Low Earth Orbit (LEO) satellite networks, such as Starlink, provide global internet access and currently serve content to millions of users. Recent work has shown that existing network infrastructures, such as Content Delivery Networks (CDNs), are not well-suited to satellite network architectures. Traditional terrestrial CDNs degrade performance for satellite network users and do not alleviate the congestion in the ground-satellite links. We design StarCDN, a new CDN architecture that caches content in space to improve user experience and reduce ground-satellite bandwidth usage. The fundamental challenge in designing StarCDN lies in the orbital motion of satellites, which causes each satellite's coverage area to change rapidly, serving vastly different regions (e.g., US and Europe) within minutes. To address this, we introduce new consistent hashing and relayed fetching schemes tailored to LEO satellite networks. Our design enables cached content to flow in the opposite direction of the orbital motion to counter satellite motion. We evaluate StarCDN against multiple baselines using real-world traces from Akamai. Our evaluation demonstrates that StarCDN can reduce the ground-to-satellite bandwidth utilization by 80% and improve user-perceived latency by 2.5X. Further, we make available an open-source trace generator, SpaceGEN, for realistic simulations of satellite-based CDNs.

A similarity cache can reply to a query for an object with similar objects stored locally. In some applications of similarity caches, queries and objects are naturally represented as points in a continuous space. This is for example the case of 360° videos where user’s head orientation—expressed in spherical coordinates—determines what part of the video needs to be retrieved, or of recommendation systems where a metric learning technique is used to embed the objects in a finite dimensional space with an opportune distance to capture content dissimilarity. Existing similarity caching policies are simple modifications of classic policies like LRU, LFU, and${q}$LRU and ignore the continuous nature of the space where objects are embedded. In this paper, we propose GRADES, a new similarity caching policy that uses gradient descent to navigate the continuous space and find appropriate objects to store in the cache. We provide theoretical convergence guarantees and show GRADES increases the similarity of the objects served by the cache in both applications mentioned above.

A major obstacle for caching research is the increasing difficulty of obtaining original traces from production caching systems. Original traces are voluminous and also may contain private and proprietary information, and hence not generally made available to the public. The lack of original traces hampers our ability to evaluate new cache designs and provides the rationale for JEDI, our new synthetic trace generation tool. JEDI generates a synthetic trace that is "similar" to the original trace collected from a production cache, in particular, the two traces have similar object-level properties and produce similar hit rates in a cache simulation. JEDI uses a novel traffic model called Popularity-Size Footprint Descriptor (pFD) that concisely captures key properties of the original trace and uses the pFD to generate the synthetic trace. We show that the synthetic traces produced by JEDI can be used to accurately simulate a wide range of cache admission and eviction algorithms and the hit rates obtained from these simulations correspond closely to those obtained from simulations that use the original traces. JEDI will be provided to the public as open-source, along with a library of pFD's computed from traffic classes hosted on Akamai's production CDN. This will allow researchers to produce realistic synthetic traces for their own caching research.

Traces from production caching systems of users accessing content are seldom made available to the public as they are considered private and proprietary. The dearth of realistic trace data makes it difficult for system designers and researchers to test and validate new caching algorithms and architectures. To address this key problem, we present TRAGEN, a tool that can generate a synthetic trace that is "similar" to an original trace from the production system in the sense that the two traces would result in similar hit rates in a cache simulation. We validate TRAGEN by first proving that the synthetic trace is similar to the original trace for caches of arbitrary size when the Least-Recently-Used (LRU) policy is used. Next, we empirically validate the similarity of the synthetic trace and original trace for caches that use a broad set of commonly-used caching policies that include LRU, SLRU, FIFO, RANDOM, MARKERS, CLOCK and PLRU. For our empirical validation, we use original request traces drawn from four different traffic classes from the world's largest CDN, each trace consisting of hundreds of millions of requests for tens of millions of objects. TRAGEN is publicly available and can be used to generate synthetic traces that are similar to actual production traces for a number of traffic classes such as videos, social media, web, and software downloads. Since the synthetic traces are similar to the original production ones, cache simulations performed using the synthetic traces will yield similar results to what might be attained in a production setting, making TRAGEN a key tool for cache system developers and researchers.

A similarity cache can reply to a query for an object with similar objects stored locally. In some applications of similarity caches, queries and objects are naturally represented as points in a continuous space. Examples include 360° videos where user's head orientation-expressed in spherical coordinates- determines what part of the video needs to be retrieved, and recommendation systems where the objects are embedded in a finite-dimensional space with a distance metric to capture content dissimilarity. Existing similarity caching policies are simple modifications of classic policies like LRU, LFU, and qLRU and ignore the continuous nature of the space where objects are embedded. In this paper, we propose Grades, a new similarity caching policy that uses gradient descent to navigate the continuous space and find the optimal objects to store in the cache. We provide theoretical convergence guarantees and show Grades increases the similarity of the objects served by the cache in both applications mentioned above.

Large organizations often have users in multiple sites which are connected over the Internet. Since resources are limited, communication between these sites needs to be carefully orchestrated for the most benefit to the organization. We present a Mission-optimized Overlay Network (MON), a hybrid overlay network architecture for maximizing utility to the organization. We combine an offline and an online system to solve non-concave utility maximization problems. The offline tier, the Predictive Flow Optimizer (PFO), creates plans for routing traffic using a model of network conditions. The online tier, MONtra, is aware of the precise local network conditions and is able to react quickly to problems within the network. Either tier alone is insufficient. The PFO may take too long to react to network changes. MONtra only has local information and cannot optimize non-concave mission utilities. However, by combining the two systems, MON is robust and achieves near-optimal utility under a wide range of network conditions. While best-effort overlay networks are well studied, our work is the first to design overlays that are optimized for mission utility.