| 1. |
- Ali-Eldin, Ahmed, et al.
(författare)
-
An adaptive hybrid elasticity controller for cloud infrastructures
- 2012
-
Ingår i: 2012 IEEE Network operations and managent symposium (NOMS). - : IEEE Communications Society. - 9781467302685 ; , s. 204-212
-
Konferensbidrag (refereegranskat)abstract
- Cloud elasticity is the ability of the cloud infrastructure to rapidly change the amount of resources allocated to a service in order to meet the actual varying demands on the service while enforcing SLAs. In this paper, we focus on horizontal elasticity, the ability of the infrastructure to add or remove virtual machines allocated to a service deployed in the cloud. We model a cloud service using queuing theory. Using that model we build two adaptive proactive controllers that estimate the future load on a service. We explore the different possible scenarios for deploying a proactive elasticity controller coupled with a reactive elasticity controller in the cloud. Using simulation with workload traces from the FIFA world-cup web servers, we show that a hybrid controller that incorporates a reactive controller for scale up coupled with our proactive controllers for scale down decisions reduces SLA violations by a factor of 2 to 10 compared to a regression based controller or a completely reactive controller.
|
|
| 2. |
- Ali-Eldin, Ahmed, et al.
(författare)
-
Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
- 2012
-
Ingår i: Proceedings of the 3rd workshop on Scientific Cloud Computing Date. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450313407 - 145031340X ; , s. 31-40
-
Konferensbidrag (refereegranskat)abstract
- Elasticity is the ability of a cloud infrastructure to dynamically change theamount of resources allocated to a running service as load changes. We build anautonomous elasticity controller that changes the number of virtual machinesallocated to a service based on both monitored load changes and predictions offuture load. The cloud infrastructure is modeled as a G/G/N queue. This modelis used to construct a hybrid reactive-adaptive controller that quickly reactsto sudden load changes, prevents premature release of resources, takes intoaccount the heterogeneity of the workload, and avoids oscillations. Using simulations with Web and cluster workload traces, we show that our proposed controller lowers the number of delayed requests by a factor of 70 for the Web traces and 3 for the cluster traces when compared to a reactive controller. Ourcontroller 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.
|
|
| 3. |
- Ali-Eldin, Ahmed, 1985-, et al.
(författare)
-
Workload Classification for Efficient Auto-Scaling of Cloud Resources
- 2013
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Elasticity algorithms for cloud infrastructures dynamically change the amount of resources allocated to a running service according to the current and predicted future load. Since there is no perfect predictor, and since different applications’ workloads have different characteristics, no single elasticity algorithm is suitable for future predictions for all workloads. In this work, we introduceWAC, aWorkload Analysis and Classification tool that analyzes workloads and assigns them to the most suitable elasticity controllers based on the workloads’ characteristics and a set of business level objectives.WAC has two main components, the analyzer and the classifier. The analyzer analyzes workloads to extract some of the features used by the classifier, namely, workloads’ autocorrelations and sample entropies which measure the periodicity and the burstiness of the workloads respectively. These two features are used with the business level objectives by the clas-sifier as the features used to assign workloads to elasticity controllers. We start by analyzing 14 real workloads available from different applications. In addition, a set of 55 workloads is generated to test WAC on more workload configurations. We implement four state of the art elasticity algorithms. The controllers are the classes to which the classifier assigns workloads. We use a K nearest neighbors classifier and experiment with different workload combinations as training and test sets. Our experi-ments show that, when the classifier is tuned carefully, WAC correctly classifies between 92% and 98.3% of the workloads to the most suitable elasticity controller.
|
|
| 4. |
- Arkian, Hamidreza, et al.
(författare)
-
An Experiment-Driven Performance Model of Stream Processing Operators in Fog Computing Environments
- 2020
-
Ingår i: SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing. - New York, NY, USA : ACM Digital Library. ; , s. 1763-1771
-
Konferensbidrag (refereegranskat)abstract
- Data stream processing (DSP) is an interesting computation paradigm in geo-distributed infrastructures such as Fog computing because it allows one to decentralize the processing operations and move them close to the sources of data. However, any decomposition of DSP operators onto a geo-distributed environment with large and heterogeneous network latencies among its nodes can have significant impact on DSP performance. In this paper, we present a mathematical performance model for geo-distributed stream processing applications derived and validated by extensive experimental measurements. Using this model, we systematically investigate how different topological changes affect the performance of DSP applications running in a geo-distributed environment. In our experiments, the performance predictions derived from this model are correct within ±2% even in complex scenarios with heterogeneous network delays between every pair of nodes.
|
|
| 5. |
- Arkian, Hamidreza, et al.
