The Harvard Systems Group at the John A. Paulson School of Engineering and Applied Sciences (SEAS) operates at the vital intersection of systems, theory, and hardware. We design and build the architectures, networks, and databases that form the backbone of modern computing.
From scaling global cloud infrastructure to optimizing the software-hardware interface, we don’t just study systems—we reinvent the layers that power the future.
eventlocation_onSEC 4.307 & 4.308personHaoda Wang (Columbia University)
Cheap launches have moved satellites from a few expensive fault-tolerant high-orbit craft to constellations of commodity SmallSats, but flight software still carries the ad-hoc infrastructure built for one-off missions. Haoda will present Orbital, which derives tight worst-case execution time bounds for hard real-time multi-tenant scheduling, and Radshield, a programming model that replicates execution to protect commodity hardware from radiation-induced silent data corruption.
eventlocation_onSEC 4.307 & 4.308personAnshvardhan Shetty (Imperial College London)
Long agentic sessions make the KV cache the dominant memory cost in LLM serving, and flash offers capacity HBM cannot, but every block written spends a fraction of the drive’s lifetime, so a flash tier needs an admission policy and not only an eviction policy. Anshvardhan will present a study of that one-shot admission decision on a 222-day production trace of agentic coding traffic, including a learned prefill-time scorer and a two-tier implementation inside vLLM.
eventlocation_onSEC 2.122 & 2.123personHaoran Ni (Nanjing University)
The same open-weight LLM is now served by many providers with very different pricing models and time-varying latencies, so routing requests well can cut cost and latency without touching model quality. Haoran will present RouteWise, a multi-provider router that unifies heterogeneous pricing and jointly optimizes cost and latency, cutting cost by 26.6% and mean time to first token by 58.4% against OpenRouter’s automatic routing.
Smart cache eviction algorithms can adapt to workloads, but they often pay for it with complexity, instability, or overhead. Learning-Augmented Heuristics takes a different approach: keep the fast heuristic on the data path, and use learning to configure it at a slower timescale.
Bursty arrivals usually cause a drop in performance and are seen as headaches in production systems. In this blog, we investigate a phenomenon where burstiness actually improves performance, uncovering what conditions produce this effect.
Cache eviction is usually presented as a ranking problem. LRU ranks objects by
recency. LFU ranks them by frequency. More advanced algorithms combine several
signals, adapt their parameters, and maintain increasingly sophisticated data
structures to decide which object is least valuable.
Most LLM servers treat a deployed model as fixed: its weights have one
precision, its KV cache has one memory budget, and both remain unchanged until
the process restarts.