Network operators need to understand how routing affects their customers, and to quickly detect routing changes. Despite its importance, today, there is no good way to summarize routing for a service, nor to differentiate between tiny changes and huge shifts. It is challenging to understand routing, even for a single destination, because no one organization has a global view of routing —routing emerges from combining the Internet's diverse routing policies as determined by business and policy constraints. We present Fenrir, a new tool that can rediscover recurring routing results. It can help operators detect routing modes (stable periods) and spot changes that need a performance check. We use Fenrir to evaluate B-Root's anycast service to show routing dynamics over five years. Fenrir shows that the routing of B Root is relatively stable except when sites are removed or added to B Root DNS service.

How do VPNs interact with IPv6? Our poster shows that VPNs often leak IPv6 traffic, failing to provide the promised privacy, and VPNs often prefer IPv4, even though IPv6 is available and working. These results use new data from a website for IP identification, coupled with experiments on specific VPN software. We identify the fraction of v6 traffic leaked, and find the root-cause of IPv6 de-preferencing in interactions between address selection in OSes and VPNs

Twelve years have passed since World IPv6 Launch Day, but what is the current state of IPv6 deployment? Prior work has examined IPv6 status as a binary: can a user do any IPv6? As deployment increases, we must consider a more nuanced, non-binary perspective on IPv6: how much and often can a user or a service use IPv6? We consider this question as a client, server, and cloud provider. Considering the client's perspective, we observe user traffic. We see that the fraction of IPv6 traffic a user sends varies greatly, both across users and day-by-day, with a standard deviation of over 15%. We show this variation occurs for two main reasons. First, IPv6 traffic is primarily human-generated, thus showing diurnal patterns. Second, some services lead with full IPv6 adoption, while others lag with partial or no support, so as users do different things their fraction of IPv6 varies. We look at server-side IPv6 adoption in two ways. First, we expand analysis of web services to examine how many are only partially IPv6 enabled due to their reliance on IPv4-only resources. Our findings reveal that only 12.6% of top 100k websites qualify as fully IPv6-ready. Finally, we examine cloud support for IPv6. Although all clouds and CDNs support IPv6, we find that tenant deployment rates vary significantly across providers. We find that ease of enabling IPv6 in the cloud is correlated with tenant IPv6 adoption rates, and recommend best practices for cloud providers to improve IPv6 adoption. Our results suggest IPv6 deployment is growing, but many services lag, presenting a potential for improvement.

Network traffic is often diurnal, with some networks peaking during the workday and many homes during evening streaming hours. Monitoring systems consider diurnal trends for capacity planning and anomaly detection. In this paper, we reverse this inference and use diurnal network trends and their absence to infer human activity. We draw on existing and new ICMP echo-request scans of more than 5.2M /24 IPv4 networks to identify diurnal trends in IP address responsiveness. Some of these networks are change-sensitive, with diurnal patterns correlating with human activity. We develop algorithms to clean this data, extract underlying trends from diurnal and weekly fluctuation, and detect changes in that activity. Although firewalls hide many networks, and Network Address Translation often hides human trends, we show about 168k to 330k (3.3-6.4% of the 5.2M) /24 IPv4 networks are change-sensitive. These blocks are spread globally, representing some of the most active 60% of 2 × 2° geographic gridcells, regions that include 98.5% of ping-responsive blocks. Finally, we detect interesting changes in human activity. Reusing existing data allows our new algorithm to identify changes, such as Work-from-Home due to the global reaction to the emergence of Covid-19 in 2020. We also see other changes in human activity, such as national holidays and government-mandated curfews. This ability to detect trends in human activity from the Internet data provides a new ability to understand our world, complementing other sources of public information such as news reports and wastewater virus observation.

Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse today. The opaque nature of the algorithms these platforms use to curate content raises societal questions. Prior studies have used black-box methods led by experts or collaborative audits driven by everyday users to show that these algorithms can lead to biased or discriminatory outcomes. However, existing auditing methods face fundamental limitations because they function independent of the platforms. Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users' private data and platforms' proprietary algorithms. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to relevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms' business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase (6.34x as an upper bound, and 4× for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.

The Domain Name System (DNS) is an essential service for the Internet which maps host names to IP addresses. The DNS Root Sever System operates the top of this namespace. RIPE Atlas observes DNS from more than 11k vantage points (VPs) around the world, reporting the reliability of the DNS Root Server System in DNSmon. DNSmon shows that loss rates for queries to the DNS Root are nearly 10% for IPv6, much higher than the approximately 2% loss seen for IPv4. Although IPv6 is "new," as an operational protocol available to a third of Internet users, it ought to be just as reliable as IPv4. We examine this difference at a finer granularity by investigating loss at individual VPs. We confirm that specific VPs are the source of this difference and identify two root causes: VP islands with routing problems at the edge which leave them unable to access IPv6 outside their LAN, and VP peninsulas which indicate routing problems in the core of the network. These problems account for most of the loss and nearly all of the difference between IPv4 and IPv6 query loss rates. Islands account for most of the loss (half of IPv4 failures and 5/6ths of IPv6 failures), and we suggest these measurement devices should be filtered out to get a more accurate picture of loss rates. Peninsulas account for the main differences between root identifiers, suggesting routing disagreements root operators need to address. We believe that filtering out both of these known problems provides a better measure of underlying network anomalies and loss and will result in more actionable alerts.

Outages from natural disasters, political events, software or hardware issues, and human error [2] place a huge cost on e-commerce ($66k/minute at Amazon [1]).

Large websites and distributed systems employ sophisticated analytics to evaluate successes to celebrate and problems to be addressed. As analytics grow, different teams often require different frameworks, with dozens of packages supporting with streaming and batch processing, SQL and no-SQL. Bringing multiple frameworks to bear on a large, changing dataset often create challenges where data transitions-these impedance mismatches can create brittle glue logic and performance problems that consume developer time. We propose Plumb, a meta-framework that can bridge three different abstractions to meet the needs of a large class of applications in a common workflow. Large-block streaming (Block-Streaming) is suitable for single-pass applications that care about the temporal and spatial locality. Windowed-Streaming allows applications to process a group of data and many reductions. Stateful-Streaming enables applications to keep a long-term state and always-on behavior. We show that it is possible to bridge abstractions, with a common, high-level workflow specification, while the system transitions data batch processing and block- and record-level streaming as required. The challenge in bridging abstractions is to minimize latency while allowing applications to select between sequential and parallel operation, while handling out-of-order data delivery, component failures, and providing clear semantics in the face of missing data. We demonstrate these abstractions evaluating a 10-stage workflow of DNS analytics that has been in production use with Plumb for 2 years, comparing to a brittle hand-built system that has run for more than 3 years.

The Covid-19 pandemic disrupted the world as businesses and schools shifted to work-from-home (WFH), and comprehensive maps have helped visualize how those policies changed over time and in different places. We recently developed algorithms that infer the onset of WFH based on changes in observed Internet usage. Measurements of WFH are important to evaluate how effectively policies are implemented and followed, or to confirm policies in countries with less transparent journalism. This paper describes a web-based visualization system for measurements of Covid-19-induced WFH. We build on a web-based world map, showing a geographic grid of observations about WFH. We extend typical map interaction (zoom and pan, plus animation over time) with two new forms of pop-up information that allow users to drill-down to investigate our underlying data. We use sparklines to show changes over the first 6 months of 2020 for a given location, supporting identification and navigation to hot spots. Alternatively, users can report particular networks (Internet Service Providers) that show WFH on a given day. We show that these tools help us relate our observations to news reports of Covid-19-induced changes and, in some cases, lockdowns due to other causes. Our visualization is publicly available at https://covid.ant.isi.edu, as is our underlying data.

TheInternet's Domain Name System (DNS) is a part of every web request and e-mail exchange, so DNS failures can be catastrophic, taking out major websites and services. This paper identifies TsuNAME, a vulnerability where some recursive resolvers can greatly amplify queries, potentially resulting in a denial-of-service to DNS services. TsuNAME is caused by cyclical dependencies in DNS records. A recursive resolver repeatedly follows these cycles, coupled with insufficient caching and application-level retries greatly amplify an initial query, stressing authoritative servers. Although issues with cyclic dependencies are not new, the scale of amplification has not previously been understood. We document real-world events in .nz (a country-level domain), where two misconfigured domains resulted in a 50% increase on overall traffic. We reproduce and document root causes of this event through experiments, and demostrate a 500× amplification factor. In response to our disclosure, several DNS software vendors have documented their mitigations, including Google public DNS and Cisco OpenDNS. For operators of authoritative DNS services we have developed and released CycleHunter, an open-source tool that detects cyclic dependencies and prevents attacks. We use CycleHunter to evaluate roughly 184 million domain names in 7 large, top-level domains (TLDs), finding 44 cyclic dependent NS records used by 1.4k domain names. The TsuNAME vulnerability is weaponizable, since an adversary can easily create cycles to attack the infrastructure of a parent domains. Documenting this threat and its solutions is an important step to ensuring it is fully addressed.

Anycast is used to serve content including web pages and DNS, and anycast deployments are growing. However, prior work examining root DNS suggests anycast deployments incur significant inflation, with users often routed to suboptimal sites. We reassess anycast performance, first extending prior analysis on inflation in the root DNS. We show that inflation is very common in root DNS, affecting more than 95\% of users. However, we then show root DNS latency \emph{hardly matters} to users because caching is so effective. These findings lead us to question: is inflation inherent to anycast, or can inflation be limited when it matters? To answer this question, we consider Microsoft's anycast CDN serving latency-sensitive content. Here, latency matters orders of magnitude more than for root DNS. Perhaps because of this need, only 35\% of CDN users experience any inflation, and the amount they experience is smaller than root DNS. We show that CDN anycast latency has little inflation due to extensive peering and engineering. These results suggest prior claims of anycast inefficiency reflect experiments on a single application rather than anycast's technical potential, and they demonstrate the importance of context when measuring system performance.

