A new whitepaper analysing the performance and scalability of the Streamr pub/sub messaging Network is now available. Take a look at some of the fascinating key results in this introductory blog
Streamr Network: Performance and Scalability Whitepaper
https://preview.redd.it/bstqyn43x4j51.png?width=2600&format=png&auto=webp&s=81683ca6303ab84ab898c096345464111d674ee5 The Corea milestone of the Streamr Network went live in late 2019. Since then a few people in the team have been working on an academic whitepaper to describe its design principles, position it with respect to prior art, and prove certain properties it has. The paper is now ready, and it has been submitted to the IEEE Access journal for peer review. It is also now published on the new Papers section on the project website. In this blog, I’ll introduce the paper and explain its key results. All the figures presented in this post are from the paper. The reasons for doing this research and writing this paper were simple: many prospective users of the Network, especially more serious ones such as enterprises, ask questions like ‘how does it scale?’, ‘why does it scale?’, ‘what is the latency in the network?’, and ‘how much bandwidth is consumed?’. While some answers could be provided before, the Network in its currently deployed form is still small-scale and can’t really show a track record of scalability for example, so there was clearly a need to produce some in-depth material about the structure of the Network and its performance at large, global scale. The paper answers these questions. Another reason is that decentralized peer-to-peer networks have experienced a new renaissance due to the rise in blockchain networks. Peer-to-peer pub/sub networks were a hot research topic in the early 2000s, but not many real-world implementations were ever created. Today, most blockchain networks use methods from that era under the hood to disseminate block headers, transactions, and other events important for them to function. Other megatrends like IoT and social media are also creating demand for new kinds of scalable message transport layers.
The latency vs. bandwidth tradeoff
The current Streamr Network uses regular random graphs as stream topologies. ‘Regular’ here means that nodes connect to a fixed number of other nodes that publish or subscribe to the same stream, and ‘random’ means that those nodes are selected randomly. Random connections can of course mean that absurd routes get formed occasionally, for example a data point might travel from Germany to France via the US. But random graphs have been studied extensively in the academic literature, and their properties are not nearly as bad as the above example sounds — such graphs are actually quite good! Data always takes multiple routes in the network, and only the fastest route counts. The less-than-optimal routes are there for redundancy, and redundancy is good, because it improves security and churn tolerance. There is an important parameter called node degree, which is the fixed number of nodes to which each node in a topology connects. A higher node degree means more duplication and thus more bandwidth consumption for each node, but it also means that fast routes are more likely to form. It’s a tradeoff; better latency can be traded for worse bandwidth consumption. In the following section, we’ll go deeper into analyzing this relationship.
Network diameter scales logarithmically
One useful metric to estimate the behavior of latency is the network diameter, which is the number of hops on the shortest path between the most distant pair of nodes in the network (i.e. the “longest shortest path”. The below plot shows how the network diameter behaves depending on node degree and number of nodes. Network diameter We can see that the network diameter increases logarithmically (very slowly), and a higher node degree ‘flattens the curve’. This is a property of random regular graphs, and this is very good — growing from 10,000 nodes to 100,000 nodes only increases the diameter by a few hops! To analyse the effect of the node degree further, we can plot the maximum network diameter using various node degrees: Network diameter in network of 100 000 nodes We can see that there are diminishing returns for increasing the node degree. On the other hand, the penalty (number of duplicates, i.e. bandwidth consumption), increases linearly with node degree: Number of duplicates received by the non-publisher nodes In the Streamr Network, each stream forms its own separate overlay network and can even have a custom node degree. This allows the owner of the stream to configure their preferred latency/bandwidth balance (imagine such a slider control in the Streamr Core UI). However, finding a good default value is important. From this analysis, we can conclude that:
The logarithmic behavior of network diameter leads us to hope that latency might behave logarithmically too, but since the number of hops is not the same as latency (in milliseconds), the scalability needs to be confirmed in the real world (see next section).
A node degree of 4 yields good latency/bandwidth balance, and we have selected this as the default value in the Streamr Network. This value is also used in all the real-world experiments described in the next section.
It’s worth noting that in such a network, the bandwidth requirement for publishers is determined by the node degree and not the number of subscribers. With a node degree 4 and a million subscribers, the publisher only uploads 4 copies of a data point, and the million subscribing nodes share the work of distributing the message among themselves. In contrast, a centralized data broker would need to push out a million copies.
