Principles of Elastic Processes

Dustdar, Schahram; Guo, Yuanping; Satzger, Benjamin; Truong, Hong-Linh

doi:10.1109/mic.2011.121

Cited by 146 publications

(80 citation statements)

References 5 publications

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“…As mentioned in our former work [8], an ERM decides how to utilize resources in an optimal way under multi-dimensional constraints. It takes into account dynamic resource, cost, and workflow information and provides not only a scheduling, but also controls Cloud resources by triggering different actions like moving services from one VM to another or starting and terminating servers.…”

Section: Elastic Reasoning Mechanismsmentioning

confidence: 99%

“…With the advent of Cloud computing, organizations presently have a much more cost-efficient alternative that enables the use of computing resources in an on-demand, utility-like fashion [6]. While resource elasticity is a common way to describe the scalability of single applications as well as workflows, elasticity is not the only constraint that should be taken into account in the context of workflow scalability [8]. Notably, Quality of Service (QoS) in terms of criteria like response time does not necessarily reflect resource elasticity in a linear way, i.e., there may be no proportional relationship between involved resources and QoS.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, Quality of Service (QoS) in terms of criteria like response time does not necessarily reflect resource elasticity in a linear way, i.e., there may be no proportional relationship between involved resources and QoS. As a result, it is necessary to define quality elasticity, which describes the responsiveness of quality regarding changes in resource usage [8]. Last but not least, many Cloud providers make use of dynamic pricing models, which should also be taken into account if Cloud resources are used in order to realize scalable processes.…”

Section: Introductionmentioning

confidence: 99%

“…Last but not least, many Cloud providers make use of dynamic pricing models, which should also be taken into account if Cloud resources are used in order to realize scalable processes. These dynamic pricing models are reflected in cost elasticity, i.e., a resource provisioner's responsiveness to changes in costs [8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Introducing the Vienna Platform for Elastic Processes

Schulte

Hoenisch

Venugopal

et al. 2013

Service-Oriented Computing

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Abstract. Resource-intensive tasks are playing an increasing role in business processes. The emergence of Cloud computing has enabled the deployment of such tasks onto resources sourced on-demand from Cloud providers. This has enabled so-called elastic processes that are able to dynamically adjust their resource usage to meet varying workloads.Traditional Business Process Management Systems (BPMSs) do not consider the needs of elastic processes such as monitoring facilities, tracking the current and future system landscape, reasoning about optimally utilizing resources given Quality of Service constraints, and executing necessary actions (e.g., start/stop servers, move services). This paper introduces ViePEP, a research BPMS capable of handling the aforementioned requirements of elastic processes.

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Section: Elastic Reasoning Mechanismsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Introducing the Vienna Platform for Elastic Processes

Schulte

Hoenisch

Venugopal

et al. 2013

Service-Oriented Computing

Self Cite

View full text Add to dashboard Cite

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“…Now, the concern is, how to price an option to be reasonable for both parties? Obviously, option pricing is an elastic process (Dustdar et al, 2011), sensitive to the fluctuation of the asset price determined by supply and demand between federation parties in spot market. As a consequence, pricing elasticity that comes in two types of Demand and Provisioning may drive a wedge between the buying and selling price of an asset.…”

Section: Motivationmentioning

confidence: 99%

Cloud Asset Pricing Tree (CAPT) - Elastic Economic Model for Cloud Service Providers

Qanbari

Dustdar

et al. 2014

Proceedings of the 4th International Conference on Cloud Computing and Services Science

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Abstract:Cloud providers are incorporating novel techniques to cope with prospective aspects of trading like resource allocation over future demands and its pricing elasticity that was not foreseen before. To leverage the pricing elasticity of upcoming demand and supply, we employ financial option theory (future contracts) as a mechanism to alleviate the risk in resource allocation over future demands. This study introduces a novel Cloud Asset Pricing Tree (CAPT) model that finds the optimal premium price of the Cloud federation options efficiently. Providers will benefit by this model to make decisions when to buy options in advance and when to exercise them to achieve more economies of scale. The CAPT model adapts its structure to address the price elasticity concerns and makes the demand, price inelastic and the supply, price elastic. Our empirical evidences suggest that using the CAPT model, exploits the Cloud market potential as an opportunity for more resource utilization and future capacity planning.

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On the performance and power consumption analysis of elastic clouds

Guo

Pang

et al. 2016

Concurrency and Computation

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Cloud computing is a novel paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Elasticity is one of the most attractive features of cloud computing. Elastic clouds are able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible. However, elasticity adds complexity, which makes quantitative analysis of cloud performance and power consumption difficult. Such analysis is required to evaluate and quantify the cost-benefit of a strategy portfolio and the quantitative runtime performance and power consumption experienced by cloud-users. In this study, we present a comprehensive analytical approach to performance and power consumption analysis of elastic clouds. Several metrics are defined and evaluated: expected task completion time, power consumption rate, and task rejection rate under different load conditions, elasticity intensities, and error intensities. To validate the proposed approach, we obtain experimental data through a real-world cloud and conduct a confidence interval analysis. The analysis results suggest the perfect coverage of theoretical results by corresponding experimental confidence intervals. 4368 K.-Y. GUO ET AL.up/out as long as the workload is high, and scaling back in/down when possible, which potentially brings energy saving and operational cost reduction. Elasticity is one of the essential characteristics of scalable cloud, which distinguish the cloud paradigm from traditional computing systems such as gird and cluster computing. However, elasticity is not always welcomed because it also leads to nonnegligible performance degradations caused by lack of available computing resources when facing bursty/high workload and extra operational cost needed by up-scaling/down-scaling activities.Another notable feature influencing performance and power consumption of elastic cloud is machine error. Such errors are caused by accidental storage unavailability, component malfunction, software bug, software aging, storage space fragmentation accumulation, memory leak, extra-longresponse message, connection failure, message loss, wrong input sequence, external intrusion, and so on. Errors are a major reason causing performance and availability degradation in cloud computing systems. Such degradation could be further amplified because of the fact that elastic cloud usually maintains a subset of active machines rather than put all machines online. Such reduced number of active machines are obviously more susceptible to machine errors than those of non-elastic clouds where all machines are kept active.Errors occur at different levels, and not all errors especially hardware and platform-related errors can be detected and repaired by operating system-level (OS-level) detectors. The complexity of error hierarchy and the fact that lower-level hardware/platform errors are more rare than higherlevel software/tas...

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Principles of Elastic Processes

Cited by 146 publications

References 5 publications

Introducing the Vienna Platform for Elastic Processes

Introducing the Vienna Platform for Elastic Processes

Cloud Asset Pricing Tree (CAPT) - Elastic Economic Model for Cloud Service Providers

On the performance and power consumption analysis of elastic clouds

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