What is the right approach to use to configure OpenSplice DDS to support 100,000 or more nodes?
Can I use a hierarchical naming scheme for partition names, so "headquarters.city.location_guid_xxx" would prevent packets from leaving a location, and "company.city*" would allow samples to align across a city, and so on? Or would all the nodes know about all these partitions just in case they wanted to publish to them?
The durability services will choose a master when it comes up. If one durability service is running on a Raspberry Pi in a remote location running over a 3G link what is to prevent it from trying becoming the master for "headquarters" and crashing?
I am experimenting with durability settings in a remote node such that I use location_guid_xxx but for the "headquarters" cloud server I use a Headquarters
On the remote client I might to do this:
<Merge scope="Headquarters" type="Ignore"/>
<Merge scope="location_guid_xxx" type="Merge"/>
so a location won't be master for the universe, but can a durability service within a location still be master for that location?
If I have 100,000 locations does this mean I have to have all of them listed in the "Merge scope" in the ospl.xml file located at headquarters? I would think this alone might limit the size of the network I can handle.
I am assuming that this product will handle this sort of Internet of Things scenario. Has anyone else tried it?
Considering the scale of your system I think you should seriously consider the use of Vortex-Cloud (see these slides http://slidesha.re/1qMVPrq). Vortex Cloud will allow you to better scale your system as well as deal with NAT/Firewall. Beside that, you'll be able to use TCP/IP to communicate from your Raspberry Pi to the cloud instance thus avoiding any problem related to NATs/Firewalls.
Before getting to your durability question, there is something else I'd like to point out. If you try to build a flat system with 100K nodes you'll generate quite a bit of discovery information. Beside generating some traffic, this will be taking memory on your end applications. If you use Vortex-Cloud, instead, we play tricks to limit the discovery information. To give you an example, if you have a data-write matching 100K data reader, when using Vortex-Cloud the data-writer would only match on end-point and thus reducing the discovery information by 100K times!!!
Finally, concerning your durability question, you could configure some durability service as alignee only. In that case they will never become master.
HTH.
A+
Related
I want to preface this by saying that I've never taken a networking class but I'm learning on the job. Things like TCP/IP networking I have a pretty basic grasp of and if you think this will hinder my attempt at this let me know.
The task I have at hand is thus: I have an Open Stack network with a bunch of nodes that can communicate with each other, all running CentOS virtual machines (just for simplicity's sake) with applications running on top of them. The task is basically to find a way to monitor the ping of every node and report whenever some kind of message (probably through http) that reports what happened. The logic of checking for the actual latency problems isn't what I'm struggling with, its the best structure to complete this task.
I'm thinking of using Nagios and setting up a distributed monitoring system. Basically my plan is to instal nagios on each node after writing my plugin (unless its already offered or exists) and it would simply ping everything else in the network once its setup and the other nodes ping it once the fact that it has joined the network is detected. I'm not sure exactly how scalable this is because if the number of nodes increase a lot would having every node pinging every other node actually be a good thing? Could it actually end up being a lot of stress on the network?
Is this a bad idea? I know a more efficient solution would be something where as long as every node is being checked (not necessarily have to have every node connected to by every other node) is more efficient. Visualizing it as a graph with a couple of points, it would be a bidirectional graph with just one path connecting each point rather than every possible point having edges between each other. But I don't know if this is the level I should be thinking about it or not.
In short, what I'm asking is: How would one go about setting up a ping monitoring system between a bunch of Open Stack nodes?
Let me know if this question makes sense. Thanks.
Still not entirely sure what you're trying to accomplish with this setup, but the Nagios setup you're describing sounds messy and likely won't cover what you need. I'd look at building packetbeat into the provisioning of each of your hosts, and then shipping that data off to Elasticsearch. That way you can watch your actual application-level traffic and response times. https://www.elastic.co/products/beats/packetbeat
I started exploring openHAB for my home automation. Looks to be a great application for the home automation. I want to automate two homes and want to run openHAB on one centrally placed server. Is it possible to segregate the data for my two homes and provide use based access for two homes.
Or I will have to have to instances running on my server.
Please suggest if anyone has done this earlier.
you can (I believe) provide different sitemaps, but the most importing question is how will a central openhab instance communicate with the "other" home?
Especially If you're going to use bindungs which require a piece of hardware like z-wave etc.
You can potentially play with MQTT and have a small Raspberry Pi running in the "other" home feeding the MQTT.
Assuming that there is not a hardware or range based issue with using OpenHab for two homes (e.g. z-wave USB dongle but second home is out of range) and there is network connectivity between the two houses, there are a number of ways you can accomplish this. Here is one.
The easiest would probably be to just use a naming convention for your items and groups to easily tell which house the item comes from. You would probably want to set up a separate sitemap for each house as well. If I understand your question this should segregate the data for you based on name and provide use based access for each home.
If you want to segregate the data even more thoroughly you can configure your persistence to save all the items from one house to one DB and all the others to a different one, though you will need two different persistence bindings set up (i.e. one uses rrd4j and another uses db4o). I'm not sure this provides any advantage.
The final step is getting the data from the remote house into openHab. How this is accomplished will depend on the nature of the sensors and triggers in the other house. You can use the HTTP binding, TCP/IP binding, or an MQTT broker. I've personally exposed a couple of my Raspberry Pi based sensors to openHAB using a python script and the paho library that publishes the sensor data read from the GPIO pins to an MQTT broker and it works great.
Centralization vs segregation - you have to decide, which one has more advantages and less risk.
