Constrained Network Slicing Games: Achieving service guarantees and network efficiency

Network slicing is a key capability for next generation mobile networks. It enables one to cost effectively customize logical networks over a shared infrastructure. A critical component of network slicing is resource allocation, which needs to ensure that slices receive the resources needed to support their mobiles/services while optimizing network efficiency. In this paper, we propose a novel approach to slice-based resource allocation named Guaranteed seRvice Efficient nETwork slicing (GREET). The underlying concept is to set up a constrained resource allocation game, where (i) slices unilaterally optimize their allocations to best meet their (dynamic) customer loads, while (ii) constraints are imposed to guarantee that, if they wish so, slices receive a pre-agreed share of the network resources. The resulting game is a variation of the well-known Fisher market, where slices are provided a budget to contend for network resources (as in a traditional Fisher market), but (unlike a Fisher market) prices are constrained for some resources to provide the desired guarantees. In this way, GREET combines the advantages of a share-based approach (high efficiency by flexible sharing) and reservation-based ones (which provide guarantees by assigning a fixed amount of resources). We characterize the Nash equilibrium, best response dynamics, and propose a practical slice strategy with provable convergence properties. Extensive simulations exhibit substantial improvements over network slicing state-of-the-art benchmarks

Scaling Deep Learning on GPU and Knights Landing clusters

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learning systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation

Design and analysis of fair, efficient and low-latency schedulers for high-speed packet-switched networks

A variety of emerging applications in education, medicine, business, and entertainment rely heavily on high-quality transmission of multimedia data over high speed networks. Packet scheduling algorithms in switches and routers play a critical role in the overall Quality of Service (QoS) strategy to ensure the performance required by such applications. Fair allocation of the link bandwidth among the traffic flows that share the link is an intuitively desirable property of packet schedulers. In addition, strict fairness can improve the isolation between users, help in countering certain kinds of denial-of-service attacks and offer a more predictable performance. Besides fairness, efficiency of implementation and low latency are among the most desirable properties of packet schedulers. The first part of this dissertation presents a novel scheduling discipline called Elastic Round Robin (ERR) which is simple, fair and efficient with a low latency bound. The perpacket work complexity of ERR is O(1). Our analysis also shows that, in comparison to all previously proposed scheduling disciplines of equivalent complexity, ERR has significantly better fairness properties as well as a lower latency bound. However, all frame-based schedulers including ERR suffer from high start-up latencies, burstiness in the output anddelayed correction of fairness. In the second part of this dissertation we propose a new scheduling discipline called Prioritized Elastic Round Robin (PERR) which overcomes the limitations associated with the round robin service order of ERR. The PERR scheduler achieves this by rearranging the sequence in which packets are transmitted in each round of the ERR scheduler. Our analysis reveals that PERR has a low work complexity which is independent of the number of flows. We also prove that PERR has better fairness and latency characteristics than other known schedulers of equivalent complexity. In addition to their obvious applications in Internet routers and switches, both the ERR and PERR schedulers also satisfy the unique requirements of wormhole switching, popular in interconnection networks of parallel systems. Finally, using real gateway traces and based on a new measure of instantaneous fairness borrowed from the field of economics, we present simulation results that demonstrate the improved fairness characteristics and latency bounds of the ERR and and PERR schedulers in comparison with other scheduling disciplines of equivalent efficiency.Ph.D., Electrical Engineering -- Drexel University, 200

Slicing in WiFi networks through airtime-based resource allocation

Network slicing is one of the key enabling technologies for 5G networks. It allows infrastructure owners to assign resources to service providers (tenants), which will afterwards use them to satisfy their end-user demands. This paradigm, which changes the way networks have been traditionally managed, was initially proposed in the wired realm (core networks). More recently, the scientific community has paid attention to the integration of network slicing in wireless cellular technologies (LTE). However, there are not many works addressing the challenges that appear when trying to exploit slicing techniques over WiFi networks, in spite of their growing relevance. In this paper we propose a novel method of proportionally distributing resources in WiFi networks, by means of the airtime. We develop an analytical model, which shed light on how such resources could be split. The validity of the proposed model is assessed by means of simulation-based evaluation over the ns-3 framework.This work has been supported in part by the European Commission and the Spanish Government (Fondo Europeo de desarrollo Regional, FEDER) by means of the EU H2020 NECOS (777067) and ADVICE (TEC2015-71329) projects, respectively