Ad platforms such as Facebook, Google and LinkedIn promise value for advertisers through their targeted advertising. However, multiple studies have shown that ad delivery on such platforms can be skewed by gender or race due to hidden algorithmic optimization by the platforms, even when not requested by the advertisers. Building on prior work measuring skew in ad delivery, we develop a new methodology for black-box auditing of algorithms for discrimination in the delivery of job advertisements. Our first contribution is to identify the distinction between skew in ad delivery due to protected categories such as gender or race, from skew due to differences in qualification among people in the targeted audience. This distinction is important in U.S. law, where ads may be targeted based on qualifications, but not on protected categories. Second, we develop an auditing methodology that distinguishes between skew explainable by differences in qualifications from other factors, such as the ad platform’s optimization for engagement or training its algorithms on biased data. Our method controls for job qualification by comparing ad delivery of two concurrent ads for similar jobs, but for a pair of companies with different de facto gender distributions of employees. We describe the careful statistical tests that establish evidence of non-qualification skew in the results. Third, we apply our proposed methodology to two prominent targeted advertising platforms for job ads: Facebook and LinkedIn. We confirm skew by gender in ad delivery on Facebook, and show that it cannot be justified by differences in qualifications. We fail to find skew in ad delivery on LinkedIn. Finally, we suggest improvements to ad platform practices that could make external auditing of their algorithms in the public interest more feasible and accurate.

Abstract Operational services run 24×7 and require analytics pipelines to evaluate performance. In mature services such as domain name system (DNS), these pipelines often grow to many stages developed by multiple, loosely coupled teams. Such pipelines pose two problems: first, computation and data storage may be duplicated across components developed by different groups, wasting resources. Second, processing can be skewed, with structural skew occurring when different pipeline stages need different amounts of resources, and computational skew occurring when a block of input data requires increased resources. Duplication and structural skew both decrease efficiency, increasing cost, latency, or both. Computational skew can cause pipeline failure or deadlock when resource consumption balloons; we have seen cases where pessimal traffic increases CPU requirements 6‐fold. Detecting duplication is challenging when components from multiple teams evolve independently and require fault isolation. Skew management is hard due to dynamic workloads coupled with the conflicting goals of both minimizing latency and maximizing utilization. We propose Plumb, a framework to abstract stream processing as large‐block streaming (LBS) for a multi‐stage, multi‐user workflow. Plumb users express analytics as a DAG of processing modules, allowing Plumb to integrate and optimize workflows from multiple users. Many real‐world applications map to the LBS abstraction. Plumb detects and eliminates duplicate computation and storage, and it detects and addresses both structural and computational skew by tracking computation across the pipeline. We exercise Plumb using the analytics pipeline for B‐Root DNS. We compare Plumb to a hand‐tuned system, cutting latency to one‐third the original, and requiring 39% fewer container hours, while supporting more flexible, multi‐user analytics and providing greater robustness to DDoS‐driven demands.

Distributed Denial-of-Service (DDoS) attacks launched from compromised Internet-of-Things (IoT) devices have shown how vulnerable the Internet is to large-scale DDoS attacks. To understand the risks of these attacks requires learning about these IoT devices: where are they? how many are there? how are they changing? This paper describes three new methods to find IoT devices on the Internet: server IP addresses in traffic, server names in DNS queries, and manufacturer information in TLS certificates. Our primary methods (IP addresses and DNS names) use knowledge of servers run by the manufacturers of these devices. Our third method uses TLS certificates obtained by active scanning. We have applied our algorithms to a number of observations. With our IP-based algorithm, we report detections from a university campus over 4 months and from traffic transiting an IXP over 10 days. We apply our DNS-based algorithm to traffic from 8 root DNS servers from 2013 to 2018 to study AS-level IoT deployment. We find substantial growth (about 3.5×) in AS penetration for 23 types of IoT devices and modest increase in device type density for ASes detected with these device types (at most 2 device types in 80% of these ASes in 2018). DNS also shows substantial growth in IoT deployment in residential households from 2013 to 2017. Our certificate-based algorithm finds 254k IP cameras and network video recorders from 199 countries around the world.

Today, outage detection systems can track outages across the whole IPv4 Internet-millions of networks. However, it becomes difficult to find meaningful, interesting events in this huge dataset, since three months of data can easily include 41 billion observations and millions of outage events. We propose an outage reporting system that sifts through this data to find the most interesting events. We explore multiple metrics to evaluate “interesting”, reflecting the size and severity of outages. We show that defining interest as the product of size by severity works well, avoiding degenerate cases like complete outages affecting a few people, and apparently large outages that affect only a small fraction of people in an area. We have integrated outage reporting into our existing public website (https://outage.ant.isi.edu) with the goal of making near-real-time outage information accessible to the general public. Such data can help answer questions like “what are the most significant outages today?”, “did Florida have major problems in an ongoing hurricane?”, and are there power outages in Venezuela?”.

DNS depends on extensive caching for good performance, and every DNS zone owner must set Time-to-Live (TTL) values to control their DNS caching. Today there is relatively little guidance backed by research about how to set TTLs, and operators must balance conflicting demands of caching against agility of configuration. Exactly how TTL value choices affect operational networks is quite challenging to understand due to interactions across the distributed DNS service, where resolvers receive TTLs in different ways (answers and hints), TTLs are specified in multiple places (zones and their parent's glue), and while DNS resolution must be security-aware. This paper provides the first careful evaluation of how these multiple, interacting factors affect the effective cache lifetimes of DNS records, and provides recommendations for how to configure DNS TTLs based on our findings. We provide recommendations in TTL choice for different situations, and for where they must be configured. We show that longer TTLs have significant promise in reducing latency, reducing it from 183 ms to 28.7 ms for one country-code TLD.

No abstract available.

Anycast-based services today are widely used commercially, with several major providers serving thousands of important websites. However, to our knowledge, there has been only limited study of how often anycast fails because routing changes interrupt connections between users and their current anycast site. While the commercial success of anycast CDNs means anycast usually works well, do some users end up shut out of anycast? In this paper, we examine data from more than 9000 geographically distributed vantage points (VPs) to 11 anycast services to evaluate this question. Our contribution is the analysis of this data to provide the first quantification of this problem, and to explore where and why it occurs. We see that about 1% of VPs are anycast unstable, reaching a different anycast site frequently (sometimes every query). Flips back and forth between two sites in 10 s are observed in selected experiments for given service and VPs. Moreover, we show that anycast instability is persistent for some VPs-a few VPs never see a stable connections to certain anycast services during a week or even longer. The vast majority of VPs only saw unstable routing toward one or two services instead of instability with all services, suggesting the cause of the instability lies somewhere in the path to the anycast sites. We point out that for highly unstable VPs, their probability to hit a given site is constant, suggesting load balancing might be the cause to anycast routing flipping. Finally, we directly examine TCP flipping and show that it is much rarer than UDP flipping, but does occur in about 0.15% (VP, letter) combinations. Moreover, we show concrete cases in which TCP connection timeout in anycast connection due to per-packet flipping. Our findings confirm the common wisdom that anycast almost always works well, but provide evidence that a small number of locations in the Internet where specific anycast services are never stable.

In Internet Domain Name System (DNS), services operate authoritative name servers that individuals query through recursive resolvers. Operators strive to provide reliability by operating multiple name servers (NS), each on a separate IP address, and by using IP anycast to allow NSes to provide service from many physical locations. To meet their goals of minimizing latency and balancing load across NSes and anycast, operators need to know how recursive resolvers select an NS, and how that interacts with their NS deployments. Prior work has shown some recursives search for low latency, while others pick an NS at random or round robin, but did not examine how prevalent each choice was. This paper provides the first analysis of how recursives select between name servers in the wild, and from that we provide guidance to operators how to engineer their name servers to reach their goals. We conclude that all NSes need to be equally strong and therefore we recommend to deploy IP anycast at every single authoritative.

IP anycast provides DNS operators and CDNs with automatic fail-over and reduced latency by breaking the Internet into catchments, each served by a different anycast site. Unfortunately, understanding and predicting changes to catchments as anycast sites are added or removed has been challenging. Current tools such as RIPE Atlas or commercial equivalents map from thousands of vantage points (VPs), but their coverage can be inconsistent around the globe. This paper proposes Verfploeter, a new method that maps anycast catchments using active probing. Verfploeter provides around 3.8M passive VPs, 430x the 9k physical VPs in RIPE Atlas, providing coverage of the vast majority of networks around the globe. We then add load information from prior service logs to provide calibrated predictions of anycast changes. Verfploeter has been used to evaluate the new anycast deployment for B-Root, and we also report its use of a nine-site anycast testbed. We show that the greater coverage made possible by Verfploeter's active probing is necessary to see routing differences in regions that have sparse coverage from RIPE Atlas, like South America and China.

