storage per Node (Logical) | 16 TB + 1 TB (NVMe) |
1.4 Networking
High-bandwidth, low-latency networking is essential for Big Data processing. Each node is equipped with a 100Gbps network interface card (NIC) connected to a non-blocking, low-latency network fabric. Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCEv2) is utilized for improved inter-node communication.
| Specification | Value |
|---|---|
| Network Interface Card (NIC) | Mellanox ConnectX-6 DX |
| Network Speed | 100 Gbps |
| Network Topology | Clos Network |
| Protocol | RoCEv2 (RDMA over Converged Ethernet) |
| Network Latency (Typical) | < 10 microseconds |
1.5 Other Hardware Components
- **Power Supply:** Redundant 1600W 80+ Platinum power supplies. See section 5 for power requirements.
- **Cooling:** Liquid cooling system for both CPUs and high-power components. See section 5 for cooling considerations.
- **Baseboard Management Controller (BMC):** Integrated BMC for remote management and monitoring. See Remote Server Management for details.
- **Operating System:** CentOS 8 (customized for Big Data workloads). See Linux Server Administration for related information.
2. Performance Characteristics
The performance of this configuration is rigorously tested using industry-standard benchmarks and real-world Big Data workloads. The following results are representative of typical performance.
2.1 Benchmark Results
- **Hadoop Distributed File System (HDFS) Read Throughput:** Average 120 GB/s across the cluster. This is heavily dependent on the number of nodes in the cluster.
- **Hadoop MapReduce:** Job completion times are significantly reduced compared to previous generations, averaging a 30% improvement for complex analytical queries.
- **Spark SQL:** Query performance on a 1TB dataset averages under 5 seconds.
- **TPC-DS Benchmark:** Achieved a TPC-DS score of X.XX (details available upon request due to proprietary data).
- **IOPS (Input/Output Operations Per Second):** NVMe SSDs consistently deliver > 500,000 IOPS. HDD IOPS are lower, around 200 IOPS, but are offset by the large capacity.
2.2 Real-World Performance
We have benchmarked the system with several customer datasets and workloads, including:
- **Log Analytics:** Processing 100 GB of log data per hour with an average latency of 2 minutes.
- **Fraud Detection:** Real-time fraud detection with a processing speed of 1 million transactions per second.
- **Recommendation Engines:** Generating personalized recommendations for 10 million users in under 30 minutes.
- **Genomic Sequencing Analysis:** Analyzing a 100-genome dataset in approximately 8 hours. See Genomic Data Processing for more information.
These results demonstrate the configuration's ability to handle a wide range of Big Data workloads with high performance and scalability. Performance will scale linearly with the number of nodes added to the cluster.
2.3 Performance Monitoring
Comprehensive performance monitoring is integrated into the system using tools such as Prometheus and Grafana. Key metrics include CPU utilization, memory usage, disk I/O, network bandwidth, and application-specific metrics. Alerting is configured to notify administrators of any performance anomalies or potential issues.
3. Recommended Use Cases
This configuration is ideal for applications requiring high-performance processing of large datasets. Specific use cases include:
- **Real-time Analytics:** Analyzing streaming data sources for immediate insights. See Stream Processing for related information.
- **Data Warehousing:** Building and maintaining large-scale data warehouses for business intelligence.
- **Machine Learning:** Training and deploying machine learning models on massive datasets. See Machine Learning Infrastructure.
- **Log Management and Analysis:** Collecting, storing, and analyzing log data from various sources.
- **Financial Modeling:** Performing complex financial simulations and risk analysis.
- **Scientific Computing:** Running computationally intensive simulations and experiments.
- **Genomics and Bioinformatics:** Processing and analyzing large genomic datasets.
- **IoT Data Analytics:** Analyzing data from a large number of connected devices.
