Results 1 -
9 of
9
VSaaS Model on DRAGON-Lab
"... Video Surveillance as a Service provides service to users using Cloud technology. The DRAGON-Lab supports an opportunity for researchers to use Cloud technology on an open platform, but the services utilize schemes have not been defined. This paper proposed two VSaaS working models, market model and ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Video Surveillance as a Service provides service to users using Cloud technology. The DRAGON-Lab supports an opportunity for researchers to use Cloud technology on an open platform, but the services utilize schemes have not been defined. This paper proposed two VSaaS working models, market model and tender/contract model. A comparision testing are given and proven the proposed models are applicable for the DRAGON-Lab, and the market model evaluated have better performance than the tender/contract model. Both of them are justified as reasonable approaches which fulfiled the lacking of this research field.
Bi-Objective Online Scheduling with Quality of Service for IaaS Clouds
"... Abstract — This paper focuses on the bi-objective experimental analysis of online scheduling in the Infrastructure as a Service model of Cloud computing. In this model, customer have the choice between different service levels. Each service level is associated with a price per unit of job execution ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
Abstract — This paper focuses on the bi-objective experimental analysis of online scheduling in the Infrastructure as a Service model of Cloud computing. In this model, customer have the choice between different service levels. Each service level is associated with a price per unit of job execution time and a slack factor that determines the maximal time span to deliver the requested amount of computing resources. It is responsibility of the system and its scheduling algorithm to guarantee the corresponding quality of service for all accepted jobs. We do not consider any optimistic scheduling approach, that is, a job cannot be accepted if its service guarantee will not be observed assuming that all accepted jobs receive the requested resources. We analyze several scheduling algorithms with different cloud configurations and workloads and use the maximization of the provider income and minimization of the total power consumption of a schedule as additional objectives. Therefore, we cannot expect finding a unique solution to a given problem but a set of nondominated solutions also known as Pareto optima. Then we assess the performance of different scheduling algorithms by using a set coverage metric to compare them in terms of Pareto dominance. Based on the presented case study, we claim that a simple algorithm can provide the best energy and income trade-offs. This scheduling algorithm performs well in different scenarios with a variety of workloads and cloud configurations.
Heavy-Traffic Revenue Maximization in Parallel Multiclass Queues
"... Motivated by revenue maximization in server farms with admission control, we investigate optimal scheduling in parallel processor-sharing queues. Incoming customers are distinguished in multiple classes and we define revenue as a weighted sum of class throughputs. Under these assumptions, we describ ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
Motivated by revenue maximization in server farms with admission control, we investigate optimal scheduling in parallel processor-sharing queues. Incoming customers are distinguished in multiple classes and we define revenue as a weighted sum of class throughputs. Under these assumptions, we describe a heavy-traffic limit for the revenue maximization problem and study the asymptotic properties of the optimization model as the number of clients increases. Our main result is a simple heuristic that is able to provide tight guarantees on the optimality gap of its solutions. In the general case with M queues and R classes, we prove that our heuristic is (1 + 1 M−1)-competitive in heavy-traffic. Experimental results indicate that the proposed heuristic is remarkably accurate, despite its negligible computational costs, both in random instances and using service rates of a web application measured on multiple cloud deployments.1 1
Design and Performance Guarantees in Cloud Computing: Challenges and Opportunities ∗ [Challenge Paper]
"... In the last years, cloud computing received an increasing attention both from academia and industry. Most of the solutions proposed in the literature strive to limit the effect of uncertain and unpredictable behaviors that may occur in cloud environments, like for example flash crowds or hard-ware f ..."
Abstract
- Add to MetaCart
(Show Context)
In the last years, cloud computing received an increasing attention both from academia and industry. Most of the solutions proposed in the literature strive to limit the effect of uncertain and unpredictable behaviors that may occur in cloud environments, like for example flash crowds or hard-ware failures. However, managing uncertainty in a cloud environment is still an open problem. In such a panorama, the service provider is not able to define suitable Service Level Objectives (SLO) that are easy to measure, and con-trol. In this work we analyze two of the critical problems that are encountered in cloud environments, but seldom dis-cussed or addressed in the literature: (1) how to reduce the uncertainty providing suitable control interfaces at different levels of the computing infrastructure; (2) how to assess per-formance evaluation in order to get probabilistic guarantees for the SLOs. We here briefly describe the two problems and envision some possible control-theoretical solutions.
