Go-With-The-Winner: Client-Side Server Selection for Content Delivery

Content delivery networks deliver much of the web and video content in the world by deploying a large distributed network of servers. We model and analyze a simple paradigm for client-side server selection that is commonly used in practice where each user independently measures the performance of a set of candidate servers and selects the one that performs the best. For web (resp., video) delivery, we propose and analyze a simple algorithm where each user randomly chooses two or more candidate servers and selects the server that provided the best hit rate (resp., bit rate). We prove that the algorithm converges quickly to an optimal state where all users receive the best hit rate (resp., bit rate), with high probability. We also show that if each user chose just one random server instead of two, some users receive a hit rate (resp., bit rate) that tends to zero. We simulate our algorithm and evaluate its performance with varying choices of parameters, system load, and content popularity.Comment: 15 pages, 9 figures, published in IFIP Networking 201

The International Mass Loading Service

The International Mass Loading Service computes four loadings: a) atmospheric pressure loading; b) land water storage loading; c) oceanic tidal loading; and d) non-tidal oceanic loading. The service provides to users the mass loading time series in three forms: 1) pre-computed time series for a list of 849 space geodesy stations; 2) pre-computed time series on the global 1deg x 1deg grid; and 3) on-demand Internet service for a list of stations and a time range specified by the user. The loading displacements are provided for the time period from 1979.01.01 through present, updated on an hourly basis, and have latencies 8-20 hours.Comment: 8 pages, 3 figures, to appear in the Proceedings of the Reference Frames for Applications in Geosciences Simposium, held in Luxemboug in October 201

MorphoSys: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798

Generalized formulation of multilevel selective harmonic elimination PWM: Case I-Non-Equal DC Sources

The paper presents optimal solutions for eliminating harmonics from the output waveform of a multilevel staircase pulse-width modulation (PWM) method with non-equal dc sources. Therefore, the degrees of freedom for specifying the cost function increased without physical changes as compared to the conventional stepped waveform. The paper discusses an efficient hybrid real coded genetic algorithm (HRCGA) that reduces significantly the computational burden resulting in fast convergence. An objective function describing a measure of effectiveness of eliminating selected order of harmonics while controlling the fundamental for any number of levels and for any number of switching angels is derived. It is confirmed that multiple independent sets of solutions exist and the ones that offer better harmonic performance are identified. Different operating points including five- and seven-level inverters are investigated and simulated. Selected experimental results are reported to verify and validate the effectiveness of the proposed method

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe