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In computer software, JSONPath is a query language for querying values in JSON. The uses of JSONPath include: Selecting a specific node in a JSON value; Retrieving a set of nodes from a JSON value, based on specific criteria; Navigating through complex JSON values to retrieve the required data.
Kubernetes provides two modes of service discovery, using environment variables or using Kubernetes DNS. [59] Service discovery assigns a stable IP address and DNS name to the service, and load balances traffic in a round-robin manner to network connections of that IP address among the pods matching the selector (even as failures cause the pods ...
JSON (JavaScript Object Notation, pronounced / ˈ dʒ eɪ s ən / or / ˈ dʒ eɪ ˌ s ɒ n /) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of name–value pairs and arrays (or other serializable values).
PER Aligned: a fixed number of bits if the integer type has a finite range and the size of the range is less than 65536; a variable number of octets otherwise; OER: 1, 2, or 4 octets (either signed or unsigned) if the integer type has a finite range that fits in that number of octets; a variable number of octets otherwise
The value q s is the sample's test statistic. (The notation | x | means the absolute value of x; the magnitude of x with the sign set to +, regardless of the original sign of x.) This q s test statistic can then be compared to a q value for the chosen significance level α from a table of the studentized range distribution.
In that case, the upper layers of the ETSI NFV MANO architecture (i.e. the NFVO and VNFM) cooperate with a container infrastructure service management (CISM) function [5] that is typically implemented using cloud-native orchestration solutions (e.g. Kubernetes). The characteristics of cloud-native network functions are: [6] [7]
PBKDF2 applies a pseudorandom function, such as hash-based message authentication code (HMAC), to the input password or passphrase along with a salt value and repeats the process many times to produce a derived key, which can then be used as a cryptographic key in subsequent operations.
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.