Bioinformatics Infrastructure in Kenya
Engineering Excellence & Technical Support
Bioinformatics Infrastructure solutions for Digital & Analytical. High-standard technical execution following OEM protocols and local regulatory frameworks.
National High-Performance Computing (HPC) Cluster
Deployment of a state-of-the-art HPC cluster, significantly accelerating complex genomic and proteomic analyses. This infrastructure provides researchers with the computational power needed for large-scale data processing, enabling breakthroughs in disease genomics, crop improvement, and biodiversity research.
Centralized Bioinformatics Data Repository
Establishment of a secure, federated data repository adhering to FAIR principles. This platform allows for standardized storage, annotation, and sharing of biological data, fostering collaboration among Kenyan institutions and facilitating reproducible research across diverse fields like infectious diseases and agricultural sciences.
Scalable Cloud-Based Bioinformatics Platforms
Leveraging scalable cloud computing solutions to host accessible, user-friendly bioinformatics workflows and tools. This offers researchers flexible access to cutting-edge analytical capabilities without the burden of local hardware maintenance, empowering a wider range of scientists and students to conduct sophisticated analyses.
What Is Bioinformatics Infrastructure In Kenya?
Bioinformatics infrastructure in Kenya refers to the integrated ecosystem of computational resources, data management systems, analytical tools, and skilled personnel that support biological research and development. It encompasses hardware (high-performance computing clusters, servers, storage), software (bioinformatics pipelines, databases, visualization tools), networks (high-speed connectivity), and institutional frameworks (research centers, training programs, data repositories). The objective is to enable the secure storage, processing, analysis, and sharing of large-scale biological data, fostering innovation in areas such as genomics, proteomics, transcriptomics, and evolutionary biology. This infrastructure is crucial for addressing national health priorities, agricultural challenges, and biodiversity conservation efforts through data-driven scientific inquiry.
| Service Involved | Who Needs It | Typical Use Cases |
|---|---|---|
| Genomic Data Analysis | Researchers in human health, infectious disease surveillance, agricultural genomics, and evolutionary biology. | Whole-genome sequencing analysis for disease association studies, pathogen strain characterization, crop improvement (e.g., marker-assisted selection), and biodiversity assessments. |
| Proteomic and Metabolomic Data Analysis | Researchers in drug discovery, environmental monitoring, and food security. | Identification of biomarkers for disease diagnosis, understanding metabolic pathways in response to environmental stimuli, and characterizing food composition. |
| Transcriptomic Data Analysis (RNA-Seq) | Researchers investigating gene expression patterns in response to various biological conditions. | Identifying differentially expressed genes in disease states, studying developmental processes, and understanding cellular responses to treatments. |
| Data Storage and Management | All research institutions and organizations generating or utilizing biological data. | Centralized repositories for research data, ensuring long-term preservation, accessibility for re-analysis, and compliance with data sharing mandates. |
| Development of Bioinformatics Tools and Pipelines | Computational biologists, software developers, and research groups requiring specialized analytical workflows. | Creating novel algorithms for data analysis, automating complex workflows, and adapting existing tools to specific research questions. |
| Capacity Building and Training | University students, early-career researchers, and established scientists seeking to enhance their computational skills. | Workshops, courses, and degree programs in bioinformatics, enabling researchers to independently conduct computational analyses. |
| Interdisciplinary Collaboration Platforms | Research consortia and multi-institutional projects. | Facilitating joint data analysis, resource sharing, and collaborative hypothesis generation among geographically dispersed research teams. |
Key Components of Kenyan Bioinformatics Infrastructure
- High-Performance Computing (HPC) clusters and cloud computing resources for intensive data processing.
- Secure and scalable data storage solutions (e.g., distributed file systems, object storage) for vast genomic and other biological datasets.
- Centralized and distributed biological databases for storing and accessing curated data (e.g., sequence data, protein structures, gene expression profiles).
- A suite of open-source and proprietary bioinformatics software packages and pipelines for sequence alignment, variant calling, phylogenetic analysis, functional genomics, etc.
- Robust networking infrastructure for efficient data transfer within and between research institutions.
- Specialized hardware for data acquisition (e.g., next-generation sequencing platforms, mass spectrometers).
- Data management frameworks and protocols for ensuring data integrity, provenance, and FAIR (Findable, Accessible, Interoperable, Reusable) principles.
- Skilled bioinformatics personnel, including bioinformaticians, computational biologists, data scientists, and IT support specialists.
