A Mobile Geo-Communication Dataset for Physiology-Aware DASH in Rural Ambulance Transport

Hosseini, Mohammad; Jiang, Yu; Yekkehkhany, Ali; Berlin, Richard R.; Sha, Lui

doi:10.1145/3083187.3083211

Cited by 14 publications

(9 citation statements)

References 10 publications

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“…We analyzed the results both under exponential distribution for the service times and log-normal distribution which has a heavy tail. The six algorithms can also be tested on real data sets like the one in [42] or [43]. Under exponential service time, the mean task completion time under the six algorithms is shown in figure 2.…”

Section: Simulation Resultsmentioning

confidence: 99%

The Power of d Choices in Scheduling for Data Centers with Heterogeneous Servers

Moaddeli¹,

Ahmadi²,

Abhar³

2019

Preprint

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MapReduce framework is the de facto in big data and its applications, in which the big data-set is split into small data chunks and the data chunks are replicated on different servers, three servers by default, among thousands of servers. The heterogeneous server structure of the system, where the pair task-server determines the task processing speed, makes the scheduling much harder than scheduling for systems with homogeneous servers. Throughput optimality of the system on one hand and delay optimality on the other hand creates a dilemma for assigning tasks to servers. Unless many research has been done on both heuristic and theoretical algorithms for scheduling for such a system with multi-level data locality, the optimal scheduling algorithm is still an open problem. The JSQ-MaxWeight and Balanced-Pandas algorithms are the states of the arts algorithms with theoretical guarantees on throughput and delay optimality for systems with two and three levels of data locality. However, the scheduling complexity of these two algorithms are way too much. Hence, we use the power of d choices algorithm combined with the Balanced-Pandas algorithm and the JSQ-MaxWeight algorithm, and compare the complexity of the simple algorithms and the power of d choices versions of them. We will further show that the Balanced-Pandas algorithm combined with the power of the d choices, Balanced-Pandas-Pod, not only performs better than simple Balanced-Pandas, but also is less sensitive to the parameter d than the combination of the JSQ-MaxWeight algorithm and the power of the d choices, JSQ-MaxWeight-Pod. In fact in our extensive simulation results, the Balanced-Pandas-Pod algorithm is performing better than the simple Balanced-Pandas algorithm in low and medium loads, where data centers are usually performing at, and performs almost the same as the Balanced-Pandas algorithm at high loads. The reason is that the combination of the two algorithms enhances the data locality of tasks and prioritize local and rack local service to remote service more than the Balanced-Pandas algorithm. Note that the load balancing complexity of Balanced-Pandas and JSQ-MaxWeight algorithms are O(M ), where M is the number of servers in the system which is in the order of thousands servers, whereas the complexity of Balanced-Pandas-Pod and JSQ-MaxWeight-Pod are O(1), that makes the central scheduler faster and saves energy. Moreover, as mentioned, our proposed Balanced-Pandas-Pod has better performance than the existing algorithms that makes it superior to the state-of-the-art algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

The Power of d Choices in Scheduling for Data Centers with Heterogeneous Servers

Moaddeli¹,

Ahmadi²,

Abhar³

2019

Preprint

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“…We took a scenario where a patient is transferred from a rural hospital to a regional center hospital via a high-speed ambulance, and profiled the two major routes in Figure 3, from Hoopeston's rural hospital to Carle Foundation Hospital in Urabana as proof of concept. To profile the available bandwidth, we developed a mobile bandwidth profiler application in collaboration with Carle Ambulance Service, and collected communication bandwidth traces under 4 major US cellular networks [26]. Figure 5 demonstrate only a small subset of our profiled data under Sprint cellular network.…”

Section: Discussionmentioning

confidence: 99%

Physiology-Aware Rural Ambulance Routing

Hosseini

Berlin

Sha

2017

2017 IEEE International Conference on Healthcare Informatics (ICHI)

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Abstract-The ultimate objective of medical cyber-physical systems is to enhance the effectiveness of patient care. During emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. The physician experts can provide vital assistance to the ambulance EMT personnel. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to lowspeed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work.In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis and effective treatment.

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“…Besides that, there are many other applications of the changepoint within the linear relations framework in, for instance, glaciology [27], empirical economics [9], dietary assessment [11], image forensics [28], or healthcare industry [29][30][31].…”

Section: Application 4: Environmental Toxicologymentioning

confidence: 99%

Changepoint in Error-Prone Relations

Pešta

2021

Mathematics

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Linear relations, containing measurement errors in input and output data, are considered. Parameters of these so-called errors-in-variables models can change at some unknown moment. The aim is to test whether such an unknown change has occurred or not. For instance, detecting a change in trend for a randomly spaced time series is a special case of the investigated framework. The designed changepoint tests are shown to be consistent and involve neither nuisance parameters nor tuning constants, which makes the testing procedures effortlessly applicable. A changepoint estimator is also introduced and its consistency is proved. A boundary issue is avoided, meaning that the changepoint can be detected when being close to the extremities of the observation regime. As a theoretical basis for the developed methods, a weak invariance principle for the smallest singular value of the data matrix is provided, assuming weakly dependent and non-stationary errors. The results are presented in a simulation study, which demonstrates computational efficiency of the techniques. The completely data-driven tests are illustrated through problems coming from calibration and insurance; however, the methodology can be applied to other areas such as clinical measurements, dietary assessment, computational psychometrics, or environmental toxicology as manifested in the paper.

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A Mobile Geo-Communication Dataset for Physiology-Aware DASH in Rural Ambulance Transport

Cited by 14 publications

References 10 publications

The Power of d Choices in Scheduling for Data Centers with Heterogeneous Servers

The Power of d Choices in Scheduling for Data Centers with Heterogeneous Servers

Physiology-Aware Rural Ambulance Routing

Changepoint in Error-Prone Relations

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