.This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS currently under construction at Brandeis University, Brown University, and M.I.T. We first provide an overview of the basic Aurora model and architecture and then describe in detail a stream-oriented set of operators.

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Aurora: a new model and architecture for data stream management

This document presents a set of requirements for Traffic Engineering over Multiprotocol Label Switching (MPLS). It identifies the functional capabilities required to implement policies that facilitate efficient and reliable network operations in an MPLS domain. These capabilities can be used to optimize the utilization of network resources and to enhance traffic oriented performance characteristics.

Requirements for Traffic Engineering Over MPLS

This paper introduces monitoring applications, which we will show differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS that is currently under construction at Brandeis University, Brown University, and M.I.T. We describe the basic system architecture, a stream-oriented set of operators, optimization tactics, and support for real-time operation.

/pdf/monitoring-streams-a-new-class-of-data-management-59yymz2awd.pdf

Monitoring streams: a new class of data management applications

A Data Stream Manager accepts push-based inputs from a set of data sources, processes these inputs with respect to a set of standing queries, and produces outputs based on Quality-of-Service (QoS) specifications. When input rates exceed system capacity, the system will become overloaded and latency will deteriorate. Under these conditions, the system will shed load, thus degrading the answer, in order to improve the observed latency of the results. This paper examines a technique for dynamically inserting and removing drop operators into query plans as required by the current load. We examine two types of drops: the first drops a fraction of the tuples in a randomized fashion, and the second drops tuples based on the importance of their content. We address the problems of determining when load shedding is needed, where in the query plan to insert drops, and how much of the load should be shed at that point in the plan. We describe efficient solutions and present experimental evidence that they can bring the system back into the useful operating range with minimal degradation in answer quality.

/pdf/load-shedding-in-a-data-stream-manager-jz2fz7gp4h.pdf

Load shedding in a data stream manager

Since its formal introduction to IP networks in 1993 as a viable complementary approach for congestion control, there has been a steady stream of research output with respect to Active Queue Management (AQM). This survey attempts to travel the trajectory of AQM research from 1993 with the first algorithm, Random Early Detection (RED), to current work in 2011. In this survey we discuss the general attributes of AQM schemes, and the design approaches taken such as heuristic, control-theoretic and deterministic optimization. Of interest is the role of AQM in QoS provisioning particularly in the DiffServ context, as well as the role of AQM in the wireless domain. For each section, example algorithms from the research literature are presented.

Active Queue Management: A Survey

The authors propose a new taxonomy for congestion control algorithms in packet switching computer networks based on control theory. They view a network as a large, distributed control system, in which a congestion control scheme is a (distributed) control policy executable at each node (host or switches) of the network in order to a certain level of stable conditions. This taxonomy provides a coherent framework for the comparative study of existing algorithms and offers clues toward the development of new congestion control strategies. >

A.V.S. Reddy

Papers

A taxonomy for congestion control algorithms in packet switching networks