- Training programs and capacity building initiatives to upskill researchers in bioinformatics methodologies.
- Collaborative platforms and computational analysis environments to facilitate interdisciplinary research.
Who Needs Bioinformatics Infrastructure In Kenya?
Bioinformatics infrastructure is crucial for advancing research, diagnostics, and public health initiatives in Kenya. It empowers various stakeholders to leverage biological data for innovation and problem-solving. The demand for robust bioinformatics infrastructure spans academic institutions, research organizations, healthcare providers, and government agencies, all seeking to unlock the potential of genomics, proteomics, and other 'omics' data.
| Customer Type | Key Departments/Units | Specific Needs/Applications |
|---|---|---|
| Academic and Research Institutions | Departments of Biology, Biotechnology, Biochemistry, Genetics, Biomedical Sciences, Bioinformatics Centers, Computer Science (for computational biology) | Genomic sequencing and analysis (human, pathogen, agricultural), transcriptomics, proteomics, metabolomics, drug discovery, evolutionary biology research, bioinformatics training and education. |
| Healthcare and Diagnostic Laboratories | Pathology Departments, Clinical Genetics Units, Infectious Disease Units, Public Health Laboratories, Diagnostic Sequencing Labs | Pathogen surveillance and outbreak investigation (e.g., malaria, HIV, COVID-19), genetic disease diagnosis, personalized medicine initiatives, antimicrobial resistance monitoring, cancer genomics. |
| Government Ministries and Agencies | Ministry of Health (National Public Health Laboratories, Disease Surveillance Units), Ministry of Agriculture, Livestock, Fisheries and Cooperatives (Kenya Agricultural and Livestock Research Organization - KALRO), National Commission for Science, Technology and Innovation (NACOSTI), Kenya National Bureau of Statistics (KNBS) | National disease surveillance, public health policy informed by genomic data, agricultural innovation (crop and livestock breeding), biosecurity, national research strategy development, data management and dissemination. |
| Biotechnology and Pharmaceutical Companies | Research and Development (R&D) Departments, Bioinformatics Units, Drug Discovery Teams | Drug target identification, vaccine development, preclinical and clinical trial data analysis, biomarker discovery, development of novel diagnostics. |
| Agricultural Research and Development | Plant Breeding Departments, Animal Genetics Units, Soil Science Departments, Crop Protection Units | Genomic selection for improved crop varieties (e.g., drought resistance, yield), livestock breeding for enhanced productivity, pest and disease management, soil microbiome analysis for sustainable agriculture. |
Target Customers and Departments for Bioinformatics Infrastructure in Kenya:
- Academic and Research Institutions
- Healthcare and Diagnostic Laboratories
- Government Ministries and Agencies
- Biotechnology and Pharmaceutical Companies
- Agricultural Research and Development
Bioinformatics Infrastructure Process In Kenya
The bioinformatics infrastructure process in Kenya involves a structured workflow to address the needs of researchers and institutions requiring bioinformatics support, data analysis, and computational resources. This process generally starts with an inquiry and progresses through various stages of assessment, planning, execution, and ongoing support, ensuring efficient and effective utilization of available resources and expertise.
| Stage | Description | Key Activities | Responsible Parties/Institutions | Typical Outputs |
|---|---|---|---|---|
| The initial stage where researchers or institutions express a need for bioinformatics services or infrastructure. | Contacting relevant bioinformatics units, submitting requests, defining research questions, specifying data types and analysis requirements. | Researchers, Students, Research Institutions, Universities, Government Agencies. | Formal inquiry, needs assessment report, preliminary project scope. |
| Determining the most suitable infrastructure, tools, and personnel to meet the identified needs. | Assessing availability of computational resources (servers, cloud), software licenses, databases, and bioinformatics expertise. | Bioinformatics Core Facilities, National Research Institutes (e.g., KEMRI, KALRO), University IT departments, Government Agencies (e.g., NACOSTI). | Resource allocation plan, proposal for infrastructure usage, service agreement. |
| Developing a detailed plan for the bioinformatics project, outlining methodologies, timelines, and deliverables. | Defining specific analytical pipelines, choosing appropriate algorithms and software, establishing data management protocols, setting timelines, risk assessment. | Bioinformatics Analysts, Computational Biologists, Principal Investigators (PIs), Collaborating Researchers. | Project proposal, detailed bioinformatics plan, experimental design documentation. |
| Gathering, cleaning, and formatting the biological data for analysis. | Data generation (e.g., sequencing), data import, quality control, data cleaning, format conversion, metadata annotation. | Researchers, Data Generators, Bioinformatics Technicians, Data Curators. | Cleaned and formatted datasets, quality control reports. |
