Cont.. from page 1How * Internxt network
Historically, the state of computing has gone through a series of platforms and environmental changes. Distributed computing holds great assurance for using computer systems effectively. As a result, supercomputer providers and datacenters have changed from providing high performance floating point computing resources to concurrently servicing a huge number of requests from billions of users. A distributed computing system uses multiple computers to solve large-scale problems over the internet. It becomes data-intensive and network-centric. The applications of distributed computing have become increasingly wide-spread. In distributed computing, the main stress is on the large-scale resource sharing and always goes for the best performance.
Distributed systems can be considered conventional networks of independent computers. They have multiple system images, as each node runs its own operating system, and the individual machines in a distributed system could be, for example, combinations of Massively Parallel Processors (MPPs), Symmetric Multiprocessors (SMPs), clusters, and individual computers.
Peer-to-peer (P2P) networks and grids are distributed computing models that enable decentralized collaboration by integrating computers into networks in which each can consume and offer services. P2P is a class of self-organizing system or application that takes advantage of available distributed resources. A grid is a geographically distributed computing platform comprising a set of heterogeneous machines that users can access through a single interface. Unlike the classic client-server model, in which roles are well separated, P2P and grid networks can assign each node a client or server role according to the operations they are to perform on the network, even if some nodes act more as server than as client in current implementations. Both are concerned with the same general problem, namely, the organization of resource sharing within virtual communities; both take the same general approach to solving this problem, namely the creation of overlay structures, but the underlaying organization of this structure greatly differs amongst these two technologies.
While computing Grids have been widely used in computational science, Peer-to-Peer computing has achieved wide prominence in the context of multimedia file exchange. It uses the computing power at the edge of a connection rather than within the network. The client/server architecture does not exist in a peer-to-peer system. Peer nodes act as both clients and servers - their roles are determined by the characteristics of the tasks and the status of the system. This architecture minimizes the workload per node, and maximizes the utilization of the overall computing resources among the network. Today, the sheer numbers of desktop systems make the potential advantages of interoperability between desktops and servers into a single Grid system quite compelling. However, these commodity systems have significantly different properties than the conventional server-based Grid systems. They are usually highly autonomous and heterogeneous systems. And their availability varies from time to time.
Cloud services are mainly divided into three services delivery models: SaaS (Software as a Service, e.g. Google Mail), PaaS (Platform as a Service, e.g. Google AppEngine) and IaaS (Infrastructure as a Service, e.g. Amazon EC2). We normally view cloud computing as providing some mixture of three core types of service: Infrastructure as a Service (IaaS), typically servers, storage and network devices provided through virtualization; Platform as a Service (PaaS), used to deploy applications that are provided by the cloud provider, provider’s partners or the cloud customer; and Software as a Service (SaaS), where software applications are accessed over the network (usually the internet) as hosted services. But there is always an intention to develop a better technology.
Clouds have evolved as the next-generation platform that facilitates creation of wide-area on-demand renting of computing or storage services for hosting application services that experience highly variable workloads and requires high availability and performance. Interconnecting Cloud computing system components (servers, virtual machines (VMs), application services) through peer-to-peer routing and information dissemination structure are essential to avoid the problems of provisioning efficiency bottleneck and single point of failure that are predominantly associated with traditional centralized or hierarchical approaches. These limitations can be overcome by connecting Cloud system components using a structured peer-to-peer network model
Internxt’s mission is to combine P2P and Grid into nonhierarchical decentralized grid system by going beyond standard P2P. We aim to build P2P Grid network, next generation peer-to-peer platform. It will be a truly decentralized structured P2P system which does not require central coordination or knowledge. On top of P2P Grid we aim to provide decentralized platform, a combination of IaaS and PaaS with multiple services that act as building blocks for variety of usages. P2P systems are commonly classified into two categories: unstructured systems exposing emergent phenomena driven from purely local interactions, and structured (DHT-based) systems with probabilistic execution guarantees. Internxt will combine both based on research done in P2P Grid computing.
In the P2P community, a fundamental distinction is made among unstructured and structured P2P systems for resource location. In unstructured P2P systems in principle peers are unaware of the resources that neighboring peers in the overlay networks maintain. Typically, they resolve search requests by flooding techniques. Using the network maintenance protocol, a peer discovers new peers in the network by flooding discovery. Using the network maintenance protocol, a peer discovers new peers in the network by flooding discovery messages. From the responses, it (randomly) selects certain peers to which direct network links are established. The property is accounted to the mechanism of how these networks are being constructed: New nodes preferentially attach to already well-connected ones. Thus, it’s a completely decentralized but also self-organizing system: From randomized interactions of peers’ global structures emerge.
In contrast, standard structured P2P systems follow a different approach with respect to network maintenance. They assign static identifiers to peers and the distributed data structures (e.g., DHTs) are constructed based on these identifiers by distributed algorithms. As a result, the overlay network structure is mainly determined by the choice of identifiers and in turn any self-organization of the system is prevented.
Load balancing as a resource allocation problem is critical to support high scalability, availability, accessibility, and throughput. For systems supporting equality-based lookup of data only, the problem of non-uniform workloads may be circumvented by applying hash functions to the data keys, thus uniformly distributing workload, both for storage and query answering. In combination with using balanced search structures, i.e., balanced distributed search trees, this approach leads to uniform load distribution among the participating peers. However, it is limited if further semantics of the data keys is exploited, for example, in the simplest case when the ordering of data keys is used to support prefix or range queries. This is critical for DB-oriented applications.
P2P Grid is a peer-to-peer lookup system based on a virtual distributed search tree, similarly structured as standard distributed hash tables, and which can solve load balancing issues present in traditional technologies. Each peer holds part of the overall tree. Every participating peer's position is determined by its path, that is, the binary bit string representing the subset of the tree's overall information that the peer is responsible for. Query routing approach is as follows: For each bit in its path, a peer stores a reference to at least one other peer that is responsible for the other side of the binary tree at that level. Thus, if a peer receives a binary query string it cannot satisfy, it must forward the query to a peer that is “closer” to the result. The salient feature of P2P Grid, in contrast to other DHT-based P2P systems, is the separation of concern between peer identifier and peer’s path. In P-Grid peer paths are not determined a priori but are acquired and changed dynamically through negotiation with other peers as part of the network maintenance protocol. Thus, P2P Grid’s prefix-routing infrastructure is constructed by means of a decentralized, self-organizing process in which it adapts to a given distribution of data keys stored by the peers.
We assume here that Grids are sharing environments implemented via the deployment of a persistent, standards-based service infrastructure that supports the creation of, and resource sharing within, distributed communities. Resources can be computers, storage space, sensors, software applications, and data, all connected through the internet and a middleware software layer that provides basic services for security, monitoring, resource management, and so forth. Because accessing these decentralized resources means operating in an environment of unstable connectivity and unpredictable IP addresses, P2P design requirements commonly include independence from DNS and significant or total autonomy from central servers. Their implementations frequently involve the creation of overlay networks  with a structure independent of that of the underlying internet
We will build upon these and foundations of distributed computing paradigms in an effort to create a decentralized secure and reliable platform. The following graph show an overview of the technology behind Internxt, which is further discussed in the coming sections.
The process of deploying application services on publicly accessible clouds that expose their capabilities as a network of virtualized services (hardware, storage, database) is known as Cloud Provisioning. The Cloud provisioning process consists of two key steps VM Provisioning, this involves instantiation of one or more VMs on physical servers hosted within public or private Cloud computing environments. The selection physical server for hosting VMs in a cloud is based on number of mapping requirements including available memory, storage space, and proximity of the parent cloud; and Application Service Provisioning, the second step is mapping and scheduling of requests to the services that are hosted within a VM on a set of VMs.
We aim to build cloud provisioning platform with following features: On-demand dynamic computing, pay-per-use, Proximity aware server location.
