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Large-scale graphs can be analyzed by lightweight systems on a single server, e.g., GraphChi, X-Stream, and GridGraph. Studies indicate that graph algorithms have different performance impacted by partitioning schemes, scheduling strategies and execution models. Existing systems of single-server graph processing often suffer from poor I/O locality, inefficient selective scheduling or expensive synchronization...
Concurrency bugs, particularly data races, are notoriously difficult to debug and are a significant source of unreliability in multithreaded applications. Many tools to catch data races rely on program instrumentation to obtain memory instruction traces. Unfortunately, this instrumentation introduces significant runtime overhead, is extremely invasive, or has a limited domain of applicability making...
Distributed application management system is important for managing applications on distributed computing platforms. One of the main caveat of using a distributed management system is that the management system itself, as a distributed application, need to be deployed and maintained continually. In this paper, we propose Self-Managed Overlay Network (SMOM) and explore the challenges associated with...
Virtual clusters are the new ways for managing the computing resources in the cluster environment. Users are presented with virtual clusters instead of physical ones. The storage system for supporting the running clusters is critical in such systems for efficient store of virtual machine images. We have used the CAS (Content Addressable Storage) based storage for manage the large number of virtual...
Distributed metadata management is an important issue in the design and implementation of Data Grid. The key challenge lies in the strategies of metadata synchronization and the representation of the distributed metadata. We have designed a Hierarchical Bloom Filter, which consists of two level Bloom filters, to facilitate the metadata management. A Recent Bloom Filter at the top level is based on...
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