The paper proposes local and global optimization schemes for efficient TCP buffer allocation in an HTTP server. The proposed local optimization scheme dynamically adjusts the TCP send-buffer size to the connection and server characteristics. The global optimization scheme divides a certain amount of buffer space among all active TCP connections. These schemes are of increasing importance due to the large scale of TCP connection characteristics. The schemes are compared to the static allocation policy employed by a typical HTTP server and shown to achieve considerable improvement to server performance and better utilization of its resources. The schemes require only minor code changes and only at the server.

The paper proposes local and global optimization schemes for efficient TCP buffer allocation in an HTTP server. The proposed local optimization scheme dynamically adjusts the TCP send-buffer size to the connection and server characteristics. The global optimization scheme divides a certain amount of buffer space among all active TCP connections. These schemes are of increasing importance due to the large scale of TCP connection characteristics. The schemes are compared to the static allocation policy employed by a typical HTTP server and shown to achieve considerable improvement to server performance and better utilization of its resources. The schemes require only minor code changes and only at the server.

The problem is to find the largest observation in a random sample of size n by asking binary-type questions of people (or items) in the sample. At each stage of the search, a threshold is calculated and a binary-type question is posed to each member of the sample. The threshold is determined from answers given to the previous questions, and no exact data is ever collected, i.e., no member is asked to explicitly provide his observation. Arrow, Pesotchinsky, and Sobel (APS) calculated the optimal threshold sequence for two different objectives: (i) minimize the average number of questions required for a solution, and (ii) maximize the probability of solving the problem in, at most, r questions. APS have assumed that the number of respondents in the affirmative at each stage of the search is exactly known. There exist applications where the number of affirmative answers is only known to be one member of the set (0, 1, more than 1). For these applications, we calculate exactly the optimal thresholds, in the sense of maximizing the probability of getting precisely one affirmative answer to the next binary question. An application of the threshold-calculation procedure is demonstrated in resolution of packet collisions over multiuser communication channel.>