To meet today's requirements, provisioning techniques and services should be able to: dynamically adapt to performance needs, failure, and leave and join of hardware and software, including VMs, servers, storage, software, applications, and networks; discover and monitor state of services in completely decentralized and distributed manner; accomplish coordinated and load-balanced provisioning of VMs and application services; handle spikes in service demand (workload) through dynamic scaling in of services from other public clouds; and handle authentication and authorization of services for users; provisioning users’ access; federated security model.
A fundamental challenge in managing the Cloud provisioning system is to maintain a consistent connectivity between the components (self-organization). This challenge cannot be overtaken by introducing a central network model to connect the components, since the information needed for managing the connectivity and making the decisions is completely decentralized and distributed.
Further, centralized network model does not scale well, lacks fault-tolerance, and requires expensive server hardware infrastructure. System components can leave (VM instance destruction), join (VM creation), and fail (service outage) in a dynamic fashion; hence it is an impossible task to manage such a network centrally. Therefore, an efficient decentralized or peer-to-peer solution is mandatory that can gracefully adapt, and scale to the changing conditions.
In peer-to-peer organization of Cloud provisioning systems both control and decisions making are decentralized by nature and where different system components interact together to adaptively maintain and achieve a desired system wide behavior. A distributed Cloud provisioning configuration is decentralized if none of the components in the system are more important than the others, in case that one of the component fails, then it is neither more nor less harmful to the system than caused by the failure of any other component in the system.
Traditionally, DHTs have been efficient for single-dimensional queries such as “finding all resources that match the given attribute value”. Since Cloud computing IaaS and PaaS level services such as servers, VMs, enterprise computers (private cloud resources), storage devices, and databases are identified by more than one attribute; therefore, a search query for these services is always multi-dimensional. These search dimensions or attributes can include service type, processor speed, architecture, installed operating system, available memory, and network bandwidth.
Internxt will be making use of Blockchain technology in different parts throughout its whole environment. Blockchain will mainly be used as a backbone for all payments throughout Internxt’s network. We will make use of Ethereum smart contracts and will create Internxt (INXT) network tokens based on the ERC20 token standard due to its robust structure. Smart contracts are instances of code that live inside the Ethereum network, and it is impossible for anyone to breach or modify them. They are public, and allow transparent payments and job running. For the standard user, smart contracts remove the need of an additional middle man when processing payments. With this in mind, we will create an interface that communicates with smart contracts, and fully automate all the tasks done in our platform like deployment of applications inside the network, or storing user files. Internxt network’s availability and consistency are of core relevance for the platform. We will therefore run algorithms for watching over the performance of network hosts. This would allow to users to increase their rewards with constant performance over time.
Automation plays a vital role in ensuring we can release software repeatedly and reliably. One key goal is to take repetitive manual processes like build, deployment, regression testing and infrastructure provisioning, and automate them. This is the part of the system that handles initial distribution of files onto the Internxt network. After the user has uploaded the files he wishes to run or store on our network, and if the number of tokens connected to his account is enough for the calculated expenses, files are delivered to the network. All the settings related to site hosting, level of security and additional encryption and distribution of files are presented to the users, and in depth explained.
We will use Ansible to provide best user experience for configuration management, and application deployment. In contrast with popular configuration management software — such as Chef, Puppet, and CFEngine — Ansible uses an agentless architecture. With an agent-based architecture, nodes must have a locally installed daemon that communicates with a controlling machine. With an agentless architecture, nodes are not required to install and run background daemons to connect with a controlling machine. This type of architecture reduces the overhead on the network by preventing the nodes from polling the controlling machine.
To orchestrate nodes, Ansible deploys modules to nodes over SSH. Modules are temporarily stored in the nodes and communicate with the controlling machine through a JSON protocol over the standard output.
Identity management, also known as identity and access management (IAM) is, in computer security, the security and business discipline that "enables the right individuals to access the right resources at the right times and for the right reasons". It addresses the need to ensure appropriate access to resources across increasingly heterogeneous technology environments and to meet increasingly rigorous compliance requirements.