Latency scales logarithmically
To see if actual latency scales logarithmically in real-world conditions, we ran large numbers of nodes in 16 different Amazon AWS data centers around the world. We ran experiments with network sizes between 32 to 2048 nodes. Each node published messages to the network, and we measured how long it took for the other nodes to get the message. The experiment was repeated 10 times for each network size. The below image displays one of the key results of the paper. It shows a CDF (cumulative distribution function) of the measured latencies across all experiments. The y-axis runs from 0 to 1, i.e. 0% to 100%. CDF of message propagation delay From this graph we can easily read things like: in a 32 nodes network (blue line), 50% of message deliveries happened within 150 ms globally, and all messages were delivered in around 250 ms. In the largest network of 2048 nodes (pink line), 99% of deliveries happened within 362 ms globally. To put these results in context, PubNub, a centralized message brokering service, promises to deliver messages within 250 ms — and that’s a centralized service! Decentralization comes with unquestionable benefits (no vendor lock-in, no trust required, network effects, etc.), but if such protocols are inferior in terms of performance or cost, they won’t get adopted. It’s pretty safe to say that the Streamr Network is on par with centralized services even when it comes to latency, which is usually the Achilles’ heel of P2P networks (think of how slow blockchains are!). And the Network will only get better with time. Then we tackled the big question: does the latency behave logarithmically? Mean message propagation delay in Amazon experiments Above, the thick line is the average latency for each network size. From the graph, we can see that the latency grows logarithmically as the network size increases, which means excellent scalability. The shaded area shows the difference between the best and worst average latencies in each repeat. Here we can see the element of chance at play; due to the randomness in which nodes become neighbours, some topologies are faster than others. Given enough repeats, some near-optimal topologies can be found. The difference between average topologies and the best topologies gives us a glimpse of how much room for optimisation there is, i.e. with a smarter-than-random topology construction, how much improvement is possible (while still staying in the realm of regular graphs)? Out of the observed topologies, the difference between the average and the best observed topology is between 5–13%, so not that much. Other subclasses of graphs, such as irregular graphs, trees, and so on, can of course unlock more room for improvement, but they are different beasts and come with their own disadvantages too. It’s also worth asking: how much worse is the measured latency compared to the fastest possible latency, i.e. that of a direct connection? While having direct connections between a publisher and subscribers is definitely not scalable, secure, or often even feasible due to firewalls, NATs and such, it’s still worth asking what the latency penalty of peer-to-peer is. Relative delay penalty in Amazon experiments As you can see, this plot has the same shape as the previous one, but the y-axis is different. Here, we are showing the relative delay penalty (RDP). It’s the latency in the peer-to-peer network (shown in the previous plot), divided by the latency of a direct connection measured with the ping tool. So a direct connection equals an RDP value of 1, and the measured RDP in the peer-to-peer network is roughly between 2 and 3 in the observed topologies. It increases logarithmically with network size, just like absolute latency. Again, given that latency is the Achilles’ heel of decentralized systems, that’s not bad at all. It shows that such a network delivers acceptable performance for the vast majority of use cases, only excluding the most latency-sensitive ones, such as online gaming or arbitrage trading. For most other use cases, it doesn’t matter whether it takes 25 or 75 milliseconds to deliver a data point.
Latency is predictable
It’s useful for a messaging system to have consistent and predictable latency. Imagine for example a smart traffic system, where cars can alert each other about dangers on the road. It would be pretty bad if, even minutes after publishing it, some cars still haven’t received the warning. However, such delays easily occur in peer-to-peer networks. Everyone in the crypto space has seen first-hand how plenty of Bitcoin or Ethereum nodes lag even minutes behind the latest chain state. So we wanted to see whether it would be possible to estimate the latencies in the peer-to-peer network if the topology and the latencies between connected pairs of nodes are known. We applied Dijkstra’s algorithm to compute estimates for average latencies from the input topology data, and compared the estimates to the actual measured average latencies: Mean message propagation delay in Amazon experiments We can see that, at least in these experiments, the estimates seemed to provide a lower bound for the actual values, and the average estimation error was 3.5%. The measured value is higher than the estimated one because the estimation only considers network delays, while in reality there is also a little bit of a processing delay at each node.
