No abstract

Large scale graph processing is a major research area for Big Data exploration. Vertex centric programming models like Pregel are gaining traction due to their simple abstraction that allows for scalable execution on distributed systems naturally. However, there are limitations to this approach which cause vertex centric algorithms to under-perform due to poor compute to communication overhead ratio and slow convergence of iterative superstep. In this paper we introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters. We introduce a sub-graph centric programming abstraction that combines the scalability of a vertex centric approach with the flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation.

Abstract-Power utilities globally are increasingly upgrading to Smart Grids that use bi-directional communication with the consumer to enable an information-driven approach to distributed energy management. Clouds offer features well suited for Smart Grid software platforms and applications, such as elastic resources and shared services. However, the security and privacy concerns inherent in an informationrich Smart Grid environment are further exacerbated by their deployment on Clouds. Here, we present an analysis of security and privacy issues in a Smart Grids software architecture operating on different Cloud environments, in the form of a taxonomy. We use the Los Angeles Smart Grid Project that is underway in the largest U.S. municipal utility to drive this analysis that will benefit both Cloud practitioners targeting Smart Grid applications, and Cloud researchers investigating security and privacy.

Contemporary continuous dataflow systems use elastic scaling on distributed cloud resources to handle variable data rates and to meet applications' needs while attempting to maximize resource utilization. However, virtualized clouds present an added challenge due to the variability in resource performance -over time and space -thereby impacting the application's QoS. Elastic use of cloud resources and their allocation to continuous dataflow tasks need to adapt to such infrastructure dynamism. In this paper, we develop the concept of "dynamic dataflows" as an extension to continuous dataflows that utilizes alternate tasks and allows additional control over the dataflow's cost and QoS. We formalize an optimization problem to perform both deployment and runtime cloud resource management for such dataflows, and define an objective function that allows trade-off between the application's value against resource cost. We present two novel heuristics, local and global, based on the variable sized bin packing heuristics to solve this NP-hard problem. We evaluate the heuristics against a static allocation policy for a dataflow with different data rate profiles that is simulated using VM performance traces from a private cloud data center. The results show that the heuristics are effective in intelligently utilizing cloud elasticity to mitigate the effect of both input data rate and cloud resource performance variabilities on QoS.