Application hosted in our network, that allows users to have an overview over the balance of Internxt tokens, how they spend them (in terms of the content that is hosted on the INX network), and how much profit they are making from the resources their provide. This includes a full fledged web and mobile apps that user can use to manage his own content. Communication between user client and the server will be encrypted. The server will then communicate with the blockchain and if the payment is successful, deployment and configuration management parts of the system are contacted. Automation and User-friendliness are key for our network. User is able to pay for what he uses, and earn tokens for resources he provide through our platform, and all of that secured by the Ethereum backbone network. Not only that Ethereum is a great solution today, it is also in constant improvement and as it evolves, so will our platform grow as well, and this will allow us to add new features in the close future.
Application containerization is an operating system level (OS-level) virtualization method for deploying and running distributed applications without launching an entire virtual machine (VM) for each app. Instead, multiple isolated systems are run on a single control host and access a single kernel. The application containers hold the components such as files, environment variables and libraries necessary to run the desired software. Because resources are shared in this way, application containers can be created that place less strain on the overall resources available. This is the layer that allows the regular users to plug in the their personal computers, or dedicated machines into Internxt. For this we plan to use OpenShift, that provides enterprise Kubernetes for developers. OpenShift Origin is a distribution of Kubernetes optimized for continuous application development and multi-tenant deployment. OpenShift adds developer and operations-centric tools on top of Kubernetes to enable rapid application development, easy deployment and scaling, and long-term lifecycle maintenance for small and large teams. We plan to use this for easy distribution of Docker images to user machines, that will allow all the users to work with our system with ease. OpenShift allows a huge specter of tech to be run out of the box, including: Java (Wildfly, JBossEAP, Tomcat), PHP, Node.js, Python, Perl, MySQL, PostgreSQL, MongoDB, Jenkins, Cron, and JBoss xPaaS Services (Fuse, BPM Suite, BRMS, Data Virtualization, Aerogear, and more). We plan to extend on their framework, and if needed adapt it to our needs. This will represent the backbone of our system that connects all the other parts: it will be used for deployment and provisioning of all the resources inside the network. It allows all the computers inside the network to be able to run as one, and create the Internxt as we imagine it. We will also provide an developer API that will allow users to connect to the network, and use it instead of others existing cloud solutions. The API will be created so that there is no danger of running malicious code or harming the network in any way. We find OpenShift superior solution compared to similar solutions like Cocaine. OpenShift runs and supports both stateful and stateless applications. This allows you to take full advantage of containers without needing to completely re architect your enterprise apps.
In computing, a distributed cache is an extension of the traditional concept of cache used in a single locale. A distributed cache may span multiple servers so that it can grow and in transactional capacity. It is mainly used to store application data residing in database and web session data. Distributed caches, although deployed on a cluster of multiple nodes, offer a single logical view (and state) of the cache. In most cases, an object stored in a distributed cache cluster
will reside on a single node in a distributed cache cluster. By means of a hashing algorithm, the cache engine can always determine on which node a key-value resides. Since there is always a single state of the cache cluster, it is never inconsistent. The idea of distributed caching has become feasible now because main memory has become very cheap and network cards have become very fast, with 1 Gbit now standard everywhere and 10 Gbit gaining traction. Also, a distributed cache works well on lower cost machines usually employed for web servers as opposed to database servers which require expensive hardware. We want to provide this, since we think it is a feature that is perfectly fit into the architecture of our system, and brings a lot of benefits in terms of speed and network reliability. A cache provides high throughput, low-latency access to commonly accessed application data, by storing the data in memory. For a cloud app, the most useful type of cache is distributed cache, which means the data is not stored on the individual web server's memory but on other cloud resources, and the cached data is made available to all of an application's web servers (or other cloud VMs that are used by the application). Since the concept of a “server” is different for us, and we have a lot of memory available in the network, it is easy to make use of, and deliver content with high speeds, regardless of scaling of applications on a high number of places. A distributed cache runs as an independent process across multiple nodes and therefore failure of a single node does not result in a complete failure of the cache. Because of a node failure, items that are no longer cached will make their way into surviving nodes on the next cache miss. Also in the case of distributed caches, the worst consequence of a complete cache failure should be degraded performance of the application as opposed to complete system failure.