The research has shown that the Streamr Network can be expected to deliver messages in roughly 150–350 milliseconds worldwide, even at a large scale with thousands of nodes subscribing to a stream. This is on par with centralized message brokers today, showing that the decentralized and peer-to-peer approach is a viable alternative for all but the most latency-sensitive applications. It’s thrilling to think that by accepting a latency only 2–3 times longer than the latency of an unscalable and insecure direct connecion, applications can interconnect over an open fabric with global scalability, no single point of failure, no vendor lock-in, and no need to trust anyone — all that becomes available out of the box. In the real-time data space, there are plenty of other aspects to explore, which we didn’t cover in this paper. For example, we did not measure throughput characteristics of network topologies. Different streams are independent, so clearly there’s scalability in the number of streams, and heavy streams can be partitioned, allowing each stream to scale too. Throughput is mainly limited, therefore, by the hardware and network connection used by the network nodes involved in a topology. Measuring the maximum throughput would basically be measuring the hardware as well as the performance of our implemented code. While interesting, this is not a high priority research target at this point in time. And thanks to the redundancy in the network, individual slow nodes do not slow down the whole topology; the data will arrive via faster nodes instead. Also out of scope for this paper is analysing the costs of running such a network, including the OPEX for publishers and node operators. This is a topic of ongoing research, which we’re currently doing as part of designing the token incentive mechanisms of the Streamr Network, due to be implemented in a later milestone. I hope that this blog has provided some insight into the fascinating results the team uncovered during this research. For a more in-depth look at the context of this work, and more detail about the research, we invite you to read the full paper. If you have an interest in network performance and scalability from a developer or enterprise perspective, we will be hosting a talk about this research in the coming weeks, so keep an eye out for more details on the Streamr social media channels. In the meantime, feedback and comments are welcome. Please add a comment to this Reddit thread or email [[email protected]](mailto:[email protected]). Originally published by. Henri atblog.streamr.networkon August 24, 2020.
Finding SHA256 partial collisions via the Bitcoin blockchain
This is not a cryptocurrency post, per se. I used Bitcoin's blockchain as a vehicle by which to study SHA256. The phrase "partial collision" is sometimes used to describe a pair of hashes that are "close" to one another. One notion of closeness is that the two hashes should agree on a large number of total bits. Another is that they should agree on a large number of specific (perhaps contiguous) bits. The goal in Bitcoin mining is essentially (slight simplification here) to find a block header which, when hashed twice with SHA256, has a large number of trailing zeros. (If you have some familiarity with Bitcoin, you may be wondering: doesn't the protocol demand a large number of leading zeros? It does, kind of, but the Bitcoin protocol reverses the normal byte order of SHA256. Perhaps Satoshi interpreted SHA256 output as a byte stream in little endian order. If so, then this is a slightly unfortunate choice, given that SHA256 explicitly uses big endian byte order in its padding scheme.) Because Bitcoin block header hashes must all have a large number of trailing zeros, they must all agree on a large number of trailing bits. Agreement or disagreement on earlier bits should, heuristically, appear independent and uniform at random. Thus, I figured it should be possible to get some nice SHA256 partial collisions by comparing block header hashes. First, I looked for hashes that agree on a large number of trailing bits. At present, block header hashes must have about 75 trailing zeros. There are a little over 2^19 blocks in total right now, so we expect to get a further ~38 bits of agreement via a birthday attack. Although this suggests we may find a hash pair agreeing on 75 + 38 = 113 trailing bits, this should be interpreted as a generous upper bound, since early Bitcoin hashes had fewer trailing zeros (as few as 32 at the outset). Still, this gave me a good enough guess to find some partial collisions without being overwhelmed by them. The best result was a hash pair agreeing on their final 108 bits. Hex encodings of the corresponding SHA256 inputs are as follows: 23ca73454a1b981fe51cad0dbd05f4e696795ba67abb28c61aea1a024e5bbeca a16a8141361ae9834ad171ec28961fc8a951ff1bfc3a9ce0dc2fcdbdfa2ccd35 (I will emphasize that these are hex encodings of the inputs, and are not the inputs themselves.) There were a further 11 hash pairs agreeing on at least 104 trailing bits. Next, I searched for hashes that agree on a large number of total bits. (In other words, hash pairs with low Hamming distance.) With a little over 2^19 blocks, we have around (2^19 choose 2) ~= 2^37 block pairs. Using binomial distribution statistics, I estimated that it should be possible to find hash pairs that agree on more than 205 bits, but probably not more than 210. Lo and behold, the best result here was a hash pair agreeing on 208 total bits. Hex encodings of the corresponding SHA256 inputs are as follows: dd9591ff114e8c07be30f0a7998cf09c351d19097766f15a32500ee4f291e7e3 c387edae394b3b9b7becdddcd829c8ed159a32879c156f2e23db73365fde4a94 There were 8 other hash pairs agreeing on at least 206 total bits. So how interesting are these results, really? One way to assess this is to estimate how difficult it would be to get equivalent results by conventional means. I'm not aware of any clever tricks that find SHA256 collisions (partial or full) faster than brute force. As far as I know, birthday attacks are the best known approach. To find a hash pair agreeing on their final 108 bits, a birthday attack would require 2^54 time and memory heuristically. Each SHA256 hash consists of 2^5 bytes, so 2^59 is probably a more realistic figure. This is "feasible", but would probably require you to rent outside resources at great expense. Writing code to perform this attack on your PC would be inadvisable. Your computer probably doesn't have the requisite ~600 petabytes of memory, anyway. The hash pair agreeing on 208 of 256 bits is somewhat more remarkable. By reference to binomial distribution CDFs, a random SHA256 hash pair should agree on at least 208 bits with probability about 2^-81. A birthday attack will cut down on the memory requirement by the normal square root factor - among ~2^41 hashes, you expect that there will be such a pair. But in this case, it is probably necessary to actually compare all hash pairs. The problem of finding the minimum Hamming distance among a set doesn't have obvious shortcuts in general. Thus, a birthday attack performed from scratch would heuristically require about 2^81 hash comparisons, and this is likely not feasible for any entity on Earth right now. I don't think these results carry any practical implications for SHA256. These partial collisions are in line with what one would expect without exploiting any "weaknesses" of SHA256. If anything, these results are a testament to just how much total work has been put into the Bitcoin blockchain. Realistically, the Bitcoin blockchain will never actually exhibit a SHA256 full collision. Still, I thought these were fun curiosities that were worth sharing.
