Graph Random Walks (GRWs) offer efficient approximations of key graph properties and have been widely adopted in many applications. However, GRW workloads are notoriously difficult to accelerate due to their strong data dependencies, irregular memory access patterns, and imbalanced execution behavior. While recent work explores FPGA-based accelerators for GRWs, existing solutions fall far short of hardware potential due to inefficient pipelining and static scheduling. This paper presents RidgeWalker, a high-performance GRW accelerator designed for datacenter FPGAs. The key insight behind RidgeWalker is that the Markov property of GRWs allows decomposition into stateless, fine-grained tasks that can be executed out-of-order without compromising correctness. Building on this insight, RidgeWalker introduces an asynchronous pipeline architecture with a feedback-driven scheduler grounded in queuing theory. This design enables perfect pipelining and adaptive load balancing. We prototype RidgeWalker on FPGAs and evaluate its performance across a range of GRW algorithms and real-world graph datasets. Experimental results demonstrate that RidgeWalker achieves an average speedup of 7.0× over state-of-the-art FPGA solutions and 8.1× over GPU solutions, with peak speedups of up to 71.0× and 22.9×, respectively. The source code is publicly available at https://github.com/Xtra-Computing/RidgeWalker.

This paper explores traffic engineering (TE) for large-scale Low-Earth-Orbit satellite constellations. While there is rich prior work on TE algorithms for global cloud wide-area networks (WANs), they are designed for static network topologies and often require significant computation time for large-scale networks. Such limitations make existing WAN TE algorithms unsuitable for large-scale satellite networks which rapidly change topology and require computing optimal traffic allocation under stringent latency constraints.

This paper presents DDS, a novel disaggregated storage architecture enabled by emerging networking hardware, namely DPUs (Data Processing Units). DPUs can optimize the latency and CPU consumption of disaggregated storage servers. However, utilizing DPUs for DBMSs requires careful design of the network and storage paths and the interface exposed to the DBMS. To fully benefit from DPUs, DDS heavily uses DMA, zero-copy, and userspace I/O to minimize overhead when improving throughput. It also introduces an offload engine that eliminates host CPUs by executing client requests directly on the DPU. Adopting DDS' API requires minimal DBMS modification. Our experimental study and production system integration show promising results---DDS achieves higher disaggregated storage throughput with an order of magnitude lower latency, and saves up to tens of CPU cores per storage server.

Memory disaggregation allows applications running on compute servers to expand their pool of available memory capacity by leveraging remote resources through low-latency networks. Unfortunately, in existing software-level disaggregation frameworks, the simple act of issuing requests to remote memory---paid on every access---can consume many CPU cycles. This overhead represents a direct cost to disaggregation, not only on the throughput of remote memory access but also on application logic, which must contend with the framework's CPU overheads.

Recent proposals for the disaggregation of compute, memory, storage, and accelerators in data centers promise substantial operational benefits. Unfortunately, for resources like memory, this comes at the cost of performance overhead due to the potential insertion of network latency into every load and store operation. This effect is particularly felt by data-intensive systems due to the size of their working sets, the frequency at which they need to access memory, and the relatively low computation per access. This performance impairment offsets the elasticity benefit of disaggregated memory. This paper presents TELEPORT, a compute pushdown framework for data-intensive systems that run on disaggregated architectures; compared to prior work on compute pushdown, TELEPORT is unique in its efficiency and flexibility. We have developed optimization prin- ciples for several popular systems including a columnar in-memory DBMS, a graph processing system, and a MapReduce system. The evaluation results show that using TELEPORT to push down simple operators improves the performance of these systems on state-of-the-art disaggregated OSes by an order of magnitude, thus fully exploiting the elasticity of disaggregated data centers.

While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory demands. Moreover, it ensures efficient resource utilization and hence prevents resource fragmentation in a data center. We have explored the design of XOV blockchain systems in a disaggregated fashion and developed a tiered key-value store that can elastically scale its memory and storage. Our design significantly speeds up the execution stage. We have also leveraged several techniques to parallelize the validation stage in FlexChain to further improve the overall blockchain performance. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively.

At-scale evaluation of new data center network innovations is becoming increasingly intractable. This is true for testbeds, where few, if any, can afford a dedicated, full-scale replica of a data center. It is also true for simulations, which while originally designed for precisely this purpose, have struggled to cope with the size of today's networks. This paper presents an approach for quickly obtaining accurate performance estimates for large data center networks. Our system,MimicNet, provides users with the familiar abstraction of a packet-level simulation for a portion of the network while leveraging redundancy and recent advances in machine learning to quickly and accurately approximate portions of the network that are not directly visible. MimicNet can provide over two orders of magnitude speedup compared to regular simulation for a data center with thousands of servers. Even at this scale, MimicNet estimates of the tail FCT, throughput, and RTT are within 5% of the true results.

Redy is a cloud service that provides high performance caches using RDMA-accessible remote memory. An application can customize the performance of each cache with a service level objective (SLO) for latency and throughput. By using remote memory, it can leverage stranded memory and spot VM instances to reduce the cost of its caches and improve data center resource utilization. Redy automatically customizes the resource configuration for the given SLO, handles the dynamics of remote memory regions, and recovers from failures. The experimental evaluation shows that Redy can deliver its promised performance and robustness under remote memory dynamics in the cloud. We augment a production key-value store, FASTER, with a Redy cache. When the working set exceeds local memory, using Redy is significantly faster than spilling to SSDs.