Serverless computing is a cloud computing execution model in which the cloud provider dynamically manages the allocation of machine resources. Pricing is based on the actual amount of resources consumed by an application, rather than on pre-purchased units of capacity. It is a form of utility computing. Serverless architectures refers to applications that significantly depend on third-party services (knows as Backend as a Service or "BaaS") or on custom code that's run in ephemeral containers (Function as a Service or "FaaS"), the best-known vendor host of which currently is AWS Lambda. Instead of thinking of applications as collections of servers, developers instead define applications with a set of functions with access to a common data store. The Lambda model has many benefits as compared to more traditional, server-based approaches. Lambda handlers from different customers share common pools of servers managed by the cloud provider, so developers need not worry about server management. The code specific to an application will typically be small, and hence it is inexpensive to send the handler code to any worker in a cluster. Finally, applications can scale up rapidly without needing to start new servers. In this manner, the Lambda model represents the logical conclusion of the evolution of sharing between applications, from hardware to operating systems to the runtime environments themselves.
Again, we consider this to be a perfect fit for the architecture that runs the Internxt network. We will be making use of Open Lambda, as an open source and reliable project, that allows serverless computing and is compatible with other technical solutions we already use (eg OpenShift, Docker). Serverless computing is used more, and it represents the future of applications. With serverless computing and Internxt network it is easy to detect and scale the needs for resources needed for application to run. We allow users to host their services on our platform, with easy integration to any existing application through standard REST interface. Additional benefit is ease of payment for the user - like for everything else in our system, it is done by smart contracts that monitor the usage of resources. By writing good code, this would allow the publishers to get an incredible scaling, at low prices.* Decentralized Content Delivery Network
Due to increase in internet usage nowadays users are accessing information from all part of the world so we need to have an effective content delivery system to deliver content in a fast and effective way. The problem of existing content distribution to users is by delivering the data through single transmission from source to one point (cache server in that branch) and the copies are created at that branch point and distributed to each requested user node. We have many existing content delivery networks which delivers content quickly and effectively by responding to the user request from the nearest geo location cache server. But enabling a decentralized content caching capability removes the need of central cache server and increases the performance, data availability and speed of content delivery process. Here content is cached in mostly every intermediate node within the network and content is fetched by its content name not based on the hostname. Requested content is delivered to the node from the nearest node holding the content cache, in case if that node is unavailable the request is shifted to the next nearest node holding the content. Content delivery networks (CDN) is a cache based technique which stores a data in cache server place at different locations. When a user request for a specific data from the server, the nearest CDN will respond with data to the user’s request. Peer-to-Peer (P2P) system also use a caching technique but here the participant node who are all accessing the data will become a cache server and respond to other users. P2P data is stored in one user’s node is made available to another user. So, the distribution load is shared among users instead of central serve. Even if
the user is respond by the CDN having single cache server for one nation or one zone can’t be the solution. In Peer-to-Peer the data requested is fetched from another participant who is requesting the same data from the server.
DNS (Domain name service) is used to resolve human readable addresses - like internxt.io, into a series of numbers, that are machine readable, but hard for users to memorize. DNS allows users to browse the internet with ease, and most of the users don’t even think about it. Communication between user and DNS server is done in multiple steps, and it only takes a few milliseconds to complete. But, while DNS is a very good and well technical solution, it has its flaws. DNS is by default decentralized, and it is very massive set of supercomputers running behind it. But on top level, it is centralized, and this has a lot of flaws users usually don’t think of. DNS allows for hacker attacks to be conducted on massive number of users. It allows also for someone who controls it, to censor the internet in the way he likes. Many countries around the world censor the internet already, and since day by day internet is more widely used, the need for this will only grow bigger, by big organizations and governments. This is where a truly decentralized DNS would set in place, and bring its many benefits onto the table. The main benefit is that decentralized DNS would allow zero down times while at the same time making censoring of content impossible.