Network-wide activity is when one computer (the originator) touches many others (the targets). Motives for activity may be benign (mailing lists, content-delivery networks, and research scanning), malicious (spammers and scanners for security vulnerabilities), or perhaps indeterminate (ad trackers). Knowledge of malicious activity may help anticipate attacks, and understanding benign activity may set a baseline or characterize growth. This paper identifies domain name system (DNS) backscatter as a new source of information about network-wide activity. Backscatter is the reverse DNS queries caused when targets or middleboxes automatically look up the domain name of the originator. Queries are visible to the authoritative DNS servers that handle reverse DNS. While the fraction of backscatter they see depends on the server's location in the DNS hierarchy, we show that activity that touches many targets appear even in sampled observations. We use information about the queriers to classify originator activity using machine-learning. Our algorithm has reasonable accuracy and precision (70-80%) as shown by data from three different organizations operating DNS servers at the root or country level. Using this technique, we examine nine months of activity from one authority to identify trends in scanning, identifying bursts corresponding to Heartbleed, and broad and continuous scanning of secure shell.

Network-wide activity is when one computer (the originator) touches many others (the targets). Motives for activity may be benign (mailing lists, CDNs, and research scanning), malicious (spammers and scanners for security vulnerabilities), or perhaps indeterminate (ad trackers). Knowledge of malicious activity may help anticipate attacks, and understanding benign activity may set a baseline or characterize growth. This paper identifies DNS backscatter as a new source of information about network-wide activity. Backscatter is the reverse DNS queries caused when targets or middleboxes automatically look up the domain name of the originator. Queries are visible to the authoritative DNS servers that handle reverse DNS. While the fraction of backscatter they see depends on the server's location in the DNS hierarchy, we show that activity that touches many targets appear even in sampled observations. We use information about the queriers to classify originator activity using machine-learning. Our algorithm has reasonable precision (70-80%) as shown by data from three different organizations operating DNS servers at the root or country-level. Using this technique we examine nine months of activity from one authority to identify trends in scanning, identifying bursts corresponding to Heartbleed and broad and continuous scanning of ssh.

The Domain Name System (DNS) seems ideal for connectionless UDP, yet this choice results in challenges of eavesdropping that compromises privacy, source-address spoofing that simplifies denial-of-service (DoS) attacks on the server and third parties, injection attacks that exploit fragmentation, and reply-size limits that constrain key sizes and policy choices. We propose T-DNS to address these problems. It uses TCP to smoothly support large payloads and to mitigate spoofing and amplification for DoS. T-DNS uses transport-layer security (TLS) to provide privacy from users to their DNS resolvers and optionally to authoritative servers. TCP and TLS are hardly novel, and expectations about DNS suggest connections will balloon client latency and overwhelm server with state. Our contribution is to show that T-DNS significantly improves security and privacy: TCP prevents denial-of-service (DoS) amplification against others, reduces the effects of DoS on the server, and simplifies policy choices about key size. TLS protects against eavesdroppers to the recursive resolver. Our second contribution is to show that with careful implementation choices, these benefits come at only modest cost: end-to-end latency from TLS to the recursive resolver is only about 9% slower when UDP is used to the authoritative server, and 22% slower with TCP to the authoritative. With diverse traces we show that connection reuse can be frequent (60 -- 95% for stub and recursive resolvers, although half that for authoritative servers), and after connection establishment, experiments show that TCP and TLS latency is equivalent to UDP. With conservative timeouts (20 s at authoritative servers and 60 s elsewhere) and estimated per-connection memory, we show that server memory requirements match current hardware: a large recursive resolver may have 24k active connections requiring about 3.6 GB additional RAM. Good performance requires key design and implementation decisions we identify: query pipelining, out-of-order responses, TCP fast-open and TLS connection resumption, and plausible timeouts.

As the Internet matures, policy questions loom larger in its operation. When should an ISP, city, or government invest in infrastructure? How do their policies affect use? In this work, we develop a new approach to evaluate how policies, economic conditions and technology correlates with Internet use around the world. First, we develop an adaptive and accurate approach to estimate block availability, the fraction of active IP addresses in each /24 block over short timescales (every 11 minutes). Our estimator provides a new lens to interpret data taken from existing long-term outage measurements, thus requiring no additional traffic. (If new collection was required, it would be lightweight, since on average, outage detection requires less than 20 probes per hour per /24 block; less than 1% of background radiation.) Second, we show that spectral analysis of this measure can identify diurnal usage: blocks where addresses are regularly used during part of the day and idle in other times. Finally, we analyze data for the entire responsive Internet (3.7M /24 blocks) over 35 days. These global observations show when and where the Internet sleeps---networks are mostly always-on in the US and Western Europe, and diurnal in much of Asia, South America, and Eastern Europe. ANOVA (Analysis of Variance) testing shows that diurnal networks correlate negatively with country GDP and electrical consumption, quantifying that national policies and economics relate to networks.

Industrial sensing applications place a premium on cost-effectiveness and accuracy. Traditional approaches often use expensive, invasive sensors from concern that inexpensive, non-invasive sensors will incur many false positives. High sensor cost leaves many lower value sites with sparse automation and no sensing. In this paper, we show how a combination of different types of sensors can provide reliable detection of industrial events while avoid false positives. Our use of multiple, low-cost sensors can enable greater levels of automation for these applications. We explore this problem by studying a specific application: blockages in oil flow lines common in cold weather. We sense changes in fluid flow from pipe skin temperature, and combine readings with acoustic data to avoid false positives and be robust to environmental changes. We demonstrate that our approach is effective with field experiments, and explore a broader range of problems in laboratory experiments. Our general approach applies to the classes of problems where false positives from one sensing modality can be resolved by multi-modal sensing.

DNS is the canonical protocol for connectionless UDP. Yet DNS today is challenged by eavesdropping that compromises privacy, source-address spoofing that results in denial-of-service (DoS) attacks on the server and third parties, injection attacks that exploit fragmentation, and size limitations that constrain policy and operational choices. We propose T-DNS to address these problems. It uses TCP to smoothly support large payloads and to mitigate spoofing and amplification for DoS. T-DNS uses transport-layer security (TLS) to provide privacy from users to their DNS resolvers and optionally to authoritative servers. Our model shows end-to-end latency from TLS to the recursive resolver is only about 9% slower when UDP is used to the authoritative server, and 22% slower with TCP to the authoritative. With diverse traces we show that frequent connection reuse is possible (60-95% for stub and recursive resolvers, although half that for authoritative servers). Our experiment shows that after connection establishment, TCP and TLS latency is equivalent to UDP. With conservative timeouts (20 s at authoritative servers and 60 s elsewhere) and conservative estimates of connection state memory requirements, we show that server memory requirements well within current, commodity server hardware. We identify the key design and implementation decisions needed to minimize overhead: query pipelining, out-of-order responses, TLS connection resumption, and plausible timeouts. This poster abstract summarizes work we describe in detail in ISI-TR-2014-693.

Modern content-distribution networks both provide bulk content and act as "serving infrastructure" for web services in order to reduce user-perceived latency. Serving infrastructures such as Google's are now critical to the online economy, making it imperative to understand their size, geographic distribution, and growth strategies. To this end, we develop techniques that enumerate IP addresses of servers in these infrastructures, find their geographic location, and identify the association between clients and clusters of servers. While general techniques for server enumeration and geolocation can exhibit large error, our techniques exploit the design and mechanisms of serving infrastructure to improve accuracy. We use the EDNS-client-subnet DNS extension to measure which clients a service maps to which of its serving sites. We devise a novel technique that uses this mapping to geolocate servers by combining noisy information about client locations with speed-of-light constraints. We demonstrate that this technique substantially improves geolocation accuracy relative to existing approaches. We also cluster server IP addresses into physical sites by measuring RTTs and adapting the cluster thresholds dynamically. Google's serving infrastructure has grown dramatically in the ten months, and we use our methods to chart its growth and understand its content serving strategy. We find that the number of Google serving sites has increased more than sevenfold, and most of the growth has occurred by placing servers in large and small ISPs across the world, not by expanding Google's backbone.

IP anycast is a central part of production DNS. While prior work has explored proximity, affinity and load balancing for some anycast services, there has been little attention to third-party discovery and enumeration of components of an anycast service. Enumeration can reveal abnormal service configurations, benign masquerading or hostile hijacking of anycast services, and help characterize anycast deployment. In this paper, we discuss two methods to identify and characterize anycast nodes. The first uses an existing anycast diagnosis method based on CHAOS-class DNS records but augments it with traceroute to resolve ambiguities. The second proposes Internet-class DNS records which permit accurate discovery through the use of existing recursive DNS infrastructure. We validate these two methods against three widely-used anycast DNS services, using a very large number (60k and 300k) of vantage points, and show that they can provide excellent precision and recall. Finally, we use these methods to evaluate anycast deployments in top-level domains (TLDs), and find one case where a third-party operates a server masquerading as a root DNS anycast node as well as a noticeable proportion of unusual DNS proxies. We also show that, across all TLDs, up to 72% use anycast.

Natural and human factors cause Internet outages---from big events like Hurricane Sandy in 2012 and the Egyptian Internet shutdown in Jan. 2011 to small outages every day that go unpublicized. We describe Trinocular, an outage detection system that uses active probing to understand reliability of edge networks. Trinocular is principled: deriving a simple model of the Internet that captures the information pertinent to outages, and populating that model through long-term data, and learning current network state through ICMP probes. It is parsimonious, using Bayesian inference to determine how many probes are needed. On average, each Trinocular instance sends fewer than 20 probes per hour to each /24 network block under study, increasing Internet "background radiation" by less than 0.7%. Trinocular is also predictable and precise: we provide known precision in outage timing and duration. Probing in rounds of 11 minutes, we detect 100% of outages one round or longer, and estimate outage duration within one-half round. Since we require little traffic, a single machine can track 3.4M /24 IPv4 blocks, all of the Internet currently suitable for analysis. We show that our approach is significantly more accurate than the best current methods, with about one-third fewer false conclusions, and about 30% greater coverage at constant accuracy. We validate our approach using controlled experiments, use Trinocular to analyze two days of Internet outages observed from three sites, and re-analyze three years of existing data to develop trends for the Internet.

