Abstract
This paper presents a set of methods and techniques to remove inefficiencies in a data analysis application used in searches by the ATLAS Experiment at the Large Hadron Collider. Profiling scientific code helped to pinpoint design and runtime inefficiencies, the former due to coding and data structure design. The data analysis code used by groups doing searches in the ATLAS Experiment contributed to clearly identify some of these inefficiencies and to give suggestions on how to prevent and overcome those common situations in scientific code to improve the efficient use of available computational resources in a parallel homogeneous platform.

Abstract
Recently, tuple-stores have become pivotal structures in many information systems. Their ability to handle large datasets makes them important in an era with unprecedented amounts of data being produced and exchanged. However, these tuple-stores typically rely on structured peer-to-peer protocols which assume moderately stable environments. Such assumption does not always hold for very large scale systems sized in the scale of thousands of machines. In this paper we present a novel approach to the design of a tuple-store. Our approach follows a stratified design based on an unstructured substrate. We focus on this substrate and how the use of epidemic protocols allow reaching high dependability and scalability.

Abstract
All companies developing their business on the Web, not only giants like Google or Facebook but also small companies focused on niche markets, face scalability issues in data management. The case study of this paper is the content management systems for classified or commercial advertisements on the Web. The data involved has a very significant growth rate and a read-intensive access pattern with a reduced update rate. Typically, data is stored in traditional file systems hosted on dedicated servers or Storage Area Network devices due to the generalization and ease of use of file systems. However, this ease in implementation and usage has a disadvantage: the centralized nature of these systems leads to availability, elasticity and scalability problems. The scenario under study, undemanding in terms of the system's consistency and with a simple interaction model, is suitable to a distributed database, such as Cassandra, conceived precisely to dynamically handle large volumes of data. In this paper, we analyze the suitability of Cassandra as a substitute for file systems in content management systems. The evaluation, conducted using real data from a production system, shows that when using Cassandra, one can easily get horizontal scalability of storage, redundancy across multiple independent nodes and load distribution imposed by the periodic activities of safeguarding data, while ensuring a comparable performance to that of a file system. Copyright © 2013 ACM.

Abstract
Today's intensive demand for data such as live broadcast or news feeds requires efficient and robust dissemination systems. Traditionally, designs focus on extremes of the efficiency/robustness spectrum by either using structures, such as trees for efficiency or by using loosely-coupled epidemic protocols for robustness. We present BRISA, a hybrid approach combining the robustness of epidemics with the efficiency of structured approaches. BRISA implicitly emerges embedded dissemination structures from an underlying epidemic substrate. The structures' links are chosen with local knowledge only, but still ensuring connectivity. Failures can be promptly compensated and repaired thanks to the epidemic substrate, and their impact on dissemination delays masked by the use of multiple independent structures. Besides presenting the protocol design, we conduct an extensive evaluation in real environments, analyzing the effectiveness of the structure creation mechanism and its robustness under dynamic conditions. Results confirm BRISA as an efficient and robust approach to data dissemination in large dynamic environments.

Abstract
NoSQL databases manage the bulk of data produced by modern Web applications such as social networks. This stems from their ability to partition and spread data to all available nodes, allowing NoSQL systems to scale. Unfortunately, current solutions' scale out is oblivious to the underlying data access patterns, resulting in both highly skewed load across nodes and suboptimal node configurations. In this paper, we first show that judicious placement of HBase partitions taking into account data access patterns can improve overall throughput by 35%. Next, we go beyond current state of the art elastic systems limited to uninformed replica addition and removal by: i) reconfiguring existing replicas according to access patterns and ii) adding replicas specifically configured to the expected access pattern. MeT is a prototype for a Cloud-enabled framework that can be used alone or in conjunction with OpenStack for the automatic and heterogeneous reconfiguration of a HBase deployment. Our evaluation, conducted using the YCSB workload generator and a TPC-C workload, shows that MeT is able to i) autonomously achieve the performance of a manual configured cluster and ii) quickly reconfigure the cluster according to unpredicted workload changes. © 2013 ACM.

Abstract
Topic-based publish/subscribe is at the core of many distributed systems, ranging from application integration middleware to news dissemination. Therefore, much research was dedicated to publish/subscribe architectures and protocols, and in particular to the design of overlay networks for decentralized topic-based routing and efficient message dissemination. Nonetheless, existing systems fail to take full advantage of shared interests when disseminating information, hence suffering from high maintenance and traffic costs, or construct overlays that cope poorly with the scale and dynamism of large networks. In this paper, we present StaN, a decentralized protocol that optimizes the properties of gossip-based overlay networks for topic-based publish/subscribe by sharing a large number of physical connections without disrupting its logical properties. StaN relies only on local knowledge and operates by leveraging common interests among participants to improve global resource usage and promote topic and event scalability. The experimental evaluation under two real workloads, both via a real deployment and through simulation, shows that StaN provides an attractive infrastructure for scalable topic-based publish/subscribe.