Quick DeFiChain update: Anchoring the DeFiChain on Bitcoin costs money, and this is taken from the 200 DFI blockreward - namely 5 DFI / block. That goes into a pool and whenever a stakeholder wants to, as the pool has grown big enough, he/she anchors the DeFiChain on Bitcoin and gets the accumulated reward. In one of the last PRs (Pull Requests) there was a faulty entry, where these 5 DFI actually should come from (from the 180 or the 20) - which led to the strange rewards for the past blocks, which the community noticed. The short term solution now is to put the accumulated pool of almost 12,000 DFI, which actually belongs to Cake for the anchoring, into the Community Development Fund. The CDF should not be punished for this. The long term solution is, that until the end of the month all DFI Stakers will vote whether the 5 DFI will be taken from the 180 (then there will be less staking returns) or from the 20 (then there will be less Community Development Funds). My suggestion would be here to take it from the 20 from the CDP, as it is now actually, as for now the staking returns seem to be more important, but at the end DFI Stakers must vote on this. DeFiChain Foundation will refrain from voting - so it is in the hands of all DFI hodlers out there. We will post details on this later this week - it will actually be our first DeFiChain vote.
On the topic of Bitcoin and currencies name confusion (Bitcoin Cash, Bitcoin SV, Bitcoin BTC, Ethereum ETH, Ethereum ETC..)
The topic of cryptocurrencies name confusion come back regularly. It is often presented as a proof that Bitcoin Cash is trying to mislead newbie and steal the “Bitcoin” brand. While I agree currencies sharing similar name can be confusing, the situation is not new and in reality extremely common. It is the norm, not the exception. It is actually relatively rare to have a currency with unique, non-shared name. Any peoples with only a bit of knowledge should know this situation exist and be prepared for it when trading currencies whatever it is crypto or regular FIAT. If anything crypto have shown so far a remarkable consistency. (Somewhat surprising as crypto are open source project while FIAT currencies are state enforced) Here I collect some examples, the list is pretty exhaustive fell free to let me know if you have other examples I will add them to the list. Dollars: US Dollars USD Australian Dollar AUD New Zealand NZD Barbadian dollars BBD Bermudian dollars BMD Brunian dollars BND Bahamian dollars BSD Belizean dollars BZD Canadian dollars CAD Finjian dollars FJD Taiwan new dollars TWD Pounds: Britsh pounds GBP Egyptian pounds EGP Falkland island pounds FKP Guerney pounds GGP Gibraltar pounds GIP Isle of man pounds IMP Jersey pounds JE Lebanese pounds LBP Sudanese pounds SDG Saint Helenian pounds SHP Syrian pounds SYP Pesos: Argentinian Pesos ARS Chilan Pesos CLP Combian Pesos COP Cuban Pesos CUC Dominican Pesos DOP Mexican Pesos MXP Philipine Pesos PHP Uruguayan Pesos UYU Rubles: Belaruzian rubles BYB Russian rubles RUB Krona: Nowegian Krona NOK Swedish Krona SEK Danish Krona DKK Icelandic Krona ISK Croatian Krona HRK Czeck Krona CZK Francs: French francs (dead) Swiss francs CHF Francs CFA XOF Burudian francs BIF Congolese francs CDF Djibutian francs DJF Guinean francs GNF Comorian francs KMF Rwanda francs RWF Dinars: Bahraini Dinars BHD Algerian Dinars DZD Iraqi Dinars IQD Jordanian Dinars JOD Kuwaiti Dinars KWD Lybian Dinars LYD Serbian Dinars RSD Tunisian Dinars TND Shillings: Kenyan shillings KES Somali shillings SOS Tanzanian shillings TZD Ugandan shillings UGX Rupee: Indian rupees INR Sri lankan rupees LKR Mauritian rupees MUR Nepalese rupees NPR Pakistan rupees PKR Seychellois rupees SCR Indonesian rupiahs IPR I recommend to link this post next time you encounter this argument again. I think there is no need wasting energy debating such points. It is a normal characteristics of currencies and while possibly annoying it has to be accepted (and is simply unavoidable). Edit: format hate me
CNIT 40: DNS Security DNS is crucial for all Internet transactions, but it is subject to numerous security risks, including phishing, hijacking, packet amplification, spoofing, snooping, poisoning, and more. Learn how to configure secure DNS servers, and to detect malicious activity with DNS monitoring. We will also cover DNSSEC principles and deployment. Students will perform hands-on projects deploying secure DNS servers on both Windows and Linux platforms.