The principles of sensor networks—low-power, wireless, in-situ sensing with many inexpensive sensors—are only recently penetrating into underwater research. Acoustic communication is best suited for underwater communication, with much lower attenuation than RF, but acoustic propagation is five orders-of-magnitude slower than RF, so propagation times stretch to hundreds of milliseconds. Low-power wakeup tones are present in new underwater acoustic modems, and when added to applications and MAC protocols they reduce energy consumption wasted on idle listening. Unfortunately, underwater acoustic tones suffer from self-multipath —echoes unique to the latency that can completely defeat their protocol advantages. We introduce Self-Reflection Tone Learning (SRTL), a novel approach where nodes use Bayesian techniques to address interference by learning to discriminate self-reflections from noise and independent communication. We present detailed experiments using an acoustic modem in controlled and uncontrolled, in-air and underwater environments. These experiments demonstrate that SRTL's knowledge corresponds to physical-world predictions, that it can cope with underwater noise and reasonable levels of artificial noise, and that it can track a changing multipath environment. Simulations confirm that these real-world experiments generalize over a wide range of conditions.

Previous measurement-based IP geolocation algorithms have focused on accuracy, studying a few targets with increasingly sophisticated algorithms taking measurements from tens of vantage points (VPs). In this paper, we study how to scale up existing measurement-based geolocation algorithms like Shortest Ping and CBG to cover the whole Internet. We show that with many vantage points, VP proximity to the target is the most important factor affecting accuracy. This observation suggests our new algorithm that selects the best few VPs for each target from many candidates. This approach addresses the main bottleneck to geolocation scalability: minimizing traffic into each target (and also out of each VP) while maintaining accuracy. Using this approach we have currently geolocated about 35% of the allocated, unicast, IPv4 address-space (about 85% of the addresses in the Internet that can be directly geolocated). We visualize our geolocation results on a web-based address-space browser.

Sensors are all around us, in buildings, vehicles and public places, from commodity thermostats to custom sensornets. Yet today these sensors are often disconnected from the world, either because they are distant from infrastructure, and wide-area networking (by 3G cellular, satellite, or other approaches) is too expensive to justify. Data muling makes communication cost-effective by leveraging short-range wireless and mobility, perhaps by zebras, buses or farmworkers. In this paper we propose that human-carried mobile phones can serve as data mules for sensornet deployments, exploiting ubiquity of mobile phones and human mobility to bring low-cost communication to sensors. We use two mobile phone datasets to show that Bluetooth can serve as a viable muling network, and humans already see many potential sensors regularly. We have implemented a mobile-phone-based data muling system, and used it in four sensornet deployments totaling ten months operation. We find that muling can be the only cost-effective option for rural deployments, where it is critical to monitoring remote sensor networks. We also show opportunistic mobility can collect data without any extra effort in residential and office environments. Finally, we systematically evaluate our deployments to understand how contact duration and data size interact, and to evaluate the effect of muling on phone batteries.

Sensornets promise to extend automated monitoring and control into industrial processes. In spite of great progress made in sensornet design, installation and operational costs can impede their widespread adoption---current practices of infrequent, manual observation are often seen as sufficient and more cost effective than automation, even for key business processes. In this paper we present two new approaches to reduce these costs, and we apply those approaches to rapidly detect blockages in steam pipelines of a production oilfield. First, we eliminate the high cost of bringing power to the field by generating electricity from heat, exploiting the high temperature of the very pipelines we monitor. We demonstrate that for temperature differences of 80 °C or more, we are able to sustain sensornet operation without grid electricity or batteries. Second, we show that non-invasive sensing can reduce the cost of sensing by avoiding sensors that pierce the pipeline and have high installation cost with interruption to production. Our system instead uses surface temperature to infer full or partial blockages in steam pipelines and full blockages in hot water pipelines. Finally, we evaluate our "steam-powered sensing" system to monitor potential blockages in steam pipeline chokes at a production oilfield. We also show the generality of our algorithm by applying it to detect water pipeline blockages in our lab. To our knowledge, this paper describes the first field-tested deployment of an industrial sensornet that employs non-solar energy harvesting.

This paper develops parametric methods to detect network anomalies using only aggregate traffic statistics, in contrast to other works requiring flow separation, even when the anomaly is a small fraction of the total traffic. By adopting simple statistical models for anomalous and background traffic in the time domain, one can estimate model parameters in real time, thus obviating the need for a long training phase or manual parameter tuning. The proposed bivariate parametric detection mechanism (bPDM) uses a sequential probability ratio test, allowing for control over the false positive rate while examining the tradeoff between detection time and the strength of an anomaly. Additionally, it uses both traffic-rate and packet-size statistics, yielding a bivariate model that eliminates most false positives. The method is analyzed using the bit-rate signal-to-noise ratio (SNR) metric, which is shown to be an effective metric for anomaly detection. The performance of the bPDM is evaluated in three ways. First, synthetically generated traffic provides for a controlled comparison of detection time as a function of the anomalous level of traffic. Second, the approach is shown to be able to detect controlled artificial attacks over the University of Southern California (USC), Los Angeles, campus network in varying real traffic mixes. Third, the proposed algorithm achieves rapid detection of real denial-of-service attacks as determined by the replay of previously captured network traces. The method developed in this paper is able to detect all attacks in these scenarios in a few seconds or less.

An understanding of Internet topology is central to answer various questions ranging from network resilience to peer selection or data center location. While much of prior work has examined AS-level connectivity, meaningful and relevant results from such an abstract view of Internet topology have been limited. For one, semantically, AS relationships capture business relationships and not physical connectivity. Additionally, many organizations often use multiple ASes, either to implement different routing policies, or as legacies from mergers and acquisitions. In this paper, we move beyond the traditional AS graph view of the Internet to define the problem of AS-to-organization mapping. We describe our initial steps at automating the capture of the rich semantics inherent in the AS-level ecosystem where routing and connectivity intersect with organizations. We discuss preliminary methods that identify multi-AS organizations from WHOIS data and illustrate the challenges posed by the quality of the available data and the complexity of real-world organizational relationships.

An Internet hitlist is a set of addresses that cover and can represent the the Internet as a whole. Hitlists have long been used in studies of Internet topology, reachability, and performance, serving as the destinations of traceroute or performance probes. Most early topology studies used manually generated lists of prominent addresses, but evolution and growth of the Internet make human maintenance untenable. Random selection scales to today's address space, but most random addresses fail to respond. In this paper we present what we believe is the first automatic generation of hitlists informed censuses of Internet addresses. We formalize the desirable characteristics of a hitlist: responsiveness, each representative responds to pings; completeness, they cover all the allocated IPv4 address space; and stability, list evolution is minimized when possible. We quantify the accuracy of our automatic hitlists, showing that only one-third of the Internet allows informed selection of representatives. Of informed representatives, 50--60% are likely to respond three months later, and we show that causes for non-responses are likely due to dynamic addressing (so no stable representative exists) or firewalls. In spite of these limitations, we show that the use of informed hitlists can add 1.7 million edge links (a 5% growth) to traceroute-based Internet topology studies Our hitlists are available free-of-charge and are in use by several other research projects.

Prior studies of Internet traffic have considered traffic at different resolutions and time scales: packets and flows for hours or days, aggregate packet statistics for days or weeks, and hourly trends for months. However, little is known about the long-term behavior of individual flows. In this paper, we study individual flows (as defined by the 5-tuple of protocol, source and destination IP address and port) over days and weeks. While the vast majority of flows are short, and most bytes are in short flows, we find that about 20% of the overall bytes are carried in flows that last longer than 10 minutes, and flows lasting 100 minutes or longer make up 2 % of traffic. We show that long-lived flows are qualitatively different from short flows: they are generally slower, less bursty, and are due to different applications and protocols. We investigate the causes of short- and long-lived flows, and show that the traffic mix varies significantly depending on duration time scale, with computer-to-computer traffic more and more dominating in larger time scales.

The principles of sensor networks, with inexpensive low-power, wireless, in-situ sensing, are today going underwater with acoustic communication. With large acoustic delays, new modems use techniques such as low-power wakeup tones to reduce energy otherwise wasted on idle listening, and recent applications and MAC protocols integrate tones in their operation. While all wireless data-networks suffer from receiver-multipath, we show that tone-echos cause self-multipath for tone-based protocols. We address this interference with Self-Reflection Tone Learning (SRTL), a novel approach which uses Bayesian techniques to discriminate echos from noise or communication from other nodes. We present experiments from controlled environments to show that SRTL's knowledge corresponds to physical-world predictions, and that it can tolerate random noise up to at least two false triggers per sample.

In many cases, sensornets require continuous monitoring, 24x7, at remote, inaccessible locations making energy management a critical part of most sensornets. The sensornet research community has explored energy conservation and energy harvesting to address this problem of long-lived sensornets. Energy conservation is a primary concern in almost all sensornet work, and techniques from low-power hardware and OSes to coordinated network protocols and applications. Complementing energy conservation, energy harvesting gathers new energy from the environment.