| Running the planned bioinformatics analyses using allocated resources and tools. | Executing scripts and pipelines, performing statistical analyses, running machine learning models, utilizing high-performance computing (HPC) clusters or cloud platforms. | Bioinformatics Analysts, Computational Biologists, Research Assistants. | Raw analysis outputs, statistical summaries, intermediate results. |
| Making sense of the analysis results and presenting them in an understandable format. | Biological interpretation of findings, generating graphs, charts, heatmaps, phylogenetic trees, pathway analysis. | Bioinformatics Analysts, Domain Experts (e.g., Geneticists, Molecular Biologists), Statisticians. | Interpreted results, graphical representations, visualizations. |
| Documenting the project and sharing the findings with the relevant stakeholders. | Writing research reports, preparing manuscripts for publication, presenting findings at conferences, creating data dashboards. | Researchers, PIs, Bioinformatics Analysts, Scientific Writers. | Research reports, publications, conference presentations, data summaries. |
| Ensuring researchers have the necessary skills to utilize bioinformatics infrastructure and perform analyses themselves. | Organizing workshops, providing hands-on training, developing online tutorials, mentoring junior researchers. | Bioinformatics Core Facilities, University Departments, National Research Institutes, Training Providers. | Trained personnel, skilled researchers, educational materials. |
| Ensuring the bioinformatics infrastructure remains functional, up-to-date, and accessible. | Regular software updates, hardware maintenance, troubleshooting technical issues, providing ongoing user support, managing user accounts and access. | IT Support Teams, Bioinformatics Infrastructure Managers, System Administrators. | Functional infrastructure, reliable services, responsive support. |
| Periodically assessing the effectiveness of the bioinformatics infrastructure and processes. | Collecting user feedback, analyzing usage statistics, identifying areas for improvement, planning for future upgrades or new resource acquisition. | Bioinformatics Steering Committees, Research Management, Infrastructure Providers. | Feedback reports, performance metrics, strategic development plans. |
Bioinformatics Infrastructure Process Workflow in Kenya
- Inquiry and Needs Assessment
- Resource Identification and Allocation
- Project Planning and Design
- Data Acquisition and Preparation
- Computational Analysis and Execution
- Data Interpretation and Visualization
- Reporting and Dissemination
- Training and Capacity Building
- Maintenance and Support
- Evaluation and Improvement
Bioinformatics Infrastructure Cost In Kenya
Bioinformatics infrastructure in Kenya encompasses a range of hardware, software, and services essential for biological data analysis, research, and development. The cost of establishing and maintaining this infrastructure is influenced by several key factors, leading to a wide spectrum of pricing. These factors include the type and scale of computing resources required, the sophistication of analytical software, data storage needs, network connectivity, and the level of technical support and expertise available. For instance, academic institutions might opt for shared computing clusters, while commercial enterprises or specialized research facilities may require dedicated high-performance computing (HPC) clusters. Cloud-based solutions are also increasingly popular, offering flexibility but with recurring operational costs. The specific biological disciplines being served also play a role; genomics and proteomics research, for example, often demand more substantial computational power and storage than, say, basic epidemiological data analysis.
Pricing in the Kenyan market for bioinformatics infrastructure can be broadly categorized. Basic hardware, such as powerful workstations for individual researchers or small labs, can range from KES 250,000 to KES 800,000, depending on specifications like CPU cores, RAM, and GPU capabilities. Dedicated servers for localized data processing might cost between KES 1,000,000 and KES 5,000,000 or more, depending on the number of nodes, storage capacity, and networking. For larger-scale needs, building or leasing HPC clusters can escalate costs significantly, potentially running into tens or hundreds of millions of Kenyan Shillings for acquisition and setup. Cloud computing services from providers like AWS, Azure, or Google Cloud, when utilized in Kenya (or accessed through regional endpoints), are typically priced per hour for compute instances and per gigabyte for storage, with monthly costs varying widely based on usage, from tens of thousands to millions of KES. Specialized bioinformatics software licenses can be a substantial expense, with annual fees ranging from KES 100,000 for single-user, less complex tools to several million KES for enterprise-level suites or multi-user licenses for advanced platforms. Data storage solutions, from local NAS/SAN systems to cloud storage, can range from KES 50,000 per terabyte for basic solutions to KES 500,000+ per terabyte for high-speed, redundant enterprise-grade storage. Finally, ongoing costs include maintenance, power, cooling, and skilled personnel, which are crucial for the operationalization of any bioinformatics infrastructure.
