Utility and Cloud Computing 

About: Utility and Cloud Computing is an academic conference. The conference publishes majorly in the area(s): Cloud computing & Virtual machine. Over the lifetime, 195 publications have been published by the conference receiving 4164 citations.

SMICloud: A Framework for Comparing and Ranking Cloud Services

Abstract: With the growth of Cloud Computing, more and more companies are offering different cloud services. From the customer's point of view, it is always difficult to decide whose services they should use, based on users' requirements. Currently there is no software framework which can automatically index cloud providers based on their needs. In this work, we propose a framework and a mechanism, which measure the quality and prioritize Cloud services. Such framework can make significant impact and will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their Quality of Services (QoS).

NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations

Abstract: As interest in adopting Cloud computing for various applications is rapidly growing, it is important to understand how these applications and systems will perform when deployed on Clouds Due to the scale and complexity of shared resources, it is often hard to analyze the performance of new scheduling and provisioning algorithms on actual Cloud test beds Therefore, simulation tools are becoming more and more important in the evaluation of the Cloud computing model Simulation tools allow researchers to rapidly evaluate the efficiency, performance and reliability of their new algorithms on a large heterogeneous Cloud infrastructure However, current solutions lack either advanced application models such as message passing applications and workflows or scalable network model of data center To fill this gap, we have extended a popular Cloud simulator (CloudSim) with a scalable network and generalized application model, which allows more accurate evaluation of scheduling and resource provisioning policies to optimize the performance of a Cloud infrastructure

A Survey on Cloud Computing Elasticity

Abstract: Elasticity is a key feature in the cloud computing context, and perhaps what distinguishes this computing paradigm of the other ones, such as cluster and grid computing. Considering the importance of elasticity in cloud computing context, the objective of this paper is to present a comprehensive study about the elasticity mechanisms available today. Initially, we propose a classification for elasticity mechanisms, based on the main features found in the analysed commercial and academic solutions. In a second moment, diverse related works are reviewed in order to define the state of the art of elasticity in clouds. We also discuss some of the challenges and open issues associated with the use of elasticity features in cloud computing.

Dynamic Resource Allocation for Spot Markets in Cloud Computing Environments

Abstract: The advent of cloud computing promises to provide computational resources to customers like public utilities such as water and electricity. To deal with dynamically fluctuating resource demands, market-driven resource allocation has been proposed and recently implemented by public Infrastructure-as-a-Service (IaaS) providers like Amazon EC2. In this environment, cloud resources are offered in distinct types of virtual machines (VMs) and the cloud provider runs an auction-based market for each VM type with the goal of achieving maximum revenue over time. However, as demand for each type of VMs can fluctuate over time, it is necessary to adjust the capacity allocated to each VM type to match the demand in order to maximize total revenue while minimizing the energy cost. In this paper, we consider the case of a single cloud provider and address the question how to best match customer demand in terms of both supply and price in order to maximize the providers revenue and customer satisfactions while minimizing energy cost. In particular, we model this problem as a constrained discrete-time optimal control problem and use Model Predictive Control (MPC) to find its solution. Simulation studies using real cloud workloads indicate that under dynamic workload conditions, our proposed solution achieves higher net income than static allocation strategies and minimizes the average request waiting time.

MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture

Abstract: The rise in popularity of mobile applications creates a growing demand to deliver richer functionality to users executing on mobile devices with limited resources. The availability of cloud computing platforms has made available unlimited and scalable resource pools of computation and storage that can be used to enhance service quality for mobile applications. This paper exploits the observation that using local resources in close proximity to the user, i.e. local clouds, can increase the quality and performance of mobile applications. In contrast, public cloud offerings (e.g. Amazon Web Services) offer scalability at the cost of higher delays, higher power consumption and higher price on the mobile device. In this paper we introduce MAP Cloud, a hybrid, tiered cloud architecture consisting of local and public clouds and show how it can be leveraged to increase both performance and scalability of mobile applications. We model the mobile application as a workflow of tasks and aim to optimally decompose the set of tasks to execute on the mobile client and 2-tier cloud architecture considering multiple QoS factors such as power, price, and delay. Such an optimization is shown to be NP-Hard, we propose an efficient simulated annealing based heuristic, called CRAM that is able to achieve about84% of optimal solutions when the number of users is high. We evaluate CRAM and the 2-tier approach via implementation(on Android G2 devices and Amazon EC2, S3 and Cloud Front)and extensive simulation using two rich mobile applications(Video-Content Augmented Reality and Image processing). Our results indicate that MAP Cloud provides improved scalability as compared to local clouds, improved efficiency (power/delay)(about 32% lower delays and power) and about 40% decrease in price in comparison to only using public cloud.