pbCAM: Probabilistically-banked Content Addressable Memory

Soyata, Tolga; Liobe, J.

doi:10.1109/socc.2012.6398372

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…In boost mode, the energy transfer requires much shorter pulses (≈ 20 µs in the Figure 7b). We have observed the output of the final back-end converter to be fixed at 8.4 V from V SC = 10.5 V up to V SC = 16 V. We leave cloud computing using sychronous optimization as future work [11]- [15]. V. RELATED WORK Solar panels and MPPT have been well studied.…”

Section: Performance Evaluationmentioning

confidence: 99%

SOLARCAP: Super capacitor buffering of solar energy for self-sustainable field systems

Fahad

Soyata

Tai

et al. 2012

2012 IEEE International SOC Conference

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Abstract-Intelligent systems like automatic highway traffic management, area surveillance, and geological activity monitoring require substantial data collection and processing in the field. Energy self-sustainability is a critical foundation for successful field systems that are away from the power grid infrastructure. Instead of the conventional battery-based energy storage, this paper argues that the super capacitor buffering of solar energy (SOLARCAP) has the advantages of precise energy lifetime awareness, low maintenance, and operational robustness. By designing and developing a prototype implementation of the circuitry required for management and harvesting of energy, we demonstrate a SOLARCAP system that ensures safe device operation within the permitted voltage range.

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Section: Performance Evaluationmentioning

confidence: 99%

SOLARCAP: Super capacitor buffering of solar energy for self-sustainable field systems

Fahad

Soyata

Tai

et al. 2012

2012 IEEE International SOC Conference

Self Cite

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“…The cloud can be modeled as a graph using the vertices as cloud server locations and the edges as the network latencies (Soyata, Friedman, & Mulligan, 1993) (Soyata, Friedman, & Mulligan, 1995) which will allow sophisticated combinatorial optimization algorithms for task scheduling in the cloud (Soyata, Friedman, & Mulligan, 1997) (Soyata T. , 2000). Additionally, rather than pure-software methods, hardware accelerators can be utilized for reducing the computational latency (Li, Ding, Hu, & Soyata, 2014) (Guo, Ipek, & Soyata, 2010) (Soyata & Liobe, 2012). While this will provide a speed-up and mitigate some of the negative impact of the network latencies, the next section focuses on the general effects of the latency on certain latency-sensitive applications.…”

Section: Fig 2 Histograms Of Latencies At Oregon Datacenter Via Wifmentioning

confidence: 99%

A Tutorial on Network Latency and Its Measurements

Kwon

Advances in Wireless Technologies and Telecommunication

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Internet latency is crucial in providing reliable and efficient networked services when servers are placed in geographically diverse locations. The trend of mobile, cloud, and distributed computing accelerates the importance of accurate latency measurement due to its nature of rapidly changing locations and interactivity. Accurately measuring latency, however, is not easy due to lack of testing resources, the sheer volume of collected data points, the tedious and repetitive aspect of measurement practice, clock synchronization, and network dynamics. This chapter discusses the techniques that use PlanetLab to measure latency in the Internet, its underlying infrastructure, representative latency results obtained from experiments, and how to use these measure latencies. The chapter covers 1) details of using PlanetLab, 2) the Internet infrastructure that causes the discrepancy between local and global latencies, and 3) measured latency results from our own experiments and analysis on the distributions, averages, and their implications.

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Accelerating Mobile-Cloud Computing

Soyata

Heinzelman

et al.

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today’s mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

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pbCAM: Probabilistically-banked Content Addressable Memory

Cited by 8 publications

References 14 publications

SOLARCAP: Super capacitor buffering of solar energy for self-sustainable field systems

SOLARCAP: Super capacitor buffering of solar energy for self-sustainable field systems

A Tutorial on Network Latency and Its Measurements

Accelerating Mobile-Cloud Computing

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