CNIT 120 - Network Security Knowledge and skills required for Network Administrators and Information Technology professionals to be aware of security vulnerabilities, to implement security measures, to analyze an existing network environment in consideration of known security threats or risks, to defend against attacks or viruses, and to ensure data privacy and integrity. Terminology and procedures for implementation and configuration of security, including access control, authorization, encryption, packet filters, firewalls, and Virtual Private Networks (VPNs).
CNIT 121 - Computer Forensics The class covers forensics tools, methods, and procedures used for investigation of computers, techniques of data recovery and evidence collection, protection of evidence, expert witness skills, and computer crime investigation techniques. Includes analysis of various file systems and specialized diagnostic software used to retrieve data. Prepares for part of the industry standard certification exam, Security+, and also maps to the Computer Investigation Specialists exam.
CNIT 123 - Ethical Hacking and Network Defense Students learn how hackers attack computers and networks, and how to protect systems from such attacks, using both Windows and Linux systems. Students will learn legal restrictions and ethical guidelines, and will be required to obey them. Students will perform many hands-on labs, both attacking and defending, using port scans, footprinting, exploiting Windows and Linux vulnerabilities, buffer overflow exploits, SQL injection, privilege escalation, Trojans, and backdoors.
CNIT 124 - Advanced Ethical Hacking Advanced techniques of defeating computer security, and countermeasures to protect Windows and Unix/Linux systems. Hands-on labs include Google hacking, automated footprinting, sophisticated ping and port scans, privilege escalation, attacks against telephone and Voice over Internet Protocol (VoIP) systems, routers, firewalls, wireless devices, Web servers, and Denial of Service attacks.
CNIT 126 - Practical Malware Analysis Learn how to analyze malware, including computer viruses, trojans, and rootkits, using disassemblers, debuggers, static and dynamic analysis, using IDA Pro, OllyDbg and other tools.
CNIT 127 - Exploit Development Learn how to find vulnerabilities and exploit them to gain control of target systems, including Linux, Windows, Mac, and Cisco. This class covers how to write tools, not just how to use them; essential skills for advanced penetration testers and software security professionals.
CNIT 128 - Hacking Mobile Devices Mobile devices such as smartphones and tablets are now used for making purchases, emails, social networking, and many other risky activities. These devices run specialized operating systems have many security problems. This class will cover how mobile operating systems and apps work, how to find and exploit vulnerabilities in them, and how to defend them. Topics will include phone call, voicemail, and SMS intrusion, jailbreaking, rooting, NFC attacks, malware, browser exploitation, and application vulnerabilities. Hands-on projects will include as many of these activities as are practical and legal.
CNIT 129S: Securing Web Applications Techniques used by attackers to breach Web applications, and how to protect them. How to secure authentication, access, databases, and back-end components. How to protect users from each other. How to find common vulnerabilities in compiled code and source code.
CNIT 140: IT Security Practices Training students for cybersecurity competitions, including CTF events and the Collegiate Cyberdefense Competition (CCDC). This training will prepare students for employment as security professionals, and if our team does well in the competitions, the competitors will gain recognition and respect which should lead to more and better job offers.
Florida State University's - Offensive Network Security This class allows students to look deep into know protocols (i.e. IP, TCP, UDP) to see how an attacker can utilize these protocols to their advantage and how to spot issues in a network via captured network traffic. The first half of this course focuses on know protocols while the second half of the class focuses on reverse engineering unknown protocols. This class will utilize captured traffic to allow students to reverse the protocol by using known techniques such as incorporating bioinformatics introduced by Marshall Beddoe. This class will also cover fuzzing protocols to see if the server or client have vulnerabilities. Overall, a student finishing this class will have a better understanding of the network layers, protocols, and network communication and their interaction in computer networks.
