39,553 research outputs found
Distributed Online Data Aggregation in Dynamic Graphs
We consider the problem of aggregating data in a dynamic graph, that is, aggregating the data that originates from all nodes in the graph to a specific node, the sink. We are interested in giving lower bounds for this problem, under different kinds of adversaries. In our model, nodes are endowed with unlimited memory and unlimited computational power. Yet, we assume that communications between nodes are carried out with pairwise interactions, where nodes can exchange control information before deciding whether they transmit their data or not, given that each node is allowed to transmit its data at most once. When a node receives a data from a neighbor, the node may aggregate it with its own data. We consider three possible adversaries: the online adaptive adversary, the oblivious adversary , and the randomized adversary that chooses the pairwise interactions uniformly at random. For the online adaptive and the oblivious adversary, we give impossibility results when nodes have no knowledge about the graph and are not aware of the future. Also, we give several tight bounds depending on the knowledge (be it topology related or time related) of the nodes. For the randomized adversary, we show that the Gathering algorithm, which always commands a node to transmit, is optimal if nodes have no knowledge at all. Also, we propose an algorithm called Waiting Greedy, where a node either waits or transmits depending on some parameter, that is optimal when each node knows its future pairwise interactions with the sink
GiViP: A Visual Profiler for Distributed Graph Processing Systems
Analyzing large-scale graphs provides valuable insights in different
application scenarios. While many graph processing systems working on top of
distributed infrastructures have been proposed to deal with big graphs, the
tasks of profiling and debugging their massive computations remain time
consuming and error-prone. This paper presents GiViP, a visual profiler for
distributed graph processing systems based on a Pregel-like computation model.
GiViP captures the huge amount of messages exchanged throughout a computation
and provides an interactive user interface for the visual analysis of the
collected data. We show how to take advantage of GiViP to detect anomalies
related to the computation and to the infrastructure, such as slow computing
units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
Monte Carlo Algorithm for Simulating Reversible Aggregation of Multisite Particles
We present an efficient and exact Monte Carlo algorithm to simulate
reversible aggregation of particles with dedicated binding sites. This method
introduces a novel data structure of dynamic bond tree to record clusters and
sequences of bond formations. The algorithm achieves a constant time cost for
processing cluster association and a cost between and
for processing bond dissociation in clusters with bonds.
The algorithm is statistically exact and can reproduce results obtained by the
standard method. We applied the method to simulate a trivalent ligand and a
bivalent receptor clustering system and obtained an average scaling of
for processing bond dissociation in acyclic
aggregation, compared to a linear scaling with the cluster size in standard
methods. The algorithm also demands substantially less memory than the
conventional method.Comment: 8 pages, 3 figure
C2MS: Dynamic Monitoring and Management of Cloud Infrastructures
Server clustering is a common design principle employed by many organisations
who require high availability, scalability and easier management of their
infrastructure. Servers are typically clustered according to the service they
provide whether it be the application(s) installed, the role of the server or
server accessibility for example. In order to optimize performance, manage load
and maintain availability, servers may migrate from one cluster group to
another making it difficult for server monitoring tools to continuously monitor
these dynamically changing groups. Server monitoring tools are usually
statically configured and with any change of group membership requires manual
reconfiguration; an unreasonable task to undertake on large-scale cloud
infrastructures.
In this paper we present the Cloudlet Control and Management System (C2MS); a
system for monitoring and controlling dynamic groups of physical or virtual
servers within cloud infrastructures. The C2MS extends Ganglia - an open source
scalable system performance monitoring tool - by allowing system administrators
to define, monitor and modify server groups without the need for server
reconfiguration. In turn administrators can easily monitor group and individual
server metrics on large-scale dynamic cloud infrastructures where roles of
servers may change frequently. Furthermore, we complement group monitoring with
a control element allowing administrator-specified actions to be performed over
servers within service groups as well as introduce further customized
monitoring metrics. This paper outlines the design, implementation and
evaluation of the C2MS.Comment: Proceedings of the The 5th IEEE International Conference on Cloud
Computing Technology and Science (CloudCom 2013), 8 page
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