TY - GEN
T1 - Lightweight Robust Framework for Workload Scheduling in Clouds
AU - Abdulazeez, Muhammed
AU - Garncarek, Pawel
AU - Kowalski, Dariusz R.
AU - Wong, Prudence W.H.
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the Polish National Science Centre grant DEC-2012/06/M/ST6/00459.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - Reliability, security and stability of cloud services without sacrificing too much resources have become a desired feature in the area of workload management in clouds. The paper proposes and evaluates a lightweight framework for scheduling a workload which part could be unreliable. This unreliability could be caused by various types of failures or attacks. Our framework for robust workload scheduling efficiently combines classic fault-tolerant and security tools, such as packet/job scanning, with workload scheduling, and it does not use any heavy resource consuming tools, e.g., cryptography or non-linear optimization. More specifically, the framework uses a novel objective function to allocate jobs to servers and constantly decides which job to scan based on a formula associated with the objective function. We show how to set up the objective function and the corresponding scanning procedure to make the system provably stable, provided it satisfies a specific stability condition. As a result, we show that our framework assures cloud stability even if naive scanning-all and scanning-none strategies are not stable. We extend the framework to decentralized scheduling and evaluate it under several popular routing procedures.
AB - Reliability, security and stability of cloud services without sacrificing too much resources have become a desired feature in the area of workload management in clouds. The paper proposes and evaluates a lightweight framework for scheduling a workload which part could be unreliable. This unreliability could be caused by various types of failures or attacks. Our framework for robust workload scheduling efficiently combines classic fault-tolerant and security tools, such as packet/job scanning, with workload scheduling, and it does not use any heavy resource consuming tools, e.g., cryptography or non-linear optimization. More specifically, the framework uses a novel objective function to allocate jobs to servers and constantly decides which job to scan based on a formula associated with the objective function. We show how to set up the objective function and the corresponding scanning procedure to make the system provably stable, provided it satisfies a specific stability condition. As a result, we show that our framework assures cloud stability even if naive scanning-all and scanning-none strategies are not stable. We extend the framework to decentralized scheduling and evaluate it under several popular routing procedures.
UR - http://www.scopus.com/inward/record.url?scp=85032273723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032273723&partnerID=8YFLogxK
U2 - 10.1109/IEEE.EDGE.2017.36
DO - 10.1109/IEEE.EDGE.2017.36
M3 - Conference contribution
AN - SCOPUS:85032273723
T3 - Proceedings - 2017 IEEE 1st International Conference on Edge Computing, EDGE 2017
SP - 206
EP - 209
BT - Proceedings - 2017 IEEE 1st International Conference on Edge Computing, EDGE 2017
A2 - Goscinski, Andrzej M
A2 - Luo, Min
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Conference on Edge Computing, EDGE 2017
Y2 - 25 June 2017 through 30 June 2017
ER -