Florida State University's - Offensive Computer Security The primary incentive for an attacker to exploit a vulnerability, or series of vulnerabilities is to achieve a return on an investment (his/her time usually). This return need not be strictly monetary, an attacker may be interested in obtaining access to data, identities, or some other commodity that is valuable to them. The field of penetration testing involves authorized auditing and exploitation of systems to assess actual system security in order to protect against attackers. This requires thorough knowledge of vulnerabilities and how to exploit them. Thus, this course provides an introductory but comprehensive coverage of the fundamental methodologies, skills, legal issues, and tools used in white hat penetration testing and secure system administration.
NYU Tandon School of Engineering - OSIRIS Lab's Hack Night Developed from the materials of NYU Tandon's old Penetration Testing and Vulnerability Analysis course, Hack Night is a sobering introduction to offensive security. A lot of complex technical content is covered very quickly as students are introduced to a wide variety of complex and immersive topics over thirteen weeks.
Rensselaer Polytechnic Institute - Malware Analysis This course will introduce students to modern malware analysis techniques through readings and hands-on interactive analysis of real-world samples. After taking this course students will be equipped with the skills to analyze advanced contemporary malware using both static and dynamic analysis.
This BIP proposal describes a concrete specification (along with a reference implementations13) for the much discussed client-side filtering reversal of BIP-37. The precise details are described in the BIP, but as a summary: we've implemented a new light-client mode that uses client-side filtering based off of Golomb-Rice coded sets. Full-nodes maintain an additional index of the chain, and serve this compact filter (the index) to light clients which request them. Light clients then fetch these filters, query the locally and maybe fetch the block if a relevant item matches. The cool part is that blocks can be fetched from any source, once the light client deems it necessary. Our primary motivation for this work was enabling a light client mode for lnd4 in order to support a more light-weight back end paving the way for the usage of Lightning on mobile phones and other devices. We've integrated neutrino as a back end for lnd, and will be making the updated code public very soon. One specific area we'd like feedback on is the parameter selection. Unlike BIP-37 which allows clients to dynamically tune their false positive rate, our proposal uses a fixed false-positive. Within the document, it's currently specified as P = 1/220. We've done a bit of analysis and optimization attempting to optimize the following sum: filter_download_bandwidth + expected_block_false_positive_bandwidth. Alex has made a JS calculator that allows y'all to explore the affect of tweaking the false positive rate in addition to the following variables: the number of items the wallet is scanning for, the size of the blocks, number of blocks fetched, and the size of the filters themselves. The calculator calculates the expected bandwidth utilization using the CDF of the Geometric Distribution. The calculator can be found here: https://aakselrod.github.io/gcs_calc.html. Alex also has an empirical script he's been running on actual data, and the results seem to match up rather nicely. We we're excited to see that Karl Johan Alm (kallewoof) has done some (rather extensive!) analysis of his own, focusing on a distinct encoding type . I haven't had the time yet to dig into his report yet, but I think I've read enough to extract the key difference in our encodings: his filters use a binomial encoding directly on the filter contents, will we instead create a Golomb-Coded set with the contents being hashes (we use siphash) of the filter items. Using a fixed fp=20, I have some stats detailing the total index size, as well as averages for both mainnet and testnet. For mainnet, using the filter contents as currently described in the BIP (basic + extended), the total size of the index comes out to 6.9GB. The break down is as follows:
Finally, here are the testnet stats which take into account the increase in the maximum filter size due to segwit's block-size increase. The max filter sizes are a bit larger due to some of the habitual blocks I created last year when testing segwit (transactions with 30k inputs, 30k outputs, etc).
