Rendezvous or highest random weight (HRW) hashing is an algorithm that allows clients to achieve distributed agreement on a set of k {\displaystyle k} options out of a possible set of n {\displaystyle n} options. A typical application is when clients need to agree on which sites (or proxies) objects are assigned to. Consistent hashing addresses the special case k = 1 {\displaystyle k=1} using a different method. Rendezvous hashing is both much simpler and more general than consistent hashing (see below). == History == Rendezvous hashing was invented by David Thaler and Chinya Ravishankar at the University of Michigan in 1996. Consistent hashing appeared a year later in the literature. Given its simplicity and generality, rendezvous hashing is now being preferred to consistent hashing in real-world applications. Rendezvous hashing was used very early on in many applications including mobile caching, router design, secure key establishment, and sharding and distributed databases. Other examples of real-world systems that use Rendezvous Hashing include the GitHub load balancer, the Apache Ignite distributed database, the Tahoe-LAFS file store, the CoBlitz large-file distribution service, Apache Druid, IBM's Cloud Object Store, the Arvados Data Management System, Apache Kafka, and the Twitter EventBus pub/sub platform. One of the first applications of rendezvous hashing was to enable multicast clients on the Internet (in contexts such as the MBONE) to identify multicast rendezvous points in a distributed fashion. It was used in 1998 by Microsoft's Cache Array Routing Protocol (CARP) for distributed cache coordination and routing. Some Protocol Independent Multicast routing protocols use rendezvous hashing to pick a rendezvous point. == Problem definition and approach == === Algorithm === Rendezvous hashing solves a general version of the distributed hash table problem: We are given a set of n {\displaystyle n} sites (servers or proxies, say). How can any set of clients, given an object O {\displaystyle O} , agree on a k-subset of sites to assign to O {\displaystyle O} ? The standard version of the problem uses k = 1. Each client is to make its selection independently, but all clients must end up picking the same subset of sites. This is non-trivial if we add a minimal disruption constraint, and require that when a site fails or is removed, only objects mapping to that site need be reassigned to other sites. The basic idea is to give each site S j {\displaystyle S_{j}} a score (a weight) for each object O i {\displaystyle O_{i}} , and assign the object to the highest scoring site. All clients first agree on a hash function h ( ⋅ ) {\displaystyle h(\cdot )} . For object O i {\displaystyle O_{i}} , the site S j {\displaystyle S_{j}} is defined to have weight w i , j = h ( O i , S j ) {\displaystyle w_{i,j}=h(O_{i},S_{j})} . Each client independently computes these weights w i , 1 , w i , 2 … w i , n {\displaystyle w_{i,1},w_{i,2}\dots w_{i,n}} and picks the k sites that yield the k largest hash values. The clients have thereby achieved distributed k {\displaystyle k} -agreement. If a site S {\displaystyle S} is added or removed, only the objects mapping to S {\displaystyle S} are remapped to different sites, satisfying the minimal disruption constraint above. The HRW assignment can be computed independently by any client, since it depends only on the identifiers for the set of sites S 1 , S 2 … S n {\displaystyle S_{1},S_{2}\dots S_{n}} and the object being assigned. HRW easily accommodates different capacities among sites. If site S k {\displaystyle S_{k}} has twice the capacity of the other sites, we simply represent S k {\displaystyle S_{k}} twice in the list, say, as S k , 1 , S k , 2 {\displaystyle S_{k,1},S_{k,2}} . Clearly, twice as many objects will now map to S k {\displaystyle S_{k}} as to the other sites. === Properties === Consider the simple version of the problem, with k = 1, where all clients are to agree on a single site for an object O. Approaching the problem naively, it might appear sufficient to treat the n sites as buckets in a hash table and hash the object name O into this table. Unfortunately, if any of the sites fails or is unreachable, the hash table size changes, forcing all objects to be remapped. This massive disruption makes such direct hashing unworkable. Under rendezvous hashing, however, clients handle site failures by picking the site that yields the next largest weight. Remapping is required only for objects currently mapped to the failed site, and disruption is minimal. Rendezvous hashing has the following properties: Low overhead: The hash function used is efficient, so overhead at the clients is very low. Load balancing: Since the hash function is randomizing, each of the n sites is equally likely to receive the object O. Loads are uniform across the sites. Site capacity: Sites with different capacities can be represented in the site list with multiplicity in proportion to capacity. A site with twice the capacity of the other sites will be represented twice in the list, while every other site is represented once. High hit rate: Since all clients agree on placing an object O into the same site SO, each fetch or placement of O into SO yields the maximum utility in terms of hit rate. The object O will always be found unless it is evicted by some replacement algorithm at SO. Minimal disruption: When a site fails, only the objects mapped to that site need to be remapped. Disruption is at the minimal possible level. Distributed k-agreement: Clients can reach distributed agreement on k sites simply by selecting the top k sites in the ordering. == O(log n) running time via skeleton-based hierarchical rendezvous hashing == The