277,372 research outputs found
Handling Network Partitions and Mergers in Structured Overlay Networks
Structured overlay networks form a major class of peer-to-peer systems, which are touted for their abilities to
scale, tolerate failures, and self-manage. Any long-lived
Internet-scale distributed system is destined to face network partitions. Although the problem of network partitions
and mergers is highly related to fault-tolerance and
self-management in large-scale systems, it has hardly been
studied in the context of structured peer-to-peer systems.
These systems have mainly been studied under churn (frequent
joins/failures), which as a side effect solves the problem
of network partitions, as it is similar to massive node
failures. Yet, the crucial aspect of network mergers has been
ignored. In fact, it has been claimed that ring-based structured
overlay networks, which constitute the majority of the
structured overlays, are intrinsically ill-suited for merging
rings. In this paper, we present an algorithm for merging
multiple similar ring-based overlays when the underlying
network merges. We examine the solution in dynamic conditions,
showing how our solution is resilient to churn during
the merger, something widely believed to be difficult or
impossible. We evaluate the algorithm for various scenarios
and show that even when falsely detecting a merger, the
algorithm quickly terminates and does not clutter the network
with many messages. The algorithm is flexible as the
tradeoff between message complexity and time complexity
can be adjusted by a parameter
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Evaluating the resilience and security of boundaryless, evolving socio-technical Systems of Systems
Addressing the Node Discovery Problem in Fog Computing
In recent years, the Internet of Things (IoT) has gained a lot of attention due to connecting various sensor devices with the cloud, in order to enable smart applications such as: smart traffic management, smart houses, and smart grids, among others. Due to the growing popularity of the IoT, the number of Internet-connected devices has increased significantly. As a result, these devices generate a huge amount of network traffic which may lead to bottlenecks, and eventually increase the communication latency with the cloud. To cope with such issues, a new computing paradigm has emerged, namely: fog computing. Fog computing enables computing that spans from the cloud to the edge of the network in order to distribute the computations of the IoT data, and to reduce the communication latency. However, fog computing is still in its infancy, and there are still related open problems. In this paper, we focus on the node discovery problem, i.e., how to add new compute nodes to a fog computing system. Moreover, we discuss how addressing this problem can have a positive impact on various aspects of fog computing, such as fault tolerance, resource heterogeneity, proximity awareness, and scalability. Finally, based on the experimental results that we produce by simulating various distributed compute nodes, we show how addressing the node discovery problem can improve the fault tolerance of a fog computing system
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Reinforcement learning (RL) constitutes a promising solution for alleviating
the problem of traffic congestion. In particular, deep RL algorithms have been
shown to produce adaptive traffic signal controllers that outperform
conventional systems. However, in order to be reliable in highly dynamic urban
areas, such controllers need to be robust with the respect to a series of
exogenous sources of uncertainty. In this paper, we develop an open-source
callback-based framework for promoting the flexible evaluation of different
deep RL configurations under a traffic simulation environment. With this
framework, we investigate how deep RL-based adaptive traffic controllers
perform under different scenarios, namely under demand surges caused by special
events, capacity reductions from incidents and sensor failures. We extract
several key insights for the development of robust deep RL algorithms for
traffic control and propose concrete designs to mitigate the impact of the
considered exogenous uncertainties.Comment: 8 page
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