Hash-based data structures such as Bloom filters are widely used in network systems for tasks including caching, anomaly detection, and machine learning pipelines. They typically provide binary indications of whether an element belongs to a set of interest, e.g., the contents of a cache. When uncertainty arises due to hash collisions, a positive indication is returned to avoid false negatives. We argue that the certainty associated with such indications can itself be useful information. This work focuses on Counting Bloom Filters (CBFs), a Bloom-filter variant that maintains counters rather than bits. Besides supporting insertions and deletions, these counters provide additional information that can be used to estimate the certainty of positive membership indications. We show how this certainty signal can be exploited in architectures that combine Bloom Filters with machine learning (ML) models.

GitHub Security Advisories (GHSA) have become a central component of open-source vulnerability disclosure and are widely used by developers and security tools. A distinctive feature of GHSA is that only a fraction of advisories are reviewed by GitHub, while the mechanisms associated with this review process remain poorly understood. In this paper, we conduct a large-scale empirical study of the GHSA review processes, analyzing over 288,000 advisories spanning 2019-2025. We characterize which advisories are more likely to be reviewed, quantify review delays, and identify two distinct review-latency regimes: a fast path dominated by GitHub Repository Advisories (GRAs) and a slow path dominated by NVD-first advisories. We further develop a queueing model that accounts for this dichotomy based on the structure of the advisory processing pipeline.

• We introduced a fully automated framework that quantifies the relationship between microservice architecture complexity, derived from multiple types of statically detected dependencies, and performance-related quality attributes. • We analyzed five complex benchmark systems, including four structurally distinct releases of the Train-Ticket microservice benchmark and one instance of the DeathStarBench suite. • Regarding the relationship between microservice-architecture complexity and overall performance, our analysis demonstrated that systems with poor structural complexity, reflected in high propagation cost and high Clique ratios, exhibited reduced scalability and supported fewer user requests. • Regarding the relationship between individual service complexity and their performance, our results show that endpoints with higher coupling scores tend to have longer response times. Using Spearman’s rank correlation to assess the correlation between each endpoint’s coupling score and its performance score, we find a statistically significant positive correlation. A microservice architecture is intended to promote modularity and evolvability. In this paper, we present an automated framework for assessing the relationship between microservice architecture complexity and performance-related quality attributes. In this framework, we use PPTAM, a performance testing tool, to evaluate system response time under varying user loads, and DV8, an architecture analysis tool, to assess architectural complexity and the complexity of individual services using coupling scores, propagation cost, and architectural antipatterns derived from various types of dependency relations. Using this approach, we evaluated five benchmark systems, including four releases of a microservice system that share similar functionalities but differ in structural design. The results show that microservice architectures with poor complexity scores also exhibited degraded performance outcomes. This automated framework, for the first time, enables a comprehensive measurement of microservice architecture complexity, formed through multiple types of statically extracted dependencies, and its correlation with dynamically obtained performance metrics.

Liquidity providers (LPs) are essential figures in the operation of automated market makers (AMMs); in exchange for transaction fees, LPs lend the liquidity that allows AMMs to operate. While many prior works have studied the incentive structures of LPs in general, we currently lack a principled understanding of a special class of LPs known as Just-In-Time (JIT) LPs. These are strategic agents who momentarily supply liquidity for a single swap, in an attempt to extract disproportionately high fees relative to the remaining passive LPs. This paper provides the first formal, transaction-level model of JIT liquidity provision for a widespread class of AMMs known as Concentrated Liquidity Market Makers (CLMMs), as seen in Uniswap V3, for instance. We characterize the landscape of price impact and fee allocation in these systems, formulate and analyze a non-linear optimization problem faced by JIT LPs, and prove the existence of an optimal strategy. By fitting our optimal solution for JIT LPs to real-world CLMMs, we observe that in liquidity pools (particularly those with risky assets), there is a significant gap between observed and optimal JIT behavior. Existing JIT LPs often fail to account for price impact; doing so, we estimate they could increase earnings by up to 69% on average over small time windows. We also show that JIT liquidity, when deployed strategically, can improve market efficiency reducing slippage for traders, albeit at the cost of eroding passive LP profits by up to 44% per trade on average.

