Autonomic communications seek to improve the ability of network and services to cope with unpredicted change, including changes in topology, load, task, the physical and logical characteristics of the networks that can be accessed, and so forth. Broad-ranging autonomic solutions require designers to account for a range of end-to-end issues affecting programming models, network and contextual modeling and reasoning, decentralised algorithms, trust acquisition and maintenance---issues whose solutions may draw on approaches and results from a surprisingly broad range of disciplines. We survey the current state of autonomic communications research and identify significant emerging trends and techniques.

We study network optimization that considers energy minimization as an objective. Studies have shown that mechanisms such as speed scaling can significantly reduce the power consumption of telecommunication networks by matching the consumption of each network element to the amount of processing required for its carried traffic. Most existing research on speed scaling focuses on a single network element in isolation. We aim for a network-wide optimization. Specifically, we study a routing problem with the objective of provisioning guaranteed speed/bandwidth for a given demand matrix while minimizing energy consumption. Optimizing the routes critically relies on the characteristic of the energy curve f(s), which is how energy is consumed as a function of the processing speed s. If f is superadditive, we show that there is no bounded approximation in general for integral routing, i.e., each traffic demand follows a single path. This contrasts with the well-known logarithmic approximation for subadditive functions. However, for common energy curves such as polynomials f(s) = ¿s^¿, we are able to show a constant approximation via a simple scheme of randomized rounding. The scenario is quite different when a non-zero startup cost ¿ appears in the energy curve, e.g. f(s) = { _{¿ + ¿s¿} ⁰ if (s = 0)/if (s > 0). For this case a constant approximation is no longer feasible. In fact, for any ¿ > 1, we show an ¿(log^¿ N) hardness result under a common complexity assumption. (Here N is the size of the network.) On the positive side we present O((¿/¿)^1/¿) and O(K) approximations, where K is the number of demands.

The recently approved Energy Efficient Ethernet standard IEEE 802.3az achieves energy savings by using a low power mode when the link is idle. However, those savings heavily depend on the traffic patterns, due to the overhead inherent in transitions between active and low power modes. This makes it impractical to estimate energy savings through measurements or simulations in all relevant scenarios. In this letter we present an analytical model to estimate the energy consumption of an Energy Efficient Ethernet link, based on simple traffic parameters. The model is validated through simulation and experimental data.

We study network optimization that considers power minimization as an objective. Studies have shown that mechanisms such as speed scaling can significantly reduce the power consumption of telecommunication networks by matching the consumption of each network element to the amount of processing required for its carried traffic. Most existing research on speed scaling focuses on a single network element in isolation. We aim for a network-wide optimization. Specifically, we study a routing problem with the objective of provisioning guaranteed speed/bandwidth for a given demand matrix while minimizing power consumption. Optimizing the routes critically relies on the characteristic of the speed-power curve f ( s ), which is how power is consumed as a function of the processing speed s . If f is superadditive, we show that there is no bounded approximation in general for integral routing, i.e., each traffic demand follows a single path. This contrasts with the well-known logarithmic approximation for subadditive functions. However, for common speed-power curves such as polynomials f ( s ) = μ s ^α , we are able to show a constant approximation via a simple scheme of randomized rounding. We also generalize this rounding approach to handle the case in which a nonzero startup cost σ appears in the speed-power curve, i.e., f ( s ) = {σ + μs ^α , if s >; 0; 0, if s = 0. We present an O ((σ/μ) ^1/α )-approximation, and we discuss why coming up with an approximation ratio independent of the startup cost may be hard. Finally, we provide simulation results to validate our algorithmic approaches.