standard version of Rendezvous Hashing described above works quite well for moderate n, but when n {\displaystyle n} is extremely large, the hierarchical use of Rendezvous Hashing achieves O ( log n ) {\displaystyle O(\log n)} running time. This approach creates a virtual hierarchical structure (called a "skeleton"), and achieves O ( log n ) {\displaystyle O(\log n)} running time by applying HRW at each level while descending the hierarchy. The idea is to first choose some constant m {\displaystyle m} and organize the n {\displaystyle n} sites into c = ⌈ n / m ⌉ {\displaystyle c=\lceil n/m\rceil } clusters C 1 = { S 1 , S 2 … S m } , C 2 = { S m + 1 , S m + 2 … S 2 m } … {\displaystyle C_{1}=\left\{S_{1},S_{2}\dots S_{m}\right\},C_{2}=\left\{S_{m+1},S_{m+2}\dots S_{2m}\right\}\dots } Next, build a virtual hierarchy by choosing a constant f {\displaystyle f} and imagining these c {\displaystyle c} clusters placed at the leaves of a tree T {\displaystyle T} of virtual nodes, each with fanout f {\displaystyle f} . In the accompanying diagram, the cluster size is m = 4 {\displaystyle m=4} , and the skeleton fanout is f = 3 {\displaystyle f=3} . Assuming 108 sites (real nodes) for convenience, we get a three-tier virtual hierarchy. Since f = 3 {\displaystyle f=3} , each virtual node has a natural numbering in octal. Thus, the 27 virtual nodes at the lowest tier would be numbered 000 , 001 , 002 , . . . , 221 , 222 {\displaystyle 000,001,002,...,221,222} in octal (we can, of course, vary the fanout at each level - in that case, each node will be identified with the corresponding mixed-radix number). The easiest way to understand the virtual hierarchy is by starting at the top, and descending the virtual hierarchy. We successively apply Rendezvous Hashing to the set of virtual nodes at each level of the hierarchy, and descend the branch defined by the winning virtual node. We can in fact start at any level in the virtual hierarchy. Starting lower in the hierarchy requires more hashes, but may improve load distribution in the case of failures. For example, instead of applying HRW to all 108 real nodes in the diagram, we can first apply HRW to the 27 lowest-tier virtual nodes, selecting one. We then apply HRW to the four real nodes in its cluster, and choose the winning site. We only need 27 + 4 = 31 {\displaystyle 27+4=31} hashes, rather than 108. If we apply this method starting one level higher in the hierarchy, we would need 9 + 3 + 4 = 16 {\displaystyle 9+3+4=16} hashes to get to the winning site. The figure shows how, if we proceed starting from the root of the skeleton, we may successively choose the virtual nodes ( 2 ) 3 {\displaystyle (2)_{3}} , ( 20 ) 3 {\displaystyle (20)_{3}} , and ( 200 ) 3 {\displaystyle (200)_{3}} , and finally end up with site 74. The virtual hierarchy need not be stored, but can be created on demand, since the virtual nodes names are simply prefixes of base- f {\displaystyle f} (or mixed-radix) representations. We can easily create appropriately sorted strings from the digits, as required. In the example, we would be working with the strings 0 , 1 , 2 {\displaystyle 0,1,2} (at tier 1), 20 , 21 , 22 {\displaystyle 20,21,22} (at tier 2), and 200 , 201 , 202
Artificial intelligence in hiring
Artificial intelligence can be used to automate aspects of the job recruitment process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants. Proponents of artificial intelligence in hiring claim it reduces bias, assists with finding qualified candidates, and frees up human resource workers' time for other tasks, while opponents worry that AI perpetuates inequalities in the workplace and will eliminate jobs. Despite the potential benefits, the ethical implications of AI in hiring remain a subject of debate, with concerns about algorithmic transparency, accountability, and the need for ongoing oversight to ensure fair and unbiased decision-making throughout the recruitment process. == Background == It is common for companies to use AI to automate aspects of their hiring process, especially the hospitality, finance, and tech industries. == Uses == === Screeners === Screeners are tests that allow companies to sift through a large applicant pool and extract applicants that have desirable features. What factors are used to screen applicants is a concern to ethicists and civil rights activists. A screener that favors people who have similar characteristics to those already employed at a company may perpetuate inequalities. For example, if a company that is predominantly white and male uses its employees' data to train its screener it may accidentally create a screening process that favors white, male applicants. The automation of screeners also has the potential to reduce biases. Biases against applicants with African American sounding names have been shown in multiple studies. An AI screener has the potential to limit human bias and error in the hiring process, allowing more minority applicants to be successful. === Recruitment === Recruitment involves the identification of potential applicants and the marketing of positions. AI is commonly utilized in the recruitment process because it can help boost the number of qualified applicants for positions. Companies are able to use AI to target their marketing to applicants who are likely to be good fits for a position. This often involves the use of social media sites advertising tools, which rely on AI. Facebook allows advertisers to target ads based on demographics, location, interests, behavior, and connections. Facebook also allows companies to target a "look-a-like" audience, that is the company supplies Facebook with a data set, typically the company's current employees, and Facebook will target the