Although the Internet is widely used today, we have little information about the edge of the network. Decentralized management, firewalls, and sensitivity to probing prevent easy answers and make measurement difficult. Building on frequent ICMP probing of 1% of the Internet address space, we develop clustering and analysis methods to estimate how Internet addresses are used. We show that adjacent addresses often have similar characteristics and are used for similar purposes (61% of addresses we probe are consistent blocks of 64 neighbours or more). We then apply this block-level clustering to provide data to explore several open questions in how networks are managed. First, we provide information about how effectively network address blocks appear to be used, finding that a significant number of blocks are only lightly used (most addresses in about one-fifth of 24 blocks are in use less than 10% of the time), an important issue as the IPv4 address space nears full allocation. Second, we provide new measurements about dynamically managed address space, showing nearly 40% of 24 blocks appear to be dynamically allocated, and dynamic addressing is most widely used in countries more recent to the Internet (more than 80% in China, while less than 30% in the U.S.). Third, we distinguish blocks with low-bitrate last-hops and show that such blocks are often underutilized.

Prior measurement studies of the Internet have explored traffic and topology, but have largely ignored edge hosts. While the number of Internet hosts is very large, and many are hidden behind firewalls or in private address space, there is much to be learned from examining the population of visible hosts, those with public unicast addresses that respond to messages. In this paper we introduce two new approaches to explore the visible Internet. Applying statistical population sampling, we use censuses to walk the entire Internet address space, and surveys to probe frequently a fraction of that space. We then use these tools to evaluate address usage, where we find that only 3.6% of allocated addresses are actually occupied by visible hosts, and that occupancy is unevenly distributed, with a quarter of responsive /24 address blocks (subnets) less than 5% full, and only 9% of blocks more than half full. We show about 34 million addresses are very stable and visible to our probes (about 16% of responsive addresses), and we project from this up to 60 million stable Internet-accessible computers. The remainder of allocated addresses are used intermittently, with a median occupancy of 81 minutes. Finally, we show that many firewalls are visible, measuring significant diversity in the distribution of firewalled block size. To our knowledge, we are the first to take a census of edge hosts in the visible Internet since 1982, to evaluate the accuracy of active probing for address census and survey, and to quantify these aspects of the Internet.

This paper introduces T-Lohi, a new class of distributed and energy-efficient media-access protocols (MAC) for underwater acoustic sensor networks (UWSN). MAC design for UWSN faces significant challenges. For example, acoustic communication suffers from latencies five orders-of-magnitude larger than radio communication, so a naive CSMA MAC would require very long listen time resulting in low throughput and poor energy efficiency. In this paper, we first identify unique characteristics in underwater networking that may affect all MACs, such as space-time uncertainty and deafness conditions. We then develop T-Lohi employing a novel tone-based contention resolution mechanism that exploits space-time uncertainty and high latency to detect collisions and count contenders, achieving good throughput across all offered loads. Lohi uses our low-power wake-up receiver to significantly reduce energy consumption. Finally, we evaluate design choices and protocol performance through extensive simulation. The results show that the energy cost of packet transmission is within 3-9 % of optimal, and that Lohi achieves good channel utilization, within 30% utilization of the theoretical maximum. We also show that Lohi is stable and fair under both low and very high offered loads.

No abstract available.

The 13 papers in this special issue focus on underwater wireless communication networks.

This paper introduces T-Lohi, a new class of distributed and energy-efficient media-access protocols (MAC) for underwater acoustic sensor networks (UWSN). MAC design for UWSN faces significant challenges. For example, acoustic communication suffers from latencies five orders-of-magnitude larger than radio communication, so a naive CSMA MAC would require very long listen time resulting in low throughput and poor energy efficiency. In this paper, we first identify unique characteristics in underwater networking that may affect all MACs, such as space-time uncertainty and deafness conditions. We then develop T-Lohi employing a novel tone-based contention resolution mechanism that exploits space-time uncertainty and high latency to detect collisions and count contenders, achieving good throughput across all offered loads. T-Lohi exploits a low-power wake-up receiver to significantly reduce energy consumption. We evaluate design choices and protocol performance through extensive simulation. Finally, we compare T-Lohi against a few canonical MAC protocols. The results show that the energy cost of packet transmission is within 3-9% of optimal, and that Lohi achieves good channel utilization, within 30% utilization of the theoretical maximum. We also show that Lohi is stable and fair under both low and very high offered loads. Finally, we compare Lohi with other alternatives, including TDMA, CSMA, and ALOHA. Except for TDMA under heavy load, Lohi provides the best utilization in all cases, and it is always the most energy efficient.

Increasingly, network operators do not directly operate computers on their network, yet are responsible for assessing network vulnerabilities to ensure compliance with policies about information disclosure, and tracking services that affect provisioning. Thus, with decentralized network management, service discovery becomes an important part of maintaining and protecting computer networks. We explore two approaches to service discovery: active probing and passive monitoring. Active probing finds all services currently on the network, except services temporarily unavailable or hidden by firewalls; however, it is often too invasive, especially if used across administrative boundaries. Passive monitoring can find transient services, but misses services that are idle. We compare the accuracy of passive and active approaches to service discovery and show that they are complimentary, highlighting the need for multiple active scans coupled with long-duration passive monitoring. We find passive monitoring is well suited for quickly finding popular services, finding servers responsible for 99 % of incoming connections within minutes. Active scanning is better suited to rapidly finding all servers, which is important for vulnerability detection–one scan finds 98 % of services in two hours, missing only a handful. External scans are an unexpected ally to passive monitoring, speeding service discovery by the equivalent of 9–15 days of additional observation. Finally, we show how the use of static or dynamic addresses changes the effectiveness of service discovery, both due to address reuse and VPN effects.

Dual-radio, dual-processor nodes are an emerging class of wireless sensor network devices that provide both low-energy operation as well as substantially increased computational performance and communication bandwidth for applications. In such systems, the secondary radio and processor operates with sufficiently low power that it may remain always vigilant, while the main processor and primary, high-bandwidth radio remain off until triggered by the application. By exploiting the high energy efficiency of the main processor and primary radio along with proper usage, net operating energy benefits are enabled for applications. The secondary radio provides a constantly available multi-hop network, while paths in the primary network exist only when required. This paper describes a topology control mechanism for establishing an end-to-end path in a network of dual-radio nodes using the secondary radios as a control channel toselectivelywake up nodes along the required end-to-end path. Using numerical models as well as testbed experimentation, we show that our proposed mechanism provides significant energy savings of more than 60% compared to alternative approaches, and that it incurs only moderately greater application latency.

Abstract — Denial of Service attacks have become a weapon for extortion and vandalism causing damages in the millions of dollars to commercial and government sites. Legal prosecution is a powerful deterrent, but requires attribution of attacks, currently a difficult task. In this paper we propose a method to automatically fingerprint and identify repeated attack scenarios—a combination of attacking hosts and attack tool. Such fingerprints not only aid in attribution for criminal and civil prosecution of attackers, but also help justify and focus response measures. Since packet contents can be easily manipulated, we base our fingerprints on the spectral characteristics of the attack stream which are hard to forge. We validate our methodology by applying it to real attacks captured at a regional ISP and comparing the outcome with header-based classification. Finally, we conduct controlled experiments to identify and isolate factors that affect the attack fingerprint. I.

Abstract — Distributed time synchronization is an important part of a sensor network where sensing and actuation must be coordinated across multiple nodes. Several time synchronization protocol that maximize accuracy and energy conservation have been developed, including FTSP, TPSN, and RBS. All of these assume nearly instantaneous wireless communication between sensor nodes; each of them work well in today’s RF-based sensor networks. We are just beginning to explore underwater sensor networks where communication is primarily via acoustic telemetry. With acoustic communication, where the propagation speed is nearly five orders of magnitude slower than RF, assumptions about rapid communication are incorrect and new approaches to time synchronization are required. We present Time Synchronization for High Latency (TSHL), designed assuming such high latency propagation. We show through analysis and simulation that it achieves precise time synchronization with minimal energy cost. Although at very short distances existing protocols are adequate, TSHL shows twice the accuracy at 500m, demonstrating the need to model both clock skew and propagation latency. I.

A new class of sensor network applications are mostly off. Exemplified by Intel's FabApp, in these applications the network alternates between being off for hours or weeks, then activating to collect data for a few minutes. While configuration of traditional sensornet applications is occasional and so need not be optimized, these applications may spend half their time while awake configuring, so they require new approaches to quickly restart after a long downtime, in effect, "sensor network suspend and resume". While there are many network services that may need to be restarted, this paper focuses on the key question of when the network can determine that all nodes are now awake and ready to interact. Current resume approaches assume worst-case clock drift and so must conservatively take minutes to reconfigure after a month-long sleep. We propose two energy efficient reconfiguration protocols to address this challenge. The first approach is low-power listening with flooding, where the network restarts quickly by flooding a control message as soon as one node can determine the whole network is up. The second protocol uses local update with suppression, where nodes only notify their one-hop neighbors about the network state, avoiding the cost of flooding. Both protocols are fully distributed algorithms. Through analysis and simulations, we show that both protocols are more energy efficient than current approaches. Flooding works best in sparse networks with 6 neighbors or less, while local update with suppression works best in dense networks (more than 6 neighbors)

We undertake a systematic experimental study of the effects of concurrent packet transmissions in low-power wireless networks. Our measurements, conducted with Mica2 motes equipped with CC1000 radios, confirm that guaranteeing successful packet reception with high probability in the presence of concurrent transmissions requires that the signal-to-interference-plus-noise-ratio (SINR) exceed a critical threshold. However, we find a significant variation of about 6 dB in the threshold for groups of radios operating at different transmission powers. We find that it is harder to estimate the level of interference in the presence of multiple interferers. We also find that the measured SINR threshold generally increases with the number of interferers. Our study offers a better understanding of concurrent transmissions and suggests richer interference models and useful guidelines to improve the design and analysis of higher layer protocols.

