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Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
- In Proceedings of the 3rd Workshop on Scientific Cloud Computing Date, ScienceCloud ’12
, 2012
"... Elasticity is the ability of a cloud infrastructure to dynami-cally change the amount of resources allocated to a running service as load changes. We build an autonomous elasticity controller that changes the number of virtual machines al-located to a service based on both monitored load changes and ..."
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Elasticity is the ability of a cloud infrastructure to dynami-cally change the amount of resources allocated to a running service as load changes. We build an autonomous elasticity controller that changes the number of virtual machines al-located to a service based on both monitored load changes and predictions of future load. The cloud infrastructure is modeled as a G/G/N queue. This model is used to con-struct a hybrid reactive-adaptive controller that quickly re-acts to sudden load changes, prevents premature release of resources, takes into account the heterogeneity of the work-load, and avoids oscillations. Using simulations with Web and cluster workload traces, we show that our proposed con-troller lowers the number of delayed requests by a factor of 70 for the Web traces and 3 for the cluster traces when com-pared to a reactive controller. Our controller also decreases the average number of queued requests by a factor of 3 for both traces, and reduces oscillations by a factor of 7 for the Web traces and 3 for the cluster traces. This comes at the expense of between 20 % and 30 % over-provisioning, as compared to a few percent for the reactive controller.
Migration of multi-tier applications to infrastructure-as-a-service clouds: An investigation using kernel-based virtual machines
- Proc. 12th IEEE/ACM Intl. Conf. On Grid Computing (GRID 2011
"... Abstract — To investigate challenges of multi-tier application migration to Infrastructure-as-a-Service (IaaS) clouds we performed an experimental investigation by deploying a processor bound and input-output bound variant of the RUSLE2 erosion model to an IaaS based private cloud. Scaling the appli ..."
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Abstract — To investigate challenges of multi-tier application migration to Infrastructure-as-a-Service (IaaS) clouds we performed an experimental investigation by deploying a processor bound and input-output bound variant of the RUSLE2 erosion model to an IaaS based private cloud. Scaling the applications to achieve optimal system throughput is complex and involves much more than simply increasing the number of allotted virtual machines (VMs). While scaling the application variants a series of bottlenecks were encountered unique to an application's processing, I/O, and memory requirements, herein referred to as an application's profile. To investigate the impact of provisioning variation for hosting multi-tier applications we tested four schemes of VM deployments across the physical nodes of our cloud. Performance degradation was more pronounced when multiple I/O or CPU resource intensive application components were co-located on the same physical hardware. We investigated the virtualization overhead incurred using Kernel-based virtual machines (KVM) by deploying our application variants to both physical and virtual machines. Overhead varied based on the unique characteristics of each application's profile. We observed ~112 % overhead for the input/output bound application and just ~ 10 % overhead for the processor bound application. Understanding an application's profile was found to be important for optimal IaaS-based cloud migration and scaling.
Performance Aware Reconfiguration of Software Systems
, 2010
"... In this paper we address the problem of building a scalable component-based system by means of dynamic reconfiguration. Specifically, we consider the system response time as the performance metric; we assume that the system components can be dynamically reconfigured to provide a degraded service w ..."
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Cited by 4 (3 self)
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In this paper we address the problem of building a scalable component-based system by means of dynamic reconfiguration. Specifically, we consider the system response time as the performance metric; we assume that the system components can be dynamically reconfigured to provide a degraded service with lower response time. Each component operating at one of the available quality levels is assigned a utility. Higher quality levels are associated to higher utility. We propose an approach for performance-aware reconfiguration of degradable software systems called PARSY (Performance Aware Reconfiguration of software SYstems). PARSY tunes individual components in order to maximize the system utility with the constraint of keeping the system response time below a pre defined threshold. PARSY uses a closed Queueing Network model to select the components to upgrade or degrade.
Formal Modeling and Evaluation of Service-based Business Process Elasticity in the
"... Abstract—Cloud computing is a new delivery model for IT services. Cloud platforms are being increasingly used for the deployment and execution of service-based business processes (SBPs). Nevertheless, the provisioning of elastic infrastructures and/or platforms is not sufficient to provide users wit ..."
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Abstract—Cloud computing is a new delivery model for IT services. Cloud platforms are being increasingly used for the deployment and execution of service-based business processes (SBPs). Nevertheless, the provisioning of elastic infrastructures and/or platforms is not sufficient to provide users with elasticity at the level of SBPs. Therefore, there is a need to provide SBPs with mechanisms to scale their resource requirements up and down whenever possible. This can be achieved using mechanisms for duplicating and consolidating business services that compose the SBPs. In this paper, we propose a formal model for SBPs elasticity in the Cloud. We show that our model preserves the semantics of SBPs when services are duplicated or consolidated. We also propose a formal framework for the evaluation of elasticity strategies that decide on when and how many resources are required to ensure elasticity of SBPs. I.
Formal Modeling and Evaluation of Stateful Service-Based
- Business Process Elasticity in the Cloud. On the Move to Meaningful Internet Systems OTM 2013 Conferences
, 2013
"... Abstract. Cloud environments are being increasingly used for deploy-ing and executing business processes and particularly Service-based Busi-ness Processes (SBPs). One of the expected features of Cloud environ-ments is elasticity at different levels. It is obvious that provisioning of elastic platfo ..."
