Performance Analysis of Communications Networks and Systems

Mieghem, Piet Van

doi:10.1017/cbo9780511616488

Cited by 206 publications

(170 citation statements)

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“…In the sequel of the paper, we denote the probabilities that a link is in the state ON Fig. 2(a)], we denote a transition probability p that a link present at the moment t will not appear at the moment (t + 1); and probability q that a link will be added at the moment (t + 1), if it was not present at the moment t. According to the Markovian model and the formula for total probability we can calculate the probability for both states ON and OFF [8,34] . In a special case where p + q = 1, there is no time correlation and we have a fixed probability q for a link appearance, which corresponds to Erdős-Rényi temporal network as shown in Fig.…”

Section: B Markov Temporal Modelmentioning

confidence: 99%

Error and attack vulnerability of temporal networks

2012

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The study of real-world communication systems via complex network models has greatly expanded our understanding on how information flows, even in completely decentralized architectures such as mobile wireless networks. Nonetheless, static network models cannot capture the time-varying aspects and, therefore, various temporal metrics have been introduced. In this paper, we investigate the robustness of time-varying networks under various failures and intelligent attacks. We adopt a methodology to evaluate the impact of such events on the network connectivity by employing temporal metrics in order to select and remove nodes based on how critical they are considered for the network. We also define the temporal robustness range, a new metric that quantifies the disruption caused by an attack strategy to a given temporal network. Our results show that in real-world networks, where some nodes are more dominant than others, temporal connectivity is significantly more affected by intelligent attacks than by random failures. Moreover, different intelligent attack strategies have a similar effect on the robustness: even small subsets of highly connected nodes act as a bottleneck in the temporal information flow, becoming critical weak points of the entire system. Additionally, the same nodes are the most important across a range of different importance metrics, expressing the correlation between highly connected nodes and those that trigger most of the changes in the optimal information spreading. Contrarily, we show that in randomly generated networks, where all the nodes have similar properties, random errors and intelligent attacks exhibit similar behavior. These conclusions may help us in design of more robust systems and fault-tolerant network architectures.

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Section: B Markov Temporal Modelmentioning

confidence: 99%

Error and attack vulnerability of temporal networks

2012

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“…The results for G 0.2 (N) are plotted in Figures 10-13. Figures 10 and 11 indicate that the difference in weight decreases when N increases, because E[w(P )] ≈ ln N Np [19]. The probability density function for w(P 2 ) − w(P 1 ) in Figure 10 seems to follow an exponential distribution, unlike the probability density function for w(P l ) − w(P 1 ) in Figure 11.…”

Section: Bounds On Path Stabilitymentioning

confidence: 95%

The stability of paths in a dynamic network

Kuipers

Wang

Mieghem

2005

Proceedings of the 2005 ACM Conference on Emerging Network Experiment and Technology - CoNEXT'05

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Dynamic networks appear in several contexts: QoS routing faces the difficult problem of accurately and efficiently maintaining, distributing and updating network state information, and in wireless ad hoc networking, signal strength fluctuations complicate the choice of stable paths. In this paper we will focus on the stability of paths in a network with dynamically changing link weights. The level of path stability has a direct relation to the number of updates that are necessary to maintain an accurate view of the network state. If a small change in the network state does not affect the shortest path, then such a change need not be distributed throughout the network. We evaluate path stability by adding noise and observing the change in paths.

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“…The broker receives the current status of data centers' physical resources and their energy information from ECE-CIS, and based on this information, assigns the VM to a physical server in a data center site. Based on [25], in today's Internet and core networks design, average number of hops a packet passes from source to destination is between 12-14 hops. Therefore, we can have data center site selection without considering network distance; especially for sites that are located in a region, such as different states in USA, as we considered in this paper.…”

Section: Ece Cloud Informationmentioning

confidence: 99%

Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud Data Centers

Khosravi

Garg

Buyya

2013

Euro-Par 2013 Parallel Processing

100

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Abstract. Due to the increasing use of Cloud computing services and the amount of energy used by data centers, there is a growing interest in reducing energy consumption and carbon footprint of data centers. Cloud data centers use virtualization technology to host multiple virtual machines (VMs) on a single physical server. By applying efficient VM placement algorithms, Cloud providers are able to enhance energy efficiency and reduce carbon footprint. Previous works have focused on reducing the energy used within a single or multiple data centers without considering their energy sources and Power Usage Effectiveness (PUE). In contrast, this paper proposes a novel VM placement algorithm to increase the environmental sustainability by taking into account distributed data centers with different carbon footprint rates and PUEs. Simulation results show that the proposed algorithm reduces the CO2 emission and power consumption, while it maintains the same level of quality of service compared to other competitive algorithms.

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Performance Analysis of Communications Networks and Systems

Cited by 206 publications

References 0 publications

Error and attack vulnerability of temporal networks

Error and attack vulnerability of temporal networks

The stability of paths in a dynamic network

Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud Data Centers

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