Many real-world systems, such as social networks, rely on mining efficiently large graphs, with hundreds of millions of vertices and edges. This volume of information requires partitioning the graph across multiple nodes in a distributed system. This has a deep effect on performance, as traversing edges cut between partitions incurs a significant performance penalty due to the cost of communication. Thus, several systems in the literature have attempted to improve computational performance by enhancing graph partitioning, but they do not support another characteristic of real-world graphs: graphs are inherently dynamic, their topology evolves continuously, and subsequently the optimum partitioning also changes over time. In this work, we present the first system that dynamically repartitions massive graphs to adapt to structural changes. The system optimises graph partitioning to prevent performance degradation without using data replication. The system adopts an iterative vertex migration algorithm that relies on local information only, making complex coordination unnecessary. We show how the improvement in graph partitioning reduces execution time by over 50 , while adapting the partitioning to a large number of changes to the graph in three real-world scenarios.
Sedge @cite_25 is a dynamic replication mechanism (as opposed to a re-partitioning one). Sedge keeps a fixed set of non overlapping partitions and then dynamically creates new ones or replicates some of them in different machines to cope with variations in workload. Replicated systems are more focused at providing low latency to multiple concurrent and short-lived queries. Our system tries to keep a few long-lasting (continuous) queries which results are modified as a consequence of changes in the information or the topology of the graph.
@cite_25
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Many proposals for managing replicated data use sites running the Snapshot Isolation (SI) concurrency control mechanism, and provide 1-copy SI or something similar, as the global isolation level. This allows good scalability, since only ww-conflicts need to be managed globally. However, 1-copy SI can lead to data corruption and violation of integrity constraints [5]. 1-copy serializability is the global correctness condition that prevents data corruption. We propose a new algorithm Replicated Serializable Snapshot Isolation (RSSI) that uses SI at each site, and combines this with a certification algorithm to guarantee 1-copy serializable global execution. Management of ww-conflicts is similar to what is done in 1-copy SI. But unlike previous designs for 1-copy serializable systems, we do not need to prevent all rw-conflicts among concurrent transactions. We formalize this in a theorem that shows that many rw-conflicts are indeed false positives that do not risk non-serializable behavior. Our proposed RSSI algorithm will only abort a transaction when it detects a well-defined pattern of two consecutive rw-edges in the serialization graph. We have built a prototype that integrates our RSSI with the existing open-source PostgresR(SI) system. Our performance evaluation shows that there is a worst-case overhead of about 15 for getting full 1copy serializability as compared to 1-copy SI in a cluster of 8 nodes, with our proposed RSSI clearly outperforming the previous work [6] for update-intensive workloads. Online services often use replication for improving the performance of user-facing services. However, using replication for performance comes at a price of weakening the consistency levels of the replicated service. To address this tension, recent proposals from academia and industry allow operations to run at different consistency levels. In these systems, the programmer has to decide which level to use for each operation. We present SIEVE, a tool that relieves Java programmers from this errorprone decision process, allowing applications to automatically extract good performance when possible, while resorting to strong consistency whenever required by the target semantics. Taking as input a set of application-specific invariants and a few annotations about merge semantics, SIEVE performs a combination of static and dynamic analysis, offline and at runtime, to determine when it is necessary to use strong consistency to preserve these invariants and when it is safe to use causally consistent commutative replicated data types (CRDTs). We evaluate SIEVE on two web applications and show that the automatic classification overhead is low. Modern cloud systems are geo-replicated to improve application latency and availability. Transactional consistency is essential for application developers; however, the corresponding concurrency control and commitment protocols are costly in a geo-replicated setting. To minimize this cost, we identify the following essential scalability properties: (i) only replicas updated by a transaction T make steps to execute T; (ii) a read-only transaction never waits for concurrent transactions and always commits; (iii) a transaction may read object versions committed after it started; and (iv) two transactions synchronize with each other only if their writes conflict. We present Non-Monotonic Snapshot Isolation (NMSI), the first strong consistency criterion to allow implementations with all four properties. We also present a practical implementation of NMSI called Jessy, which we compare experimentally against a number of well-known criteria. Our measurements show that the latency and throughput of NMSI are comparable to the weakest criterion, read-committed, and between two to fourteen times faster than well-known strong consistencies. Searching and mining large graphs today is critical to a variety of application domains, ranging from community detection in social networks, to de novo genome sequence assembly. Scalable processing of large graphs requires careful partitioning and distribution of graphs across clusters. In this paper, we investigate the problem of managing large-scale graphs in clusters and study access characteristics of local graph queries such as breadth-first search, random walk, and SPARQL queries, which are popular in real applications. These queries exhibit strong access locality, and therefore require specific data partitioning strategies. In this work, we propose a Self Evolving Distributed Graph Management Environment (Sedge), to minimize inter-machine communication during graph query processing in multiple machines. In order to improve query response time and throughput, Sedge introduces a two-level partition management architecture with complimentary primary partitions and dynamic secondary partitions. These two kinds of partitions are able to adapt in real time to changes in query workload. (Sedge) also includes a set of workload analyzing algorithms whose time complexity is linear or sublinear to graph size. Empirical results show that it significantly improves distributed graph processing on today's commodity clusters.