Varying interference levels make broadcasting an unreliable operation in low-power wireless networks. Many routing and resource discovery protocols depend on flooding (repeated per-node broadcasts) over the network. Unreliability at the broadcast-level can result in either incomplete flooding coverage or excessive re-flooding, making path maintenance either unreliable or expensive. We present RBP, a very simple protocol that bolsters the reliability of broadcasting in such networks. Our protocol requires only local information, and resides as a service between the MAC and network layer, taking information from both. We show that RBP improves reliability while balancing energy efficiency. RBP is based on two principles: First, we exploit network density to achieve near-perfect flooding reliability by requiring moderate (50-70%) broadcast reliability when nodes have many neighbors. Second, we identify areas of sparse connectivity where important links bridge dense clusters of nodes, and strive for guaranteed reliability over those links. We demonstrate, through both testbed experiments and controlled simulations, that this hybrid approach is advantageous to providing near-perfect reliability for flooding with good efficiency. Testbed experiments show 99.8% reliability with 48% less overhead than the level of flooding required to get equivalent reliability, suggesting that routing protocols will benefit from RBP.

Energy is a critical resource in sensor networks. MAC protocols such as S-MAC and T-MAC coordinate sleep schedules to reduce energy consumption. Recently, lowpower listening (LPL) approaches such as WiseMAC and B-MAC exploit very brief polling of channel activity combined with long preambles before each transmission, saving energy particularly during low network utilization. Synchronization cost, either explicitly in scheduling, or implicitly in long preambles, limits all these protocols to duty cycles of 1-2%. We demonstrate that ultra-low duty cycles of 0.1% and below are possible with a new MAC protocol called scheduled channel polling (SCP). This work prompts three new contributions: First, we establish optimal configurations for both LPL and SCP under fixed conditions, developing a lower bound of energy consumption. Under these conditions, SCP can extend lifetime of a network by a factor of 3-6 times over LPL. Second, SCP is designed to adapt well to variable traffic. LPL is optimized for known, periodic traffic, and long preambles become very costly when traffic varies. In one experiment, SCP reduces energy consumption by a factor of 10 under bursty traffic. We also show how SCP adapts to heavy traffic and streams data in multi-hop networks, reducing latency by 85% and energy by 95% at 9 hops. Finally, we show that SCP can operate effectively on recent hardware such as 802.15.4 radios. In fact, power consumption of SCP decreases with faster radios, but that of LPL increases.

This paper explores applications and challenges for underwater sensor networks. We highlight potential applications to off-shore oilfields for seismic monitoring, equipment monitoring, and underwater robotics. We identify research directions in short-range acoustic communications, MAC, time synchronization, and localization protocols for high-latency acoustic networks, long-duration network sleeping, and application-level data scheduling. We describe our preliminary design on short-range acoustic communication hardware, and summarize results of high-latency time synchronization

Providing approximate max-min fair bandwidth allocation among flows within a network or at a single router has been an important research problem. In this paper, we study the space complexity of fairness algorithms, and the communication complexity of distributed global fairness algorithms. We show that in order to enforce max-min fairness with bounded errors, a router must maintain per-flow state. Then we present a practical edge-marking based architecture to demonstrate the enforcement of approximate global max-min fairness for representative scenarios with multiple bottlenecks and non-responsive traffic. We validate our architecture using packet level simulations.

Data dissemination in sensor networks requires four components: resource discovery, route establishment, packet forwarding, and route maintenance. Resource discovery can be the most costly aspect if meta-data does not exist to guide the search. Geographic routing can minimize search cost when resources are defined by location, and hash-based techniques like data-centric storage can make searching more efficient, subject to increased storage cost. In general, however, flooding is required to locate all resources matching a specification. In this paper, we propose BARD, Bayesian-assisted resource discovery, an approach that optimizes resource discovery in sensor networks by modeling search and routing as a stochastic process. BARD exploits the attribute structure of diffusion and prior routing history to avoid flooding for similar queries. BARD models attributes as random variables and finds routes to arbitrary value sets via Bayesian estimation. Results of occasional flooded queries establish a baseline probability distribution, which is used to focus additional queries. Since this process is probabilistic and approximate, even partial matches from prior searches can still reduce the scope of search. We evaluate the benefits of BARD by extending directed diffusion and examining control overhead with and without our Bayesian filter. These simulations demonstrate a 28% to 73% reduction in control traffic, depending on the number and locations of sources and sinks.

Network models today are often derived from two different methods. On one hand, detailed traffic models are generated based on traces from a single tap into the network. Alternatively, one can collect higher-level traffic-matrix data with SNMP from many routers. However, inferring flow-level details from such data is still an open research issue. Today it is infeasible to collect a fine-grained, packet-level representation of a complete, multi-router network. Even if it were economically feasible to synchronize and monitor every router in a large network, the amount of data generated would tax storage and computation resources. In this work, we propose a methodology to infer flow-level traffic across a network by exploiting the correlations between user populations across different networks. The contribution of this paper is twofold. First, based on traces of Web traffic collected from two different sources, we observe that the user-behavior parameters of the traffic (such as user "think" time in Web traffic) are correlated across time, while the application-specific parameters of the traffic (such as object size) are correlated across "similar" networks. Second, by utilizing the correlations between similar networks, we propose a methodology for inferring traffic at places where continuously taking measurements is infeasible. We evaluate the effectiveness of our methodology via simulation

Recently, several MAC protocols, such as S-MAC and T-MAC, have exploited scheduled sleep/wakeup cycles to conserve energy in sensor networks. Until now, most protocols have assumed all nodes in the network were configured to follow the same schedule, or have assumed border nodes would follow multiple schedules, but those cases have not been evaluated. The paper develops two new algorithms to control and exploit the presence of multiple schedules to reduce energy consumption and latency. The first one is the global schedule algorithm (GSA). Through experiments, we demonstrate that, because of radio propagation vagaries, large sensor networks have very ragged, overlapping borders where many nodes listen to two or more schedules. GSA is a fully distributed algorithm that allows a large network to converge on a single global schedule to conserve energy. Secondly, we demonstrate that strict schedules incur a latency penalty in a multi-hop network when packets must wait for the next schedule for transmission. To reduce latency in multi-hop paths, we develop the fast path algorithm (FPA). FPA provides fast data forwarding paths by adding additional wake-up periods on the nodes along paths from sources to sinks. We evaluate both algorithms through experiments on Berkeley motes and demonstrate that the protocols accomplish their goals of reducing energy consumption and latency in large sensor networks.

We design an adaptive admission control mechanism, network early warning system (NEWS), to protect servers and networks from flash crowds and maintain high performance for end-users. NEWS detects flash crowds from performance degradation in responses and mitigates flash crowds by admitting incoming requests adaptively. We evaluate NEWS performance with both simulations and testbed experiments. We first investigate a network-limited scenarion in simulations. We find that NEWS detects flash crowds within 20 seconds. By discarding 32% of incoming requests, NEWS protects the target server and networks from overloading, reducing the response packet drop rate from 25% to 2%. For admitted requests, NEWS increases their response rate by two times. This performance is similar to the best static rate limiter deployed in the same scenario. We also investigate the impact of detection intervals on NEWS performance, showing it affects both detection delay and false alarm rate. We further consider a server memory-limited scenario in testbed experiments, confirming that NEWS is also effective in this case. We also examine the runtime cost of NEWS traffic monitoring in practice and find that it consumes little CPU time and relatively small memory. Finally, we show NEWS effectively protects bystander traffic from flash crowds.

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. This article addresses two key challenges in wireless sensor networks: in-network storage and distributed search. The need for these techniques arises from the inability to provide persistent, centralized storage and querying in many sensor networks. Centralized storage requires multihop transmission of sensor data to Internet gateways which can quickly drain battery-operated nodes.Constructing a storage and search system that satisfies the requirements of data-rich scientific applications is a daunting task for many reasons: (a) the data requirements may be large compared to available storage and communication capacity of resource-constrained nodes, (b) user requirements are diverse and range from identification and collection of interesting event signatures to obtaining a deeper understanding of long-term trends and anomalies in the sensor events, and (c) many applications are in new domains where a priori information may not be available to reduce these requirements.This article describes a lossy, gracefully degrading storage model . We believe that such a model is necessary and sufficient for many scientific applications since it supports both progressive data collection for interesting events as well as long-term in-network storage for in-network querying and processing. Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multiresolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.

Sensor network applications have a diverse set of requirements - some involve extraction of sensor data to a single point, others exploit sensor-to-sensor communication; some employ long-lasting data streams while connections in others are mainly ephemeral. Different variants of the directed diffusion routing protocol - pull-based, push-based and hybrid rendezvous-based - have been developed, along with in-network processing and geographic routing techniques. We mathematically model and analyze the performance of these routing techniques across a range of application scenarios (with varying numbers of nodes, sources, sinks, data settings etc.). Besides quantifying the conditions under which the different routing algorithms outperform each other, we obtain a number of useful design insights. Our analysis shows that algorithms mismatched to applications can result in drastically poor performance; demonstrates the desirability of reducing flooded interest and exploratory messages when data aggregation is used; and suggests that it may be difficult to implement efficient hybrid schemes because their performance is very sensitive to the optimal placement of rendezvous points.