Effective resource allocation in sensor networks, IoT systems, and distributed computing is essential for applications such as environmental monitoring, surveillance, and smart infrastructure. Sensors or agents must optimize their resource allocation to maximize the accuracy of parameter estimation. In this work, we consider a group of sensors or agents, each sampling from a different variable of a multivariate Gaussian distribution and having a different estimation objective. We formulate a sensor or agent’s data collection and collaboration policy design problem as a Fisher information maximization (or Cramer-Rao bound minimization) problem. This formulation captures a novel trade-off in energy use, between locally collecting univariate samples and collaborating to produce multivariate samples. When knowledge of the correlation between variables is available, we analytically identify two cases: (1) where the optimal data collection policy entails investing resources to transfer information for collaborative sampling, and (2) where knowledge of the correlation between samples cannot enhance estimation efficiency. When knowledge of certain correlations is unavailable, but collaboration remains potentially beneficial, we propose novel approaches that apply multi-armed bandit algorithms to learn the optimal data collection and collaboration policy in our sequential distributed parameter estimation problem. We illustrate the effectiveness of the proposed algorithms,DOUBLE-F, DOUBLE-Z, UCB-F,UCB-Z, through simulation.

Deep Neural Networks (DNNs) have demonstrated exceptional performance in diverse tasks. However, deploying DNNs on resource-constrained devices presents challenges due to energy consumption and delay overheads. To mitigate these issues, early-exit DNNs (EE-DNNs) incorporate exit branches within intermediate layers to enable early inferences. These branches estimate prediction confidence and employ a fixed threshold to determine early termination. Nonetheless, fixed thresholds yield suboptimal performance in dynamic contexts, where context refers to distortions caused by environmental conditions, in image classification, or variations in input distribution due to concept drift, in NLP. In this article, we introduce Upper Confidence Bound in EE-DNNs (UCBEE), an online algorithm that dynamically adjusts early exit thresholds based on context. UCBEE leverages confidence levels at intermediate layers and learns without the need for true labels. Through extensive experiments in image classification and NLP, we demonstrate that UCBEE achieves logarithmic regret, converging after just a few thousand observations across multiple contexts. We evaluate UCBEE for image classification and text mining. In the latter, we show that UCBEE can reduce cumulative regret and lower latency by approximately 10%–20% without compromising accuracy when compared to fixed threshold alternatives. Our findings highlight UCBEE as an effective method for enhancing EE-DNN efficiency.

Caching plays a central role in networked systems, reducing the load on servers and the delay experienced by users. Despite their relevance, networked caching systems still pose a number of challenges pertaining their long term behavior. In this paper, we formally show and experimentally evidence conditions under which networked caches tend to synchronize over time. Such synchronization, in turn, leads to performance degradation and aging, motivating the monitoring of caching systems for eventual rejuvenation, as well as the deployment of diverse cache replacement policies across caches to promote diversity and preclude synchronization and its aging effects. Based on trace-driven simulations with real workloads, we show how hit probability is sensitive to varying channel reliability, cache sizes, and cache separation, indicating that the mix of simple policies, such as Least Recently Used (LRU) and Least Frequently Used (LFU), provide competitive performance against state-of-art policies. Indeed, our results suggest that diversity in cache replacement policies, rejuvenation and intentional dropping of requests are strategies that build diversity across caches, preventing or mitigating performance degradation due to caching aging.

Deep Neural Networks (DNNs) are widely used to solve a growing number of tasks, such as image classification. However, their deployment at resource-constrained devices still poses challenges related to energy consumption and delay over-heads. Early-Exit DNNs (EE-DNNs) address the challenges by adding side branches through their architecture. Under an edge-cloud co-inference, if the confidence at a side branch is larger than a fixed confidence threshold, the inference is performed completely at the edge device, saving computation for more difficult observations. Otherwise, the edge device offloads the inference task to the cloud, incurring overhead. Despite its success, EE-DNNs for image classification have to cope with distorted images. The baseline distortion level depends on the environmental context, e.g., time of the day, lighting, and weather conditions. To cope with varying distortion, we propose Adaptive Early-Exit in Deep Neural Networks (AdaEE), a novel algorithm to dynamically adjust the confidence threshold based on context, leveraging the Upper Confidence Bound (UCB) for that matter. AdaEE provably achieves logarithmic regret under mild conditions. We experimentally verify that 1) convergence occurs after collecting a few thousand observations for images with different distortion levels and overhead values, and 2) AdaEE obtains a lower cumulative regret when compared against alternatives using the Caltech-256 dataset subject to varying distortion.