ad to profiles that are similar to the profiles in the data set. Additionally, job sites like Indeed, Glassdoor, and ZipRecruiter target job listings to applicants that have certain characteristics employers are looking for. Targeted advertising has many advantages for companies trying to recruit such being a more efficient use of resources, reaching a desired audience, and boosting qualified applicants. This has helped make it a mainstay in modern hiring. Who receives a targeted ad can be controversial. In hiring, the implications of targeted ads have to do with who is able to find out about and then apply to a position. Most targeted ad algorithms are proprietary information. Some platforms, like Facebook and Google, allow users to see why they were shown a specific ad, but users who do not receive the ad likely never know of its existence and also have no way of knowing why they were not shown the ad. === Interviews === Chatbots were one of the first applications of AI and are commonly used in the hiring process. Interviewees interact with chatbots to answer interview questions, and an analysis of their responses can be generated by AI. HireVue has created technology that analyzes interviewees' responses and gestures during recorded video interviews. Over 12 million interviewees have been screened by the more than 700 companies that utilize the service. == Controversies == Artificial intelligence in hiring confers many benefits, but it also has some challenges that have concerned experts. AI is only as good as the data it is using. Biases can inadvertently be baked into the data used in AI. Often companies will use data from their employees to decide what people to recruit or hire. This can perpetuate bias and lead to more homogenous workforces. Facebook Ads was an example of a platform that created such controversy for allowing business owners to specify what type of employee they are looking for. For example, job advertisements for nursing and teach could be set such that only women of a specific age group would see the advertisements. Facebook Ads has since then removed this function from its platform, citing the potential problems with the function in perpetuating biases and stereotypes against minorities. The growing use of Artificial Intelligence-enabled hiring systems has become an important component of modern talent hiring, particularly through social networks such as LinkedIn and Facebook. However, data overflow embedded in the hiring systems, based on Natural Language Processing (NLP) methods, may result in unconscious gender bias. Utilizing data driven methods may mitigate some bias generated from these systems It can also be hard to quantify what makes a good employee. This poses a challenge for training AI to predict which employees will be best. Commonly used metrics like performance reviews can be subjective and have been shown to favor white employees over black employees and men over women. Another challenge is the limited amount of available data. Employers only collect certain details about candidates during the initial stages of the hiring process. This requires AI to make determinations about candidates with very limited information to go off of. Additionally, many employers do not hire employees frequently and so have limited firm specific data to go off. To combat this, many firms will use algorithms and data from other firms in their industry. AI's reliance on applicant and current employees personal data raises privacy issues. These issues effect both the applicants and current employees, but also may have implications for third parties who are linked through social media to applicants or current employees. For example, a sweep of someone's social media will also show their friends and people they have tagged in photos or posts. == AI and the future of hiring == Artificial intelligence along with other technological advances such as improvements in robotics have placed 47% of jobs at risk of being eliminated in the near future. In 2016 the founder of the World Economic Forum, Klaus Schwab, called AI and related technology the "Fourth Industrial Revolution". According to some scholars, however, the transformative impact of AI on labor has been overstated. The "no-real-change" theory holds that an IT revolution has already occurred, but that the benefits of implementing new technologies does not outweigh the costs associated with adopting them. This theory claims that the result of the IT revolution is thus much less impactful than had originally been forecasted. Other scholars refute this theory claiming that AI has already led to significant job loss for unskilled labor and that it will eliminate middle skill and high skill jobs in the future. This position is based around the idea that AI is not yet a technology of general use and that any potential 4th industrial revolution has not fully occurred. A third theory holds that the effect of AI and other technological advances is too complicated to yet be understood. This theory is centered around the idea that while AI will likely eliminate jobs in the short term it will also likely increase the demand for other jobs. The question then becomes will the new jobs be accessible to people and will they emerge near when jobs are eliminated. == AI use in hiring for candidates == Job seekers now commonly encounter AI-driven tools at multiple stages, including automated resume parsing, video interview analysis, chatbots for frequently asked questions, and real‑time application updates. Some candidates also employ AI career agents, designed to optimize job searches, tailor applications, and interface with hiring teams. A 2025 Australian study found that AI-driven video interviews exhibited transcription error rates of up to 22% for non‑native speakers and those with speech-related disabilities, raising concerns of discrimination. A 2017 study in the Journal of Sociology found persistent gender and racial disparities in AI screening tools, even when fairness interventions are applied. Industry observers describe a growing “AI arms race” in recruitment, where both employers and candidates increasingly rely on automated agents. Employers use recruiting systems to source and filter applicants, while candidates deploy AI agents to prepare and submit applications. == Regulations == The Artifici
