Provably Efficient Adaptive Scheduling for Parallel Jobs

Scheduling competing jobs on multiprocessors has always been an important issue for parallel and distributed systems. The challenge is to ensure global, system-wide efficiency while offering a level of fairness to user jobs. Various degrees of successes have been achieved over the years. However, few existing schemes address both efficiency and fairness over a wide range of work loads. Moreover, in order to obtain analytical results, most of them require prior information about jobs, which may be difficult to obtain in real applications. This paper presents two novel adaptive scheduling algorithms -- GRAD for centralized scheduling, and WRAD for distributed scheduling. Both GRAD and WRAD ensure fair allocation under all levels of workload, and they offer provable efficiency without requiring prior information of job's parallelism. Moreover, they provide effective control over the scheduling overhead and ensure efficient utilization of processors. To the best of our knowledge, they are the first non-clairvoyant scheduling algorithms that offer such guarantees. We also believe that our new approach of resource request-allotment protocol deserves further exploration. Specifically, both GRAD and WRAD are O(1)-competitive with respect to mean response time for batched jobs, and O(1)-competitive with respect to makespan for non-batched jobs with arbitrary release times. The simulation results show that, for non-batched jobs, the makespan produced by GRAD is no more than 1.39 times of the optimal on average and it never exceeds 4.5 times. For batched jobs, the mean response time produced by GRAD is no more than 2.37 times of the optimal on average, and it never exceeds 5.5 times.Singapore-MIT Alliance (SMA

Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

The Boston University Photonics Center annual report 2013-2014

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2013-2014 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This annual report summarizes activities of the Boston University Photonics Center in the 2013–2014 academic year.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $14.5M in new research grants and contracts this year. Faculty and staff also expanded their efforts in education and training, through National Science Foundation–sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Innovations at the Intersections of Micro/Nanofabrication Technology, Biology, and Biomedicine at our annual Future of Light Symposium. We took a leadership role in running national workshops on emerging photonic fields, including an OSA Incubator on Controlled Light Propagation through Complex Media, and an NSF Workshop on Noninvasive Imaging of Brain Function. Highlights of our research achievements for the year include a distinctive Presidential Early Career Award for Scientists and Engineers (PECASE) for Assistant Professor Xue Han, an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, launch of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and successful completion of the ambitious IARPA-funded contract for Next Generation Solid Immersion Microscopy for Fault Isolation in Back-Side Circuit Analysis led by Professor Bennett Goldberg. These three programs, which represent more than$20M in research funding for the University, are indicative of the breadth of Photonics Center research interests: from fundamental modeling of optoelectronic materials to practical development of cancer diagnostics, from exciting new discoveries in optogenetics for understanding brain function to the achievement of world-record resolution in semiconductor circuit microscopy. Our community welcomed an auspicious cohort of new faculty members, including a newly hired assistant professor and a newly hired professor (and Chair of the Mechanical Engineering Department). The Industry/University Cooperative Research Center—the centerpiece of our translational biophotonics program—continues to focus on advancing the health care and medical device industries, and has entered its fourth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base

The Boston University Photonics Center annual report 2013-2014

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2013-2014 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This annual report summarizes activities of the Boston University Photonics Center in the 2013–2014 academic year.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $14.5M in new research grants and contracts this year. Faculty and staff also expanded their efforts in education and training, through National Science Foundation–sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Innovations at the Intersections of Micro/Nanofabrication Technology, Biology, and Biomedicine at our annual Future of Light Symposium. We took a leadership role in running national workshops on emerging photonic fields, including an OSA Incubator on Controlled Light Propagation through Complex Media, and an NSF Workshop on Noninvasive Imaging of Brain Function. Highlights of our research achievements for the year include a distinctive Presidential Early Career Award for Scientists and Engineers (PECASE) for Assistant Professor Xue Han, an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, launch of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and successful completion of the ambitious IARPA-funded contract for Next Generation Solid Immersion Microscopy for Fault Isolation in Back-Side Circuit Analysis led by Professor Bennett Goldberg. These three programs, which represent more than$20M in research funding for the University, are indicative of the breadth of Photonics Center research interests: from fundamental modeling of optoelectronic materials to practical development of cancer diagnostics, from exciting new discoveries in optogenetics for understanding brain function to the achievement of world-record resolution in semiconductor circuit microscopy. Our community welcomed an auspicious cohort of new faculty members, including a newly hired assistant professor and a newly hired professor (and Chair of the Mechanical Engineering Department). The Industry/University Cooperative Research Center—the centerpiece of our translational biophotonics program—continues to focus on advancing the health care and medical device industries, and has entered its fourth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base

Energy efficiency using cloud management of LTE networks employing fronthaul and virtualized baseband processing pool