The two houses will store data on the server (openhab2, mqtt, DB/rr4d) and each one have access to it - that must be clarified.
The network connectivity is obvious, it should be stable between the two sites. Security is another issue - not only digital, but safety of life (hvac controlling or safety appliances with network outage?).
Configuration is pretty supported in both ways, separate config files (items, rules, persistence, etc) and connectivity in a hierarchy has endless approaches and capabilities.
In the newest version of the android app you can add multiple openhab servers. Why don't just use two instances of openHAB?
I have a question related to MPI.
In order to keep track of the communication volume used by my implementation, I would like to get the currently-transferred data amount since the mpi-process' start until the current measure-point.
I checked the specification as well as the mpi.h header file of mpich and did not find a matching function to call or variable that keeps track of the network transfer costs. It would, of course, be possible to implement a small traffic registry or define a macro for tracking communication sizes, but maybe it can be read out from somewhere.
Do you know a method to gain the current transfer size, maybe it is also possible to get this number using a system call to get the network traffic size of the process?
Is it maybe possible to access the proc information of the current process, maybe the /proc/net is maintained per process as well, such as /proc/self/net?
Thank you in advance,
Martin
I want to create a P2P application on the internet. What is the best or if none exist a good enough way to do auto-discovery of other nodes in a decentralized network?
Grothoff and GauthierDickey from the GNUnet project (an anonymous censorship-resistant file-sharing network) researched on the question of bootstrapping a p2p network without any central hostlist.
They found that for the Gnutella (Limewire) network a random ip search needed on average 2500 connection attempts to find a peer.
In the paper they proposed a method which reduced the required connection attempts to 817 for Gnutella and 51 for the E2DK network.
Achieved was this through creating a statistical profile of p2p users for every DNS organization, this small (around 100kb) discovery database has to be created in advance and shipped with the p2p client.
This is the holy grail of P2P. There isn't a magic solution really - there's no way a node can discover other nodes without a good known point to act as a reference (well, you can do so on a LAN by using broadcasting, but not on the internet). P2P filesharing tends to work by having known websites distributing 'start points' for discovery, and then further discovery (I would expect) can come from asking nodes what other nodes they know about.
A good place to start on research would be Distributed Hash Tables.
As for security, that topic will be in the literature somewhere, I should think - again I would recommend Wikipedia. Non-existent ones are trivially dealt with: if you can't contact an IP/port, don't keep it on your list, and if a node regularly provides non-existent pointers, consider de-prioritising it or removing it from your list entirely.
For evil nodes, it depends on your use case, but let's say you are doing file sharing. If you request a section of a file, check with several nodes what the file section's hash should be, and then request by hash. If the evil node gives you a chunk that has a different hash, then you can again de-prioritise or forget that node.
Distributed processing systems work a little differently: they tend to ask several unrelated nodes to perform the same work, and then they use a voting system (probably using hashing again) to determine whether evilness is at hand. If a node provides consistently bad results, the administrator is contacted or the IP is removed from the known nodes list.
ok, for two peers to find each other they both have to know a common, lets say, mediator to exchange IPs once. You can use anything for this kind of the first handshake whilst being able to WRITE and READ from that "channel". i.e: DNS (your well known domains), e-Mail, IRC, Twitter, Facebook, dropbox, etc.
I am looking for a way to replicate a small and simple relational database (like SQLite) across peers. This should work in an environment with unstable network connections, hence the need for each peer to have a full copy of the database. This should allow a peer to continue working off-line in the event of network failure.
To keep things simple, replication should only have to support the replication of addition of data, i.e. only INSERTs, not DELETEs or UPDATEs.
Does anyone know of a good - and ideally cross-platform - technology or method of creating such a system? I am currently looking at JXTA and JXSE, but I am put off by its complexity and apparant lack of life in its community after the takeover of Sun by Oracle.
Thanks!
Frans
rqlite uses the raft consensus algorithm, so it should be fairly resilient to unstable network connection.
Also, it seems to be possible to configure rqlite to accept reads even in the case of a network failure.
A similar project, dqlite, exists as a library, available in various languages, but it seems less explicit about the event of a network failure.
You may want to explore JGroups for the communication layer if you don't like JXTA. For the replication, I think you will have to implement your own code.
I am working on something similar (though the code is far from ready). I'll describe a little about my intended approach, but whether that is suitable for you depends on some key design points you'd need to consider. I am not aware of any ready-built projects that will do this, unfortunately.
In particular we'd need to know what language you wish to use, or which languages you'd rather avoid.
Also, consider how you intend to do peer dicovery - can you set up trust between node pairs manually, or do you want them to auto-discover?
Presumably all peers may insert data?
If you are able to use PHP, and are happy manually peering node pairs, then my approach may be of interest. Set up an ORM such as Doctrine, Propel or NotORM, and get each node to regularly sync with an internet time source. For each new row in a db, grab the data (either in an array or ORM object), serialise it, and push it out to all nodes that you have a trust relationship with. Where a push fails, keep a note of this and retry at periodic intervals (potentially giving up after a remote node fails to answer a large number of retries).
Pushes can either be kicked off by your application that creates the row, or can be called by whatever scheduler is available on each machine. A push message can be XML, or for simplicity can be just a POST message containing the new row and whatever metadata (e.g. timestamp of save, so as to resolve INSERT order from several nodes).
If your nodes do not have static IP addresses, they could be registered with a dynamic DNS addressing service so as to allow each node to stay in touch with peers even if their IP changes. You might also consider adding a message signing system, to ensure that messages between nodes are genuine.