We introduce the ParClusterers Benchmark Suite (PCBS)---a collection of highly scalable parallel graph clustering algorithms and benchmarking tools that streamline comparing different graph clustering algorithms and implementations. The benchmark includes clustering algorithms that target a wide range of modern clustering use cases, including community detection, classification, and dense subgraph mining. The benchmark toolkit makes it easy to run and evaluate multiple instances of different clustering algorithms with respect to both the running time and quality. We evaluate the PCBS algorithms empirically and find that they deliver both the state of the art quality and the running time. In terms of the running time, they are on average over 4x faster than the fastest library we compared to. In terms of quality, the correlation clustering algorithm [Shi et al., VLDB'21] optimizing for the LambdaCC objective, which does not have a direct counterpart in other libraries, delivers the highest quality in the majority of datasets that we used.

Cloud-management stacks have become an increasingly important element in cloud computing, serving as the resource manager of cloud platforms. While the functionality of this emerging layer has been constantly expanding, its fault resilience remains under-studied. This paper presents a systematic study of the fault resilience of OpenStack---a popular open source cloud-management stack. We have built a prototype fault-injection framework targeting service communications during the processing of external requests, both among OpenStack services and between OpenStack and external services, and have thus far uncovered 23 bugs in two versions of OpenStack. Our findings shed light on defects in the design and implementation of state-of-the-art cloud-management stacks from a fault-resilience perspective.

While commercial off-the-shelf (COTS) operating systems (OS) have long been widely used, the issue regarding their robustness is far from being solved. Although many efforts have been made in this research domain, people still find it difficult to make choices among various OSs for robustness concerns. This paper proposes a reference model for OS robustness forecast and selection that aims to forecast the robustness of specific OSs under given operational profiles. At the same time, the model can select appropriate OSs as development/operating platforms that meet the particular robustness requirements of the target workloads. Our model combines OSspsila overall robustness with operational profiles and uses extensive tests on OS APIs to make our calculation. We have measured 255 APIs and C-library functions on windows XP and Vista, and 197 C-library functions on Linux 2.6.22 (Ubuntu 7.10). Our results show that on average Windows XP and Vista are more robust than Linux, but their performances are comparable under compute-intensive workloads. A demonstration of how these results are used in the proposed reference model for OSs robustness forecast and selection is given at the end.