Zobrazeno 1 - 10
of 23
pro vyhledávání: '"XUNYUN LIU"'
Publikováno v:
IEEE Access, Vol 9, Pp 48851-48859 (2021)
Pommerman is a popular reinforcement learning environment because it imposes several challenges such as sparse and deceptive rewards and delayed action effects. In this paper, we propose an efficient reinforcement learning approach that uses a more e
Externí odkaz:
https://doaj.org/article/d92e636dbec64d20adc100a6fb23cb49
Publikováno v:
Future Generation Computer Systems. 128:117-131
An elastic stream computing system is expected to process dynamic and volatile data streams with low latency and high throughput in timely manner. Effective management of stream application is considered one of the keys to achieve elastic computing b
Autor:
XUNYUN LIU1 liuxunyun@nudt.edu.cn, BUYYA, RAJKUMAR2 rbuyya@unimelb.edu.au
Publikováno v:
ACM Computing Surveys. May2021, Vol. 53 Issue 3, p1-41. 41p.
Publikováno v:
Future Generation Computer Systems. 114:243-258
Low latency and high throughput are two of the most critical performance requirements for big data stream computing systems. As multi-source high-speed data streams arrive in real time, it is essential to study latency-aware and resource-aware schedu
Publikováno v:
Future Generation Computer Systems. 112:193-208
An elastic stream computing system needs elastic adjustment of computing resource allocation and vertex parallelism to improve latency and throughput, which includes continuously or periodically scaling in/out the workload of computing nodes at runti
Autor:
Xunyun Liu, Rajkumar Buyya
Publikováno v:
ACM Computing Surveys. 53:1-41
Stream processing is an emerging paradigm to handle data streams upon arrival, powering latency-critical application such as fraud detection, algorithmic trading, and health surveillance. Though there are a variety of Distributed Stream Processing Sy
Dynamic Resource-Efficient Scheduling in Data Stream Management Systems Deployed on Computing Clouds
Publikováno v:
Internet of Things ISBN: 9783031055270
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d89c582b65697c423de155043d3b6a44
https://doi.org/10.1007/978-3-031-05528-7_5
https://doi.org/10.1007/978-3-031-05528-7_5
Publikováno v:
Advanced Data Mining and Applications ISBN: 9783030954048
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f246e38ebb80bac1288361d2c553bb7
https://doi.org/10.1007/978-3-030-95405-5_24
https://doi.org/10.1007/978-3-030-95405-5_24
Publikováno v:
Journal of Network and Computer Applications. 206:103462
Publikováno v:
Future Generation Computer Systems. 97:194-209
State and runtime-aware scheduling is one of the problems that is hard to resolve in elastic big data stream computing systems, as the state of each vertex is different, and the arrival rate of data streams fluctuates over time. A state and runtime-a