Paper Info
Title
Towards workload-aware fine-grained control over cloud resources: student research abstract.
Abstract
The systems deployed over cloud are subject to unpredictable workload conditions that vary from time to time, e.g. an ecommerce website may face higher workloads than normal during festivals or promotional schemes. In order to maintain the performance of such systems, an efficient elastic resource provisioning strategy is required. However, providing such a strategy that determines the right amount of cloud resources that fulfills the Quality of Service (QoS) demand is a challenging task. Over the period, many proposals have been introduced using techniques like threshold based rules, reinforcement learning and control theory, etc. The existing proposals, however, suffer from issues like lack of expertise to appropriately set the quantitative specification of thresholds, online training time overhead of the algorithm, too specific to work well in particular situation like when there is sudden burst in workload or work well in nominal conditions for stable workload, etc. Moreover, the existing approaches do not address uncertainty. Our proposed framework is a step forward to address the mentioned issues for systems that hold time varying workload conditions.
Year
DOI
Venue
2016
10.1145/2851613.2852009
SAC 2016: Symposium on Applied Computing Pisa Italy April, 2016
Field
DocType
ISBN
Dependability,Computer security,Workload,Computer science,Quality of service,Provisioning,Software as a service,Fault tolerance,Distributed computing,Reinforcement learning,Cloud computing
Conference
978-1-4503-3739-7
Citations 
PageRank 
References 
0
0.34
6
Authors
1
Name
Order
Citations
PageRank
Amjad Ullah181.48