By Lars Nielsen
Noted platforms developer Lars Nielsen finds the dynamic mixture of innovation, pageant and (often eccentric) entrepreneurship that went to form today's tech panorama.
From the military's ENIAC of 1946 to the ripening of the net and e-commerce, Nielsen's COMPUTING: A company background info the evolution of computing because it has impacted the types and practices of markets providers world wide.
Nielsen explains not just the improvement of applied sciences, however the ongoing saga of strategic rivalries among such tech enterprises as IBM, Cisco, Apple, Microsoft, Google and fb.
He surveys the increase and fall of businesses and structures from the early days of the mainframe whilst the likes of Sperry Rand and DEC laid unsuccessful siege to IBM's market-share, via to the period of home windows vs. Mac and past.
And he vividly depicts the intense yet frequently rapacious personalities in the back of the contest that formed the company computing surroundings we all know this day.
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Additional resources for Computing: A Business History
100, 14086–14090 (2003) 12. : Attractor selection and perturbation for robust networks in ﬂuctuating environments. IEEE Netw. 24, 14–18 (2010) 13. : On reducing time spent in M/G/1 systems. Eur. J. Oper. Res. 9, 286–294 (1982) 14. The network simulator ns-2. pl NASK Research and Academic Computer Network, 02-796 Warsaw, Poland Abstract. This paper introduces the Reactive ASR-FA algorithm which is a novel ant routing algorithm that utilizes statistical models of packet delay to detect changes in the network conditions.
Yet, the NTT network structure is much more complex, so a change of the traﬃc conditions near one node does not necessarily have to imply a global change. Thus, it is worth to verify the merits in using the separate of Ψ and ψ for each possible destination of a given node. In this set of experiments we test the same scenario as in Sect. 1. The load level is increased after 500 s and returns to the primary value after 1800 s from the beginning of the simulation. We compare the results obtained for Actions 1-3 with the original ASR-FA algorithm for diﬀerent values of the load level jump.
If at some moment of time, Ψ is unnecessarily decreased, suboptimal paths may be used and the mean delay may grow until Ψ is increased again. We also noticed that — after the adaptation porcess — the mean delay converges to a higher value in case of Action 1 The Reactive ASR-FA 45 and Action 3 (modifying Ψ parameter) than in case of Action 2 and the original ASR-FA algorithm (not changing beta). Additional experiments showed that we can eliminate both problems by setting Ψm = 2, yet this results in a signiﬁcant increase of the adaptation times and reduces the advantages of our algorithm.