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The paper focuses on dynamic resource provisioning which minimizes carbon footprint of data centers interconnected via optical networks. The main contribution of this paper is a schema of fitting energy aware anycast strategies to different types of cloud services in order to reduce greenhouse gases emission. The proposed schema was compared to the cases when all types of cloud services were handled using the same anycast strategy. It is shown that the proposed schema is able to significantly reduce greenhouse gases emission without significant deterioration of network performance.

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The paper focuses on cooperation between cloud and network operators, as well as on fitting particular routing strategies to various cloud services. Three cooperation models are presented, analyzed and compared in the paper: the proposed model and two widely used reference models. The main difference between the models is the set of information being exchanged between the involved parties. Additionally, we analyze the applicability of four fitting schemas for each considered model. It is shown that the proposed model, alongside with an appropriate fitting schema, is able to reduce the blocking probability of cloud services requests. At the same time, thanks to the use of green anycast strategies, it is able to significantly reduce carbon dioxide emission.

SummaryThis paper focuses on the problem of time‐efficient traffic prediction. The prediction enables the proactive and globally scoped optimisation in software‐defined networks (SDNs). We propose the shrinkage and selection heuristic method for the trigonometric Fourier‐based traffic models in SDNs. The proposed solution allows us to optimise the network for an upcoming time window by installing flow entries in SDN nodes before the first packet of a new flow arrives. As the mechanism is designed to be a part of a sophisticated routing‐support system, several critical constraints are considered and taken into account. Specifically, the system is traffic‐ and topology‐agnostic, thus the prediction mechanism must be applicable to the networks with highly variable traffic loads (e.g., observed inside intra‐DCNs: datacentre networks). Furthermore, the system must effectively optimise routing in large‐scale SDNs comprised of numerous nodes and handling millions of flows of a dynamic nature. Therefore, the prediction must be simultaneously accurate as well as being time efficient and scalable. These requirements are met by our Fourier‐based solution, which subtracts consecutive harmonics from the original signal and compares the result with an adaptive threshold adjusted to the signal's standard deviation. The evaluation is performed by comparing the proposed heuristic with the well‐known Lasso method of proven accuracy. The results show that our solution is able to retain prediction accuracy at a comparable level. Moreover, in accordance with our main aim, we operate in a manner which is always significantly faster. In some cases, computation times are reduced by as much as 50 times.