| Infrastructure Component | Typical Price Range (KES) | Notes |
|---|---|---|
| High-Performance Workstation | 250,000 - 800,000 | For individual researchers or small labs; depends on CPU, RAM, GPU. |
| Dedicated Server(s) | 1,000,000 - 5,000,000+ | For localized data processing; scales with nodes and storage. |
| HPC Cluster (Acquisition/Setup) | 10,000,000 - 100,000,000+ | For large-scale, intensive research projects. |
| Cloud Computing (Monthly Usage) | 50,000 - 5,000,000+ | Highly variable based on compute, storage, and data transfer. |
| Specialized Software License (Annual) | 100,000 - 5,000,000+ | Depends on software complexity, user count, and vendor. |
| Data Storage (Per Terabyte) | 50,000 - 500,000+ | Ranges from basic NAS to high-speed, redundant enterprise solutions. |
| Network Connectivity (Monthly) | 20,000 - 200,000+ | Depends on bandwidth, latency, and service provider. |
| Maintenance & Support Contract (Annual) | 5% - 15% of initial hardware/software cost | Essential for ensuring uptime and performance. |
Key Factors Influencing Bioinformatics Infrastructure Costs in Kenya
- Type and Scale of Computing Resources (Workstations, Servers, HPC Clusters)
- Data Storage Capacity and Speed Requirements
- Software Licenses and Subscriptions (Proprietary vs. Open Source)
- Cloud Computing Service Usage (Compute, Storage, Networking)
- Network Bandwidth and Connectivity
- Technical Support, Maintenance, and Personnel Costs
- Power, Cooling, and Physical Infrastructure Needs
- Specific Biological Disciplines and Data Complexity (e.g., Genomics, Proteomics)
Affordable Bioinformatics Infrastructure Options
Navigating the world of bioinformatics infrastructure can be daunting, especially when budget is a primary concern. Fortunately, a range of affordable options exist, often built around the concepts of value bundles and strategic cost-saving measures. Value bundles typically combine essential software, hardware, and support services into a single, more cost-effective package, reducing the need for individual procurement and integration. Cost-saving strategies focus on maximizing resource utilization, leveraging open-source technologies, and adopting flexible deployment models.
| Strategy/Option | Description | Value Proposition | Cost-Saving Mechanism |
|---|---|---|---|
| Cloud Computing (IaaS/PaaS) | Leveraging providers like AWS, Google Cloud, Azure for compute, storage, and specialized bioinformatics services. | Scalability, access to cutting-edge hardware and software, reduced upfront capital expenditure. | Pay-as-you-go pricing, avoiding over-provisioning, utility-based cost model. |
| Open-Source Software Stacks | Utilizing freely available bioinformatics pipelines and workflows (e.g., Galaxy, Nextflow). | Eliminates software licensing costs, fosters community-driven development and support. | Zero licensing fees, reduced dependency on proprietary vendors. |
| Containerization (Docker/Singularity) | Packaging applications and their dependencies into portable containers for consistent execution. | Improved reproducibility, simplified deployment, efficient resource utilization across different environments. | Reduced setup time, less hardware-specific configuration, potential for increased compute density. |
| HPC Consortia/Shared Resources | Joining or utilizing shared high-performance computing clusters within research institutions or collaborations. | Access to powerful computing resources without individual acquisition costs. | Shared infrastructure costs, economies of scale, avoidance of capital expenditure. |
| Value Bundles (Managed Services) | Purchasing integrated packages of software, hardware, and support for specific bioinformatics tasks. | Simplified procurement, pre-configured solutions, expert support, faster time-to-results. | Potentially lower overall cost compared to individual component purchases and integration, predictable operational expenses. |
Key Affordable Bioinformatics Infrastructure Options & Strategies
- Cloud-based computing platforms (e.g., AWS, Google Cloud, Azure) offering pay-as-you-go models and pre-configured bioinformatics environments.
- Hybrid cloud solutions, blending on-premises hardware with cloud scalability for cost optimization.
- Open-source bioinformatics software stacks (e.g., Galaxy, Nextflow, Snakemake) minimizing licensing fees.
- Shared computing clusters and high-performance computing (HPC) resources within academic or institutional consortia.
- Virtual Machine (VM) and Containerization technologies (e.g., Docker, Singularity) for efficient resource allocation and portability.