The widespread adoption of 5G cellular technology will evolve as one of the major drivers for the growth of IoT-based applications. In this paper, we consider a Service Provider (SP) that launches a smart city service based on IoT data readings: in order to serve IoT data collected across different locations, the SP dynamically negotiates and rescales bandwidth and service functions. 5G network slicing functions are key to lease appropriate amount of resources over heterogeneous access technologies and different site types. Also, different infrastructure providers will charge slicing service depending on specific access technology supported across sites and IoT data collection patterns. We introduce a pricing mechanism based on Age of Information (AoI) to reduce the cost of SPs. It provides incentives for devices to smooth traffic by shifting part of the traffic load from highly congested and more expensive locations to lesser charged ones, while meeting QoS requirements of the IoT service. The proposed optimal pricing scheme comprises a two-stage decision process, where the SP determines the pricing of each location and devices schedule uploads of collected data based on the optimal uploading policy. Simulations show that the SP attains consistent cost reductions tuning the trade-off between slicing costs and the AoI of uploaded IoT data.

Data on software vulnerabilities, products, and exploits are typically collected from multiple non-structured sources. Valuable information, e.g., on which products are affected by which exploits, is conveyed by matching data from those sources, i.e., through their relations. In this paper, we leverage this simple albeit unexplored observation to introduce a statistical relational learning (SRL) approach for the analysis of vulnerabilities, products, and exploits. In particular, we focus on the problem of determining the existence of an exploit for a given product, given information about the relations between products and vulnerabilities, and vulnerabilities and exploits, focusing on Industrial Control Systems (ICS), the National Vulnerability Database, and ExploitDB. Using RDN-Boost, we were able to reach an AUC ROC of 0.80 and an AUC PR of 0.65 for the problem at hand. To reach that performance, we indicate that it is instrumental to include textual features, e.g., extracted from the description of vulnerabilities, as well as structured information, e.g., about product categories. In addition, using interpretable relational regression trees, we report simple rules that shed insight on factors impacting the weaponization of ICS products.

Similarity caching allows requests for an item$i$to be served by a similar item i’. Applications include recommendation systems, multimedia retrieval, and machine learning. Recently, many similarity caching policies have been proposed, but still we do not know how to compute the hit rate even for simple policies, like SIM-LRU and RND-LRU that are straightforward modifications of classic caching algorithms. This paper proposes the first algorithm to compute the hit rate of similarity caching policies under the independent reference model for the request process. In particular, we show how to extend the popular time-to-live approximation in classic caching to similarity caching. The algorithm is evaluated on both synthetic and real world traces.

Content recommendation systems, also known as recommenders, are pervasive and impact a significant portion of users demands over the Internet. Although recommenders have been primarily devised to account for users interests with respect to the content catalog, mobile users are typically served by a network that is unreliable and subject to losses and low QoS. Can content recommenders compensate for low QoS? To answer this question, we conducted experiments over the Internet, and report our findings on (i) the characterization of QoS and (ii) the compensation for low QoS. Our measurements suggest that content that is far from the trends tends to be far from the user. We quantify the extent at which unpopular content tends to be served with lower QoS and establish a methodology to determine the relationship between contents' popularity and its physical proximity to the users. Then, we verify that making requests a bit trendier can hit much closer content. In particular, our results suggest conditions under which a recommender can compensate for low QoS, at zero costs for operators.

Joint caching and recommendation has been recently proposed as a new paradigm for increasing the efficiency of mobile edge caching. Early findings demonstrate significant gains for the network performance. However, previous works evaluated the proposed schemes exclusively on simulation environments. Hence, it still remains uncertain whether the claimed benefits would change in real settings. In this paper, we propose a methodology that enables to evaluate joint network and recommendation schemes in real content services by only using publicly available information. We apply our methodology to the YouTube service, and conduct extensive measurements to investigate the potential performance gains. Our results show that significant gains can be achieved in practice; e.g., 8 to 10 times increase in the cache hit ratio from cache-aware recommendations. Finally, we build an experimental testbed and conduct experiments with real users; we make available our code and datasets to facilitate further research. To our best knowledge, this is the first realistic evaluation (over a real service, with real measurements and user experiments) of the joint caching and recommendations paradigm. Our findings provide experimental evidence for the feasibility and benefits of this paradigm, validate assumptions of previous works, and provide insights that can drive future research.