(författare)
-
Model-based Stream Processing Auto-scaling in Geo-Distributed Environments
- 2021
-
Ingår i: 2021 International Conference on Computer Communications and Networks (ICCCN). - : IEEE.
-
Konferensbidrag (refereegranskat)abstract
- Data stream processing is an attractive paradigm for analyzing IoT data at the edge of the Internet before transmitting processed results to a cloud. However, the relative scarcity of fog computing resources combined with the workloads' nonstationary properties make it impossible to allocate a static set of resources for each application. We propose Gesscale, a resource auto-scaler which guarantees that a stream processing application maintains a sufficient Maximum Sustainable Throughput to process its incoming data with no undue delay, while not using more resources than strictly necessary. Gesscale derives its decisions about when to rescale and which geo-distributed resource(s) to add or remove on a performance model that gives precise predictions about the future maximum sustainable throughput after reconfiguration. We show that this auto-scaler uses 17% less resources, generates 52% fewer reconfigurations, and processes more input data than baseline auto-scalers based on threshold triggers or a simpler performance model.
|
|
| 6. |
- Armstrong, Django, et al.
(författare)
-
Contextualization : dynamic configuration of virtual machines
- 2015
-
Ingår i: Journal of Cloud Computing. - : Springer. - 2192-113X. ; 4:17
-
Tidskriftsartikel (refereegranskat)abstract
- New VM instances are created from static templates that contain the basic configuration of the VM to achieve elasticity with regards to capacity. Instance specific settings can be injected into the VM during the deployment phase through means of contextualization. So far this is limited to a single data source and data remains static throughout the lifecycle of the VM.We present a layered approach to contextualization that supports different classes of contextualization data available from several sources. The settings are made available to the VM through virtual devices. Inside each VM data from different classes are layered on top of each other to create a unified file hierarchy.Context data can be modified during runtime by updating the contents of the virtual devices, making our approach the first contextualization approach to natively support recontextualization. Recontextualization enables runtime reconfiguration of an executing service and can act as a trigger and key enabler of self-* techniques. This trigger provides a service with a mechanism to adapt or optimize itself in response to a changing environment. The runtime reconfiguration using recontextualization and its potential gains are illustrated in an example with a distributed file system, demonstrating the feasibility of our approach.
|
|
| 7. |
- Badia, Rosa M., et al.
(författare)
-
Demonstration of the OPTIMIS Toolkit for Cloud Service Provisioning
- 2011
-
Ingår i: Towards a Service-Based Internet. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642247545 - 9783642247552 ; , s. 331-333
-
Konferensbidrag (refereegranskat)abstract
- We demonstrate the OPTIMIS toolkit for scalable and dependable service platforms and architectures that enable flexible and dynamic provisioning of Cloud services. The innovations demonstrated are aimed at optimizing Cloud services and infrastructures based on aspects such as trust, risk, eco-efficiency, cost, performance and legal constraints. Adaptive self-preservation is part of the toolkit to meet predicted and unforeseen changes in resource requirements. By taking into account the whole service life cycle, the multitude of future Cloud architectures, and a by taking a holistic approach to sustainable service provisioning, the toolkit provides a foundation for a reliable, sustainable, and trustful Cloud computing industry.
|
|
| 8. |
|
|
| 9. |
|
|
| 10. |
- Berglund, Ann-Charlotte, et al.
(författare)
-
Combining local and grid resources in scientific workflows (for Bioinformatics)
- 2009
-
Konferensbidrag (refereegranskat)abstract
- We examine some issues that arise when using both local and Gridresources in scientific workflows. Our previous work addresses and illustratesthe benefits of a light-weight and generic workflow engine that manages andoptimizes Grid resource usage. Extending on this effort, we hereillustrate how a client tool for bioinformatics applications employs the engine tointerface with Grid resources. We also explore how to define data flowsthat transparently integrates local and Grid subworkflows. In addition, the benefits of parameter sweep workflows are examined and a means for describing this type of workflows in an abstract and concise manner is introduced. Finally, the above mechanisms are employed to perform an orthology detection analysis.
|
|