- Managed services for specific bioinformatics tasks (e.g., sequencing data analysis, variant calling) that can be more economical than building in-house expertise.
- Strategic procurement of hardware, including refurbished equipment or bulk purchasing agreements.
- Tiered support models, opting for community support for less critical components and paid support for mission-critical systems.
Verified Providers In Kenya
In the competitive landscape of healthcare providers in Kenya, distinguishing between genuine and fraudulent services is paramount for individuals seeking quality medical care. Franance Health has emerged as a beacon of trust and reliability, consistently upholding the highest standards of healthcare delivery. Their commitment to verified credentials and ethical practices makes them a standout choice for patients across the nation.
| Credential Type | Franance Health Verification Standard | Patient Benefit |
|---|---|---|
| Medical Practitioner Licenses | All doctors, nurses, and specialists hold current and valid licenses from the Kenya Medical Practitioners and Dentists Council (KMPDC) and relevant professional bodies. | Ensures practitioners are legally qualified and competent to practice, guaranteeing safe and professional medical care. |
| Facility Accreditation | Hospitals and clinics are accredited by the National Hospital Insurance Fund (NHIF) and/or recognized international accreditation bodies, signifying adherence to stringent operational and safety standards. | Guarantees that facilities meet high standards for patient care, safety, hygiene, and operational efficiency. |
| Specialist Certifications | Specialists possess recognized board certifications and fellowships in their respective fields, often from reputable international institutions. | Confirms advanced expertise and specialized knowledge, providing access to high-quality, specialized medical treatments. |
| Pharmaceutical Compliance | All medications are sourced from licensed and reputable pharmaceutical suppliers and dispensed by registered pharmacists, adhering to Pharmacy and Poisons Board regulations. | Ensures the authenticity, efficacy, and safety of all prescribed medications, preventing the use of counterfeit drugs. |
| Continuous Professional Development (CPD) | All medical staff are mandated to participate in ongoing CPD programs to stay updated with the latest medical advancements and techniques. | Ensures patients receive care based on current medical knowledge and best practices, leading to improved health outcomes. |
Why Franance Health Stands Out:
- Unwavering Commitment to Quality: Franance Health prioritizes patient well-being above all else, implementing rigorous quality control measures across all their facilities and services.
- Experienced and Certified Professionals: The backbone of Franance Health is its team of highly skilled and certified medical practitioners, who undergo continuous professional development.
- Patient-Centric Approach: Every aspect of Franance Health's operations is designed with the patient in mind, ensuring accessible, compassionate, and effective healthcare.
- Adherence to Regulatory Standards: Franance Health operates in strict compliance with all Kenyan healthcare regulations and international best practices.
- Technological Integration: Embracing innovation, Franance Health leverages modern medical technology to enhance diagnosis, treatment, and patient experience.
Scope Of Work For Bioinformatics Infrastructure
This document outlines the Scope of Work (SOW) for establishing and maintaining robust bioinformatics infrastructure. It details the technical deliverables required to support advanced genomic and proteomic data analysis, along with standard specifications for hardware, software, and networking components. The goal is to provide a scalable, secure, and performant computing environment for research scientists.
| Component | Specification/Requirement | Quantity/Scale | Notes |
|---|---|---|---|
| Compute Nodes | Multi-core CPUs (e.g., Intel Xeon Scalable, AMD EPYC), minimum 32 cores per node; ample RAM (e.g., 128GB - 512GB per node); GPU acceleration optional based on workload. | Scalable, starting with 20 nodes | Prioritize power efficiency and high clock speeds for bioinformatic tasks. |
| High-Speed Interconnect | InfiniBand (e.g., HDR, NDR) or equivalent low-latency, high-bandwidth network. | Dedicated network infrastructure | Crucial for inter-node communication in parallel processing. |
| Compute Storage | Fast NVMe SSDs for scratch space (e.g., 1-2TB per node); shared parallel file system (e.g., Lustre, BeeGFS). | Minimum 100TB of scratch space, 500TB+ for shared filesystem | Optimized for high read/write operations. |
| Long-Term Storage | High-capacity, cost-effective NAS or object storage (e.g., Ceph, MinIO). | Minimum 1PB, expandable | Consider tiered storage for active vs. archival data. |
| Networking | 10/40/100 GbE Ethernet for management and data transfer to/from storage. | Redundant network paths | Ensure adequate bandwidth for data ingress/egress. |
| Operating System | Linux (e.g., CentOS Stream, Rocky Linux, Ubuntu LTS). | Standardized across all nodes | Ensure compatibility with bioinformatics software. |
| Container Runtime | Docker, Singularity/Apptainer. | Pre-installed on compute nodes | Essential for reproducible research. |
| Job Scheduler | Slurm Workload Manager. | Configured and optimized | Support for various job types (serial, parallel, array). |
| Monitoring Tools | Prometheus, Grafana, Ganglia. | Comprehensive system and application monitoring | Real-time dashboards and alerting. |
Key Technical Deliverables
- High-Performance Computing (HPC) Cluster: Procurement, installation, and configuration of compute nodes, high-speed interconnects, and storage.