Caching is at the core of most modern communication systems, where caches are used to store content and traffic classification rules. While network components can leverage caching in a cooperative manner, one important aspect of such systems concerns possible dependencies among stored items. A major use case of such dependencies appears in rule placement across software-defined networks (SDNs). Despite the tremendous success of SDNs in datacenters, their wide adoption still poses a key challenge: the packet-forwarding rules in switches require fast and power-hungry memories. Rule tables, which serve as caches, are of limited size in cheap and energy-constrained devices, motivating novel solutions to achieve high hit rates. We leverage device connectivity in the fast data plane, where delays are in the order of few milliseconds, and propose multiple switches to work together to avoid accessing the control plane, where delays are orders of magnitude greater. As a low priority rule in a cache entails caching higher priority rules, we pose the problem of cooperative caching with dependencies. We provide models and algorithms accounting for dependencies among rules implied by existing switch memory types, andlay the foundations of cooperative caching with dependencies.

In this paper, we report results on a large scale measurement campaign to collect temporal information about events associated with software vulnerabilities. The data is curated so as to extract dates from each of the analyzed security advisories. The resulting time series are our object of study. From our measurements we were able to identify which role was assumed by different platforms (such as websites and forums) in the security landscape, including sources and aggregators of information about vulnerabilities. Then, we propose an analytical model to express the flow of information through security advisories across multiple platforms. The model is based on a queueing network, where each platform corresponds to a queue which adds a delay in the information propagation. Such delays, in turn, have an impact on the visibility of the information at different platforms. Leveraging the proposed model and the collected data, we assess how different system parameters, such as the delays incurred by each platform to propagate its messages, impact the overall flow of information across platforms.

Microservices have emerged as an architectural style for developing distributed applications. Assessing the performance of architecture deployment configurations — e.g., with respect to deployment alternatives — is challenging and must be aligned with the system usage in the production environment. In this paper, we introduce an approach for using operational profiles to generate load tests to automatically assess scalability pass/fail criteria of microservice configuration alternatives. The approach provides a Domain-based metric for each alternative that can, for instance, be applied to make informed decisions about the selection of alternatives and to conduct production monitoring regarding performance-related system properties, e.g., anomaly detection. We have evaluated our approach using extensive experiments in a large bare metal host environment and a virtualized environment. First, the data presented in this paper supports the need to carefully evaluate the impact of increasing the level of computing resources on performance. Specifically, for the experiments presented in this paper, we observed that the evaluated Domain-based metric is a non-increasing function of the number of CPU resources for one of the environments under study. In a subsequent series of experiments, we investigate the application of the approach to assess the impact of security attacks on the performance of architecture deployment configurations.

In this work, we study the optimal configuration of the physical layer in wireless networks by means of Semi-Markov Decision Process (SMDP) modeling. In particular, assume the physical layer is characterized by a set of potential operating points, with each point corresponding to a rate and reliability pair; for example, these pairs might be obtained through a now-standard diversity-multiplexing tradeoff characterization. Given the current network state (e.g., buffer occupancies), a Decision Maker (DM) needs to dynamically decide which operating point to use. The SMDP problem formulation allows us to choose from these points. A solution to the SMDP problem is an optimal selection of operating points, which is expressed by a decision rule as a function of the number of packets in the source's finite queue, the channel state, and the size of the packet to be transmitted. We derive a general solution to the SMDP which covers various model configurations, packet size distributions and channel dynamics. For the specific case of exponential transmission times, we analytically prove the optimal policy has a threshold structure. Numerical results validate this finding, as well as depict muti-threshold policies for time varying channels such as the Gilbert-Elliott channel.

