Silver uses the Replex replication protocol to address these issues. Driving scale-out distributed software requires more than merely spreading data across clusters and replicating it for fault tolerance; it requires name services to place data and locate it; ownership management, locking and fail over atomicity and isolation of multi-object transactions, in-memory caching, snapshot, checkpoint; and so on. As a result, the software stack of most distributed systems in production today contains a plethora of tools–Cassandra,Redis, ZooKeeper, and others. Each tool comes with its own data-model, proprietary API and query language. In order to make use of any specific tool, a developer needs to cast its own application’s state into the tool’s format, and use the tool’s API or query language in order to extract information about the state and to manipulate it. As an example, consider the life cycle of a typical distributed platform: developers may begin with the need for a distributed database such as Cassandra to store data. As the system grows and scales out, programmers begin to realize they need a way to manage Cassandra itself, so Zoo Keeper is deployed to manage configuration data and provide a mechanism for coordinating clients. Soon, a programmer realizes that funneling everything through ZooKeeper is becoming a bottleneck, so Kafka is bolted on to provide reliable high speed messaging. To further improve performance, Redis is added to implement a distributed cache. Finally, because the programmers still need a way to query data, everything in Kafka is also inserted into Cassandra. Getting different tools to work together and sharing updates across them is a nightmare.

The same information ends up duplicated and translated between multiple tools, resulting in data redundancies,maceta cuadrada plastico inefficiencies, inconsistencies, and difficult maintenance. On boarding new programmers now requires learning multiple tools and picking the correct one for each task.In an on-going joint venture, the NSX team and the VM ware Research Group are contemplating what a Clustered Management PLAT form for driving the control of VM ware’s SDN technology should look like. The design and implementation of Corfu specifically draws motivation from this venture. Figure 6.6 depicts the components of the CMPLAT-Corfu design. The left side of the figure portrays a deployment scenario. Every component in Corfu is built with redundancy and completely automated life-cycle management: Initialization, failure-monitoring, reconfiguration and fail-over. This obviates the operation of CMPLAT as a service with 24/7 availability, driving network control of large, mission critical clusters. The right side portrays the live network model CMPLAT maintains of a real network. Like previous network control planes , examples of items reflected inside CMPLAT are ports, switches, nodes, transport zones, and others. These objects are grouped into maps, e.g., a map of ports, a map of switches. Object and maps may reference one another, e.g., ports and switches belong to zones, and each port resides on some node. CMPLAT exposes an API for admins to manipulate virtual network components, e.g., create a virtual switch containing a certain number of ports, connect ports to virtual machines, and so on.This necessitates back-and-forth translation between the management plane app-servers, which use Java hash-maps to represent the model, and the DBMS. There is a rather complex a data abstraction layer that performs the translation, internally using a SQL-like query-interface for storing and retrieving information from the remote DMBS. In contrast, since Corfu supports arbitrary data-structures, CMPLAT simply uses the most natural data representation, e.g., a hash-map of ports, a linked-list of zones, and so on.

Persisting updates to the data-structure is done transparently and seamlessly, without developer awareness. An example of a NSX logical switch modeled in Corfu is shown in figure 6.7. Since CMPLAT drives the network of entire data centers, it has stringent availability and consistency guarantees. This makes it a catastrophic experience to build over platforms with weaker guarantees. Additionally, there are radically different requirements from different components, for example, live feeds from the network require high-throughput and cannot go through a slow and heavy database service, whereas admin directives must never be lost, and require strong commit guarantees. But building a management platform out of a hybrid system of components is problem-prone. For example, Onix reports anomalous situations in which a node has been updated in a “nodes base”, but the port states this node references have not been updated in the “ports base”. Corfu has the capacity to sustain millions of updates per second, and app-servers efficiently consume updates by selective filtering. This makes it possible to use Corfu as the single data platform for all the information CMPLAT processes. In this way, all of CMPLAT needs are addressed in one place, providing consistency across updates to any part, while avoiding unnecessary duplication and translation of the same information.The era of cloud-scale computing has resulted in the exponential growth of workloads. To cope with the barrage, system designers chose to trade consistency for scalability by partitioning the system and eliminating features which require communication across partitions. As cloud-scale applications became more sophisticated, programmers realized that those features were necessary for building robust, reliable distributed applications. Many of these features were then retrofitted back on, resulting in decreased performance and sometimes serious bugs in an attempt to achieve consistency across a now heavily partitioned system. This dissertation has explored Corfu, a platform for scalable consistency which answers the question: “If we were to build a distributed system from scratch, taking into consideration both the desire for consistency and the need for scalability, what would it look like?”. At the heart of this dissertation is the Corfu distributed log.