JotterPad
JotterPad is a text editor app for Android, developed by Two App Studio. It is proprietary software that uses the freemium pricing strategy. == Features == Jotterpad supports the markdown and fountain markup languages. Among its features are themes, synchronisation with Google Drive and Dropbox, dictionary and thesaurus, and snapshots. JotterPad uses a freemium pricing model, which means that a restricted version of the app is offered for free, while access to additional functionality requires payment. About half of the features are available in the free version. The synchronisation feature was originally limited to one account, and in Jotterpad 12 the option to synchronise using multiple accounts was added as a monthly subscription service.
National Cyber Security Policy 2013
National Cyber Security Policy is a policy framework by Department of Electronics and Information Technology (DeitY) It aims at protecting the public and private infrastructure from cyber attacks. The policy also intends to safeguard "information, such as personal information (of web users), financial and banking information and sovereign data". This was particularly relevant in the wake of US National Security Agency (NSA) leaks that suggested the US government agencies are spying on Indian users, who have no legal or technical safeguards against it. Ministry of Communications and Information Technology (India) defines Cyberspace as a complex environment consisting of interactions between people, software services supported by worldwide distribution of information and communication technology. == Reason for Cyber Security policies == India had no Cyber security policy before 2013. In 2013, The Hindu newspaper, citing documents leaked by NSA whistle-blower Edward Snowden, has alleged that much of the NSA surveillance was focused on India's domestic politics and its strategic and commercial interests. This sparked a furore among people. Under pressure, the government unveiled a National Cyber Security Policy 2013 on 2 July 2013. == Vision == To build a secure and resilient cyberspace for citizens, business, and government and also to protect anyone from intervening in user's privacy.It mentioned a five year target of training five lakh cyber security personnel by 2018. == Mission == To protect information and information infrastructure in cyberspace, build capabilities to prevent and respond to cyber threat, reduce vulnerabilities and minimize damage from cyber incidents through a combination of institutional structures, people, processes, technology, and cooperation. == Objective == Ministry of Communications and Information Technology (India) define objectives as follows: To create a secure cyber ecosystem in the country, generate adequate trust and confidence in IT system and transactions in cyberspace and thereby enhance adoption of IT in all sectors of the economy. To create an assurance framework for the design of security policies and promotion and enabling actions for compliance to global security standards and best practices by way of conformity assessment (Product, process, technology & people). To strengthen the Regulatory Framework for ensuring a SECURE CYBERSPACE ECOSYSTEM. To enhance and create National and Sectoral level 24x7 mechanism for obtaining strategic information regarding threats to ICT infrastructure, creating scenarios for response, resolution and crisis management through effective predictive, preventive, protective response and recovery actions. -To improve visibility of integrity of ICT products and services by establishing infrastructure for testing & validation of security of such product. To create workforce for 500,000 professionals skilled in next 5 years through capacity building skill development and training. To provide fiscal benefit to businesses for adoption of standard security practices and processes. To enable Protection of information while in process, handling, storage & transit so as to safeguard privacy of citizen's data and reducing economic losses due to cyber crime or data theft. To enable effective prevention, investigation and prosecution of cybercrime and enhancement of law enforcement capabilities through appropriate legislative intervention. == Strategies == Creating a secured Ecosystem. Creating an assurance framework. Encouraging Open Standards. Strengthening The regulatory Framework. Creating a mechanism for Security Threats Early Warning, Vulnerability management, and response to security threats. Securing E-Governance services. Protection and resilience of Critical Information Infrastructure. Promotion of Research and Development in cyber security. Reducing supply chain risks Human Resource Development (fostering education and training programs both in formal and informal sectors to Support the Nation's cyber security needs and build capacity. Creating cyber security awareness. Developing effective Public-Private partnerships. To develop bilateral and multilateral relationships in the area of cyber security with another country. (Information sharing and cooperation) a Prioritized approach for implementation.
