An unprecedented amount of data generated in space missions triggers lots of practical challenges and concerns with its transfer, storage, and analysis. As Lunar and deep space missions emerge, we need to also face the challenges of distributed computing and big data analytics. In this paper, we outline these issues and discuss how to design and analyze Lunar data centers, being space data centers designed for distributed computing, and data analysis for (not only) Lunar missions. We investigate the opportunities and chances of such space architectures to lay the foundations for practical space data centers and real-life use cases.

The rapid growth of the space industry is creating an increasing amount of data in orbit, which at the current time is not matched yet by a corresponding increase in data download capacity. Using data analytics at the edge, i.e., data processing on-board satellites, holds the promise to significantly mitigate this problem. Newly available and highly efficient artificial intelligence (AI) hardware accelerators and other off-the-shelf compute-hardware has already been successfully exploited for demonstrating satellite on-board deep learning. Aggregating such components and technologies at scale and introducing resource sharing concepts beyond individual spacecrafts, will yield the equivalent of a space data center—a space-based system that will collect, process, store, and relay data from own sensors or from "client" satellites and work in orchestration with other SDCs in a network. In this paper, we outline new opportunities for such data- and compute-sharing schemes in space and how current limitations of existing systems can be overcome.

Distributed DRAM stores have become an attractive option for providing fast data accesses to analytics applications. To accelerate the performance of these stores, researchers have proposed using RDMA technology. RDMA offers high bandwidth and low latency data access by carefully separating resource setup from IO operations, and making IO operations fast by using rich network semantics and offloading. Despite recent interest, leveraging the full potential of RDMA in a distributed environment remains a challenging task. In this paper, we present RDMA Store or RStore, a DRAM-based data store that delivers high performance by extending RDMA's separation philosophy to a distributed setting. RStore achieves high aggregate bandwidth (705 Gb/s) and close-to-hardware latency on our 12-machine testbed. We developed a distributed graph processing framework and a Key-Value sorter using RStore's unique memory-like API. The graph processing framework, which relies on RStore for low-latency graph access, outperforms state-of-the-art systems by margins of 2.6 -- 4.2× when calculating Page Rank. The Key-Value sorter can sort 256 GB of data in 31.7 sec, which is 8× better than Hadoop TeraSort in a similar setting.

The wide deployment of virtualization in datacenters catalyzes the emergence of virtual traffic that delivers the network demands between the physical network and the virtual machines hosting clients' services. Virtual traffic presents new opportunities for reducing physical network demands, as well as challenges of increasing management complexity. Given the plethora of prior art on virtualization technologies in datacenters, surprisingly little is still known about such virtual traffic, and its dependence on the physical network and virtual machines. This paper provides a multi-faceted analysis of the patterns and impacts of multiplexing the virtual traffic onto the physical network, particularly from the perspective of the network edge. We use a large collection of field data from production datacenters hosting a large number of diversified services from multiple enterprise tenants. Our first focus is on uncovering the temporal and spatial characteristics of the virtual and physical traffic, i.e., network demand growth and communication patterns, with special attention paid to the traffic of migrating virtual machines. The second focus is on characterizing the effect of network multiplexing in terms of communication locality, traffic load heterogeneity, and the dependency on CPU processing power at the edges of the network. Last but not least, we conduct a mirroring analysis on service QoS, defined by the service unavailability induced by network related issues, e.g., loads. We qualitatively and quantitatively discuss the implications and opportunities that virtual traffic presents for network capacity planning of virtualized networks and datacenters.

The Square Kilometre Array (SKA) is a future radio telescope, currently being designed by the worldwide radio-astronomy community. During the first of two construction phases, more than 250,000 antennas will be deployed, clustered in aperture-array stations. The antennas will generate 2.5 Pb/s of data, which needs to be processed in real time. For the processing stages from A/D conversion to central correlation, we propose an ASIC solution using only three chip architectures. The architecture is scalable - additional chips support additional antennas or beams - and versatile - it can relocate its receiver band within a range of a few MHz up to 4GHz. This flexibility makes it applicable to both SKA phases 1 and 2. The proposed chips implement an antenna and station processor for 289 antennas with a power consumption on the order of 600W and a correlator, including corner turn, for 911 stations on the order of 90 kW.

In this letter, we show how a prefilter can be automatically adapted to open the data eye in a nonreturn to zero transmission system using only two binary samples per bit. Although the equalizer primarily aims to minimize timing jitter with a zero-forcing criterion, this equalization method also results in nearly optimum vertical eye opening, thereby causing very little overhead in complexity and power consumption. The target application is low-power high-speed serial links to transfer data between chips over a printed circuit board