4,692 research outputs found
Fairness-aware scheduling on single-ISA heterogeneous multi-cores
Single-ISA heterogeneous multi-cores consisting of small (e.g., in-order) and big (e.g., out-of-order) cores dramatically improve energy- and power-efficiency by scheduling workloads on the most appropriate core type. A significant body of recent work has focused on improving system throughput through scheduling. However, none of the prior work has looked into fairness. Yet, guaranteeing that all threads make equal progress on heterogeneous multi-cores is of utmost importance for both multi-threaded and multi-program workloads to improve performance and quality-of-service. Furthermore, modern operating systems affinitize workloads to cores (pinned scheduling) which dramatically affects fairness on heterogeneous multi-cores. In this paper, we propose fairness-aware scheduling for single-ISA heterogeneous multi-cores, and explore two flavors for doing so. Equal-time scheduling runs each thread or workload on each core type for an equal fraction of the time, whereas equal-progress scheduling strives at getting equal amounts of work done on each core type. Our experimental results demonstrate an average 14% (and up to 25%) performance improvement over pinned scheduling through fairness-aware scheduling for homogeneous multi-threaded workloads; equal-progress scheduling improves performance by 32% on average for heterogeneous multi-threaded workloads. Further, we report dramatic improvements in fairness over prior scheduling proposals for multi-program workloads, while achieving system throughput comparable to throughput-optimized scheduling, and an average 21% improvement in throughput over pinned scheduling
Controlled Matching Game for Resource Allocation and User Association in WLANs
In multi-rate IEEE 802.11 WLANs, the traditional user association based on
the strongest received signal and the well known anomaly of the MAC protocol
can lead to overloaded Access Points (APs), and poor or heterogeneous
performance. Our goal is to propose an alternative game-theoretic approach for
association. We model the joint resource allocation and user association as a
matching game with complementarities and peer effects consisting of selfish
players solely interested in their individual throughputs. Using recent
game-theoretic results we first show that various resource sharing protocols
actually fall in the scope of the set of stability-inducing resource allocation
schemes. The game makes an extensive use of the Nash bargaining and some of its
related properties that allow to control the incentives of the players. We show
that the proposed mechanism can greatly improve the efficiency of 802.11 with
heterogeneous nodes and reduce the negative impact of peer effects such as its
MAC anomaly. The mechanism can be implemented as a virtual connectivity
management layer to achieve efficient APs-user associations without
modification of the MAC layer
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
THE OUTPUT CONCEPT AND PUBLIC SECTOR SERVICES
Units of output are sometimes defined in terms of the achievement of some pre-defined outcome (for example, a specified level of educational achievement), or alternatively in terms of some quality standard interpreted as a ‘conformance to specifications’ activity test. For most public-sector outputs, these definitions of a unit of output are flawed and may have undesirable behavioral consequences. Output measures cannot, in general, do double duty as outcome measures. Outcomes need to be measured separately. Moreover, the activity content of many types of outputs may legitimately vary both over time (as a result of qualitative rationing arising from the budget constraint), and also between clients (as a result of tailoring to varying client needs). Only for a sub-set of services is it appropriate to define a unit of output as complete only when either a specified proximate outcome has been achieved, or alternatively when a pre-defined minimum set of activities has been carried out.
Uncovering distinct protein-network topologies in heterogeneous cell populations
Background: Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation. Results: Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins. Conclusions: Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.Fil: Wieczorek, Jakob. Universitat Dortmund; AlemaniaFil: Malik Sheriff, Rahuman S.. Institut Max Planck fur Molekulare Physiologie; Alemania. Imperial College London; Reino Unido. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino UnidoFil: Fermin, Yessica. Universitat Dortmund; AlemaniaFil: Grecco, Hernan Edgardo. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de FÃsica de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de FÃsica de Buenos Aires; Argentina. Institut Max Planck fur Molekulare Physiologie; AlemaniaFil: Zamir, Eli. Institut Max Planck fur Molekulare Physiologie; AlemaniaFil: Ickstadt, Katja. Universitat Dortmund; Alemani
Sampling the proteome by emerging single-molecule and mass-spectrometry methods
Mammalian cells have about 30,000-fold more protein molecules than mRNA
molecules. This larger number of molecules and the associated larger dynamic
range have major implications in the development of proteomics technologies. We
examine these implications for both liquid chromatography-tandem mass
spectrometry (LC-MS/MS) and single-molecule counting and provide estimates on
how many molecules are routinely measured in proteomics experiments by
LC-MS/MS. We review strategies that have been helpful for counting billions of
protein molecules by LC-MS/MS and suggest that these strategies can benefit
single-molecule methods, especially in mitigating the challenges of the wide
dynamic range of the proteome. We also examine the theoretical possibilities
for scaling up single-molecule and mass spectrometry proteomics approaches to
quantifying the billions of protein molecules that make up the proteomes of our
cells.Comment: Recorded presentation: https://youtu.be/w0IOgJrrvN
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