Abstract-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) = µs α , 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 σ

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The REliable CApacity Provisioning and enhanced remediation for distributed cloud applications (RECAP) project aims to advance cloud and edge computing technology, to develop mechanisms for reliable capacity provisioning, and to make application placement, infrastructure management, and capacity provisioning autonomous, predictable and optimized. This paper presents the RECAP vision for an integrated edge-cloud architecture, discusses the scientific foundation of the project, and outlines plans for toolsets for continuous data collection, application performance modeling, application and component auto-scaling and remediation, and deployment optimization. The paper also presents four use cases from complementing fields that will be used to showcase the advancements of RECAP.

Abstract-Energy conservation is drawing increasing attention in data networking. One school of thought believes that a dominant amount of energy saving comes from turning off network elements. The difficulty is that transitioning between the active and sleeping modes consumes considerable energy and time. This results in an obvious trade-off between saving energy and provisioning performance guarantees such as end-toend delays.We study the following routing and scheduling problem in a network in which each network element either operates in the full-rate active mode or the zero-rate sleeping mode. For a given network and traffic matrix, routing determines the path along which each traffic stream traverses. For frame-based periodic scheduling, a schedule determines the active period per element within each frame and prioritizes packets within each active period. For a line topology, we present a schedule with close-tominimum delay for a minimum active period per element. For an arbitrary topology, we partition the network into a collection of lines and utilize the near-optimal schedule along each line. Additional delay is incurred only when a path switches from one line to another. By minimizing the number of switchings via routing, we show a logarithmic approximation for both energy consumption and end-to-end delays.If routing is given as input, we present two schedules one of which has active period proportional to the traffic load per network element, and the other proportional to the maximum load over all elements. The end-to-end delay of the latter is much improved compared to the delay for the former. This demonstrates the trade-off between energy and delay.

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