A major threat to the Internet infrastructure and, more broadly, to its culture is posed by DDoS attacks. To mitigate their impact, detection should preferably occur close to the attack origin, e.g., at home-routers. However, these devices typically have limited resources and an approach that relies on packet inspection does not bode well with such devices.We propose a lightweight approach for DDoS detection that solely employs network interface byte and packet counts. To detect attacks with such a limited amount of information, our key insight consists in training classifiers to make use of workload data from 1,823 home-users augmented with attacks generated in a controlled environment. In our experiments, we selected seven attack vectors generated using Mirai and BASHLITE malwares. We then conduct a device-agnostic detection of attacks vectors, obtaining F1 scores typically higher than 0.99. To cope with the evolving nature of DDoS attacks, we also report results indicating the detection power of the proposed methodology when different attack vectors are used for training and testing.

Users of online social networks are faced with a conundrum of trying to be always informed without having enough time or attention budget to do so. The retention of users on online social networks has important implications, encompassing economic, psychological and infrastructure aspects. In this paper, we pose the following question: what is the optimal rate at which users should access a social network? To answer this question, we propose an analytical model to determine the value of an access (VoA) to the social network. In the simple setting considered in this paper, VoA is defined as the chance of a user accessing the network and obtaining new content. Clearly, VoA depends on the rate at which sources generate content and on the filtering imposed by the social network. Then, we pose an optimization problem wherein the utility of users grows with respect to VoA but is penalized by costs incurred to access the network. Using the proposed framework, we provide insights on the optimal access rate. Our results are parameterized using Facebook data, indicating the predictive power of the approach.

The demand for Internet services that require frequent updates through small messages, also known as microblogging, has tremendously grown in the past few years. Although the use of such applications by domestic users is usually free, their access from mobile devices is subject to fees and consumes energy from limited batteries. If a user activates his mobile device and is in the range of a publisher, an update is received at the expense of monetary and energy costs. Thus, users face a tradeoff between such costs and their messages aging. The goal of this paper is to show how to cope with such a tradeoff, by devising aging control policies. An aging control policy consists of deciding, based on the utility of the owned content, whether to activate the mobile device, and if so, which technology to use (WiFi or cellular). We present a model that yields the optimal aging control policy. Our model is based on a Markov Decision Process (MDP) in which states correspond to content ages. Using our model, we show the existence of an optimal strategy in the class of threshold strategies, wherein users activate their mobile devices if the age of their poadcasts surpasses a given threshold and remain inactive otherwise. The accuracy of our model is validated against traces from the UMass DieselNet bus network.

In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.

Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithms usually lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements and an analytical model to capture the visibility of publishers over a News Feed. First, measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility bias incurred by the users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we found that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views.

Swarming systems, such as BitTorrent, are one of the most common solutions for scalable, robust and inexpensive content distribution. Although the service capacity of swarming systems has been studied for decades through modeling and analysis, there is a lack of experimental evidence about how the throughput of such systems behaves in under-provisioned regimes. The aim of this paper is to fill this gap. In this paper, we consider a closed-loop model to assess the throughput of peer-to-peer systems. Then, we show through controlled experiments using BitTorrent clients that some analytical findings recently reported in the literature, such as the missing piece syndrome, occur in practice. In particular, we indicate that when seeds have a small effective service capacity, or when seeds are intermittent, the throughput saturates as the population size grows. Finally, we discuss the implications of such findings on the modeling and design of swarming systems.

Renewable energy (e.g., solar energy) is an attractive option to provide green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either (i) requiring batteries to store surplus energy, which typically incurs ∼ 20% energy conversion losses, or (ii) using net metering to transmit surplus energy via the electric grid’s AC lines, which severely limits the maximum percentage of renewable penetration possible. In this article, we propose an alternative structure where nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when to minimize system-wide energy transmission losses in the microgrid. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system (i) reduces the energy loss on the AC line by 64% without requiring large batteries, (ii) performance scales up with larger battery capacities, and (iii) is robust to different energy consumption patterns and energy prediction accuracy in the microgrid.

In order to match the energy demand of electric vehicles to the capacity of the power grid, it is fundamental to understand the occupancy of charging stations and to react accordingly. A Markov model of a fast charging station for lithium-ion (Li-ion) batteries, i.e., the most prevalent type today, is proposed. Li-Ion batteries present a two-step charging profile, making energy management particularly challenging. A wide range of situations is covered by considering three types of scenarios with and without waiting lines. The analytical results obtained from the steady-state solution of the Markov model reveal the behavior of multiple variables of interest: availability of the charging station to accept new customers (in terms of space and energy), number of customers, energy consumption, and power utilization. From the results, indicators for assessing the quality of service of the charging station are derived. Based on these indicators, customers may decide either to wait or to head toward another station. The owners of the stations, in turn, can predict the impact of investments in space and energy provisioning, when devising capacity planning strategies.