Automotive security
Automotive security refers to the branch of computer security focused on the cyber risks related to the automotive context. The increasingly high number of ECUs in vehicles and, alongside, the implementation of multiple different means of communication from and towards the vehicle in a remote and wireless manner led to the necessity of a branch of cybersecurity dedicated to the threats associated with vehicles. Not to be confused with automotive safety. == Causes == The implementation of multiple ECUs (Electronic Control Units) inside vehicles began in the early '70s thanks to the development of integrated circuits and microprocessors that made it economically feasible to produce the ECUs on a large scale. Since then the number of ECUs has increased to up to 100 per vehicle. These units nowadays control almost everything in the vehicle, from simple tasks such as activating the wipers to more safety-related ones like brake-by-wire or ABS (Anti-lock Braking System). Autonomous driving is also strongly reliant on the implementation of new, complex ECUs such as the ADAS, alongside sensors (lidars and radars) and their control units. Inside the vehicle, the ECUs are connected with each other through cabled or wireless communication networks, such as CAN bus (controller area network), MOST bus (Media Oriented System Transport), FlexRay (Automotive Network Communications Protocol) or RF (radio frequency) as in many implementations of TPMSs (tire-pressure monitoring systems). Many of these ECUs require data received through these networks that arrive from various sensors to operate and use such data to modify the behavior of the vehicle (e.g., the cruise control modifies the vehicle's speed depending on signals arriving from a button usually located on the steering wheel). Since the development of cheap wireless communication technologies such as Bluetooth, LTE, Wi-Fi, RFID and similar, automotive producers and OEMs have designed ECUs that implement such technologies with the goal of improving the experience of the driver and passengers. Safety-related systems such as the OnStar from General Motors, telematic units, communication between smartphones and the vehicle's speakers through Bluetooth, Android Auto and Apple CarPlay. == Threat model == Threat models of the automotive world are based on both real-world and theoretically possible attacks. Most real-world attacks aim at the safety of the people in and around the car, by modifying the cyber-physical capabilities of the vehicle (e.g., steering, braking, accelerating without requiring actions from the driver), while theoretical attacks have been supposed to focus also on privacy-related goals, such as obtaining GPS data on the vehicle, or capturing microphone signals and similar. Regarding the attack surfaces of the vehicle, they are usually divided in long-range, short-range, and local attack surfaces: LTE and DSRC can be considered long-range ones, while Bluetooth and Wi-Fi are usually considered short-range although still wireless. Finally, USB, OBD-II and all the attack surfaces that require physical access to the car are defined as local. An attacker that is able to implement the attack through a long-range surface is considered stronger and more dangerous than the one that requires physical access to the vehicle. In 2015 the possibility of attacks on vehicles already on the market has been proven possible by Miller and Valasek, that managed to disrupt the driving of a Jeep Cherokee while remotely connecting to it through remote wireless communication. === Controller area network attacks === The most common network used in vehicles and the one that is mainly used for safety-related communication is CAN, due to its real-time properties, simplicity, and cheapness. For this reason the majority of real-world attacks have been implemented against ECUs connected through this type of network. The majority of attacks demonstrated either against actual vehicles or in testbeds fall in one or more of the following categories: ==== Sniffing ==== Sniffing in the computer security field generally refers to the possibility of intercepting and logging packets or more generally data from a network. In the case of CAN, since it is a bus network, every node listens to all communication on the network. It is useful for the attacker to read data to learn the behavior of the other nodes of the network before implementing the actual attack. Usually, the final goal of the attacker is not to simply sniff the data on CAN, since the packets passing on this type of network are not usually valuable just to read. ==== Denial of service ==== Denial of service (DoS) in information security is usually described as an attack that has the objective of making a machine or a network unavailable. DoS attacks against ECUs connected to CAN buses can be done both against the network, by abusing the arbitration protocol used by CAN to always win the arbitration, and targeting the single ECU, by abusing the error handling protocol of CAN. In this second case the attacker flags the messages of the victim as faulty to convince the victim of being broken and therefore shut itself off the network. ==== Spoofing ==== Spoofing attacks comprise all cases in which an attacker, by falsifying data, sends messages pretending to be another node of the network. In automotive security usually spoofing attacks are divided into masquerade and replay attacks. Replay attacks are defined as all those where the attacker pretends to be the victim and sends sniffed data that the victim sent in a previous iteration of authentication. Masquerade attacks are, on the contrary, spoofing attacks where the data payload has been created by the attacker. == Real life automotive threat example == Security researchers Charlie Miller and Chris Valasek have successfully demonstrated remote access to a wide variety of vehicle controls using a Jeep Cherokee as the target. They were able to control the radio, environmental controls, windshield wipers, and