The Corfu log achieves strong consistency by presenting the abstraction of a log – clients may read from anywhere in the log but they may only append to the end of the log. The ordering of updates on the log are decided by a high throughput sequencer, which we show can issue millions of tokens per second. The log is scalable as every update to the log is replicated independently, and every append merely needs to acquire a token before beginning replication. This means that we can scale the log by merely adding additional replicas, and our only limit is the number of tokens the sequencer can issue. We have shown that we can build a sequencer using low-level networking interfaces capable of issuing more than half a million tokens per second. We have also built a prototype FPGA storage unit which can interface directly with SSDs and raw flash,maceta 7 litros which can easily saturate a gigabit network a uses a simplified UDP-based protocol. On top of the Corfu distributed log, we have shown how multiple applications may share the same log. By sharing the same log, updates across multiple applications can ordered with respect to one another, which for the basic building block for advanced operations such as transactions. We presented two designs for virtualizing the log: streaming, which divides the log into streams built using log entries which point to one another, and stream materialization, which virtualizes the log by radically changing how data is replicated in the shared log. Materializing streams greatly improves the performance of random reads, and allows applications to exploit locality by placing virtualized logs on a single replica. Efficiently virtualizing the log turns out to be important for implementing distributed objects in Corfu, a convenient and powerful abstraction for interacting with the Corfu distributed log introduced in Chapter 5. Rather than reading and appending entries to a log, distributed objects enable programmers to interact with in-memory objects which resemble traditional data structures such as maps, trees and linked lists. Under the covers, the Corfu runtime, a library which client applications link to, translates accesses and modifications to in-memory objects into operations on the Corfu distributed log. The Corfu runtime provides rich support for objects. An automated translation process converts plain old Java objects directly into Corfu objects through both runtime and compile-time transformation of code. This allows programmers to quickly adapt existing code to run on top of Corfu. The Corfu runtime also provides strong support for transactions, which enable multiple applications to read and modify objects without relaxing consistency guarantees. We show that with stream materialization, Corfu can support storing large amounts of state while supporting strong consistency and transactions. In Chapter 6, we describe our experience in both writing new applications and adapting existing applications to Corfu. We start by building an adapter for Zookeeper clients to run on top of Corfu, then describe the implementation of Silver, a new distributed file system which leverages the power of the vCorfu stream store.

We then conclude the chapter by describing our efforts to retrofit a large and complex application: a software defined network switch controller, and detail how the strong transaction model and rich object interface greatly reduce the burden on distributed system programmers. Overall, Corfu demonstrates a highly scalable yet strongly consistent system, and shows that such a system greatly simplifies development without sacrificing performance. A slow sand filtration system is a filtration process which contaminated water percolates through a sand medium and through various physical, chemical, and biological processes, the contaminants are removed. The first known slow sand filtration system was made in 1804 by John Gibb in Scotland to produce drinking water. Since then, this technique has been widely used not only for drinking water production , but also for improving the quality of wastewater before being reused or discharged into the environment.Bio-filtration generally encompasses any type of filtration of contaminated water through sand, soil, or other various media that contains biomass to aid degradation and removal. Several types of bio-filtration have been extensively studied in literature: bio-swales, trickling filters, constructed wetlands and natural wetlands, treatment ponds, riparian zones, bank filtration, and slow sand filtration. An effective filter is the result of biological degradation and physical/chemical processes such as adsorption and straining of contaminants on the bio-filter media. Both of these processes can be effective as a result of the slow flow rates and long hydraulic residence times that allow the formation of a biological active layer composed of alga, protozoa, bacterium, fungus, actinomycetes, plankton, diatoms, and rotifer population. This layer, called the schmutzdecke, develops within the top centimeters of the filter as a result of the accumulation of the organic matter, microbes, and other particulates that settle from the fluid. Thus, as leachate water is passed through, pathogens and contaminants are trapped and broken down by these microbes as a food source, aiding to the physical and biological processes required for filtration. Depending on the raw and target effluent water quality, a slow sand filtration system can be used by itself or in series to other additional treatments, like pretreatment to protect sensible processes such as reverse osmosis or membrane filtrations , or as a polishing process to eliminate disinfection by-products after ozonation or chlorination. The benefits of SSF combines a high efficiency system in reducing cloudiness and harmful bacteria and viruses along with an economical edge. SSF uses minimal power input and no chemical requirements, does not require close operator supervision, uses locally available materials and labor, and does not produce unwanted by-products. This cost-effective technique that was once used in big cities like London, now has special application in the treatment of water at smaller scales such as isolated households in rural areas, in developing countries, or in small businesses with high water consumption, like plant nurseries.Some other media used in biofiltration are biochar , compost , woodchips , activated carbon , pressmud , anthracite , agricultural wastes , etc. In a study by Nyberg et al. , various substrates were researched as the best effective SSF medium to remove zoospores of P. nicotianae from nursery production effluent. Substrates included sand, crushed brick, calcined clay, Kaldes medium, and polyethylene beads. They discovered that within 21 days, all substrate treatments removed more zoospores than day 0. Of all the substrate treatments evaluated, the columns with 10 cm of sand removed the most zoospores on day 21. By their research, sand was the most effective medium using physical filtration alone at depths of 40 cm and 60 cm.