pDCell: an End-to-End Transport Protocol for Mobile Edge Computing Architectures

Pendiente publicación 2019To deal with increasingly demanding services and the rapid growth in number of devices and traffic, 5G and beyond mobile networks need to provide extreme capacity and peak data rates at very low latencies. Consequently, applications and services need to move closer to the users into so-called edge data centers. At the same time, there is a trend to virtualize core and radio access network functionalities and bring them to edge data centers as well. However, as is known from conventional data centers, legacy transport protocols such as TCP are vastly suboptimal in such a setting. In this work, we present pDCell, a transport design for mobile edge computing architectures that extends data center transport approaches to the mobile network domain. Specifically, pDCell ensures that data traffic from application servers arrives at virtual radio functions (i.e., C-RAN Central Units) timely to (i) minimize queuing delays and (ii) to maximize cellular network utilization. We show that pDCell significantly improves flow completion times compared to conventional transport protocols like TCP and data center transport solutions, and is thus an essential component for future mobile networks.This work is partially supported by the European Research Council grant ERC CoG 617721, the Ramon y Cajal grant from the Spanish Ministry of Economy and Competitiveness RYC-2012-10788, by the European Union H2020-ICT grant 644399 (MONROE), by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586) and the Madrid Regional Government through the TIGRE5-CM program (S2013/ICE-2919). Further, the work of Dr. Kogan is partially supported by a grant from the Cisco University Research Program Fund, an advised fund of Silicon Valley Community Foundation.No publicad

A Dynamic Run-Profile Energy-Aware Approach for Scheduling Computationally Intensive Bioinformatics Applications

High Performance Computing (HPC) resources are housed in large datacenters, which consume exorbitant amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In an earlier work, we introduced a dynamic model for energy aware scheduling (EAS) in a HPC environment; the model is domain agnostic and incorporates both the deadline parameter as well as energy parameters for computationally intensive applications. Our proposed EAS model incorporates 2-phases. In the Offline Phase, we use a run profile based approach to generate the initial schedule. In the Online Phase a feedback mechanism is incorporated between the EAS Engine and the master scheduling process. As scheduled tasks are completed, actual execution times are used to adjust the resources required for scheduling remaining tasks using the least number of nodes while meeting a given deadline. In this paper we study the impact of the quality of initial schedule using different run profiles which is the starting point for the EAS algorithm on the number of adjustments which is critical to the overall energy optimization as every adjustment made has an overhead. The conducted experiments show that the proposed approach succeeded in meeting preset deadlines while minimizing the number of nodes; thus reducing overall energy utilized and that choosing the right profile in the Offline phase has an impact on the energy optimization achieved by the EAS algorithm

Adaptive runtime techniques for power and resource management on multi-core systems

Energy-related costs are among the major contributors to the total cost of ownership of data centers and high-performance computing (HPC) clusters. As a result, future data centers must be energy-efficient to meet the continuously increasing computational demand. Constraining the power consumption of the servers is a widely used approach for managing energy costs and complying with power delivery limitations. In tandem, virtualization has become a common practice, as virtualization reduces hardware and power requirements by enabling consolidation of multiple applications on to a smaller set of physical resources. However, administration and management of data center resources have become more complex due to the growing number of virtualized servers installed in data centers. Therefore, designing autonomous and adaptive energy efficiency approaches is crucial to achieve sustainable and cost-efficient operation in data centers. Many modern data centers running enterprise workloads successfully implement energy efficiency approaches today. However, the nature of multi-threaded applications, which are becoming more common in all computing domains, brings additional design and management challenges. Tackling these challenges requires a deeper understanding of the interactions between the applications and the underlying hardware nodes. Although cluster-level management techniques bring significant benefits, node-level techniques provide more visibility into application characteristics, which can then be used to further improve the overall energy efficiency of the data centers. This thesis proposes adaptive runtime power and resource management techniques on multi-core systems. It demonstrates that taking the multi-threaded workload characteristics into account during management significantly improves the energy efficiency of the server nodes, which are the basic building blocks of data centers. The key distinguishing features of this work are as follows: We implement the proposed runtime techniques on state-of-the-art commodity multi-core servers and show that their energy efficiency can be significantly improved by (1) taking multi-threaded application specific characteristics into account while making resource allocation decisions, (2) accurately tracking dynamically changing power constraints by using low-overhead application-aware runtime techniques, and (3) coordinating dynamic adaptive decisions at various layers of the computing stack, specifically at system and application levels. Our results show that efficient resource distribution under power constraints yields energy savings of up to 24% compared to existing approaches, along with the ability to meet power constraints 98% of the time for a diverse set of multi-threaded applications