certain engine and brake functions. The method used to hack the system was implementation of pre-programmed chip into the controller area network (CAN) bus. By inserting this chip into the CAN bus, he was able to send arbitrary message to CAN bus. One other thing that Miller has pointed out is the danger of the CAN bus, as it broadcasts the signal which the message can be caught by the hackers throughout the network. The control of the vehicle was all done remotely, manipulating the system without any physical interaction. Miller states that he could control any of some 1.4 million vehicles in the United States regardless of the location or distance, the only thing needed is for someone to turn on the vehicle to gain access. The work by Miller and Valasek replicated earlier work completed and published by academics in 2010 and 2011 on a different vehicle. The earlier work demonstrated the ability to compromise a vehicle remotely, over multiple wireless channels (including cellular), and the ability to remotely control critical components on the vehicle post-compromise, including the telematics unit and the car's brakes. While the earlier academic work was publicly visible, both in peer-reviewed scholarly publications and in the press, the Miller and Valesek work received even greater public visibility. == Security measures == The increasing complexity of devices and networks in the automotive context requires the application of security measures to limit the capabilities of a potential attacker. Since the early 2000 many different countermeasures have been proposed and, in some cases, applied. Following, a list of the most common security measures: Sub-networks: to limit the attacker capabilities even if he/she manages to access the vehicle from remote through a remotely connected ECU, the networks of the vehicle are divided in multiple sub-networks, and the most critical ECUs are not placed in the same sub-networks of the ECUs that can be accessed from remote. Gateways: the sub-networks are divided by secure gateways or firewalls that block messages from crossing from a sub-network to the other if they were not intended to. Intrusion Detection Systems (IDS): on each critical sub-network, one of the nodes (ECUs) connected to it has the goal of reading all data passing on the sub-network and detect messages that, given some rules, are considered malicious (made by an attacker). The arbitrary messages can be caught by the passenger by using IDS which will notify the owner regarding with unexpected message. Authentication protocols: in order to implement authentication on networks where it is not already implemented (such as CAN), it is possible to design an authentication protocol that works on the higher layers of the ISO OSI model, by using part of the data payload of a message to authenticate the message itself. Hardware Security Modules: since many ECUs are not powerful enough to keep real-time delays whi
Observability (software)
In software engineering, more specifically in distributed computing, observability is the ability to collect data about programs' execution, modules' internal states, and the communication among components. To improve observability, software engineers use a wide range of logging and tracing techniques to gather telemetry information, and tools to analyze and use it. Observability is foundational to site reliability engineering, as it is the first step in triaging a service outage. One of the goals of observability is to minimize the amount of prior knowledge needed to debug an issue. == Etymology, terminology and definition == The term is borrowed from control theory, where the "observability" of a system measures how well its state can be determined from its outputs. Similarly, software observability measures how well a system's state can be understood from the obtained telemetry (metrics, logs, traces, profiling). The definition of observability varies by vendor: Observability is the process of making a system’s internal state more transparent. Systems are made observable by the data they produce, which in turn helps you to determine if your infrastructure or application is healthy and functioning normally. a measure of how well you can understand and explain any state your system can get into, no matter how novel or bizarre [...] without needing to ship new code software tools and practices for aggregating, correlating and analyzing a steady stream of performance data from a distributed application along with the hardware and network it runs onobservability starts by shipping all your raw data to central service before you begin analysisthe ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces Observability is tooling or a technical solution that allows teams to actively debug their system. Observability is based on exploring properties and patterns not defined in advance. proactively collecting, visualizing, and applying intelligence to all of your metrics, events, logs, and traces—so you can understand the behavior of your complex digital system The term is frequently referred to as its numeronym o11y (where 11 stands for the number of letters between the first letter and the last letter of the word). This is similar to other computer science abbreviations such as i18n and l10n and k8s. === Observability vs. monitoring === Observability and monitoring are sometimes used interchangeably. As tooling, commercial offerings and practices evolved in complexity, "monitoring" was re-branded as observability in order to differentiate new tools from the old. The terms are commonly contrasted in that systems are monitored using predefined sets of telemetry, and monitored systems may be observable. Majors et al. suggest that engineering teams that only have monitoring tools end up relying on expert foreknowledge (seniority), whereas teams that have observability tools rely on exploratory analysis (curiosity). == Telemetry types == Observability relies on three main types of telemetry data: metrics, logs and