Wireless sensor networks are characterized by collections of small, low-power nodes that collect information about the physical world. Concurrent transmissions caused by the well-known hidden terminal problem result in collisions and packet corruption. Since corrupted packets must be retransmitted, collisions add an additional burden to the already energy constrained system. We present an application-based approach to collision avoidance. We propose two specific algorithms; the first one follows TCP's congestion avoidance algorithm and adjusts the transmission rate when a collision occurs, while the second one shifts packet transmission times to minimize collisions. We evaluated both algorithms through simulations and our results show that our approach can reduce the number of collision-induced retransmissions by a factor of 8 and the energy consumption by up to 50%.

Unpredictable and heterogeneous links in a wireless sensor network require techniques to avoid low delivery rate and high delivery cost. Three commonly used techniques to help discover high quality paths include (1) link-layer retransmission, (2) blacklisting bad links, and (3) end-to-end routing metrics. Using simulation and testbed experiments, we present the first systematic exploration of the tradeoffs of combinations of these approaches, quantifying the effects of each of these three techniques. We identify several key results: one is that per-hop retransmissions (ARQ) is a necessary addition to any other mechanism if reliable data delivery is a goal. Additional interactions between the services are more subtle. First, in a multihop network, either blacklisting or reliability metrics like ETX can provide consistent high-reliability paths when added to ARQ. Second, at higher deployment densities, blacklisting has a lower routing overhead than CTX. But at lower densities, blacklisting becomes less stable as the network partitions. These results are consistent across both simulation and testbed experiments. We conclude that ETX with retransmissions is the best choice in general, but that blacklisting may be worth considering at higher densities, either with or without ETX.

We experimentally investigate the impact of variable transmission power on link quality, and propose variable power link quality control techniques to enhance the performance of data delivery in wireless sensor networks. This study extends the state of the art in two key respects: first, while there are a number of previous results on power control techniques for wireless ad hoc and sensor networks, to our knowledge, nearly all of them have been simulated and analytically studied that assumes the idealized link conditions; second, while there are several recent experimental studies that have shown the prevalence of non-ideal unreliable communication links in sensor networks, the paper has not thoroughly investigated the impact of variable transmission power. We perform a systematic set of experiments to analyze how the transmission power changes affect the quality of low power RF wireless links between nodes. These experiments show how significant variation in link qualities occur in real-world deployments and how these effects strongly influence the effectiveness of transmission power control. We then present a packet-based transmission power control mechanism that incorporates blacklisting to enhance link reliability while minimizing interference. The effectiveness of the proposed scheme is demonstrated via test bed experiments.

No abstract available.

Embedded networked sensors promise to revolutionize the way we interact with our physical environment and require scalable, ad hoc deployable and energy-efficient node localization/positioning.This paper describes the motivation, design, implementation, and experimental evaluation (on sharply resource-constrained devices) of a self-configuring localization system using radio beacons. We identify beacon density as an important parameter in determining localization quality, which saturates at a transition density. We develop algorithms to improve localization quality by (i) automating placement of new beacons at low densities (HEAP) and (ii) rotating functionality among redundant beacons while increasing system lifetime at high densities (STROBE).

This paper proposes S-MAC, a medium access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with nodes remaining largely inactive for long time, but becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in several ways: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important. S-MAC uses a few novel techniques to reduce energy consumption and support self-configuration. It enables low-duty-cycle operation in a multihop network. Nodes form virtual clusters based on common sleep schedules to reduce control overhead and enable traffic-adaptive wake-up. S-MAC uses in-channel signaling to avoid overhearing unnecessary traffic. Finally, S-MAC applies message passing to reduce contention latency for applications that require in-network data processing. The paper presents measurement results of S-MAC performance on a sample sensor node, the UC Berkeley Mote, and reveals fundamental tradeoffs on energy, latency and throughput. Results show that S-MAC obtains significant energy savings compared with an 802.11-like MAC without sleeping.

BGP has been deployed in Internet for more than a decade. However, the events that cause BGP topological changes are not well understood. Although large traces of routing updates seen in BGP operation are collected by RIPE RlS and University of Oregon RouteViews, previous work examines this data set as individual routing updates. This paper describes methods that group routing updates into events. Since one event (a policy change or peering failure) results in many update messages, we cluster updates both temporally and topologically (based on the path vector information). We propose a new approach to analyzing the update traces, classifying the topological impact of muting events, and approximating the distance to the autonomous system originating the event. Our analysis provides some insight into routing behavior: First, at least 45% path changes are caused by events on transit peerings. Second, a significant number (23-37%) of path changes are transient, in that routing updates indicate temporary path changes, but they ultimately converge on a path that is identical from the previously stable path. These observations suggest that a content provider cannot guarantee end-to-end routing stability based solely on its relationship with its immediate ISP, and that better detection of transient changes may improve routing stability.

We study the feasibility of extending the lifetime of a wireless sensor network by exploiting mobility. In our system, a small percentage of network nodes are autonomously mobile, allowing them to move in search of energy, recharge, and delivery energy to immobile, energy-depleted nodes. We term this approach energy harvesting. We characterize the problem of uneven energy consumption, suggest energy harvesting as a possible solution, and provide a simple analytical framework to evaluate energy consumption and our scheme. Data from initial feasibility experiments using energy harvesting show promising results.

An oblivious active queue management scheme is one which does not differentiate between packets belonging to different flows. In this paper, we study the existence and the quality of Nash equilibria imposed by oblivious AQM schemes on selfish agents. Oblivious AQM schemes are of obvious importance because of the ease of implementation and deployment, and Nash equilibrium offers valuable clues into network performance under noncooperative user behavior. Specifically, we ask the following three questions: 1) do there exist oblivious AQM schemes that impose Nash equilibria on selfish agents? 2) Are the imposed equilibria, if they exist, efficient in terms of the goodput obtained and the drop probability experienced at the equilibrium? 3) How easy is it for selfish users to reach the Nash equilibrium state? We assume that the traffic sources are Poisson but the users can control the average rate. We show that drop-tail and RED do not impose Nash equilibria. We modify RED slightly to obtain an oblivious scheme, VLRED, that imposes a Nash equilibrium, but is not efficient. We then present another AQM policy, EN-AQM, that can impose an efficient Nash equilibrium. Finally, we show that for any oblivious AQM, the Nash equilibrium imposed on selfish agents is highly sensitive as the number of agents increases, thus making it hard for the users to converge to the Nash equilibrium, and motivating the need for equilibria-aware protocols.

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. Centralized data collection and analysis adversely impact sensor node lifetime. Previous sensor network research has, therefore, focused on in network aggregation and query processing, but has done so for applications where the features of interest are known a priori. When features are not known a priori, as is the case with many scientific applications in dense sensor arrays, efficient support for multi-resolution storage and iterative, drill-down queries is essential.Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multi-resolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.

A distinguishing characteristic of wireless sensor networks is the opportunity to exploit characteristics of the application at lower layers. This approach is encouraged by device resource constraints, and acceptable because devices are inexpensive and numerous enough that they can be dedicated to specific applications. Many data dissemination protocols have been proposed for multi-hop communication in sensor networks, each evaluated in some scenario. The premise of this paper is that, if protocols are designed to exploit application requirements, then no one protocol can be optimized for all applications.Instead, a family of protocols are needed, with guidance to match protocol to application. We show through field experiments with two tracking applications that choice of diffusion algorithm can affect application performance by 40--60%. These applications motivate the design of two new diffusion algorithms: push and one-phase pull diffusion. We describe these algorithms in comparison to previous algorithms, then systematically explore their performance as the number of sinks and sources, the traffic rate and node placement varies, and with and without geographic proximity in node placement and with and without geographically scoped communication. We characterize algorithm performance and highlight the effect of the choice of algorithm parameters. The end result of this work are guidelines to help application developers to match dissemination algorithms to application performance requirements.

Launching a denial of service (DoS) attack is trivial, but detection and response is a painfully slow and often a manual process. Automatic classification of attacks as single- or multi-source can help focus a response, but current packet-header-based approaches are susceptible to spoofing. This paper introduces a framework for classifying DoS attacks based on header content, transient ramp-up behavior and novel techniques such as spectral analysis. Although headers are easily forged, we show that characteristics of attack ramp-up and attack spectrum are more difficult to spoof. To evaluate our framework we monitored access links of a regional ISP detecting 80 live attacks. Header analysis identified the number of attackers in 67 attacks, while the remaining 13 attacks were classified based on ramp-up and spectral analysis. We validate our results through monitoring at a second site, controlled experiments, and simulation. We use experiments and simulation to understand the underlying reasons for the characteristics observed. In addition to helping understand attack dynamics, classification mechanisms such as ours are important for the development of realistic models of DoS traffic, can be packaged as an automated tool to aid in rapid response to attacks, and can also be used to estimate the level of DoS activity on the Internet. 1.

Advances in processor, memory, and radio technology enable small and cheap nodes capable of sensing, communication, and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. We explore the directed diffusion paradigm for such coordination. Directed diffusion is data-centric in that all communication is for named data. All nodes in a directed-diffusion-based network are application aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network (e.g., data aggregation). We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network analytically and experimentally. Our evaluation indicates that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes (e.g., omniscient multicast) under the investigated scenarios.

In-network data aggregation is essential for wireless sensor networks where energy resources are limited. In a previously proposed data dissemination scheme (directed diffusion with opportunistic aggregation), data is opportunistically aggregated at intermediate nodes on a low-latency tree. In this paper, we explore and evaluate greedy aggregation, a novel approach that adjusts aggregation points to increase the amount of path sharing, reducing energy consumption. Our preliminary results suggest that, under investigated scenarios, greedy aggregation can achieve up to 45% energy savings over opportunistic aggregation in high-density networks without adversely impacting latency or robustness.