Our nodes will feature a mapping layer that will be self-organizing. These mapping nodes could be HTTP ingress proxies that route client requests from a given locale to the appropriate data centers, the model adopted by Google and Yahoo. Or the mapping nodes could be authoritative DNS servers that resolve local queries for the names of Web sites, the model adopted by Akamai and most CDNs. Furthermore, these DNS servers may use IP anycast to leverage BGP-based failover and to minimize client request latency. Whatever the mechanism for interacting with clients, each mapping nodes has only a partial view of the global space of clients. Node will be developed based on research in decentralized server selection.
Service-Oriented Computing (SOC) is emerging as a paradigm for developing distributed applications. A critical issue of utilizing SOC is to have a scalable, reliable, and robust service discovery mechanism, that would work perfect in combination with likes of network like Internxt, with a lot of nodes in form of user computers. This will allow for new services that enter network to be discovered the moment they are available to use. Since it is all decentralized, there is no need for update of some central table that everyone reads from, and makes updates visible to users in an instant. However, traditional service discovery methods using centralized registries can easily suffer from problems such as performance bottleneck and vulnerability to failures in large scalable service networks, thus functioning abnormally.
A weak point of web services is the lack of suitable technologies for implementing service registries. Universal Description, Discovery and Integration (UDDI), as a centralized approach, conflicts with the distributed nature of service-oriented architectures and did not prevail in practice. Internxt novel service discovery scheme is scalable. Each service provider runs one or more discovery proxies in its internet domain and sets DNS records pointing to them. Then, local services can register at these proxies. Service users from the local network as well as from the internet can now locate these proxies using the DNS and query them for available services in this domain. Thus, the local proxies are linked together by the DNS forming one coherent global service directory.
Centralized approaches to service discovery, monitoring, and load balancing have several critical design limitations including: single point of failure; lack scalability; high network communication cost (such as network bottleneck, congestion) at links leading to the service; requirement of high computational power (which may be not feasible with commodity machines that public clouds offer) to serve a large number of resource look-up and updated queries on the server running the central service.
Decentralized service discovery is considered as a promising approach to addressing the problems caused by centralized infrastructures. Internxt is solving this, allowing extremely high scalability, data availability and quality of service aware discovery. As new nodes are added to the network, it allows us to scale the services better, and have shortened waiting times when loading content. This allows the user to always download content from the closest node. In perfect scenario, if the user is to have someone in the same local network with him already serving the content, it can be downloaded using local network, and allow the content to be shown instantly, saving on DNS and file download times. Otherwise, the closest and most performant node is selected, and the content is served from there. Performance of node and delivery is calculated based on node load, network speed, latency and other factors that may decrease the overall experience.
When building a system on top of a set of wildly uncooperative and unruly computers you have knowledge problems: knowing when other nodes are dead; knowing when nodes become alive; getting information about other nodes so you can make local decisions, like knowing which node should handle a request based on a scheme for assigning nodes to a certain range of users; learning about new configuration data; agreeing on data values; Failure Detection is valuable for system management, replication, load balancing, and other distributed services. To date, Failure Detection Services scale badly in the number of members that are being monitored.
A gossip protocol is a style of computer-to-computer communication protocol inspired by the form of gossip seen in social networks. Modern distributed systems often use gossip protocols to solve problems that might be difficult to solve in other ways, either because the underlying network has an inconvenient structure, is extremely large, or because gossip solutions are the most efficient ones available.
Since this is a key point in scaling networks with high number of nodes, like ours is, we will implement algorithms that prevent content from being unavailable at any moment. The protocol here needs to take into consideration many negative effects, like power outage, internet failing to deliver messages between nodes, etc. We would have this running on each separate node, and on federated nodes, also run by users. Such nodes would need more processing, storage, and network power, and would logically be rewarded with higher number of tokens for their work. Our solution will be based on scientific research and current proven open source solutions.
Network tomography is the study of a network's internal characteristics using information derived from end data. Network tomography advocates that it is possible to map the path data takes through the internet by examining information from "edge nodes," the computers in which the data are originated and from which they are requested.
