PhD Aging & Rejuvenation Survey MCP Server

Endpoints MCP & Relatório Semanal (Cron: 0 9 * * 1)

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Endpoint Streamable HTTP para mcp_config.json: https://phd-aging-survey-mcp.pages.dev/mcp

Pilares de Investigação (A1–A4) & Research Questions

• RQ1-SEPARABILITY (A1): How can progressive software aging in long-running LLM inference runtimes (e.g., Ollama / llama.cpp) be statistically isolated from thermal throttling and DVFS governor drift under variable arrival workloads?
• RQ2-LAYER-LOCALIZATION (A2): Which intervention (container/process restart, KV-cache flush, or OS page-cache drop via /proc/sys/vm/drop_caches) restores throughput and Joules-per-token efficiency without requiring a full node reboot?
• RQ3-JOULE-REJUVENATION (A3): When is the energy and carbon cost of a rejuvenation action (e.g., reloading a 4.7 GB–8.9 GB GGUF model into RAM) amortized by the post-rejuvenation reduction in Joules per token?
• RQ4-VERIFIABLE-PROTOCOL (A4): What minimal continuous out-of-band telemetry invariants are required to certify that a multi-day edge AI experiment actually maintained its declared hardware and workload state?
• RQ5-WORKLOAD-NONSTATIONARITY (A1): Does bursty/diurnal prompt arrival accelerate KV-cache allocator fragmentation compared to closed-loop saturation at equal total token volume?
• RQ6-MOE-EXPERT-HOTSPOTS (A2): Do Mixture-of-Experts (MoE) models (e.g., DeepSeek-v2 Lite) exhibit different page-cache and RSS aging trajectories than dense models due to sparse expert routing?
• RQ7-CARBON-AWARE-REJUVENATION-WINDOW (A3): Can rejuvenation actions (process restart / cold model reload) be co-scheduled with low-carbon grid windows (e.g., solar midday in PT/ES) to reduce net rejuvenation carbon cost by >40%?