24 research outputs found
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
Flexible processing architecture for maintaining QoS in embedded systems applications
Comunicaci贸n presentada en las V Jornadas de Computaci贸n Empotrada, Valladolid, 17-19 Septiembre 2014The growing available capacity on a single chip is leading to increasingly sophisticated applications in the field of embedded systems. In addition, the cloud computing paradigm, allows the extension of the capabilities of these systems using remote resources. Among the wide range of applications that can arise in this context, are those in which it is critical to meet certain quality of service (QoS) requirements, such as limited latency. In these cases, real-time operating systems (RTOS) provide a valid solution to guarantee predictability and response time using the resources of the embedded system. However, in applications where the elements to process can grow and decrease in a variable way, the load can exceed the capabilities of the embedded system, which is an important limitation. In this paper, a new architecture is proposed, aiming to take the most of remotely available resources only when the load temporarily exceeds the capabilities of the embedded system. The access to the remote resources is done by using cloud platforms maintaining an acceptable level of QoS for the application
CAM04-1: Admission control in self aware networks
The worldwide growth in broadband access and multimedia traffic has led to an increasing need for Quality- of-Service (QoS) in networks. Real time network applications require a stable, reliable, and predictable network that will guarantee packet delivery under QoS constraints. Network self- awareness through on-line measurement and adaptivity in response to user needs is one way to advance user QoS when overall network conditions can change, while admission control (AC) is an approach that has been commonly used to reduce traffic congestion and to satisfy users' QoS requests. The purpose of this paper is to describe a novel measurement-based admission control algorithm which bases its decision on different QoS metrics that users can specify. The self-observation and self- awareness capabilities of the network are exploited to collect data that allows an AC algorithm to decide whether to admit users based on their QoS needs, and the QoS impact they will have on other users. The approach we propose finds whether feasible paths exist for the projected incoming traffic, and estimates the impact that the newly accepted traffic will have on the QoS of pre-existing connections. The AC decision is then taken based on the outcome of this analysis
CAM04-1: Admission control in self aware networks
The worldwide growth in broadband access and multimedia traffic has led to an increasing need for Quality- of-Service (QoS) in networks. Real time network applications require a stable, reliable, and predictable network that will guarantee packet delivery under QoS constraints. Network self- awareness through on-line measurement and adaptivity in response to user needs is one way to advance user QoS when overall network conditions can change, while admission control (AC) is an approach that has been commonly used to reduce traffic congestion and to satisfy users' QoS requests. The purpose of this paper is to describe a novel measurement-based admission control algorithm which bases its decision on different QoS metrics that users can specify. The self-observation and self- awareness capabilities of the network are exploited to collect data that allows an AC algorithm to decide whether to admit users based on their QoS needs, and the QoS impact they will have on other users. The approach we propose finds whether feasible paths exist for the projected incoming traffic, and estimates the impact that the newly accepted traffic will have on the QoS of pre-existing connections. The AC decision is then taken based on the outcome of this analysis
Routing Diverse Crowds in Emergency with Dynamic Grouping
Evacuee routing algorithms in emergency typically adopt one single criterion
to compute desired paths and ignore the specific requirements of users caused
by different physical strength, mobility and level of resistance to hazard. In
this paper, we present a quality of service (QoS) driven multi-path routing
algorithm to provide diverse paths for different categories of evacuees. This
algorithm borrows the concept of Cognitive Packet Network (CPN), which is a
flexible protocol that can rapidly solve optimal solution for any user-defined
goal function. Spatial information regarding the location and spread of hazards
is taken into consideration to avoid that evacuees be directed towards
hazardous zones. Furthermore, since previous emergency navigation algorithms
are normally insensitive to sudden changes in the hazard environment such as
abrupt congestion or injury of civilians, evacuees are dynamically assigned to
several groups to adapt their course of action with regard to their on-going
physical condition and environments. Simulation results indicate that the
proposed algorithm which is sensitive to the needs of evacuees produces better
results than the use of a single metric. Simulations also show that the use of
dynamic grouping to adjust the evacuees' category and routing algorithms with
regard for their on-going health conditions and mobility, can achieve higher
survival rates.Comment: Contains 6 pages, 5 pages. Accepted by PerNEM' 201
A denial of service detector based on maximum likelihood detection and the random neural network
In spite of extensive research in defence against De- nial of Service (DoS), such attacks remain a predom- inant threat in today鈥檚 networks. Due to the sim- plicity of the concept and the availability of the rele- vant attack tools, launching a DoS attack is relatively easy, while defending a network resource against it is disproportionately difficult. The first step of any comprehensive protection scheme against DoS is the detection of its existence, ideally long before the de- structive traffic build-up. In this paper we propose a generic approach for DoS detection which uses multi- ple Bayesian classifiers and random neural networks (RNN). Our method is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood esti- mation and fusing the information gathered from the individual input features using likelihood averaging and different architectures of RNNs. We present and compare seven different implementations of it and evaluate our experimental results obtained in a large networking testbed
Controlling Access To Conserve Qos In Autonomous Network Using Network Simulator
Continuous applications made a requirement for system Quality of Service (QoS). This significance prompted the improvement of self-sufficient systems that utilization versatile bundle directing with the end goal to give the most ideal QoS. Affirmation Control (AC) is a system which makes those systems a pace further in ensuring bundle conveyance even under strict QoS imperatives. QoS all through the time of every single acknowledged association in the system. The effect that the new call will have, on the QoS of both the new and the current clients, is assessed by sending test parcels and checking the systems. The choice of whether to acknowledge another call is made utilizing a novel math of QoS measurements, encourage by Warshall's calculation, which searches for a way with adequate QoS values that can oblige the new stream. The fundamental scientific standards and present trial results acquired by assessing the strategy in an expansive research center proving ground working the Self-Aware Cognitive Packet Network (CPN) convention