Michael G. Kallitsis

Estimating internet address space usage through passive measurements

One challenge in understanding the evolution of Internet infrastructure is the lack of systematic mechanisms for monitoring the extent to which allocated IP addresses are actually used. Address utilization has been monitored via actively scanning the entire IPv4 address space. We evaluate the potential to leverage passive network traffic measurements in addition to or instead of active probing. Passive traffic measurements introduce no network traffic overhead, do not rely on unfiltered responses to probing, and could potentially apply to IPv6 as well. We investigate two challenges in using passive traffic for address utilization inference: the limited visibility of a single observation point; and the presence of spoofed IP addresses in packets that can distort results by implying faked addresses are active. We propose a methodology for removing such spoofed traffic on both darknets and live networks, which yields results comparable to inferences made from active probing. Our preliminary analysis reveals a number of promising findings, including novel insight into the usage of the IPv4 address space that would expand with additional vantage points.

Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network.

A Framework for Optimizing Measurement-Based Power Distribution under Communication Network Constraints

In this paper, we propose a framework for coupling the communication network of a smart grid with the power distribution network in an effort to better utilize the scarce energy resources. We believe that an agile, robust and effective communication infrastructure, relying on a service-oriented network paradigm is essential for proper operation of a greener and smarter electricity grid. The communication and power components of the grid should not be studied in isolation and we suggest a model where the communication delays are integrated in an electricity market pricing model such that the power demand affects the allocation of communication resources and vice versa. Power demand could be monitored and predicted via smart meters that would allow a dynamic pricing scheme. This serves as a feedback mechanism towards the consumers which can then reduce their consumption or activate alternative sources of energy to alleviate the local distribution system.

Measurement-based optimal resource allocation for network services with pricing differentiation

In this paper, we introduce a model for allocating available resources in service-oriented network, with particular focus on delay sensitive services. The model is based on a pricing scheme for the offered services and also takes into consideration the quality of service requirements of each service class through a probabilistic delay-bound constraint. The proposed policy is dynamic in nature and relies on online measurements of the incoming traffic for adjusting the class allocations. We illustrate its performance and its robustness to various tuning parameters through an extensive simulation study that considers various simulation scenarios including experiments based on real network traces.

Understanding IPv6 internet background radiation

We report the results of a study to collect and analyze IPv6 Internet background radiation. This study, the largest of its kind, collects unclaimed traffic on the IPv6 Internet by announcing five large covering prefixes; these cover the majority of allocated IPv6 space on todays Internet. Our analysis characterizes the nature of this traffic across regions, over time, and by the allocation and routing status of the intended destinations, which we show help to identify the causes of this traffic. We compare results to unclaimed traffic in IPv4, and highlight case studies that explain a large fraction of the data or highlight notable properties. We describe how announced covering prefixes differ from traditional network telescopes, and show how this technique can help both network operators and the research community identify additional potential issues and misconfigurations in this critical Internet transition period.

Optimal Power Allocation Under Communication Network Externalities

Efficient resource allocation is an important problem that aims for a “greener” and more environmentally friendly electric power grid. The smart behavior of the newly emerged grid, combined with two-way communication between users and the operator allows for actions like measurement, monitoring, prediction, and control signaling so as to maximize social welfare. We introduce a framework for optimal resource allocation in smart grids that also considers the uncertainty in message signaling. This introduces communication network externalities, added on top of the existing transmission network ones. The task at hand resembles the so called local public goods problem in mathematical economics terminology, a problem impractical to solve using centralized mechanisms. We propose an iterative, decentralized algorithm for its solution. The algorithm is scalable for deployment in large networks since it requires only messages per network user per iteration, where is the number of users. Moreover, it is guaranteed to converge, does not require revelation of private information from each user and all algorithm actions can be realized by programmable smart devices of the grid.

Distributed and Dynamic Resource Allocation for Delay Sensitive Network Services

In this paper, we present a distributed algorithm to dynamically allocate the available resources of a service-oriented network to delay sensitive network services. We use a utility-based framework to differentiate services based on both their relative profitability and quality-of-service requirements. Our performance metric is the end-to-end delay that a service class experiences in the network. We use network calculus to obtain a deterministic upper bound of this delay and we incorporate this information into our optimization problem formulation. We leverage a moving average control scheme to capture traffic shifts in real time, which makes our solution to react adaptively to traffic dynamics. Finally, we evaluate our system using real traces of instant messaging service traffic.

Pricing and optimal resource allocation in next generation network services

In this paper, we introduce a pricing model that ensures efficient resource allocation that provides guaranteed quality of service while maximizing profit in multiservice networks. Specifically, a dynamic allocation policy is examined that relies on online measurements while each service class operates under a probabilistic bound delay constraint. We present a rigorous analysis of the properties of the policy that provides insights into its workings as well as its sensitivity to various parameters. Finally, its performance is evaluated through an extensive numerical study.

Pricing and Measurement-based Optimal Resource Allocation in Next Generation Network Services

In this paper, we enhance our previous work regarding optimal resource allocation of next generation network services under a flat pricing scheme and quality of service policies. We present a complete framework that makes our model to dynamically allocate the resources whenever required. In order to do that, we apply an online traffic estimator and we monitor traffic changes using an Exponentially Weighted Moving Average control chart. Hence, the profit maximization of the provider is done efficiently. Finally, the performance of our framework is investigated through various simulation scenarios.

A decentralized algorithm for optimal resource allocation in smartgrids with communication network externalities

We introduce a framework for optimal resource allocation in smart grids. We consider two components of the smart grid; the power distribution network and the data communication network. By defining suitable utility functions, the power and bandwidth resources are optimally allocated. This requires the solution of the, so called, local public goods problem, in mathematical economics terminology. We propose an iterative, distributed algorithm for its solution. The algorithm is scalable for deployment in large networks since it requires only O(N) messages per network user per iteration, where N is the number of users. Moreover, it is guaranteed to converge, does not require revelation of private information from each user and all algorithm actions can be realized by programmable smart devices of the smart grid.