TY - GEN
T1 - Artificial intelligent agent for energy savings in cloud computing environment
T2 - 14th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2020
AU - Ismail, Leila
AU - Materwala, Huned
N1 - Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - The gaining popularity of the Internet of Things (IoT), big data analytics, and blockchain to make the digital world connected, smart, and secure in the context of smart cities have led to increasing use of the cloud computing technology. Consequently, cloud data centers become hungry for energy consumption. This has an adverse effect on the environment in addition to the high operational and maintenance costs of large-scale data centers. Several works in the literature have proposed energy-efficient task scheduling in a cloud computing environment. However, most of these works use a scheduler that predicts the power consumption of an incoming task based on a static model. In most scenarios, the scheduler considers the CPU utilization of a server for power prediction and task allocations. This might give misleading results as the power consumption of a server, handling a variety of requests in smart cities, depends on other metrics such as memory, disk, and network in addition to CPU. Our proposed Intelligent Autonomous Agent Energy-Aware Task Scheduler in Virtual Machines (IAA-EATSVM) uses the multi-metric machine learning approach for scheduling of incoming tasks. IAA-EATSVM outperforms the mostly used Energy Conscious Task Consolidation (ECTC) based on a static approach. The detailed performance analysis is elaborated in the paper.
AB - The gaining popularity of the Internet of Things (IoT), big data analytics, and blockchain to make the digital world connected, smart, and secure in the context of smart cities have led to increasing use of the cloud computing technology. Consequently, cloud data centers become hungry for energy consumption. This has an adverse effect on the environment in addition to the high operational and maintenance costs of large-scale data centers. Several works in the literature have proposed energy-efficient task scheduling in a cloud computing environment. However, most of these works use a scheduler that predicts the power consumption of an incoming task based on a static model. In most scenarios, the scheduler considers the CPU utilization of a server for power prediction and task allocations. This might give misleading results as the power consumption of a server, handling a variety of requests in smart cities, depends on other metrics such as memory, disk, and network in addition to CPU. Our proposed Intelligent Autonomous Agent Energy-Aware Task Scheduler in Virtual Machines (IAA-EATSVM) uses the multi-metric machine learning approach for scheduling of incoming tasks. IAA-EATSVM outperforms the mostly used Energy Conscious Task Consolidation (ECTC) based on a static approach. The detailed performance analysis is elaborated in the paper.
KW - Cloud computing
KW - Energy-efficiency
KW - Intelligent autonomous agents
KW - Machine learning
KW - Task scheduling
UR - http://www.scopus.com/inward/record.url?scp=85086072815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086072815&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-5764-4_12
DO - 10.1007/978-981-15-5764-4_12
M3 - Conference contribution
AN - SCOPUS:85086072815
SN - 9789811557637
T3 - Smart Innovation, Systems and Technologies
SP - 127
EP - 140
BT - Agents and Multi-Agent Systems
A2 - Jezic, G.
A2 - Kusek, M.
A2 - Chen-Burger, J.
A2 - Sperka, R.
A2 - Howlett, Robert J.
A2 - Howlett, Robert J.
A2 - Jain, Lakhmi C.
A2 - Jain, Lakhmi C.
A2 - Jain, Lakhmi C.
PB - Springer
Y2 - 17 June 2020 through 19 July 2020
ER -