- Data Storage Solutions: Implementation of scalable and reliable storage for raw sequencing data, processed data, and archives.
- Bioinformatics Software Suite: Installation and licensing of essential bioinformatics tools and pipelines (e.g., alignment, variant calling, gene expression analysis, assembly).
- Containerization Platform: Setup and management of a container orchestration system (e.g., Kubernetes, Docker Swarm) for reproducible analysis.
- Data Transfer and Access Mechanisms: Secure and efficient methods for data ingestion, export, and researcher access.
- Job Scheduling System: Configuration of a workload manager (e.g., Slurm, LSF) for optimal resource utilization.
- Monitoring and Alerting System: Implementation of tools to track system performance, resource usage, and identify potential issues.
- Backup and Disaster Recovery Plan: Development and testing of comprehensive data backup and recovery strategies.
- Security Implementation: Configuration of firewalls, access controls, encryption, and audit logging to protect sensitive data.
- User Training and Documentation: Provision of training materials and comprehensive documentation for users on accessing and utilizing the infrastructure.
Service Level Agreement For Bioinformatics Infrastructure
This Service Level Agreement (SLA) outlines the guaranteed response times and uptime for the provided Bioinformatics Infrastructure. It aims to ensure reliable and efficient access to computational resources and data storage for research purposes.
| Service Component | Uptime Guarantee | Response Time Target (Best Effort) | Notification Period for Scheduled Maintenance | Escalation Process for Incidents |
|---|---|---|---|---|
| High-Performance Computing (HPC) Cluster | 99.5% (excluding scheduled maintenance) | Job queue processing: < 30 minutes for typical jobs under normal load | 48 hours for planned maintenance | Critical Incident: < 2 hours for initial diagnosis and mitigation plan; < 8 hours for resolution/restoration. Major Incident: < 8 business hours for initial diagnosis and mitigation plan; < 24 business hours for resolution/restoration. |
| Data Storage (e.g., NAS, Object Storage) | 99.8% (excluding scheduled maintenance) | File access/transfer: < 10 seconds for typical operations under normal load | 24 hours for planned maintenance | Critical Incident: < 1 hour for initial diagnosis and mitigation plan; < 4 hours for resolution/restoration. Major Incident: < 4 business hours for initial diagnosis and mitigation plan; < 12 business hours for resolution/restoration. |
| Bioinformatics Software Platform (e.g., Galaxy, specific pipelines) | 99.0% (excluding scheduled maintenance) | Web interface response: < 5 seconds for typical page loads | 72 hours for planned maintenance | Critical Incident: < 4 hours for initial diagnosis and mitigation plan; < 16 hours for resolution/restoration. Major Incident: < 12 business hours for initial diagnosis and mitigation plan; < 48 business hours for resolution/restoration. |
| Network Connectivity | 99.9% (excluding scheduled maintenance) | General network access: < 1 second for ping response | 48 hours for planned maintenance | Critical Incident: < 1 hour for initial diagnosis and mitigation plan; < 4 hours for resolution/restoration. Major Incident: < 8 business hours for initial diagnosis and mitigation plan; < 24 business hours for resolution/restoration. |
Key Definitions
- Uptime: The percentage of time the Bioinformatics Infrastructure is operational and accessible to authorized users.
- Downtime: The period during which the Bioinformatics Infrastructure is not operational or accessible.
- Scheduled Maintenance: Planned periods of downtime for updates, upgrades, or system checks, communicated in advance to users.
- Unscheduled Outage: Any downtime that is not a result of scheduled maintenance.
- Response Time: The time taken for the system to acknowledge and begin processing a user request (e.g., job submission, data retrieval).
- Critical Incident: An event that renders a significant portion or the entirety of the Bioinformatics Infrastructure unavailable.
- Major Incident: An event that impacts a specific service or a subset of users, but does not render the entire infrastructure unavailable.
Frequently Asked Questions
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