Abstract-While networked systems hold and generate vast amounts of configuration and operational data, this data is not accessible through a simple, uniform mechanism. Rather, it must be gathered using a range of different protocols and interfaces. Our vision is to make all this data available in a simple format through a realtime search process which runs within the network and aggregates the data into a form needed by applicationsa concept we call network search. We believe that such an approach, though challenging, is technically feasible and will enable rapid development of new management applications and advanced network functions. The paper motivates and formulates the concept of network search, compares it to related concepts like web search, outlines a search architecture, describes the design space and research challenges, and reports on a testbed implementation with management applications built for exploratory purposes of this new paradigm.
Abstract-Network search makes operational data available in real-time to management applications. In contrast to traditional monitoring, neither the data location nor the data format needs to be known to the invoking process, which simplifies application development, but requires an efficient search plane inside the managed system. The search plane is realized as a network of search nodes that process search queries in a distributed fashion. This paper introduces matching and ranking for network search queries. We are proposing a semantic for matching and ranking, which is configurable to support different types of management applications-from exact matching for database-style queries to loose, approximate matching, which is appropriate for exploratory purposes. We describe an echo protocol for efficient distributed query processing that supports matching and ranking. Further, we present the design of a search node, which maintains a real-time database of operational information and allows for parallel processing of search queries. A prototype implementation on a cloud testbed shows that the network search system, on a 9-node cluster with 24 core servers, executes 200 global search queries/sec with the 75th percetile latency below 100 milliseconds and with a CPU utilization below 5%. The performance measurements, together with our design, suggest that a system of 100,000 servers processing the same load would exhibit the same overhead per server and a query latency of below 1 sec.
Information in networked systems often has spatial semantics: routers, sensors, or virtual machines have coordinates in a geographical or virtual space, for instance. In this paper, we propose a design for a spatial search system that processes queries against spatial information that is maintained in local databases inside a large networked system. In contrast to previous works in spatial databases and peer-to-peer designs, our design is bottom-up, which makes query routing network aware and thus efficient, and which facilitates system bootstrapping and adaptation. Key to our design is a protocol that creates and maintains a distributed index of object locations based on information from local databases and the underlying network topology. The index builds upon minimum bounding rectangles to efficiently encode locations. We present a generic search protocol that is based on an echo protocol and uses the index to prune the search space and perform query routing. The response times of search queries increase with the diameter of the network, which is asymptotically optimal. We study the performance of the protocol through simulation in static and dynamic network environments, for different network topologies, and for network sizes up to 100 000 nodes. In most experiments, the overhead incurred by our protocol lies well below 30% of a hypothetical optimal protocol. In addition, the protocol provides high accuracy under significant churn. Int J Network Mgmt. 2018;28:e2041. wileyonlinelibrary.com/journal/nemConsider a networking scenario where locations for routers, servers, and virtual machines are produced by a network coordinate system, such as Vivaldi. 4 In this case, the Euclidean distance between locations refers to the round-trip-time between the network entities at those locations. Spatial queries include finding a server that is closest to a client application or finding a server with a similar distance to a given set of clients. Second, consider a networking environment where locations refer to geographic coordinates and distances refer to geographical distances. A spatial query for this case is finding backup servers outside a given area to improve availability in case of failures. Third, consider an Internet of Things (IoT) scenario with garbage containers at different geographical locations. A spatial query is finding full containers within a certain distance from a given place, for example, to facilitate garbage collection. Fourth, consider an information and communication technology infrastructure, whereby locations are IP addresses mapped onto R 4 (in case of IPv4). A spatial query in this case is finding physical or virtual machines in a given address range for the purpose of security management. Lastly, consider the case where a router searches for the closest gateway in the context of performance-based routing in order to optimize service levels.We illustrate the use of the Euclidean spatial model with two spatial queries. We assume a search space O of objects, whereby each object o ∈ O has a locat...
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