The cloud radio access network (C-RAN) emerges as one of the future solutions to handle the ever-growing data traffic, which is beyond the physical resources of current mobile networks. The C-RAN decouples the traffic management operations from the radio access technologies, leading to a new combination of a virtualized network core and a fronthaul architecture. This new resource coordination provides the necessary network control to manage dense Long-Term Evolution (LTE) networks overlaid with femtocells. However, the energy expenditure poses a major challenge for a typical C-RAN that consists of extended virtualized processing units and dense fronthaul data interfaces. In response to the power efficiency requirements and dynamic changes in traffic, this paper proposes C-RAN solutions and algorithms that compute the optimal backup topology and network mapping solution while denying interfacing requests from low-flow or inactive femtocells. A graph-coloring scheme is developed to label new formulated fronthaul clusters of femtocells using power as the performance metric. Additional power savings are obtained through efficient allocations of the virtualized baseband units (BBUs) subject to the arrival rate of active fronthaul interfacing requests. Moreover, the proposed solutions are used to reduce power consumption for virtualized LTE networks operating in the Wi-Fi spectrum band. The virtualized network core use the traffic load variations to determine those femtocells who are unable to transmit to switch them off for additional power savings. The simulation results demonstrate an efficient performance of the given solutions in large-scale network models

Resource Sharing for Multi-Tenant Nosql Data Store in Cloud

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloud providers because it enables resource sharing at low operating cost. Multi-tenancy takes several forms depending on whether the back-end file system is a local file system (LFS) or a parallel file system (PFS), and on whether tenants are independent or share data across tenants In this thesis I focus on and propose solutions to two cases: independent data-local file system, and shared data-parallel file system. In the independent data-local file system case, resource contention occurs under certain conditions in Cassandra and HBase, two state-of-the-art NoSQL stores, causing performance degradation for one tenant by another. We investigate the interference and propose two approaches. The first provides a scheduling scheme that can approximate resource consumption, adapt to workload dynamics and work in a distributed fashion. The second introduces a workload-aware resource reservation approach to prevent interference. The approach relies on a performance model obtained offline and plans the reservation according to different workload resource demands. Results show the approaches together can prevent interference and adapt to dynamic workloads under multi-tenancy. In the shared data-parallel file system case, it has been shown that running a distributed NoSQL store over PFS for shared data across tenants is not cost effective. Overheads are introduced due to the unawareness of the NoSQL store of PFS. This dissertation targets the key-value store (KVS), a specific form of NoSQL stores, and proposes a lightweight KVS over a parallel file system to improve efficiency. The solution is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes, giving capability that embedded KVSs are not designed for. Results show the proposed system outperforms Cassandra and Voldemort in several different workloads

Coupling carbon allocation with leaf and root phenology predicts tree-grass partitioning along a savanna rainfall gradient

© Author(s) 2016. The relative complexity of the mechanisms underlying savanna ecosystem dynamics, in comparison to other biomes such as temperate and tropical forests, challenges the representation of such dynamics in ecosystem and Earth system models. A realistic representation of processes governing carbon allocation and phenology for the two defining elements of savanna vegetation (namely trees and grasses) may be a key to understanding variations in tree-grass partitioning in time and space across the savanna biome worldwide. Here we present a new approach for modelling coupled phenology and carbon allocation, applied to competing tree and grass plant functional types. The approach accounts for a temporal shift between assimilation and growth, mediated by a labile carbohydrate store. This is combined with a method to maximize long-term net primary production (NPP) by optimally partitioning plant growth between fine roots and (leaves + stem). The computational efficiency of the analytic method used here allows it to be uniquely and readily applied at regional scale, as required, for example, within the framework of a global biogeochemical model. We demonstrate the approach by encoding it in a new simple carbon-water cycle model that we call HAVANA (Hydrology and Vegetation-dynamics Algorithm for Northern Australia), coupled to the existing POP (Population Orders Physiology) model for tree demography and disturbance-mediated heterogeneity. HAVANA-POP is calibrated using monthly remotely sensed fraction of absorbed photosynthetically active radiation (fPAR) and eddy-covariance-based estimates of carbon and water fluxes at five tower sites along the North Australian Tropical Transect (NATT), which is characterized by large gradients in rainfall and wildfire disturbance. The calibrated model replicates observed gradients of fPAR, tree leaf area index, basal area, and foliage projective cover along the NATT. The model behaviour emerges from complex feedbacks between the plant physiology and vegetation dynamics, mediated by shifting above- versus below-ground resources, and not from imposed hypotheses about the controls on tree-grass co-existence. Results support the hypothesis that resource limitation is a stronger determinant of tree cover than disturbance in Australian savannas