In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.

This paper provides a summary of the First International Workshop on Model Based Design for Cyber- Physical Systems (MB4CP 2015) in conjunction with DSN 2015 conference in Rio de Janeiro, Brazil.

Recommendation systems and the performance of computer network systems have fundamental implications over each other. While recommendation systems impact system performance, the latter can be used to guide the former. In this paper, we study the interconnections between recommendation systems and the performance of the network. Focusing on swarming systems à la Bittorrent, we propose an analytical model to capture the revenue and the cost to a content provider as a function of the quality of its recommendations and the cost to serve the content. The model is then used to suggest heuristics on how to recommend content accounting for service costs and user preferences.

In this paper we propose a new framework to assess survivability of software projects accounting for media capability details as introduced in Media Synchronicity Theory (MST). Specifically, we add to our global engineering framework the assessment of the impact of inadequate conveyance and convergence available in the communication infrastructure selected to be used by the project, on the system ability to recover from project disasters. We propose an analytical model to assess how the project recovers from project disasters related to process and communication failures. Our model is based on media synchronicity theory to account for how information exchange impacts recovery. Then, using the proposed model we evaluate how different interventions impact communication effectiveness. Finally, we parameterize and instantiate the proposed survivability model based on a data gathering campaign comprising thirty surveys collected from senior global software development experts at ICGSE'2014 and GSD'2015.

Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using a small four-node topology, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.

Software projects that are engineered using global software development techniques are required to deploy processes and tools to support collaboration over large geographies. Specifically, these projects involve the deployment of processes and tools to support project management, communication, and risk management. Whereas a traditional software development effort can use standard processes and tools to support communication and collaboration, global software development efforts require a unified and holistic project management, development process, collaboration, and communication approach taking into consideration the interplay of time zones, number of sites and cultural diversity. In this paper, we introduce a novel approach for modeling and quantification of global software engineering frameworks. In our approach, we apply transient survivability metrics to support the design of global software engineering projects. Therefore, our approach combines survivability analysis and global software engineering frameworks analysis. The survivability metric used in this paper is the time required to recover from a software project disaster (e.g., break down in communication between remote teams) for a given global software engineering framework. The global software engineering modeling framework we use is composed of models supporting the evaluation of communication tools, software development processes and cultural diversity management. We illustrate the application of our approach by applying it to the analysis of an example derived from a real global software engineering project. Our results indicate that the combination of survivability analysis and modeling of global software engineering frameworks can provide meaningful insights when designing global software engineering frameworks.

SUMMARY Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage, and intelligent features such as automated fault detection, isolation, and restoration (FDIR) and demand response. In this paper, we present an analytical model and metrics for the survivability assessment of the distribution power grid network. The proposed metrics extend the system average interruption duration index, accounting for the fact that after a failure, the energy demand and supply will vary over time during a multi‐step recovery process. The analytical model used to compute the proposed metrics is built on top of three design principles: state space factorization, state aggregation, and initial state conditioning. Using these principles, we reduce a Markov chain model with large state space cardinality to a set of much simpler models that are amenable to analytical treatment and efficient numerical solution. In case demand response is not integrated with FDIR, we provide closed form solutions to the metrics of interest, such as the mean time to repair a given set of sections. Under specific independence assumptions, we show how the proposed methodology can be adapted to account for multiple failures. We have evaluated the presented model using data from a real power distribution grid, and we have found that survivability of distribution power grids can be improved by the integration of the demand response feature with automated FDIR approaches. Our empirical results indicate the importance of quantifying survivability to support investment decisions at different parts of the power grid distribution network. Copyright © 2014 John Wiley & Sons, Ltd.

