Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Ardagna, Danilo; Casolari, Sara; Colajanni, Michele; Panicucci, Barbara

doi:10.1016/j.jpdc.2012.02.014

Cited by 76 publications

(78 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scheduling is often assumed to be first-come first-served (FCFS) or processor sharing (PS). In particular, the M/G/1 PS queue is a common abstraction used to model a CPU and it has been adopted in many cloud studies [47,48], thanks to its simplicity and the suitability to apply the model to multi-class workloads. For instance, an SLA-aware capacity allocation mechanism for cloud applications is derived in [47] using an M/G/1 PS queue as the QoS model.…”

Section: Performance Modelsmentioning

confidence: 99%

Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

Self Cite

211

View full text Add to dashboard Cite

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

show abstract

Section: Performance Modelsmentioning

confidence: 99%

Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

Self Cite

211

View full text Add to dashboard Cite

show abstract

“…Hence, the SaaS faces the problem of determining every hour the optimal number of VMs for each WS class in order to minimize costs and penalties. Resource allocation is performed on the basis of a prediction of future WS workloads (Ardagna et al 2012a, Zhu et al 2009b) and we will denote with Λ k the prediciton of the arrival rate for WS application k for the next time horizon. The SaaS needs also an estimate of the future performance of each VM in order to determine application average response time.…”

Section: Problem Statementmentioning

confidence: 99%

“…If reserved resources are not sufficient (step 3d), reserved instances are rescaled and the number of desired on spot instances is computed (step 3e). As in (Ardagna et al 2012a, Zhu et al 2009a), we set U = 0.6.…”

Section: Equilibria Efficiencymentioning

confidence: 99%

Service Provisioning Problem in Cloud and Multi-Cloud Systems

Passacantando

Ardagna

Savi

2016

INFORMS Journal on Computing

Self Cite

View full text Add to dashboard Cite

Cloud Computing is a new emerging paradigm that aims at streamlining the on-demand provisioning of resources as services, providing end-user with flexible and scalable services accessible through the Internet on a pay-per-use basis. Since modern Cloud systems operate in an open and dynamic world characterized by continuous changes, the development of efficient resource provisioning policies for Cloud-based services becomes increasingly challenging. This paper aims to study the hourly basis service provisioning problem through a generalized Nash game model. We take the perspective of SaaS (Software as a Service) providers which want to minimize the costs associated with the virtual machine instances allocated in a multi-IaaSs (Infrastructure as a Service) scenario, while avoiding incurring in penalties for requests execution failures and providing quality of service guarantees. SaaS providers compete and bid for the use of infrastructural resources, while the IaaSs want to maximize their revenues obtained providing virtualized resources.We propose a solution algorithm based on the best-reply dynamics, which is suitable for a distributed implementation. We demonstrate the effectiveness of our approach by performing numerical tests, considering multiple workloads and system configurations. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (5% on average but up to 260% for individual SaaS providers). Furthermore, varying the number of IaaS providers 8-15% cost savings can be achieved from the workload distribution on multiple IaaSs.

show abstract

“…This is especially necessary when the load balancer also acts as a resource allocator deciding not only where to route the current request but also how much resources it would have to execute, like in [3]. In these cases, the induced sudden lack of resources can result in poor performance.…”

Section: Related Workmentioning

confidence: 99%

Control-theoretical load-balancing for cloud applications with brownout

Dürango

Dellkrantz

Maggio

et al. 2014

53rd IEEE Conference on Decision and Control

View full text Add to dashboard Cite

Abstract-Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by introducing replicas -copies of the applications having the same functionalities -for redundancy and adding a load-balancer to direct incoming traffic.However, existing load-balancing strategies interfere with brownout self-adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing.In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a selfadaptive application simulator, together with some state-of-theart solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.

show abstract

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Cited by 76 publications

References 24 publications

Quality-of-service in cloud computing: modeling techniques and their applications

Quality-of-service in cloud computing: modeling techniques and their applications

Service Provisioning Problem in Cloud and Multi-Cloud Systems

Control-theoretical load-balancing for cloud applications with brownout

Contact Info

Product

Resources

About