traces. Those are often referred to as "pillars of observability". === Metrics === A metric is a point in time measurement (scalar) that represents some system state. Examples of common metrics include: number of HTTP requests per second; total number of query failures; database size in bytes; time in seconds since last garbage collection. Monitoring tools are typically configured to emit alerts when certain metric values exceed set thresholds. Thresholds are set based on knowledge about normal operating conditions and experience. Metrics are typically tagged to facilitate grouping and searchability. Application developers choose what kind of metrics to instrument their software with, before it is released. As a result, when a previously unknown issue is encountered, it is impossible to add new metrics without shipping new code. Furthermore, their cardinality can quickly make the storage size of telemetry data prohibitively expensive. Since metrics are cardinality-limited, they are often used to represent aggregate values (for example: average page load time, or 5-second average of the request rate). Without external context, it is impossible to correlate between events (such as user requests) and distinct metric values. === Logs === Logs, or log lines, are generally free-form, unstructured text blobs that are intended to be human readable. Modern logging is structured to enable machine parsability. As with metrics, an application developer must instrument the application upfront and ship new code if different logging information is required. Logs typically include a timestamp and severity level. An event (such as a user request) may be fragmented across multiple log lines and interweave with logs from concurrent events. === Traces === ==== Distributed traces ==== A cloud native application is typically made up of distributed services which together fulfill a single request. A distributed trace is an interrelated series of discrete events (also called spans) that track the progression of a single user request. A trace shows the causal and temporal relationships between the services that interoperate to fulfill a request. Instrumenting an application with traces means sending span information to a tracing backend. The tracing backend correlates the received spans to generate presentable traces. To be able to follow a request as it traverses multiple services, spans are labeled with unique identifiers that enable constructing a parent-child relationship between spans. Span information is typically shared in the HTTP headers of outbound requests. === Continuous profiling === Continuous profiling is another telemetry type used to precisely determine how an application consumes resources. === Instrumentation === To be able to observe an application, telemetry about the application's behavior needs to be collected or exported. Instrumentation means generating telemetry alongside the normal operation of the application. Telemetry is then collected by an independent backend for later analysis. In fast-changing systems, instrumentation itself is often the best possible documentation, since it combines intention (what are the dimensions that an engineer named and decided to collect?) with the real-time, up-to-date information of live status in production. Instrumentation can be automatic, or custom. Automatic instrumentation offers blanket coverage and immediate value; custom instrumentation brings higher value but requires more intimate involvement with the instrumented application. Instrumentation can be native - done in-code (modifying the code of the instrumented application) - or out-of-code (e.g. sidecar, eBPF). Verifying new features in production by shipping them together with custom instrumentation is a practice called "observability-driven development". == "Pillars of observability" == Metrics, logs and traces are most commonly listed as the pillars of observability. Majors et al. suggest that the pillars of observability are high cardinality, high-dimensionality, and explorability, arguing that runbooks and dashboards have little value because "modern systems rarely fail in precisely the same way twice." == Self monitoring == Self monitoring is a practice where observability stacks monitor each other, in order to reduce the risk of inconspicuous outages. Self monitoring may be put in place in addition to high availability and redundancy to further avoid correlated failures.
Common Image Generator Interface
The Common Image Generator Interface (CIGI) (pronounced sig-ee), is an on-the-wire data protocol that allows communication between an Image Generator and its host simulation. The interface is designed to promote a standard way for a host device to communicate with an image generator (IG) within the industry. CIGI enables plug-and-play by standard-compliant image generator vendors and reduces integration costs when upgrading visual systems. == Background == Most high-end simulators do not have everything running on a single machine the way popular home software flight simulators are currently implemented. The airplane model is run on one machine, normally referred to as the host, and the out the window visuals or scene graph program is run on another, usually referred to as an Image Generator (IG). Frequently there are multiple IGs required to display the surrounding environment created by a host. CIGI is the interface between the 'host' and the IGs. The main goal of CIGI is to capitalize on previous investments through the use of a common interface. CIGI is designed to assist suppliers and integrators of IG systems with ease of integration, code reuse, and overall cost reduction. In the past most image generators provided their own proprietary interface; every host had to implement that interface making changing image generators a costly ordeal. CIGI was created to standardize the interface between the host and the image generator so that little modification would be needed to switch image generators. The CIGI initiative was largely spearheaded