Progressive Ripple-Based Service Discovery for High Response Time in Autonomous Decentralized Community System

2008 ◽  
Author(s):  
Khalid Mahmood ◽  
Yuji Horikoshi ◽  
Satoshi Niki ◽  
Xiaodong Lu ◽  
Kinji Mori
Keyword(s):  
Response Time ◽  
Service Discovery ◽  
High Response ◽  
Community System

scholarly journals Spatio-temporal epidemiology of emergency medical requests in a large urban area. A scan-statistic approach

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Soheil Hashtarkhani ◽  
Behzad Kiani ◽  
Alireza Mohammadi ◽  
Shahab MohammadEbrahimi ◽  
Mohammad Dehghan-Tezerjani ◽  
...  
Keyword(s):  
Response Time ◽  
Urban Area ◽  
High Response ◽  
Scan Statistics ◽  
Care Providers ◽  
Large Urban Area ◽  
Altered Level ◽  
Median Response ◽  
Emergency Medical ◽  
Spatio Temporal

Pre-hospital care is provided by emergency medical services (EMS) staff, the initial health care providers at the scene of disaster. This study aimed to describe the characteristics of EMS callers and space-time distribution of emergency requests in a large urban area. Descriptive thematic maps of EMS requests were created using an empirical Bayesian smoothing approach. Spatial, temporal and spatio-temporal clustering techniques were applied to EMS data based on Kulldorff scan statistics technique. Almost 225,000 calls were registered in the EMS dispatch centre during the study period. Approximately two-thirds of these calls were associated with an altered level of patient consciousness, and the median response time for rural and urban EMS dispatches was 12.2 and 10.1 minutes, respectively. Spatio-temporal clusters of EMS requests were mostly located in central parts of the city, particularly near the downtown area. However, high-response time clustered areas had a low overlap with these general, spatial clusters. This low convergence shows that some unknown factors, other than EMS requests, influence the high-response times. The findings of this study can help policymakers to better allocate EMS resources and implement tailored interventions to enhance EMS system in urban areas.

Considerable humidity response of a well-aligned SOMS micro-wire flexible sensor by moisture-induced releasing of trapped electrons

2017 ◽  
Vol 46 (33) ◽  
pp. 10859-10866 ◽  
Author(s):  
Kuei-Lin Chan ◽  
Min-Han Yang ◽  
Hsin-Tien Chiu ◽  
Chi-Young Lee
Keyword(s):  
Response Time ◽  
Production Process ◽  
Mass Production ◽  
Molecular Sieves ◽  
Environmentally Friendly ◽  
High Response ◽  
Trapped Electrons ◽  
Short Response Time ◽  
Flexible Sensor ◽  
Humidity Response

Sandia Octahedral Molecular Sieves micro-wires (SOMS MWs) that exhibit ultra-high response to moisture and a short response time can be produced easily in an environmentally friendly mass production process.

scholarly journals Optimizing Service Placement for Microservice Architecture in Clouds

2019 ◽  
Vol 9 (21) ◽  
pp. 4663
Author(s):  
Yang Hu ◽  
Cees de Laat ◽  
Zhiming Zhao
Keyword(s):  
Response Time ◽  
Resource Constraints ◽  
Performance Degradation ◽  
High Response ◽  
New Approach ◽  
Service Placement ◽  
Overall Performance ◽  
Microservice Architecture ◽  
Different Parts

As microservice architecture is becoming more popular than ever, developers intend to transform traditional monolithic applications into service-based applications (composed by a number of services). To deploy a service-based application in clouds, besides the resource demands of each service, the traffic demands between collaborative services are crucial for the overall performance. Poor handling of the traffic demands can result in severe performance degradation, such as high response time and jitter. However, current cluster schedulers fail to place services at the best possible machine, since they only consider the resource constraints but ignore the traffic demands between services. To address this problem, we propose a new approach to optimize the placement of service-based applications in clouds. The approach first partitions the application into several parts while keeping overall traffic between different parts to a minimum and then carefully packs the different parts into machines with respect to their resource demands and traffic demands. We implement a prototype scheduler and evaluate it with extensive experiments on testbed clusters. The results show that our approach outperforms existing container cluster schedulers and representative heuristics, leading to much less overall inter-machine traffic.

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