Coverage Areas
HyperFRAME Research will be covering the following technology domains in our research and coverage
Cloud Infrastructure
• Public Cloud: Major IaaS, PaaS, and SaaS providers (AWS,
Azure, Google Cloud, Oracle Cloud)
• Private Cloud: On premises cloud platforms (VMware Cloud, OpenStack, HPE GreenLake, Dell APEX, Lenovo TruScale and IBM Cloud Private)
• Hybrid & Multi cloud Architectures: Interoperability,
orchestration, data migration strategies, edge to cloud
integration
• Sovereign Cloud: Regional compliance, data residency, and
security requirements
• Cost Management & Optimization: FinOps, workload
optimization, cloud cost governance
Operations & Observability
• IT Operations Management (ITOM): Automation, operational intelligence, configuration management • Observability Platforms: Fullstack observability, distributed tracing, metrics, logs, and telemetry • Application Performance Monitoring (APM): End to end monitoring, synthetic and real user monitoring • Log Management & Analysis: Log aggregation, log based alerting, SIEM integration • Event Management & Response: Incident management, automated alerting, rootcause analysis
IT Service Management (ITSM)
• Service Desk Management: Ticketing, incident management, SLA management • Asset Management & CMDB: Asset tracking, configuration management, lifecycle management • Change Management: ITIL best practices, risk management, change advisory boards • Self-Service & Knowledge Management: Knowledge base integration, user self-service portals • Experience Management: Employee experience monitoring, end-user feedback loops
Server Infrastructure
• High Performance Compute (HPC): Data center class processors, GPUs, TPUs, AI chips • Server Virtualization:: VMware VCF Microsoft HyperV, KVM, Xen • Server Virtualization: VMware vSphere, Microsoft HyperV, KVM, Xen • UNIX and UNIXBased Systems: AIX, Solaris, HPUX • Edge Computing & Edge Servers: Edgenative applications, infrastructure, realtime processing • Mainframe Modernization: z/OS, LinuxONE, mainframe-as-a-service, workload transformation to cloud • RISC-V, x86, ARM Architectures: Emerging processor designs for server workloads, power efficiency, performance enhancements
AIOps & Automation
• AIOps Platforms: Predictive analytics, anomaly detection, event correlation • Automation & Orchestration: Workflow automation, infrastructure as code (IaC), self healing systems • ChatOps & Conversational AI: Integrations with collaboration platforms, AIdriven IT support • Intelligent Virtual Assistants (IVAs): Chatbots, NLP driven help desk support • Decision Intelligence: Machine learning based decision making frameworks in IT operations
Virtualization & Containerization
• Virtual Machine Management: VM provisioning, performance optimization, resource allocation • Container Orchestration (Kubernetes): Kubernetes distributions (OpenShift, Tanzu, AKS, EKS, GKE), management and scaling • Serverless & Functionasa Service (FaaS): Serverless frameworks, functions on cloud providers • Container Security: Container image scanning, runtime security, Kubernetes security • MultiTenancy & Isolation: Resource isolation, performance management, tenant management within virtualized/containerized environments
AI Infrastructure
• Large and Small Language Models • GPU, DPU and XPU • AI Accelerators • AI processing frameworks • MLOps
Storage and Data Infrastructure
• Storage Area Networks • Network attached storage • Block storage devices • Filers and file based storage • Ceph and open source
Mainframe Systems
• Mainframe Operations & Modernization: Mainframe DevOps, workload offloading, integration with cloud services • Mainframe Observability: Monitoring z/OS environments, performance tuning, mainframe AIOps • Mainframe Security & Compliance: Access controls, identity management, regulatory compliance (e.g., SOX, GDPR) • Mainframe-as-a-Service: Managed mainframe services, cloud integrated mainframe environments • Mainframe Development Tools: Modern IDEs, version control, mainframe APIs
AI Edge
• Edge Devices: IoT sensors, smart cameras, and mobile devices collecting and processing data locally. • Edge Gateways: Intermediary devices aggregating and filtering data between edge devices and cloud. • Edge Servers: Localized servers handling compute-intensive tasks like real-time AI inference. • Cloud Data Centers: Centralized infrastructure for storage, large-scale AI training, and analytics. • Networking Infrastructure: High-speed connections (5G, Wi-Fi, fiber) linking edge and cloud. • AI Algorithms: Models optimized for edge (lightweight) or cloud (complex) processing in Hybrid AI. • Management Software: Orchestration tools for workload distribution, security, and monitoring across edge and cloud.
AI Networking
• AI-Powered Network Automation: Intelligent systems streamline operations, reducing manual tasks. • Machine Learning for Network Optimization: Adaptive algorithms enhance performance and efficiency. • Edge Computing Integration: AI-driven processing closer to data sources improves responsiveness. • AI-Based Security Solutions: Advanced threat detection and mitigation bolster cybersecurity. • Software-Defined Networking (SDN): AI enhances dynamic network management and scalability. • AI Algorithms: Models optimized for edge (lightweight) or cloud (complex) processing in Hybrid AI. • 5G and AI Collaboration: AI optimizes network traffic and improves connectivity for next-gen infrastructure. • AI-Driven Predictive Analytics: Proactive insights help in network planning and performance forecasting.