by The Boeing Company during the early 21st century. The latest version of CIGI (CIGI 4.0) was developed by the Simulation Interoperability Standards Organization (SISO) in the form of SISO-STD-013-2014, Standard for Common Image Generator Interface (CIGI), Version 4.0, dated 22 August 2014. SISO-STD-013-2014 is freely available from SISO. == Definitions == Image generator – In this context an image generator consists of one or more rendering channels that produce an image that can be used to visualize an “Out-The-Window” scene, or images produced by various sensor simulations such as Infra-red, Day TV, electro-optical, and night vision. Host simulation – In this context a “Host” is the computational system that provides information about the device being simulated so that the image generator can portray the correct scenery to the user. This information is passed via CIGI to the image generator. == Maturation == CIGI 4 is the latest version of the standard as was approved by the Simulation Interoperability Standards Organization on August 22, 2014. CIGI became an international SISO standard known as SISO-STD-013-2014; which contains the CIGI version 4.0 Interface Control Document (ICD). CIGI 4.0 is the official standard, published by SISO. Previous versions of CIGI were spearheaded by Boeing include CIGI v3.3, in November 2008, v3.2 April 2006, v3.1 June 2004, v3 November 2003, v2 in March 2002, and the original (v1) in March 2001 == Protocol dependencies == Typically, CIGI uses UDP as its transport protocol, but CIGI does not require a specific transport mechanism, only packet definition conformance. CIGI traffic does not have a well known port; however, the use of ports 8004-8005 has been widely adopted by commercial image generator vendors implementations. == Development tools == === Host Emulator === The Host Emulator can be used as a surrogate to manipulate the interface when a simulation Host is not available. It is a Windows-based image generator Host application used to develop, integrate and test image generators that use the CIGI protocol. It provides a graphical user interface (GUI) for the creation, modification and deletion of entities; manipulation of views; control of environmental attributes and phenomena; and other host functions. The Host Emulator has several features that are useful for integration and testing. A free-flight mode allows for fixed-wing and rotorcraft flight, movement along entity axes and free rotation using a joystick or a joystick-like widget. Scripting and record/playback features support regression testing, demonstrations and other tasks needing exact reproduction of certain sequences of events. A packet-level snoop feature allows the user to examine the contents of CIGI messages, image generator response times and latencies. A Heartbeat Monitor Window shows a graphical timing history of the Image Generator's data frame rate. Other features include explicit packet creation, animation control, missile flyouts and a situation display window (Host Emulator 3.x only). === Multi-Purpose Viewer === The Multi-Purpose Viewer (MPV) provides the basic functionality expected of an Image Generator, such as loading and displaying a terrain database, displaying entities and so forth. The Multi-Purpose Viewer can be used as a surrogate to manipulate the interface when a real Image Generator is not available. The MPV is capable of operating with both the Windows and Linux operating systems. === CIGI Class Library === The CCL is an object-oriented software interface that automatically handles message composition and decomposition (i.e. packing, unpacking and byte swapping to the ICD specification) on both the Host and Image Generator sides of the interface. The CCL interprets Host or Image Generator messages based on compile time parameters. It also performs error handling and translation between different versions of CIGI. Each packet type has its own class. The individual packet members are accessed through packet class accessors. Outgoing messages are constructed by placing each packet into the outgoing buffer using a streaming operator. Incoming messages are parsed using callback or event-based mechanisms that supply the using program with fully populated packet objects. === Current tool suite === A set of CIGI development tools are managed and maintained by the SISO CIGI Product Support Group. The latest packages are available on SourceForge. Comments/Suggestions to the package can be directed to the SISO discussion board at: https://discussions.sisostds.org/index.htm?A0=SAC-PSG-CIGI Archived 2017-09-13 at the Wayback Machine === Wireshark === Wireshark is a free and open source packet analyzer. It is used for network troubleshooting, analysis, software and communications protocol development, and education. Wireshark provides a dissector for CIGI packets. As of October 2016, “The CIGI dissector is fully functional for CIGI version 2 and 3. Version 1 is not yet implemented.” === Older versions of CIGI === A CIGI Interface Control Document (ICD) and development suite is available in open source format. The tools, ICD, and accompanying user documentation can be found and downloaded from the CIGI sourceforge web site. The SourceForge version of the MPV is limited in its support of CIGI data packets and is intended to grow as needs arise. The MPV uses CIGI 3 as its interface, but the MPV is backward-compatible with earlier CIGI versions through the use of the CCL. The MPV uses the Open Scene Graph library to render a scene. The scene graph is manipulated according to the CIGI commands received from the Host via the CCL. The MPV itself is an application layer that consists of a small kernel leveraging heavily on a plug-in architecture for ease of maintainability and flexibility. An implementer can implement the interface from scratch, however a full suite of integration tools is available. These tools consist of three elements. The Host Emulator (HE), the Multi-Purpose Viewer (MPV), and the CIGI Class Library (CCL).