30 research outputs found
On a Catalogue of Metrics for Evaluating Commercial Cloud Services
Given the continually increasing amount of commercial Cloud services in the
market, evaluation of different services plays a significant role in
cost-benefit analysis or decision making for choosing Cloud Computing. In
particular, employing suitable metrics is essential in evaluation
implementations. However, to the best of our knowledge, there is not any
systematic discussion about metrics for evaluating Cloud services. By using the
method of Systematic Literature Review (SLR), we have collected the de facto
metrics adopted in the existing Cloud services evaluation work. The collected
metrics were arranged following different Cloud service features to be
evaluated, which essentially constructed an evaluation metrics catalogue, as
shown in this paper. This metrics catalogue can be used to facilitate the
future practice and research in the area of Cloud services evaluation.
Moreover, considering metrics selection is a prerequisite of benchmark
selection in evaluation implementations, this work also supplements the
existing research in benchmarking the commercial Cloud services.Comment: 10 pages, Proceedings of the 13th ACM/IEEE International Conference
on Grid Computing (Grid 2012), pp. 164-173, Beijing, China, September 20-23,
201
A New Analytical Model for Calculating Elasticity in Cloud Computing
International audienceOne of the fundamental characteristics of Cloud Computing is its elasticity. It is about the ability to dynamically adapt computer resources consumption to workload while maintaining performance and quality of service. Most current industrial as well as academic solutions have limitations in terms of elasticity control, which affects the availability and performance of systems. In this paper, we propose a modeling of an elastic Cloud platform in terms of the markovian queuing model where the number of active servers depends on the current workload. A quantitative analysis of the steady state of our model allows to analyze and calculate the value of the elasticity in a precise way
Variability in Behavior of Application Service Workload in a Utility Cloud
Using the elasticity feature of a utility cloud, users can acquire and release resources as required and pay for what they use. Applications with time-varying workloads can request for variable resources over time that makes cloud a convenient option for such applications. The elasticity in current IaaS cloud provides mainly two options to the users: horizontal and vertical scaling. In both ways of scaling the basic resource allocation unit is fixed-sized VM, it forces the cloud users to characterize their workload based on VM size, which might lead to under-utilization or over-allocation of resources. This turns out to be an inefficient model for both cloud users and providers. In this paper we discuss and calculate the variability in different kinds of application service workload. We also discuss different dynamic provisioning approaches proposed by researchers. We conclude with a brief introduction to the issues or limitations in existing solutions and our approach to resolve them in a way that is suitable and economic for both cloud user and provider
Analysis of the EDoS attack impact on elastic cloud services using finite queuing model
This paper proposes a logical model to examine the effect of the EDoS attack in cloud environment using finite queuing model and enhanced with experimental model. Due to this sophisticated attacks the computing resources are busy and buffer capacity of the cloud gets exhausted by both the legitimate and malicious user requests, because of this both types of requests could not get the service. The legitimate customers are unable to get service of web application. In this backdrop this paper investigates and evaluates the vendor loss factor from the cost factor of view since the legitimate client requests are denied service. The objective of this analysis is twofold i) to identify the dynamics of the EDoS attacks with different attack rates and to measure the various performance metrics (total number of busy virtual machines, utilization of the cloud resources, request response time, request loss probability, and throughput). ii) The cost function is defined and evaluated based on these performance metrics. Finally compared analytical and experimental results are presented and conclusions are drawn
On Evaluating Commercial Cloud Services: A Systematic Review
Background: Cloud Computing is increasingly booming in industry with many
competing providers and services. Accordingly, evaluation of commercial Cloud
services is necessary. However, the existing evaluation studies are relatively
chaotic. There exists tremendous confusion and gap between practices and theory
about Cloud services evaluation. Aim: To facilitate relieving the
aforementioned chaos, this work aims to synthesize the existing evaluation
implementations to outline the state-of-the-practice and also identify research
opportunities in Cloud services evaluation. Method: Based on a conceptual
evaluation model comprising six steps, the Systematic Literature Review (SLR)
method was employed to collect relevant evidence to investigate the Cloud
services evaluation step by step. Results: This SLR identified 82 relevant
evaluation studies. The overall data collected from these studies essentially
represent the current practical landscape of implementing Cloud services
evaluation, and in turn can be reused to facilitate future evaluation work.
Conclusions: Evaluation of commercial Cloud services has become a world-wide
research topic. Some of the findings of this SLR identify several research gaps
in the area of Cloud services evaluation (e.g., the Elasticity and Security
evaluation of commercial Cloud services could be a long-term challenge), while
some other findings suggest the trend of applying commercial Cloud services
(e.g., compared with PaaS, IaaS seems more suitable for customers and is
particularly important in industry). This SLR study itself also confirms some
previous experiences and reveals new Evidence-Based Software Engineering (EBSE)
lessons
An adaptive trust based service quality monitoring mechanism for cloud computing
Cloud computing is the newest paradigm in distributed computing that delivers computing resources over the Internet as services. Due to the attractiveness of cloud computing, the market is currently flooded with many service providers. This
has necessitated the customers to identify the right one meeting their requirements in terms of service quality. The existing monitoring of service quality has been limited only to quantification in cloud computing. On the other hand, the continuous
improvement and distribution of service quality scores have been implemented in other distributed computing paradigms but not specifically for cloud computing. This research investigates the methods and proposes mechanisms for quantifying and
ranking the service quality of service providers. The solution proposed in this thesis consists of three mechanisms, namely service quality modeling mechanism, adaptive trust computing mechanism and trust distribution mechanism for cloud computing.
The Design Research Methodology (DRM) has been modified by adding phases, means and methods, and probable outcomes. This modified DRM is used throughout this study. The mechanisms were developed and tested gradually until the expected
outcome has been achieved. A comprehensive set of experiments were carried out in a simulated environment to validate their effectiveness. The evaluation has been carried out by comparing their performance against the combined trust model and
QoS trust model for cloud computing along with the adapted fuzzy theory based trust computing mechanism and super-agent based trust distribution mechanism, which were developed for other distributed systems. The results show that the mechanisms are faster and more stable than the existing solutions in terms of reaching the final trust scores on all three parameters tested. The results presented in this thesis are significant
in terms of making cloud computing acceptable to users in verifying the performance of the service providers before making the selection