Performance Evaluation of Infrastructure as Service Clouds with SLA Constraints
"... Abstract. In this paper, we present an experimental study of job scheduling algorithms in infrastructure as a service type in clouds. We analyze different system service levels which are distinguished by the amount of computing power a customer is guaranteed to receive within a time frame and a pric ..."
Abstract
- Add to MetaCart
Abstract. In this paper, we present an experimental study of job scheduling algorithms in infrastructure as a service type in clouds. We analyze different system service levels which are distinguished by the amount of computing power a customer is guaranteed to receive within a time frame and a price for a processing time unit. We analyze different scenarios for this model. These scenarios combine a single service level with single and parallel machines. We apply our algorithms in the context of executing real workload traces available to HPC community. In order to provide performance comparison, we make a joint analysis of several metrics. A case study is given.
SLA-Based Resource Provisioning for Hosted Software-as-a-Service Applications in Cloud Computing Environments
"... Abstract—Cloud computing is a solution for addressing challenges such as licensing, distribution, configuration, and operation of enterprise applications associated with the traditional IT infrastructure, software sales and deployment models. Migrating from a traditional model to the Cloud model red ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—Cloud computing is a solution for addressing challenges such as licensing, distribution, configuration, and operation of enterprise applications associated with the traditional IT infrastructure, software sales and deployment models. Migrating from a traditional model to the Cloud model reduces the maintenance complexity and cost for enterprise customers, and provides on-going revenue for Software as a Service (SaaS) providers. Clients and SaaS providers need to establish a Service Level Agreement (SLA) to define the Quality of Service (QoS). The main objectives of SaaS providers are to minimize cost and to improve Customer Satisfaction Level (CSL). In this paper, we propose customer driven SLA-based resource provisioning algorithms to minimize cost by minimizing resource and penalty cost and improve CSL by minimizing SLA violations. The proposed provisioning algorithms consider customer profiles and providers ’ quality parameters (e.g., response time) to handle dynamic customer requests and infrastructure level heterogeneity for enterprise systems. We also take into account customer-side parameters (such as the proportion of upgrade requests), and infrastructure-level parameters (such as the service initiation time) to compare algorithms. Simulation results show that our algorithms reduce the total cost up to 54 percent and the number of SLA violations up to 45 percent, compared with the previously proposed best algorithm. Index Terms—Cloud computing, Service Level Agreement (SLA), resource allocation, scheduling, software as a service, customer-driven, Key Performance Indicator (KPI), resource provisioning Ç 1
i SLA-based Resource Provisioning for Management of Cloud-based Software-as-a-Service Applications
, 2014
"... by ..."
(Show Context)
Benefits, Weaknesses, Opportunities and Risks of SaaS
"... adoption from Iranian organizations perspective ..."
(Show Context)
SLA-based Resource Provisioning for Software-as-a-Service Applications in Cloud Computing Environments
"... Abstract — Cloud computing is a solution for addressing challenges such as licensing, distribution, configuration, and operation of enterprise applications associated with the traditional IT infrastructure, software sales and deployment models. Migrating from a traditional model to the Cloud model r ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract — Cloud computing is a solution for addressing challenges such as licensing, distribution, configuration, and operation of enterprise applications associated with the traditional IT infrastructure, software sales and deployment models. Migrating from a traditional model to the Cloud model reduces the maintenance complexity and cost for enterprise customers, and provides on-going revenue for Software as a Service (SaaS) providers. Clients and SaaS providers need to establish a Service Level Agreement (SLA) to define the Quality of Service (QoS). The main objectives of SaaS providers are to minimize cost and to improve Customer Satisfaction Level (CSL). In this paper, we propose customer driven SLAbased resource provisioning algorithms to minimize cost by minimizing resource and penalty cost and improve CSL by minimizing SLA violations. The proposed provisioning algorithms consider customer profiles and providers ’ quality parameters (e.g. response time) to handle dynamic customer requests and infrastructure level heterogeneity for enterprise systems. We also take into account customer-side parameters (such as the proportion of upgrade requests), and infrastructure-level parameters (such as the service initiation time) to compare algorithms. Simulation results show that our algorithms reduce the total cost up to 54 % and the number of SLA violations up to 45%, compared with the previously proposed best algorithm.
