About: Keepalive is a research topic. Over the lifetime, 125 publications have been published within this topic receiving 1858 citations. The topic is also known as: KA.
TL;DR: This paper shows how multiple-input, multiple-output (MIMO) control theory can be used to enforce policies for interrelated metrics in Apache, and how MIMO is used both to model the target system, Apache in this case, and to design feedback controllers.
Abstract: Policy-based management provides a means for IT systems to operate according to business needs. Unfortunately, there is often an "impedance mismatch" between the policies administrators want and the controls they are given. Consider the Apache Web server. Administrators want to control CPU and memory utilizations, but this must be done indirectly by manipulating tuning parameters such as MaxClients and KeepAlive. There has been much interest in using feedback control to bridge the impedance mismatch. However, these efforts have focused on a single metric that is manipulated by a single control and hence have not considered interactions between controls such as those that are common in computing systems. This paper shows how multiple-input, multiple-output (MIMO) control theory can be used to enforce policies for interrelated metrics. MIMO is used both to model the target system, Apache in our case, and to design feedback controllers. The MIMO model captures the interactions between KA and MC, and can be used to identify infeasible metric policies. In addition, MIMO control techniques can provide considerable benefit in handling trade-offs between speed of metric convergence and sensitivity to random fluctuations while enforcing the desired policies.
TL;DR: In this article, the authors present a method and apparatus for monitoring packet loss activity in an Internet Protocol (IP) network clustering system which can provide a useful discrete and tangible mechanism for controlled failover of the TCP/IP network cluster system.
Abstract: The present invention is a method and apparatus for monitoring packet loss activity in an Internet Protocol (IP) network clustering system which can provide a useful discrete and tangible mechanism for controlled failover of the TCP/IP network cluster system. An adaptive interval value is determined as a function of the average packet loss in the system, and this adaptive interval value used to determine when a cluster member must send a next keepalive message to alt other cluster members, and wherein the keepalive message is used to determine network packet loss.
TL;DR: In this article, a network device determines a path from itself to a source device in a computer network, where the source device utilizes the path in reverse to reach the network device.
Abstract: In one embodiment, a network device determines a path from itself to a source device in a computer network, where the source device utilizes the path in reverse to reach the network device. Based on determining a reliability of the path in reverse, the network device may dynamically adjust one or more keepalive parameters for keepalive messages sent on the path. Accordingly, the network device may then send keepalive messages on the path based on the dynamically adjusted keepalive parameters.
TL;DR: In this article, the authors propose a method of managing connections in a mobile communications network, the method comprising: transmitting over a connection from a network entity to a mobile device natural traffic and keepalive messages at a frequency determined by at least one keep-alive parameter.
Abstract: A method of managing connections in a mobile communications network, the method comprising: transmitting over a connection from a network entity to a mobile device natural traffic and keepalive messages at a frequency determined by at least one keepalive parameter; at the mobile device, monitoring receipt of natural traffic and keepalive messages, and in the event of inadequate natural traffic and missing keepalive messages, closing the connection; and dynamically adjusting the at least one keepalive parameter for subsequent transmission of keepalive messages from the network entity so as to maintain the connection at a minimum frequency of keepalive messages.
TL;DR: In this article, a method for determining how often a client station in a network should send keepalive messages is presented, based on a measure of network load, which is a time interval in which the client station needs to send a keepalative message, and the presence server reports this keepalivity period to the client stations.
Abstract: A method is disclosed for determining how often a client station in a network should send keepalive messages. Based on a measure of network load, a presence server determines a keepalive period, which is a time interval in which a client station needs to send a keepalive message, and the presence server reports this keepalive period to the client station. The client station responsively sends a keepalive message to the presence server within the determined keepalive period.