Anecdotal evidence suggests that misconfiguration of backbone routers occasionally leads to an injection of large routing tables into the BGP routing system. In this paper, we investigate the detailed mechanics of router response to large BGP routing tables. We examine three commercial routers, and find that their responses vary significantly. Some routers exhibit table-size oscillations that have the potential to cause cascading failure. Others need operator intervention to recover from large routing tables. We also find that deployed resource control mechanisms, such as prefix limits and route flap damping, are only partially suceessful in mitigating the impact of large routing tables.

This paper proposes S-MAC, a medium-access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with individual nodes remaining largely inactive for long periods of time, but then becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in almost every way: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important. S-MAC uses three novel techniques to reduce energy consumption and support self-configuration. To reduce energy consumption in listening to an idle channel, nodes periodically sleep. Neighboring nodes form virtual clusters to auto-synchronize on sleep schedules. Inspired by PAMAS, S-MAC also sets the radio to sleep during transmissions of other nodes. Unlike PAMAS, it only uses in-channel signaling. Finally, S-MAC applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data move through the network. We evaluate our implementation of S-MAC over a sample sensor node, the Mote, developed at University of California, Berkeley. The experiment results show that, on a source node, an 802.11-like MAC consumes 2-6 times more energy than S-MAC for traffic load with messages sent every 1-10 s.

Beacon placement strongly affects the quality of spatial localization, a critical service for context-aware applications in wireless sensor networks; yet this aspect of localization has received little attention. Fixed beacon placement approaches such as uniform and very dense placement are not always viable and will be inadequate in very noisy environments in which sensor networks may be expected to operate (with high terrain and propagation uncertainties). We motivate the need for empirically adaptive beacon placement and outline a general approach based on exploration and instrumentation of the terrain conditions by a mobile human or robot agent. We design, evaluate and analyze three novel adaptive beacon placement algorithms using this approach for localization based on RF-proximity. In our evaluation, we find that beacon density rather than noise level has a more significant impact on beacon placement algorithms. Our beacon placement algorithms are applicable to a low (beacon) density regime of operation. Noise makes moderate density regimes more improvable.

Wireless communication is an enabling factor in multiple mobile robot systems. There is significant interaction between robot controllers and communications subsystems. We present a method for evaluating combined robot control/communication strategies for a team of wireless-networked robots performing a resource transportation task. Two alternative controller designs are compared under established communication and radio propagation models. For each we measure the overall performance of the robot team including the cost of communication. The study illustrates how our evaluation tools can be used for designing controllers for robots operating in wireless communication environments.

We introduce a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks. GAF conserves energy by identifying nodes that are equivalent from a routing perspective and then turning off unnecessary nodes, keeping a constant level of routing fidelity. GAF moderates this policy using application- and system-level information; nodes that source or sink data remain on and intermediate nodes monitor and balance energy use. GAF is independent of the underlying ad hoc routing protocol; we simulate GAF over unmodified AODV and DSR. Analysis and simulation studies of GAF show that it can consume 40% to 60% less energy than an unmodified ad hoc routing protocol. Moreover, simulations of GAP suggest that network lifetime increases proportionally to node density; in one example, a four-fold increase in node density leads to network lifetime increase for 3 to 6 times (depending on the mobility pattern). More generally, GAF is an example of adaptive fidelity, a technique proposed for extending the lifetime of self-configuring systems by exploiting redundancy to conserve energy while maintaining application fidelity.

In most distributed systems, naming of nodes for low-level communication leverages topological location (such as node addresses) and is independent of any application. In this paper, we investigate an emerging class of distributed systems where low-level communication does not rely on network topological location. Rather, low-level communication is based on attributes that are external to the network topology and relevant to the application. When combined with dense deployment of nodes, this kind of named data enables in-network processing for data aggregation, collaborative signal processing, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi-application sensor-network. We show that approaches such as in-network aggregation and nested queries can significantly affect network traffic. In one experiment aggregation reduces traffic by up to 42% and nested queries reduce loss rates by 30%. Although aggregation has been previously studied in simulation, this paper demonstrates nested queries as another form of in-network processing, and it presents the first evaluation of these approaches over an operational testbed.

The delivery of multimedia content is a facet of Internet traffic that is rapidly growing in importance. The new generation of World Wide Web sites rely heavily on extensive multimedia content such as graphics, sound, music and video to attract and retain visitors. While there have been extensive studies on the growth and effects of hyper text transfer protocol (HTTP) traffic used on the Web, little or no work has been performed in analyzing streaming multimedia traffic. We present the results of a brief study to examine the traffic emanating from a popular Internet audio service using the RealAudio program. We found protocol distributions that show a bias towards non-TCP friendly protocols. In addition, we observed consistencies in audio traffic packet sizes and data rate patterns may be useful as a tool for identifying audio data flows. Our results show that audio flows exhibit significant consistency in data rates and are considerably more persistent than HTTP connections.

Article Free Access Share on Next century challenges: scalable coordination in sensor networks Authors: Deborah Estrin USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CAView Profile , Ramesh Govindan USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CAView Profile , John Heidemann USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CAView Profile , Satish Kumar USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CAView Profile Authors Info & Claims MobiCom '99: Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networkingAugust 1999 Pages 263–270https://doi.org/10.1145/313451.313556Published:01 August 1999Publication History 1,787citation7,746DownloadsMetricsTotal Citations1,787Total Downloads7,746Last 12 Months255Last 6 weeks29 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Due to the complexity and scale of the current Internet, large scale simulation is an increasingly important tool to evaluate network protocol design. Parallel and distributed simulation is one appropriate approach to the simulation scalability problem, but it can require expensive hardware and have high overhead. We investigate a complementary solution-simulation abstraction. Just as a custom simulator includes only details necessary for the task at hand, a general simulator can support configurable levels of detail for different simulations. We demonstrate two abstraction techniques in multicast simulations and show that they each help to gain one order of magnitude in performance. Although abstraction simulations are not identical to more detailed simulations, in many cases these differences are small and result in minimal changes in the conclusions drawn from simulations.

This research proposes and tests an approach to engineering distributed file systems that are aimed at wide-scale, Internet-based use. The premise is that replication is essential to deliver performance and availability, yet the traditional conservative replica consistency algorithms do not scale to this environment. Our Ficus replicated file system uses a single-copy availability, optimistic update policy with reconciliation algorithms that reliably detect concurrent updates and automatically restore the consistency of directory replicas. The system uses the peer-to-peer model in which all machines are architectural equals but still permits configuration in a client-server arrangement where appropriate. Ficus has been used for six years at several geographically scattered installations. This paper details and evaluates the use of optimistic replica consistency, automatic update conflict detection and repair, the peer-to-peer (as opposed to client-server) interaction model, and the stackable file system architecture in the design and construction of Ficus. The paper concludes with a number of lessons learned from the experience of designing, building, measuring, and living with an optimistically replicated file system. © 1998 John Wiley & Sons, Ltd.

This paper considers the interaction of HTTP with several transport protocols, including TCP, Transaction TCP, a UDP-based request-response protocol, and HTTP with persistent TCP connections. We present an analytic model for each of these protocols and use that model to evaluate network overhead carrying HTTP traffic across a variety of network characteristics. This model includes an analysis of the transient effects of TCP slow-start. We validate this model by comparing it to network packet traces measured with two protocols (HTTP and persistent HTTP) over local and wide-area networks. We show that the model is accurate within 5% of measured performance for wide-area networks, but can underestimate latency when the bandwidth is high and delay is low. We use the model to compare the connection-setup costs of these protocols, bounding the possible performance improvement. We evaluate these costs for a range of network characteristics, finding that setup optimizations are relatively unimportant for current modem, ISDN, and LAN users but can provide moderate to substantial performance improvement over high-speed WANs. We also use the model to predict performance over future network characteristics.

Stackable design of filing systems constructs sophisticated services from mukiple, independently developed layers.This approach has been advocated to address development problems from code re-use, to extensibility, to version management.Individual layers of such a system often need to cache data to improve performance orprovide desired functionality.Whenaccessto different layers isallowed, cache incoherencies can occur.Without a cache coherence solution, layer designers must either restrict layer access and flexibility or compromise the layered structure to avoid potential data corruption.Thevalue of modular designs such as stacking can be questioned without a suitable solution to this problem.This paper presents a general cache coherence architecture for stackable tiling, including a standard approach to data identifications a key component tolayered coherence protocols.We also present a detailed performance analysis of one implementation of stack cache-coherence, which suggests that very low overheads can be achieved in practice.

Filing services have experienced a number of innovations in recent years, but many of these promising ideas have failed to enter into broad use. One reason is that current filing environments present several barriers to new development. For example, file systems today typically stand alone instead of building on the work of others, and support of new filing services often requires changes that invalidate existing work. Stackable file-system design addresses these issues in several ways. Complex filing services are constructed from layer “building blocks,” each of which may be provided by independent parties. There are no syntactic constraints to layer order, and layers can occupy different address spaces, allowing very flexible layer configuration. Independent layer evolution and development are supported by an extensible interface bounding each layer. This paper discusses stackable layering in detail and presents design techniques it enables. We describe an implementation providing these facilities that exhibits very high performance. By lowering barriers to new filing design, stackable layering offers the potential of broad third-party file-system development not feasible today.