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#EXTINF:-1,UNIVISION EAST #EXTINF:-1,UNIVISION WEST #EXTINF:-1,GALAVISION. #EXTINF:-1,MEGA TV #EXTINF:-1,TELEMUNDO EAST. #EXTINF:-1,TELEMUNDO WEST. #EXTINF:-1,UNIMAS #EXTINF:-1,NBC UNIVERSO. #EXTINF:-1,DISCOVERY ESP #EXTINF:-1,CNN ESPANOL #EXTINF:-1,CINELATINO #EXTINF:-1,CENTRO AMERICA TV #EXTINF:-1,TELECENTRO #EXTINF:-1,HOLA TV #EXTINF:-1,NAT GEOMUNDO #EXTINF:-1,VME #EXTINF:-1,HISTORY ESP #EXTINF:-1,PASIONES #EXTINF:-1,CN SONY #EXTINF:-1,VIENDO MOVIES #EXTINF:-1,UNIVISION TELENOVELAS #EXTINF:-1,HBO ESP #EXTINF:-1,CINEMAX #EXTINF:-1,CINEMAX BACKUP #EXTINF:-1,CANAL DE LAS ESTRELLAS #EXTINF:-1,ECUADOR TV #EXTINF:-1,ECUAVISA #EXTINF:-1,TYC SPORTS #EXTINF:-1,CARACOL #EXTINF:-1,NTN 24 #EXTINF:-1,TV CHILE #EXTINF:-1,LAT | INVESTIGATION DISCOVERY #EXTINF:-1,LAT | ESPN VIVO #EXTINF:-1,LAT | FOX SPORTS PREMIUM #EXTINF:-1,LAT | UNIVISION #EXTINF:-1,LAT | TELEMUNDO #EXTINF:-1,LAT | TELEMUNDO INTERNACIONAL #EXTINF:-1,LAT | UNIMAS #EXTINF:-1,LAT | BEIN SPORTS #EXTINF:-1,LAT | DE PELICULA #EXTINF:-1,LAT | GOL TV #EXTINF:-1,LAT | HBO #EXTINF:-1,LAT | NOTICIAS NTN24 #EXTINF:-1,LAT | RT NOTICIAS #EXTINF:-1,LAT | TELECINCO #EXTINF:-1,LAT | TELENOVELAS #EXTINF:-1,LAT | TNT SERIES HD #EXTINF:-1,LAT | WARNER CHANNEL HD #EXTINF:-1,ECU | CNT SPORTS 1 #EXTINF:-1,ECU | ECUADOR TV #EXTINF:-1,ECU | ECUAVISA #EXTINF:-1,ECU | OROMAR TV #EXTINF:-1,ECU | RTS #EXTINF:-1,ECU | TC HD #EXTINF:-1,ECU | TC MI CANAL #EXTINF:-1,ECU | TELEAMAZONAS #EXTINF:-1,EPS | COL CARACOL #EXTINF:-1,URU | TELE HD #EXTINF:-1,URU | TNU URG #EXTINF:-1,URU | VTV #EXTINF:-1,URU | VTV + #EXTINF:-1,URU | MONTE CARLO #EXTINF:-1,URU | MONTE CARLO BACKUP #EXTINF:-1,ARG | TV CRONICA #EXTINF:-1,ARG | TV CRONICA 2 #EXTINF:-1,ARG | TV CRONICA BACKUP #EXTINF:-1,ARG | TNT SPORTS #EXTINF:-1,ARG | TYC SPORTS. #EXTINF:-1,ARG | TYC SPORTS 2 #EXTINF:-1,ARG | TYC SPORTS BACKUP #EXTINF:-1,ARG | TN #EXTINF:-1,ARG | CANAL 26 #EXTINF:-1,ARG | EL TRECE #EXTINF:-1,ARG | Noticias NQN #EXTINF:-1,ARG | Power HD #EXTINF:-1,ARG | TELEFE #EXTINF:-1,ARG | Telemax #EXTINF:-1,CHI | 13 #EXTINF:-1,CHI | CDF PREMIUN #EXTINF:-1,CHI | CHILEVISON #EXTINF:-1,CHI | MEGA #EXTINF:-1,CHI | TVN #EXTINF:-1,COL | CABLE NOTICIAS #EXTINF:-1,COL | CARACOL #EXTINF:-1,COL | RCN #EXTINF:-1,COL | RCN NOVELAS #EXTINF:-1,COL | WINSPORTS HD #EXTINF:-1,COL | RCN NUESTRATELE #EXTINF:-1,COL | TELEMEDELLIN #EXTINF:-1,MEX | AZTECA NOTICIAS #EXTINF:-1,MEX | CANAL 5 #EXTINF:-1,MEX | ESPN 1 #EXTINF:-1,MEX | ESPN 2 #EXTINF:-1,MEX | ESPN 3 #EXTINF:-1,MEX | FOX SPORTS 1 #EXTINF:-1,MEX | FOX SPORTS 2 #EXTINF:-1,MEX | LAS ESTRELLAS #EXTINF:-1,MEX | TDN DEPORTES #EXTINF:-1,PER | AMERICA TV #EXTINF:-1,PER | ATV #EXTINF:-1,PER | IPE #EXTINF:-1,PER | PANAMERICANA #EXTINF:-1,PER | PERU HD #EXTINF:-1,PER | PERU MAGICO #EXTINF:-1,VEN | TELESUR #EXTINF:-1,VEN | VE PLUS TV #EXTINF:-1,VEN | VENEVISION #EXTINF:-1,DOM | CANAL 25 SANTIAGO #EXTINF:-1,DOM | CINEVISION CANAL 19 #EXTINF:-1,DOM | COLOR VISION CANAL 9 #EXTINF:-1,DOM | TELE UNIVERSO #EXTINF:-1,DOM | TELEFUTURO #EXTINF:-1,DOM | TELEMICRO 5
[USA-FL][H] Chromebox with Kodi, Apple Laptop, Windows netbook, HP Microservers, Android tablet, Cell Phones, 3d Printers, Xbox 360, Other misc items. [W] Paypal, Google Wallet
I have a bunch of different items for sale. All items do not include shipping. Please PM me your zip and I will calculate shipping. I will consider best offer on anything. I will also consider trades however I do not need anything specific so would prefer cash. I have prices listed for Paypal. I prefer google wallet and will deduct paypal fees. All items come in original box. If you would like any additional pictures please let me know. I also have available a $50 Google Play Credit that I am looking to sell for $40 Google Wallet Only. Must be activated by tomorrow (31st). Will send code via PM right after payment. Chromebox