4. Comparison with Similar Configurations
The following table compares our Big Data solution with other common configurations.
| Configuration | CPU | RAM | Storage | Networking | Cost (Approximate per Node) | Performance |
|---|---|---|---|---|---|---|
| **Our Cloud-Based Big Data Solution** | Dual Intel Xeon Platinum 8380 | 512 GB DDR4 | 16 TB HDD + 1 TB NVMe | 100 Gbps RoCEv2 | $15,000/month | High (Optimized for scalability and performance) |
| **Standard Hadoop Cluster (On-Premises)** | Dual Intel Xeon Gold 6248R | 256 GB DDR4 | 8 TB HDD | 10 Gbps Ethernet | $8,000 (Capital Expenditure) | Medium (Limited by network bandwidth and storage capacity) |
| **Amazon EMR (m5.2xlarge)** | Intel Xeon Platinum 8000 Series | 32 GB DDR4 | 80 GB SSD | 10 Gbps Ethernet | $0.88/hour | Medium (Cost-effective for smaller datasets, limited scalability) |
| **Google Cloud Dataproc (n1-standard-16)** | Intel Xeon E5-2680 v4 | 64 GB DDR4 | 1 TB SSD | 10 Gbps Ethernet | $0.48/hour | Medium (Similar to Amazon EMR, limited by CPU and network) |
- Key Differences:**
- **Scalability:** Our cloud-based solution offers unparalleled scalability, allowing you to easily add or remove nodes as needed.
- **Performance:** The combination of high-core-count CPUs, large memory capacity, and low-latency networking delivers superior performance.
- **Cost:** While the monthly cost per node is higher than on-premises solutions, the total cost of ownership (TCO) is often lower due to reduced operational expenses (power, cooling, maintenance, and IT staff).
- **Management:** The solution is fully managed, freeing up your IT staff to focus on other priorities. See Cloud Service Management.
5. Maintenance Considerations
Maintaining this high-performance Big Data infrastructure requires careful attention to several key areas.
5.1 Cooling
The high-density compute environment generates significant heat. Our data centers utilize a liquid cooling system to efficiently dissipate heat from the CPUs and other high-power components. Regular monitoring of temperature sensors is crucial to ensure optimal cooling performance. The system is designed to operate within a temperature range of 18-24°C (64-75°F). Redundant cooling units are in place to provide failover protection.
5.2 Power Requirements
Each node consumes approximately 800W of power at full load. The data center infrastructure provides redundant power supplies and uninterruptible power supplies (UPS) to ensure continuous operation. Each rack is equipped with power distribution units (PDUs) with monitoring capabilities to track power consumption. A dedicated power engineer monitors the overall power usage and capacity planning. See Data Center Power Management for details.
5.3 Hardware Maintenance
While the cloud-based nature of the solution minimizes the need for direct hardware maintenance, regular preventative maintenance is still performed on the underlying infrastructure. This includes:
- **Component Monitoring:** Proactive monitoring of all hardware components for potential failures. Utilizing Predictive Maintenance techniques.
- **Firmware Updates:** Regularly applying firmware updates to ensure optimal performance and security.
- **Hardware Replacements:** Replacing failed components promptly to minimize downtime.
- **Network Maintenance:** Performing scheduled maintenance on the network infrastructure to ensure optimal bandwidth and latency.
5.4 Data Backup and Disaster Recovery
Data is backed up regularly using a combination of snapshots and replication to ensure data durability and availability. A disaster recovery plan is in place to ensure business continuity in the event of a major outage. Data is replicated across multiple geographically diverse data centers. See Data Backup and Recovery for detailed information.
5.5 Security Considerations
Security is paramount. The infrastructure is protected by multiple layers of security, including:
- **Physical Security:** Strict access controls to the data centers.
- **Network Security:** Firewalls, intrusion detection systems, and other network security measures.
- **Data Encryption:** Data is encrypted both in transit and at rest. See Data Encryption Best Practices.
- **Access Control:** Role-based access control to limit access to sensitive data.
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
Order Your Dedicated Server
Configure and order your ideal server configuration
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️