In the recent years, the scale of graph datasets has increased to such a degree that a single machine is not capable of efficiently processing large graphs. Thereby, efficient graph partitioning is necessary for those large graph applications. Traditional graph partitioning generally loads the whole graph data into the memory before performing partitioning; this is not only a time consuming task but it also creates memory bottlenecks. These issues of memory limitation and enormous time complexity can be resolved using stream-based graph partitioning. A streaming graph partitioning algorithm reads vertices once and assigns that vertex to a partition accordingly. This is also called an one-pass algorithm. This paper proposes an efficient window-based streaming graph partitioning algorithm called WStream. The WStream algorithm is an edge-cut partitioning algorithm, which distributes a vertex among the partitions. Our results suggest that the WStream algorithm is able to partition large graph data efficiently while keeping the load balanced across different partitions, and communication to a minimum. Evaluation results with real workloads also prove the effectiveness of our proposed algorithm, and it achieves a significant reduction in load imbalance and edge-cut with different ranges of dataset.
Recently, there has been considerable interest in the design of an algorithm and framework to handle massive graph data in a streaming manner. Steaming graph-data can be partitioned into a cluster of nodes; the graph access pattern could be done via online or offline processing. Streaming graph partitioning is considerably efficient because the graph loader or partitioner does the partitioning task while receiving the graph data in a streaming manner. A near-optimal traditional graph partitioning algorithm called METIS was proposed in the early graph-partitioning era @cite_11 . METIS is the de facto standard for near-optimal partitioning in distributed graph partitioning. METIS can reduce the communication costs among distributed machines despite having a lengthy processing time for small graphs. However, METIS is not suitable for processing medium or large graph datasets @cite_11 .
@cite_11
2006023152 2128479026 2166860697 1971630691
Balanced graph partitioning in the streaming setting is a key problem to enable scalable and efficient computations on massive graph data such as web graphs, knowledge graphs, and graphs arising in the context of online social networks. Two families of heuristics for graph partitioning in the streaming setting are in wide use: place the newly arrived vertex in the cluster with the largest number of neighbors or in the cluster with the least number of non-neighbors. In this work, we introduce a framework which unifies the two seemingly orthogonal heuristics and allows us to quantify the interpolation between them. More generally, the framework enables a well principled design of scalable, streaming graph partitioning algorithms that are amenable to distributed implementations. We derive a novel one-pass, streaming graph partitioning algorithm and show that it yields significant performance improvements over previous approaches using an extensive set of real-world and synthetic graphs. Surprisingly, despite the fact that our algorithm is a one-pass streaming algorithm, we found its performance to be in many cases comparable to the de-facto standard offline software METIS and in some cases even superiror. For instance, for the Twitter graph with more than 1.4 billion of edges, our method partitions the graph in about 40 minutes achieving a balanced partition that cuts as few as 6.8 of edges, whereas it took more than 81 2 hours by METIS to produce a balanced partition that cuts 11.98 of edges. We also demonstrate the performance gains by using our graph partitioner while solving standard PageRank computation in a graph processing platform with respect to the communication cost and runtime. Balanced graph partitioning is a well known NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems including the optimal storage of large sets of graph-structured data over several hosts-a key problem in today's Cloud infrastructure. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable, because they typically involve frequent global operations over the entire graph. In this paper, we propose a fully distributed algorithm, called JA-BE-JA, that uses local search and simulated annealing techniques for graph partitioning. The algorithm is massively parallel: there is no central coordination, each node is processed independently, and only the direct neighbors of the node, and a small subset of random nodes in the graph need to be known locally. Strict synchronization is not required. These features allow JA-BE-JA to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We perform a thorough experimental analysis, which shows that the minimal edge-cut value achieved by JA-BE-JA is comparable to state-of-the-art centralized algorithms such as METIS. In particular, on large social networks JA-BEJA outperforms METIS, which makes JA-BE-JA-a bottom-up, self-organizing algorithm-a highly competitive practical solution for graph partitioning. As the study of large graphs over hundreds of gigabytes becomes increasingly popular for various data-intensive applications in cloud computing, developing large graph processing systems has become a hot and fruitful research area. Many of those existing systems support a vertex-oriented execution model and allow users to develop custom logics on vertices. However, the inherently random access pattern on the vertex-oriented computation generates a significant amount of network traffic. While graph partitioning is known to be effective to reduce network traffic in graph processing, there is little attention given to how graph partitioning can be effectively integrated into large graph processing in the cloud environment. In this paper, we develop a novel graph partitioning framework to improve the network performance of graph partitioning itself, partitioned graph storage and vertex-oriented graph processing. All optimizations are specifically designed for the cloud network environment. In experiments, we develop a system prototype following Pregel (the latest vertex-oriented graph engine by Google), and extend it with our graph partitioning framework. We conduct the experiments with a real-world social network and synthetic graphs over 100GB each in a local cluster and on Amazon EC2. Our experimental results demonstrate the efficiency of our graph partitioning framework, and the effectiveness of network performance aware optimizations on the large graph processing engine. Extracting knowledge by performing computations on graphs is becoming increasingly challenging as graphs grow in size. A standard approach distributes the graph over a cluster of nodes, but performing computations on a distributed graph is expensive if large amount of data have to be moved. Without partitioning the graph, communication quickly becomes a limiting factor in scaling the system up. Existing graph partitioning heuristics incur high computation and communication cost on large graphs, sometimes as high as the future computation itself. Observing that the graph has to be loaded into the cluster, we ask if the partitioning can be done at the same time with a lightweight streaming algorithm. We propose natural, simple heuristics and compare their performance to hashing and METIS, a fast, offline heuristic. We show on a large collection of graph datasets that our heuristics are a significant improvement, with the best obtaining an average gain of 76 . The heuristics are scalable in the size of the graphs and the number of partitions. Using our streaming partitioning methods, we are able to speed up PageRank computations on Spark, a distributed computation system, by 18 to 39 for large social networks.