@inproceedings{saovapakhiran_michailidis_devetsikiotis_2012, title={An algorithm for joint guidance and power control for electric vehicles in the smart grid}, DOI={10.1109/icc.2012.6364112}, abstractNote={A massive amount of energy consumption currently stems from the transportation sector. Therefore, improvements in power usage by commuting vehicles are being studied and becoming an increasingly popular research topic. In particular, there is a growing need to model the envisioned smart infrastructure, including charging stations, some of which might include energy storage devices and swappable, pre-charged batteries. For such new stations, power management is indeed crucial for operation costs, driver convenience, and overall smart grid efficiency. Information technology, communications and vehicle intelligence need to play a crucial role in this process. In this paper, we describe a quantitative model and propose a guiding and control system for the charging of PHEVs in a future smart infrastructure. Specifically, we describe an algorithm that can be used for the joint guidance and power control of smarter electric vehicles in the smart grid. We envision it as part of a larger Smart Guide for the Smart Grid (SGSG) system. Its function is to guide PHEV drivers, directing them to the appropriate charging station, while attempting to achieve an optimization goal at the same time. Our algorithm aims at a joint guiding and power control, in order to heuristically maximize the weighted sum of the average of throughput and energy cost consumption from multiple vehicle charging stations, while satisfying a cost constraint at each station, as well as system stability.}, booktitle={2012 ieee international conference on communications (icc)}, author={Saovapakhiran, B. and Michailidis, G. and Devetsikiotis, M.}, year={2012} }
 @inproceedings{saovapakhiran_devetsikiotis_michailidis_viniotis_2012, title={Average delay SLAs in cloud computing}, DOI={10.1109/icc.2012.6364548}, abstractNote={In this paper, we conduct feasibility studies on the average delay space for Cloud computing, and we propose a heuristic method to control the vector of average delays, subject to predefined delay constraints. Our work is strongly motivated by the fact that delay control plays a critical role to improve Service Level Agreements (SLA) between users and Cloud service providers, which is necessary for empowering online business. Specifically, our main contributions are two-fold: First, the feasible regions of various routing algorithms for the system's dispatcher are investigated in depth. Second, a simple heuristic algorithm is designed, to move the average delay point along the feasible direction until achieving the delay constraints. Average delay is dependent on multiple factors such as job size, inter-arrival time, flow rate, and the dispatching rules of the system. Therefore, we vary their distribution, parameters and routing rules to examine how the feasible regions move or change. After establishing the feasible delay space, then by moving along the feasible directions, we show that a simple heuristic algorithm can achieve the delay constraints for a two queue system.}, booktitle={2012 ieee international conference on communications (icc)}, author={Saovapakhiran, B. and Devetsikiotis, M. and Michailidis, G. and Viniotis, Y.}, year={2012} }
 @inproceedings{saovapakhiran_michailidis_devetsikiotis_2011, title={Aggregated-DAG Scheduling for Job Flow Maximization in Heterogeneous Cloud Computing}, DOI={10.1109/glocom.2011.6133611}, abstractNote={Heterogeneous computing platforms such as Grid and Cloud computing are becoming prevalent and available online. As a result, resource management in these platforms is fundamentally critical to their global performance. Under the assumption of jobs comprised of subtasks forming DAG jobs, we focus on how to increase utilization and achieve near-optimal throughput performance on heterogeneous platforms. Our analysis and proposed algorithm are analytically derived and establish that, by aggregating multiple jobs using good scheduling, a near-optimal throughput can be achieved. Consequently, its limit is asymptotically converging to a certain value and can be written in the form of the service time of subtasks. Furthermore, our analysis shows how to explicitly compute the optimal throughput of computing systems, an important task for such a complex scheduling problem. In addition, we derive a simple super-job scheduling and show that its performance in term of throughput is better than the well-known Heterogeneous Earliest-Finish-Time (HEFT) algorithm.}, booktitle={2011 ieee global telecommunications conference (globecom 2011)}, author={Saovapakhiran, B. and Michailidis, G. and Devetsikiotis, M.}, year={2011} }