Capacity Based Evacuation with Dynamic Exit Signs

Exit paths in buildings are designed to minimise evacuation time when the building is at full capacity. We present an evacuation support system which does this regardless of the number of evacuees. The core concept is to even-out congestion in the building by diverting evacuees to less-congested paths in order to make maximal usage of all accessible routes throughout the entire evacuation process. The system issues a set of flow-optimal routes using a capacity-constrained routing algorithm which anticipates evolutions in path metrics using the concept of "future capacity reservation". In order to direct evacuees in an intuitive manner whilst implementing the routing algorithm's scheme, we use dynamic exit signs, i.e. whose pointing direction can be controlled. To make this system practical and minimise reliance on sensors during the evacuation, we use an evacuee mobility model and make several assumptions on the characteristics of the evacuee flow. We validate this concept using simulations, and show how the underpinning assumptions may limit the system's performance, especially in low-headcount evacuations

Routing Diverse Evacuees with Cognitive Packets

This paper explores the idea of smart building evacuation when evacuees can belong to different categories with respect to their ability to move and their health conditions. This leads to new algorithms that use the Cognitive Packet Network concept to tailor different quality of service needs to different evacuees. These ideas are implemented in a simulated environment and evaluated with regard to their effectiveness.Comment: 7 pages, 7 figure

Adaptive Dispatching of Tasks in the Cloud

The increasingly wide application of Cloud Computing enables the consolidation of tens of thousands of applications in shared infrastructures. Thus, meeting the quality of service requirements of so many diverse applications in such shared resource environments has become a real challenge, especially since the characteristics and workload of applications differ widely and may change over time. This paper presents an experimental system that can exploit a variety of online quality of service aware adaptive task allocation schemes, and three such schemes are designed and compared. These are a measurement driven algorithm that uses reinforcement learning, secondly a "sensible" allocation algorithm that assigns jobs to sub-systems that are observed to provide a lower response time, and then an algorithm that splits the job arrival stream into sub-streams at rates computed from the hosts' processing capabilities. All of these schemes are compared via measurements among themselves and with a simple round-robin scheduler, on two experimental test-beds with homogeneous and heterogeneous hosts having different processing capacities.Comment: 10 pages, 9 figure

Search in the Universe of Big Networks and Data

Searching in the Internet for some object characterised by its attributes in the form of data, such as a hotel in a certain city whose price is less than something, is one of our most common activities when we access the Web. We discuss this problem in a general setting, and compute the average amount of time and the energy it takes to find an object in an infinitely large search space. We consider the use of N search agents which act concurrently. Both the case where the search agent knows which way it needs to go to find the object, and the case where the search agent is perfectly ignorant and may even head away from the object being sought. We show that under mild conditions regarding the randomness of the search and the use of a time-out, the search agent will always find the object despite the fact that the search space is infinite. We obtain a formula for the average search time and the average energy expended by N search agents acting concurrently and independently of each other. We see that the time-out itself can be used to minimise the search time and the amount of energy that is consumed to find an object. An approximate formula is derived for the number of search agents that can help us guarantee that an object is found in a given time, and we discuss how the competition between search agents and other agents that try to hide the data object, can be used by opposing parties to guarantee their own success.Comment: IEEE Network Magazine - Special Issue on Networking for Big Data, July-August 201

Signalling Storms in 3G Mobile Networks

We review the characteristics of signalling storms that have been caused by certain common apps and recently observed in cellular networks, leading to system outages. We then develop a mathematical model of a mobile user's signalling behaviour which focuses on the potential of causing such storms, and represent it by a large Markov chain. The analysis of this model allows us to determine the key parameters of mobile user device behaviour that can lead to signalling storms. We then identify the parameter values that will lead to worst case load for the network itself in the presence of such storms. This leads to explicit results regarding the manner in which individual mobile behaviour can cause overload conditions on the network and its signalling servers, and provides insight into how this may be avoided.Comment: IEEE ICC 2014 - Communications and Information Systems Security Symposiu

Graph and Analytical Models for Emergency Evacuation

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