3 research outputs found
Characterizing Workload of Web Applications on Virtualized Servers
With the ever increasing demands of cloud computing services, planning and
management of cloud resources has become a more and more important issue which
directed affects the resource utilization and SLA and customer satisfaction.
But before any management strategy is made, a good understanding of
applications' workload in virtualized environment is the basic fact and
principle to the resource management methods. Unfortunately, little work has
been focused on this area. Lack of raw data could be one reason; another reason
is that people still use the traditional models or methods shared under
non-virtualized environment. The study of applications' workload in virtualized
environment should take on some of its peculiar features comparing to the
non-virtualized environment. In this paper, we are open to analyze the workload
demands that reflect applications' behavior and the impact of virtualization.
The results are obtained from an experimental cloud testbed running web
applications, specifically the RUBiS benchmark application. We profile the
workload dynamics on both virtualized and non-virtualized environments and
compare the findings. The experimental results are valuable for us to estimate
the performance of applications on computer architectures, to predict SLA
compliance or violation based on the projected application workload and to
guide the decision making to support applications with the right hardware.Comment: 8 pages, 8 figures, The Fourth Workshop on Big Data Benchmarks,
Performance Optimization, and Emerging Hardware in conjunction with the 19th
ACM International Conference on Architectural Support for Programming
Languages and Operating Systems (ASPLOS-2014), Salt Lake City, Utah, USA,
March 1-5, 201
Patterns in the Chaos - a Study of Performance Variation and Predictability in Public IaaS Clouds
Benchmarking the performance of public cloud providers is a common research
topic. Previous research has already extensively evaluated the performance of
different cloud platforms for different use cases, and under different
constraints and experiment setups. In this paper, we present a principled,
large-scale literature review to collect and codify existing research regarding
the predictability of performance in public Infrastructure-as-a-Service (IaaS)
clouds. We formulate 15 hypotheses relating to the nature of performance
variations in IaaS systems, to the factors of influence of performance
variations, and how to compare different instance types. In a second step, we
conduct extensive real-life experimentation on Amazon EC2 and Google Compute
Engine to empirically validate those hypotheses. At the time of our research,
performance in EC2 was substantially less predictable than in GCE. Further, we
show that hardware heterogeneity is in practice less prevalent than anticipated
by earlier research, while multi-tenancy has a dramatic impact on performance
and predictability
Service level agreement specification for IoT application workflow activity deployment, configuration and monitoring
PhD ThesisCurrently, we see the use of the Internet of Things (IoT) within various domains
such as healthcare, smart homes, smart cars, smart-x applications, and smart
cities. The number of applications based on IoT and cloud computing is projected
to increase rapidly over the next few years. IoT-based services must meet
the guaranteed levels of quality of service (QoS) to match users’ expectations.
Ensuring QoS through specifying the QoS constraints using service level agreements
(SLAs) is crucial. Also because of the potentially highly complex nature
of multi-layered IoT applications, lifecycle management (deployment, dynamic
reconfiguration, and monitoring) needs to be automated. To achieve this it is
essential to be able to specify SLAs in a machine-readable format.
currently available SLA specification languages are unable to accommodate
the unique characteristics (interdependency of its multi-layers) of the IoT domain.
Therefore, in this research, we propose a grammar for a syntactical structure
of an SLA specification for IoT. The grammar is based on a proposed conceptual
model that considers the main concepts that can be used to express the requirements
for most common hardware and software components of an IoT application
on an end-to-end basis. We follow the Goal Question Metric (GQM) approach to
evaluate the generality and expressiveness of the proposed grammar by reviewing
its concepts and their predefined lists of vocabularies against two use-cases
with a number of participants whose research interests are mainly related to IoT.
The results of the analysis show that the proposed grammar achieved 91.70% of
its generality goal and 93.43% of its expressiveness goal.
To enhance the process of specifying SLA terms, We then developed a toolkit
for creating SLA specifications for IoT applications. The toolkit is used to simplify
the process of capturing the requirements of IoT applications. We demonstrate
the effectiveness of the toolkit using a remote health monitoring service (RHMS)
use-case as well as applying a user experience measure to evaluate the tool by
applying a questionnaire-oriented approach. We discussed the applicability of our
tool by including it as a core component of two different applications: 1) a contextaware
recommender system for IoT configuration across layers; and 2) a tool for
automatically translating an SLA from JSON to a smart contract, deploying it
on different peer nodes that represent the contractual parties. The smart contract
is able to monitor the created SLA using Blockchain technology. These two
applications are utilized within our proposed SLA management framework for IoT.
Furthermore, we propose a greedy heuristic algorithm to decentralize workflow
activities of an IoT application across Edge and Cloud resources to enhance
response time, cost, energy consumption and network usage. We evaluated the
efficiency of our proposed approach using iFogSim simulator. The performance
analysis shows that the proposed algorithm minimized cost, execution time, networking,
and Cloud energy consumption compared to Cloud-only and edge-ward
placement approaches