Abstract
Large scale information systems, such as public information systems for light-train/metro networks, must be able to fulfill contractualized Service Level Agreements (SLAs) in terms of end-to-end latencies and jitter, even in the presence of faults. Failure to do so has potential legal and financial implications for the software developers. Current middleware solutions have a hard time coping with these demands due, fundamentally, to a lack of adequate, simultaneous, support for fault-tolerance (FT) and real-time (RT) tasks. In this paper we present Stheno, a general purpose peer-to-peer (P2P) middleware system that builds on previous work from TAO and MEAD to provide: (a) configurable, transparent, FT support by taking advantage of the P2P layer topology awareness to efficiently implement Common Of The Shelf (COTS) replication algorithms and replica management strategies, and; (b) kernel-level resource reservation integrated with well-known threading strategies based on priorities to provide more robust support for soft real-time tasks. An evaluation of the first (unoptimized) prototype for the middleware shows that Stheno is able to match and often greatly exceed the SLA agreements provided by our target system, the light-train/metro information system developed and maintained by EFACEC, and currently deployed at multiple cities in Europe and Brazil. Copyright 2012 ACM.

Abstract
With recent advances in high-throughput cell biology, the amount of cellular biological data has grown drastically. Such data is often modeled as graphs (also called networks) and studying them can lead to new insights intomolecule-level organization. A possible way to understand their structure is by analyzing the smaller components that constitute them, namely network motifs and graphlets. Graphlets are particularly well suited to compare networks and to assess their level of similarity due to the rich topological information that they offer but are almost always used as small undirected graphs of up to five nodes, thus limiting their applicability in directed networks. However, a large set of interesting biological networks such asmetabolic, cell signaling, or transcriptional regulatory networks are intrinsically directional, and using metrics that ignore edge direction may gravely hinder information extraction. Our main purpose in this work is to extend the applicability of graphlets to directed networks by considering their edge direction, thus providing a powerful basis for the analysis of directed biological networks. We tested our approach on two network sets, one composed of synthetic graphs and another of real directed biological networks, and verified that they were more accurately grouped using directed graphlets than undirected graphlets. It is also evident that directed graphlets offer substantially more topological information than simple graph metrics such as degree distribution or reciprocity. However, enumerating graphlets in large networks is a computationally demanding task. Our implementation addresses this concern by using a state-of-the-art data structure, the g-trie, which is able to greatly reduce the necessary computation. We compared our tool to other state-of-the art methods and verified that it is the fastest general tool for graphlet counting.

Abstract
Computing the frequency of small subgraphs on a large network is a computationally hard task. This is, however, an important graph mining primitive, with several applications, and here we present a novel multicore parallel algorithm for this task. At the core of our methodology lies a state-of-the-art data structure, the g-trie, which represents a collection of subgraphs and allows for a very efficient sequential search. Our implementation was done using Pthreads and can run on any multicore personal computer. We employ a diagonal work sharing strategy to dynamically and effectively divide work among threads during the execution. We assess the performance of our Pthreads implementation on a set of representative networks from various domains and with diverse topological features. For most networks, we obtain a speedup of over 50 for 64 cores and an almost linear speedup up to 32 cores, showcasing the flexibility and scalability of our algorithm. This paves the way for the usage of such counting algorithms on larger subgraph and network sizes without the obligatory access to a cluster.

Abstract
Crowd-sourcing the resources of mobile devices is a hot topic of research given the game-changing applications it may enable. In this paper we study the feasibility of using edge-clouds of mobile devices to reduce the load in traditional WiFi infrastructures for video dissemination applications. For this purpose, we designed and implemented a mobile application for video dissemination in sport venues that retrieves replays from a central server, through the access points in the WiFi infrastructure, into a smartphone. The fan's smartphones organize themselves into WiFi-Direct groups and exchange video replays whenever possible, bypassing the central server and access points. We performed a real-world experiment using the live TV feed for the Champions League game Benfica-Besiktas with the help of a group of volunteers using the application at the student's union lounge. The analysis of the logs strongly suggests that edge-clouds can significantly reduce the load in the access points at such large venues and improve quality of experience. Indeed, the edge-clouds formed were able to serve up to 80% of connected users and provide 56% of all downloads requested from within.

Abstract

Abstract
In the last decade, technological advances and improved manufacturing processes have significantly dropped the price tag of mobile devices such as smartphones and tablets whilst augmenting their storage and computational capabilities. Their ubiquity fostered research on mobile edge-clouds, formed by sets of such devices in close proximity, with the goal of mastering their global computational and storage resources. The development of crowdsourcing applications that take advantage of such edge-clouds is, however, hampered by the complexity of network formation and maintenance, the intrinsic instability of wireless links and the heterogeneity of the hardware and operating systems in the devices. In this paper we present a middleware to deal with this complexity, providing a building block upon which crowd-sourcing applications may be built.We motivate the development of the middleware through a discussion of real-world applications, and present the middleware's architecture along with the associated components and current development status. The middleware takes form as a Java API for Android devices that allows for the establishment of links using heterogeneous communication technologies (e.g., Wifi-Direct, Bluetooth), and the combination of these links to form a logical edge-cloud network. On top of this functionality, services for edge computation, storage, and streaming are also being developed. © 2017 Association for Computing Machinery.