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Abstract. Cloud environments are being increasingly used for deploy-ing and executing business processes and particularly Service-based Busi-ness Processes (SBPs). One of the expected features of Cloud environ-ments is elasticity at different levels. It is obvious that provisioning of elastic platforms is not sufficient to provide elasticity of the deployed business process. Therefore, SBPs should be provided with elasticity so that they would be able to adapt to the workload changes while ensur-ing the desired functional and non-functional properties. In this paper, we propose a formal model for stateful SBPs elasticity that features a duplication/consolidation mechanisms and a generic controller to define and evaluate elasticity strategies. Key words: Cloud computing, stateful service-based business processes, elasticity, evaluation of elasticity strategies 1
An Intelligent Approach for Virtual Machine and QoS Provisioning in Cloud Computing
"... Abstract—Cloud Computing has become the most popular distributed computing environment because it does not require any user level management and controlling on the low-level imple-mentation of the system. However, efficient resource provisioning is a key challenge for cloud computing and resolving s ..."
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Abstract—Cloud Computing has become the most popular distributed computing environment because it does not require any user level management and controlling on the low-level imple-mentation of the system. However, efficient resource provisioning is a key challenge for cloud computing and resolving such kind of problem can reduce under or over utilization of resources, increase user satisfaction by serving more users during peak hours, reduce implementation cost for providers and service cost for users. Existing works on cloud computing focuses to accurate estimation of the capacity needs, static or dynamic VM (Virtual Machine) creation and scheduling. But significant amount of time is required to create and destroy VMs which could be used to serve more user requests. In this paper, an adaptive QoS (Quality of Service) aware VM provisioning mechanism is developed that ensures efficient utilization of the system resources. The VM for similar type of requests has been recycled so that the VM creation time can be minimized and used to serve more user requests. In the proposed model, QoS is ensured by serving all the tasks within the requirements described in SLA. Tasks are separated using multilevel queue and the most urgent task is given high priority. The simulation-based experimental results shows that a great number of tasks can be served compared to others which will help to satisfy customers during the peak hour. I.
Implementing a Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment By
, 2013
"... Implementing a dynamic scaling of web applications in a virtualized cloud computing environment ..."
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Implementing a dynamic scaling of web applications in a virtualized cloud computing environment
Apply AHP for Resource Allocation Problem in Cloud
"... Abstract Cloud computing is an emerging paradigm with many applications that are integrated with IT organization having the freedom to migrate services between different physical servers. Analytic Hierarchy Process (AHP) with a pairwise comparison matrix technique for applications has been used for ..."
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Abstract Cloud computing is an emerging paradigm with many applications that are integrated with IT organization having the freedom to migrate services between different physical servers. Analytic Hierarchy Process (AHP) with a pairwise comparison matrix technique for applications has been used for serving resources. AHP is a mathematical technique for multi-criteria decision-making used in cloud computing. The growth in cloud computing for resource allocation is sudden and raises complex issues with quality of services for selecting applications. Finally, based on the selected criteria, applications are ranked using the pairwise comparison matrix of AHP to determine the most effective scheme. The presented AHP technique represents a well-balanced multi criteria priorities synthesis of various applications effect factors that must be taken into consideration when making complex decisions of this nature. Keeping in view wide range of applications of cloud computing an attempt has been made to develop multiple criteria decision making model.
Comparative Study of Scheduling and Service Broker Algorithms in Cloud Computing
"... ABSTRACT: Cloud computing is a virtual pool of resources which are provided to users via Internet. It gives users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. Cloud computing service providers one of the goals is to use the resour ..."
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ABSTRACT: Cloud computing is a virtual pool of resources which are provided to users via Internet. It gives users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. Cloud computing service providers one of the goals is to use the resources efficiently focusing scheduling to a cloud environment enables the use of various cloud services to help framework implementation. Thus the comprehensive way of different type of scheduling and service broker algorithms in cloud computing environment is surveyed. This study gives an elaborate idea about scheduling and service broker algorithms in cloud computing. Here we have compared
A Model-Based Approach for Optimizing Power Consumption of IaaS
"... Abstract—Virtual Machine Image (VMI) provisioning is an important process of Infrastructure as a Service delivery model to provide virtual images in Cloud Computing. The power consumption and energy efficiency of VMI provisioning process depend not only on the hardware infrastructure, but also on th ..."
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Abstract—Virtual Machine Image (VMI) provisioning is an important process of Infrastructure as a Service delivery model to provide virtual images in Cloud Computing. The power consumption and energy efficiency of VMI provisioning process depend not only on the hardware infrastructure, but also on the VMI ’s configuration, which helps to compose, configure and deploy VMIs in Cloud Computing environments. The major issue of improving the energy efficiency of VMI provisioning process is how to reduce the power consumption while ensuring the compatibility of software components installed in a virtual machine image. This paper describes a model-driven approach to improve the energy efficiency of VMI provisioning in Cloud Computing. This approach considers virtual images as product lines and uses feature models to represent their configurations. It uses model-based techniques to handle VMI specialization, automatic deployment and reconfiguration. The approach aims at minimizing the amount of unneeded software installed in VMIs, and thus to reduce the power consumption of VMI provisioning as well as the data transfer through the network. Keywords-Model-driven deployment, feature models, cloud computing, virtual image provisioning, power consumption. I.