Chromebox with Kodi installed - $160 each 3 available. They are all brand new. I have it setup to boot to Kodi. No addons or extras preinstalled (But can install on own) just Kodi. I find these much faster and more stable then android.
Zotac Zbox with Kodi installed - $200 Dual core Intel Atom with Nvidia ION Video, 2gb memory, 32gb SSD, Bluray reader, Card Reader, HDMI and optical audio out. I have kodi installed on it now but can install windows or linux as you choose. I had this as a media streamer on my tv. Worked great.
ASUS EeeBook X205TA 11.6" Laptop with 2GB RAM 32GB Flash - $150 This laptop is Like New. I purchased to install Linux on and find out wifi on board is not compatible with linux. I did finally get arch installed but wound up getting something that was more compatible. Currently has Windows 10 installed and updated.
Macbook Pro - $700 13" Aluminum Macbook Pro Model MD101LL/A Includes 2 AC adapters and Orange hard plastic spec case. 2.5ghz Core i5 Processor, Upgraded to 16 gb ram, Upgraded 320gb 7200rpm Hard drive (for data), Upgraded 120gb SSD for the OS (Due to 2 Hard drives, the dvd drive has been removed but is included)
FlashForge Creator Pro - $850 Each I just completely ran through and cleaned both printers. I have tested both with Abs and Pla with a few various prints. Each will come with a new roll of Abs filament. All parts are included and printers are fully functional. (2 available)
01-04 00:12 - 'GDAX Has kept over $25,000 worth of my LTC for hostage Pending for 23 days and counting!!!!!!!!!!!!!' (self.Bitcoin) by /u/Cryptomic7777 removed from /r/Bitcoin within 5-15min
''' The LTC I deposited into Gdax on 12/12/17 has not credited me. Coinbase says it received it on 12/12/2017 9:12 , and transferred to Gdax on 12/12/2014 9:44AM. When I look in GDAX it just says pending. I've sent several emails, Called on the phone, and am even thinking about getting a lawyer. When I transferred the LTC it was worth 42k now it's about 25k. I'm depress and distraught. I was planing on selling for Christmas to buy a new car for my family and have lost so much money. GDax is a nice platform but I think they can't handle everyone's money. I heard Gemini is better. I don't know what to do. I need help. Block ID [link]1 ''' GDAX Has kept over $25,000 worth of my LTC for hostage Pending for 23 days and counting!!!!!!!!!!!!! Go1dfish undelete link unreddit undelete link Author: Cryptomic7777 1: l*ve.blockcy**e*.**m**tc/**/2d6*fe23e5a175*1ff5ab84**b2497**9292af*c6f*16a5eb4b88f*cdf*57***/ Unknown links are censored to prevent spreading illicit content.
Did I miss anything? Do you have a Dogecoin community you want featured? Let me know! All of these places are seeds. Their potential is infinite. You do not have to ‘Leave’ Reddit in order to help build up these other communities. But take part in them. Take part in one of them. Make it your own. Each of these different communities offer Shibes different options, different speeds, different conversations. What will you do there? What will you build there? It’s 8:16AM EST and Sunday is FunDay, right? Right? Our Global Hashrate is holding at ~1070 Gigahashes per second and our Difficulty is down from ~21645 to ~20614. As always, I appreciate your support! GoodShibe
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