TY - GEN
T1 - Reinforcing Cloud Environments via Index Policy for Bursty Workloads
AU - Balasubramanian, Venkatraman
AU - Aloqaily, Moayad
AU - Tunde-Onadele, Olufogorehan
AU - Yang, Zhengyu
AU - Reisslein, Martin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - In recent years, the amounts of network traffic targeted towards cloud data centers have fluctuated based on user requests. This traffic is bursty and requires a high degree of attention. Due to the variable nature of this traffic, some requests need to be re-allocated on-the-fly. Such circumstances result in performance degradations due to resource management. As appropriate solutions can be proposed only based on understanding the workload and the environment, Reinforcement Learning (RL) is a strategy that is predominantly used. Further, it has been shown that the Poisson arrival rates do not capture real-world burstiness. Thus, we mainly have a two-fold problem to address: (i) the traffic requires a new modelling approach that can characterize the burstiness, and (ii) balancing the load that can maximize the reward to the provider in such circumstances. In this paper, we propose a novel, yet simple traffic modelling that enables burst detection based on an index policy. We show that the throughput constraints play a crucial role in scheduling and our proposed RL technique produces reliable results in such a scenario. Our RL algorithm decides what instance of the request traffic needs to be processed so that the cloud provider can maximize its profit and the decisions made in hindsight are non-biased. We compare the proposed policy with two state-of-the-art approaches and draw key inferences as to why an index policy performs better in scenarios that demand RL. We observe over five times shorter average wait times while bursty workload crosses a saturation limit of 150% compared to conventional policies.
AB - In recent years, the amounts of network traffic targeted towards cloud data centers have fluctuated based on user requests. This traffic is bursty and requires a high degree of attention. Due to the variable nature of this traffic, some requests need to be re-allocated on-the-fly. Such circumstances result in performance degradations due to resource management. As appropriate solutions can be proposed only based on understanding the workload and the environment, Reinforcement Learning (RL) is a strategy that is predominantly used. Further, it has been shown that the Poisson arrival rates do not capture real-world burstiness. Thus, we mainly have a two-fold problem to address: (i) the traffic requires a new modelling approach that can characterize the burstiness, and (ii) balancing the load that can maximize the reward to the provider in such circumstances. In this paper, we propose a novel, yet simple traffic modelling that enables burst detection based on an index policy. We show that the throughput constraints play a crucial role in scheduling and our proposed RL technique produces reliable results in such a scenario. Our RL algorithm decides what instance of the request traffic needs to be processed so that the cloud provider can maximize its profit and the decisions made in hindsight are non-biased. We compare the proposed policy with two state-of-the-art approaches and draw key inferences as to why an index policy performs better in scenarios that demand RL. We observe over five times shorter average wait times while bursty workload crosses a saturation limit of 150% compared to conventional policies.
KW - Bursty workload
KW - Index policy
KW - Mobile Cloud
KW - Reinforcement learning (RL)
UR - http://www.scopus.com/inward/record.url?scp=85086756595&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086756595&partnerID=8YFLogxK
U2 - 10.1109/NOMS47738.2020.9110417
DO - 10.1109/NOMS47738.2020.9110417
M3 - Conference contribution
AN - SCOPUS:85086756595
T3 - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020
BT - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
Y2 - 20 April 2020 through 24 April 2020
ER -