Resource disaggregation is a new architecture for data centers in which resources like memory and storage are decoupled from the CPU, managed independently, and connected through a high-speed network. Recent work has shown that although disaggregated data centers (DDCs) provide operational benefits, applications running on DDCs experience degraded performance due to extra network latency between the CPU and their working sets in main memory. DBMSs are an interesting case study for DDCs for two main reasons: (1) DBMSs normally process data-intensive workloads and require data movement between different resource components; and (2) disaggregation drastically changes the assumption that DBMSs can rely on their own internal resource management. We take the first step to thoroughly evaluate the query execution performance of production DBMSs in disaggregated data centers. We evaluate two popular open-source DBMSs (MonetDB and PostgreSQL) and test their performance with the TPC-H benchmark in a recently released operating system for resource disaggregation. We evaluate these DBMSs with various configurations and compare their performance with that of single-machine Linux with the same hardware resources. Our results confirm that significant performance degradation does occur, but, perhaps surprisingly, we also find settings in which the degradation is minor or where DDCs actually improve performance.

This paper presents GraphRex, an efficient, robust, scalable, and easy-to-program framework for graph processing on datacenter infrastructure. To users, GraphRex presents a declarative, Datalog-like interface that is natural and expressive. Underneath, it compiles those queries into efficient implementations. A key technical contribution of GraphRex is the identification and optimization of a set of global operators whose efficiency is crucial to the good performance of datacenter-based, large graph analysis. Our experimental results show that GraphRex significantly outperforms existing frameworks---both high- and low-level---in scenarios ranging across a wide variety of graph workloads and network conditions, sometimes by two orders of magnitude.

A key ingredient to a startup's success is its ability to raise funding at an early stage. Crowdfunding has emerged as an exciting new mechanism for connecting startups with potentially thousands of investors. Nonetheless, little is known about its effectiveness, nor the strategies that entrepreneurs should adopt in order to maximize their rate of success. In this paper, we perform a longitudinal data collection and analysis of AngelList - a popular crowdfunding social platform for connecting investors and entrepreneurs. Over a 7-10 month period, we track companies that are actively fund-raising on AngelList, and record their level of social engagement on AngelList, Twitter, and Facebook. Through a series of measures on social en- gagement (e.g. number of tweets, posts, new followers), our analysis shows that active engagement on social media is highly correlated to crowdfunding success. In some cases, the engagement level is an order of magnitude higher for successful companies. We further apply a range of machine learning techniques (e.g. decision tree, SVM, KNN, etc) to predict the ability of a company to success- fully raise funding based on its social engagement and other metrics. Since fund-raising is a rare event, we explore various techniques to deal with class imbalance issues. We observe that some metrics (e.g. AngelList followers and Facebook posts) are more signi cant than other metrics in predicting fund-raising success. Furthermore, despite the class imbalance, we are able to predict crowdfunding success with 84% accuracy.

Graph analytics systems have gained significant popularity due to the prevalence of graph data. Many of these systems are designed to run in a shared-nothing architecture whereby a cluster of machines can process a large graph in parallel. In more recent proposals, others have argued that a single-machine system can achieve better performance and/or is more cost-effective. There is however no clear consensus which approach is better. In this paper, we classify existing graph analytics systems into four categories based on the architectural differences, i.e., processing infrastructure (centralized vs distributed), and memory consumption (in-memory vs out-of-core). We select eight open-source systems to cover all categories, and perform a comparative measurement study to compare their performance and cost characteristics across a spectrum of input data, applications, and hardware settings. Our results show that the best performing configuration can depend on the type of applications and input graphs, and there is no dominant winner across all categories. Based on our findings, we summarize the trends in performance and cost, and provide several insights that help to illuminate the performance and resource cost tradeoffs across different graph analytics systems and categories.

Inspired by Google's Pregel, many distributed graph processing systems have been developed recently to process big graphs. These systems expose a vertex-centric programming interface to users, where a programmer thinks like a vertex when designing parallel graph algorithms. However, existing systems are designed for tasks where most vertices in a graph participate in the computation, and they are not suitable for processing light-workload graph queries which only access a small portion of vertices. This is because their programming model can seriously under-utilize the resources in a cluster for processing graph queries. In this demonstration, we introduce a general-purpose system for querying big graphs, called Quegel, which treats queries as first-class citizens in the design of its computing model. Quegel adopts a novel superstep-sharing execution model to overcome the weaknesses of existing systems. We demonstrate it is user-friendly to write parallel graph-querying programs with Quegel's interface; and we also show that Quegel is able to achieve real-time response time in various applications, including the two applications that we plan to demonstrate: point-to-point shortest-path queries and XML keyword search.

Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems employ a user-friendly "think like a vertex" programming model, and exhibit good scalability for tasks where the majority of graph vertices participate in computation. However, the design of these systems can seriously under-utilize the resources in a cluster for processing light-workload graph queries, where only a small fraction of vertices need to be accessed. In this work, we develop a new open-source system, called Quegel , for querying big graphs. Quegel treats queries as first-class citizens in its design: users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand, using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.