Over the past few years Content-Centric Networking, a networking model in which host-to-content communication protocols are introduced, has been gaining much attention. A central component of such an architecture is a large-scale interconnected caching system. To date, the way these Cache Networks operate and perform is still poorly understood. In this work, we demonstrate that certain cache networks are non-ergodic in that their steady-state characterization depends on the initial state of the system. We then establish several important properties of cache networks, in the form of three independently-sufficient conditions for a cache network to comprise a single ergodic component. Each property targets a different aspect of the system - topology, admission control and cache replacement policies. Perhaps most importantly we demonstrate that cache replacement can be grouped into equivalence classes, such that the ergodicity (or lack-thereof) of one policy implies the same property holds for all policies in the class.

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams, smart grids require a unified and holistic approach taking into consideration the interplay of distributed generation, distribution automation topology, intelligent features, and others. In this paper, we use transient survivability metrics to create better distribution automation network designs. Our approach combines survivability analysis and power flow analysis to assess the survivability of the distribution power grid network. Additionally, we present an initial approach to automatically optimize available investment decisions with respect to survivability and investment costs. We have evaluated the feasibility of this approach by applying it to the design of a real distribution automation circuit. Our empirical results indicate that the combination of survivability analysis and power flow can provide meaningful investment decision support for power systems engineers.

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage and intelligent features such as automated failure detection, isolation and restoration (FDIR) and demand response.

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams, smart grids require a unified and holistic approach taking into consideration the interplay of distributed generation, distribution automation topology, intelligent features, and others.

BitTorrent, the immensely popular file swarming system, suffers a fundamental problem: content unavailability. Although swarming scales well to tolerate flash crowds for popular content, it is less useful for unpopular content as peers arriving after the initial rush find it unavailable. In this paper, we present a model to quantify content availability in swarming systems. We use the model to analyze the availability and the performance implications of bundling, a strategy commonly adopted by many BitTorrent publishers today. We find that even a limited amount of bundling exponentially reduces content unavailability. For swarms with highly unavailable publishers, the availability gain of bundling can result in a net decrease in average download time. We empirically confirm the model's conclusions through experiments on PlanetLab using the Mainline BitTorrent client.

No abstract available.

This work investigates reciprocity in peer-to-peer systems. The scenario is one where users arrive to the network with a set of contents and content demands. Peers exchange contents to satisfy their demands, following either a direct reciprocity principle (I help you and you help me) or indirect reciprocity principle (I help you and someone helps me). First, we prove that any indirect reciprocity schedule of exchanges, in the absence of relays, can be replaced by a direct reciprocity schedule, provided that users (1) are willing to download undemanded content for bartering purposes and (2) use up to twice the bandwidth they would use under indirect reciprocity. Motivated by the fact that, in the absence of relays, the loss of efficiency due to direct reciprocity is at most two, we study various distributed direct reciprocity schemes through simulations, some of them involving a broker to facilitate exchanges.

BitTorrent, the immensely popular file swarming system, suffers a fundamental problem: unavailability. Although swarming scales well to tolerate flash crowds for popular content, it is less useful for unpopular or rare files as peers arriving after the initial rush find the content unavailable.

We consider a scenario in which users share an access point and are mainly interested in VoIP applications. Each user is allowed to adapt to varying network conditions by choosing the transmission rate at which VoIP traffic is received. We denote this adaptation process by end-user congestion control, our object of study. The two questions that we ask are: (1) what are the performance consequences of letting the users to freely choose their rates? and (2) how to explain the adaptation process of the users? We set a controlled lab experiment having students as subject to answer the first question, and we extend an evolutionary game-theoretic model to address the second. Our partial answers are the following: (1) free users with local information can reach an equilibrium which is close to optimal from the system perspective. However, the equilibrium can be unfair; (2) the adaptation of the users can be explained using a game theoretic model. We propose a methodology to parameterize the latter, which involves active network measurements, simulations and an artificial neural network to estimate the QoS perceived by the users in each of the states of the model.

We consider the situation where N nodes share a common access point. With each node i there is an associated buffer and channel state that change in time. Node i dynamically chooses both the power and the admission control to be adopted so as to maximize the expected capacity, which depends on the actions and states of all the players, given its power and delay constraints. The information structure that we consider is such that each player knows the state of its own buffer and channel and its own actions. It does not know the states of, and the actions taken by other players. Using Markov Decision Processes we analyze the single player optimal policies under different model parameters. In the context of stochastic games we study the equilibria of the N player scenario.