Network coordinates (NC) system is an efficient mechanism for internet distance prediction with limited measurements. The goal of these systems is mainly to develop public infrastructure that provides distance information between any two arbitrary points on the internet. The ability to predict latency without prior communication allows systems to use proximity for better performance with less measurement overhead. A coordinate system could be used to select which of a number of replicated servers to fetch a data item from; such a system is particularly helpful when the number of potential servers is large or the amount of data is small.
While Vivaldi is a representative NC, our solution will be based on research done in Pharos and PCoord papers. Pharos is a fully decentralized NC system. All nodes in Pharos form two levels of overlays, namely base overlay for long link prediction, and local cluster overlay for short link prediction. Vivaldi algorithm is applied to both base overlay and local cluster. As a result, each Pharos node has two sets of coordinates. PCoord is a fully decentralized network coordinate system with each host updating its coordinate iteratively to refine the prediction accuracy of its estimated position. Each host updates its coordinates to minimize a loss function that measures the difference between the actual and the geometric distances between itself and a small set of other hosts. PCoord does not require any special bootstrap process - each node goes through an iterative calibration process to refine its coordinates.
Evaluation of performance done in both papers clearly shows benefits over Vivaldi and with that reason it will be basis of our implementation.
The significance of efficient security mechanisms in P2P and Grid systems is unquestionable, since security is a quality of service factor for such systems. Traditional security mechanisms like encryption and sand-boxing incur additional overhead. By using trust and reputation, the additional overhead is minimized. Trust mechanisms result in safer communication between P2P
and Grid nodes, increasing the quality of service and making P2P and Grid technology more appealing.
Due to the openness of distributed networks, security issue becomes one of the most important challenges when deploying these networks into application. Traditional strategies, such as traditional encryption and access control, because of their poor scalability, are no longer suited for resolving security issues of distributed P2P system. Trust management resolves the security issues in semantic and behavioral levels and filters malicious nodes based on their real-time behaviors between transactions. Trust mechanism can transfer between heterogeneous mixed networks seamlessly, and the researches of trust management are of considerable interest in recent years. P2P grid computing combines and integrates grid and P2P technologies to implement peer-to-peer communications with greater distribution and scalability. Trust management is a complicated and difficult task in such an environment, because resources are geographically distributed and belong to distinct organizations. In a P2P environment, some peers may provide services with low quality and may not promise to satisfy user requirements. An unfavorable situation arises when providers offer incorrect information about their resources/services to exaggerate the quality of their services. To inspire resource sharing among nodes and protect against malicious node behaviors, a reputation system for trust management is necessary. Peer-to-Peer (P2P) reputation systems are essential to evaluate the trustworthiness of participating peers and to combat the selfish, dishonest, and malicious peer behaviors.
Our solution will be based on recent advances in trusted grids and P2P computing systems. PowerTrust paper will be basis for our solution. In comparison with two established P2P reputation systems, EigenTrust and PeerTrust, we believe PowerTrust is more robust and scalable. Inspired by the power-law findings in peer feedbacks, the PowerTrust system dynamically selects a few power nodes that are most reputable by using a distributed ranking mechanism. The good reputation of power nodes is accumulated from the running history of the system. Like a democratic system, power nodes are dynamically replaceable, if they become less active or demonstrate unacceptable behavior.
We are providing a completely revolutionary technology that outperforms the current solutions in the market. As it has already been mentioned, our main competitive competences are privacy, security and efficiency, providing a price at least as good as the one traditional solutions provide. We also want to provide a user experience at least as good as the ones they offer. There already exist a few direct competitors to Internxt though, which provide a decentralized cloud solution that aims to improve the way the internet is organize. Although there are not many competitors, their decentralized technologies are sometimes outstanding. Even though this is the case, these competitors have not yet become mainstream. We believe that the main issue resides in user experience. The reality is that, although decentralized and blockchain technology is better than the traditional technology we use, it is too complex for the average user to make a transition from one technology to the other. We are putting the focus on providing an outstanding user interface, that’s as easy to use as the current solutions offered by big corporations. By providing a competitive price and a seamless user experience, we believe we can become mainstream with our decentralized technology, which in some cases does not have to be better than the one offered by our direct competitors.
