Elastic Scaling of Cloud Application Performance Based on Western Electric Rules by Injection of Aspect-oriented Code

AbstractWhile a multitude of cloud vendors exist today offering flexible application hosting services, the application adaptation capabilities provided in terms of autoscaling are rather limited. In most cases, a static adaptation action is used having a fixed scaling response. In the cases that a dynamic adaptation action is provided, this is based on a single scaling variable. We propose Severity, a novel algorithmic approach aiding the adaptation of cloud applications. Based on the input of the DevOps, our approach detects situations, calculates their Severity and proposes adaptations which can lead to better application performance. Severity can be calculated for any number of application QoS attributes and any type of such attributes, whether bounded or unbounded. Evaluation with four distinct workload types and a variety of monitoring attributes shows that QoS for particular application categories is improved. The feasibility of our approach is demonstrated with a prototype implementation of an application adaptation manager, for which the source code is provided.

Download Full-text

Severity: A QoS-aware approach to cloud application elasticity

10.21203/rs.3.rs-260258/v1 ◽

2021 ◽

Author(s):

Andreas Tsagkaropoulos ◽

Yiannis Verginadis ◽

Nikos Papageorgiou ◽

Fotis Paraskevopoulos ◽

Dimitris Apostolou ◽

...

Keyword(s):

Source Code ◽

Dynamic Adaptation ◽

Application Performance ◽

Algorithmic Approach ◽

Cloud Application ◽

Application Hosting ◽

Application Adaptation ◽

Adaptation Action ◽

Flexible Application ◽

Cloud Applications

Abstract While a multitude of cloud vendors exist today offering flexible application hosting services, the application adaptation capabilities provided in terms of autoscaling are rather limited. In most cases, a static adaptation action is used having a fixed scaling response. In the cases that a dynamic adaptation action is provided, this is based on a single scaling variable. We propose Severity, a novel algorithmic approach aiding the adaptation of cloud applications. Based on the input of the DevOps, our approach detects situations, calculates their Severity and proposes adaptations which can lead to better application performance. Severity can be calculated for any number of application QoS attributes and any type of such attributes, whether bounded or unbounded. Evaluation with four distinct workload types and a variety of monitoring attributes shows that QoS for particular application categories is improved. The efficacy of our approach is demonstrated with a prototype implementation of an application adaptation manager, for which the source code is provided.

Download Full-text

Let Latency Guide You: Towards Characterization of Cloud Application Performance

2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom) ◽

10.1109/cloudcom.2015.44 ◽

2015 ◽

Cited By ~ 2

Author(s):

Hamed Saljooghinejad ◽

Felix Cuadrado ◽

Steve Uhlig

Keyword(s):

Application Performance ◽

Cloud Application

Download Full-text

Higher bandwidth may sometimes reduce cloud application performance

2014 IEEE 20th International Workshop on Local & Metropolitan Area Networks (LANMAN) ◽

10.1109/lanman.2014.7028634 ◽

2014 ◽

Author(s):

Jing Zhu ◽

Wei Liang ◽

Zhixiong Jiang ◽

Jianping Wu ◽

Ming Zhu

Keyword(s):

Application Performance ◽

Cloud Application

Download Full-text

Estimating Cloud Application Performance Based on Micro-Benchmark Profiling

2018 IEEE 11th International Conference on Cloud Computing (CLOUD) ◽

10.1109/cloud.2018.00019 ◽

2018 ◽

Cited By ~ 6

Author(s):

Joel Scheuner ◽

Philipp Leitner

Keyword(s):

Application Performance ◽

Cloud Application

Download Full-text

Enhancing the use of Object Based Contents Addressable Storage

Computing Trendz - The Journal of Emerging Trends in Information Technology ◽

10.21844/cttjetit.v6i1.6697 ◽

2016 ◽

Vol 6 (1) ◽

Author(s):

Simab Hasan Rizvi

Keyword(s):

File System ◽

Application Performance ◽

Shared Storage ◽

Data Intensive ◽

Network Bandwidth ◽

Storage Performance ◽

Efficient Management ◽

Object Based ◽

Data Volume ◽

Potential Benefits

In Today's age of Tetra Scale computing, the application has become more data intensive than ever. The increased data volume from applications, in now tackling larger and larger problems, and has fuelled the need for efficient management of this data. In this paper, a technique called Content Addressable Storage or CAS, for managing large volume of data is evaluated. This evaluation focuses on the benefits and demerits of using CAS it focuses, i) improved application performance via lockless and lightweight synchronization ofaccess to shared storage data, ii) improved cache performance, iii) increase in storage capacity and, iv) increase network bandwidth. The presented design of a CAS-Based file store significantly improves the storage performance that provides lightweight lock less user defined consistency semantics. As a result, this file system shows a 28% increase in read bandwidth and 13% increase in write bandwidth, over a popular file system in common use. In this paper the potential benefits of using CAS for a virtual machine are estimated. The study also explains mobility application for active use and public deployment.

Download Full-text