A Bi-Criteria Algorithm for Low-Carbon and QoS-Aware Routing in Cloud Computing Infrastructures

Cloud computing is an emerging form of distributed computing that enables a reliable on-demand access to critical Information Technology (IT) services. This flexible access generally allows organizations to improve both their productivity and profitability compared to previous ownership-based computing paradigms. On the other side of the coin, the IT sector as a whole and data centers used in cloud computing infrastructures in particular, are responsible for a significant contribution to global carbon dioxide emissions. Energy consumption and carbon imprints of Cloud infrastructures have thus become a key environmental concern, and several approaches have been proposed with the aim of increasing their energy-efficiency, both within individual data centers and at the level of the infrastructure as a whole. Among these approaches, low-carbon routing algorithms offer operational solutions, which consist in dynamically routing connection requests in order to maximize the use of renewable energy or, equivalently, minimize the use of non- renewable energy. The main claim of this paper is that the existing body of methods suffers from some practical limitations due to the fact that they consider a single objective, at the possible expense of other important aspects such as Quality of Service and cost. Therefore, we propose a bi-criteria routing model, in which both carbon reduction and quality of service, in the form of data transmission delay, are considered as objectives to optimize. Under this model, which to the the best of our knowledge is the fist bi-criteria formulation of the dynamic routing problem in Cloud computing environments, We describe an algorithm to compute efficient supported and non-supported solutions in order to dynamically route connection requests. The application of our algorithm is illustrated through a case study simulated under the same conditions as a previously published work considering a single criterion approach and comparison results are - rawn.