Internxt Bounty Program
* Reddit - 90,000INXT
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* YouTube - 120,000INXT
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* Article - 350,000INXT
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* Address to your ETH wallet*
Some last points: We'll manually review your entries from September 6th onwards and assign you INXT according to our guidelines. You must keep your Article, Twitter Follow, Forum Signature etc at least until September 28th when the ICO is over, so that we also have enough time to review your entries. When you Tweet about us, Write an article about us, Add us to your signature etc remember to at least include a link to our website, and be original!
ICO overview The goal of the ICO is to allow users to participate in the development of our platform. The minimum goal in EUR for the project is 500ETH, and the cap is set at 150,000ETH. With maximum founding, we would be able to develop the complete environment we had envisioned. With the minimum raised cap of 500ETH we would be able to develop a basic version of our decentralized file storage technology (dCDN). If we fail to raise the minimum cap, although we will still try to develop our technology, users will have the chance to request a refund of their ETH (ETH gas will be subtracted during the refund process) during a period of 2 weeks from the time at which the ICO ends. The ICO will last for 3 weeks, starting on September 7th. First 15% INXT sold will give 10% bonus tokens, or 15% bonus for those who purchase over €30k worth of tokens. Second 15% set of tokens (so up to 30%) will give a 5% bonus or a 10% for those who purchase over €30k worth of tokens. INXT will be used as a payment method for the services provided in our network. The tokens will be issued according to ERC20 Ethereum standard. In depth guides for buying tokens during ICO, and later exchange of ICO tokens to regular ones, are available on our website, and for any help needed there will be live support during the ICO, and before it starts, though our slack channel. Minimum and maximum amounts of tokens that are sold in the ICO sale can be seen in the table below. For each ETH collected, there will be 300 INXT tokens generated. The number of tokens created for Internxt’s developers and the company is fixed in percentage, and the amount is calculated based on the total tokens created during the ICO sale. These tokens will mainly be used to test the current technology internally. Tokens generated for the team will be “frozen” for 6 months after the ICO sale, since we consider that will be the period that will be needed for the initial technology to be developed and to start getting tested. Internxt is a legal entity registered in Spain as Internxt Universal Technologies SLU. Internxt Tokens (INXT) are not securities -investment contracts. We have designed these with the objective of fundraising the development of our technology, and once this is ready, those who purchased the tokens will be able to trade them for our service. Although it can happen, INXT are not designed to help those who purchase them to make a profit by selling the tokens. To avoid conflicts with the US Securities and Exchange Commission, and although we are sure our token does not fall within the definition of a security, US residents are not allowed to participate in our token sale. This action is taken due to the still high risk involved in selling tokens to US residents.
Tokens issued per ETH : 300
Minimum ether to collect : 500 ETH
Maximum ether to collect : 150,000 ETH
Tokens generated for Internxt’s team : 2.5% of total sold tokens after the ICO Tokens generated for Internxt : 5% of total sold tokens after the ICO Date and Time of start : September 7th 2017, 07.00PM GMT End Date and Time : September 28th 2017, 07.00PM GMT Maximum tokens sold during ICO : 45,000,000 Maximum tokens for the team, company and bounty program (770,000INXT dedicated to bounty program) : 4,145,000 Maximum tokens generated (not including bonus tokens, which are variable) : 49,145,000
The total amount of circulating tokens = Tokens sold during ICO + Bounty + Bonus + 7.5% of tokens sold during ICO for the company and development
* Post ICO budget allocation
Based on the amount of money collected during the ICO, we will be able to develop a bigger or smaller number of features for our technology. In the event that we do not raise the required amount to build all the features we aim to develop, we will create a functional, less complete technology up to the extent that we are able to. Maximum financing scenario would allow us to follow the delivery process without running into insufficient budget. The biggest part of the budget will go into the actual development of the project, with other expenses such as marketing and taxes. We want to go into the market as fast as possible, and expand the value of the tokens for everyone holding these, by giving an actual value to the product. Once the ICO takes place, we will start working in the development of the technology and expect a stable version our technology to be readily available by the end of 2018.