| 1. |
- Kihl, Maria, et al.
(författare)
-
The Challenge of Cloud Control
- 2013
-
Ingår i: The 8th International Workshop on Feedback Computing (Feedback Computing '13).
-
Konferensbidrag (refereegranskat)abstract
- Today’s cloud data center infrastructures are not even near being able to cope with the enormous and rapidly vary-ing capacity demands that will be reality in a near future. So far, very little is understood about how to transform today’s data centers (being large, power-hungry facilities, and operated through heroic efforts by numerous adminis-trators) into a self-managed, dynamic, and dependable infrastructure, constantly delivering expected QoS with rea-sonable operation costs and acceptable carbon footprint for large-scale services with sometimes dramatic variations in capacity demands. In this paper, we discuss some of the major challenges for resource-optimized cloud data cen-ter. We propose a new research area called Cloud Control, which is a control theoretic approach to a range of cloud management problems, aiming to transform today´s static and energy consuming cloud data centers into self-managed, dynamic, and dependable infrastructures, constantly delivering expected quality of service with acceptable operation costs and carbon footprint for large-scale services with varying capacity demands.
|
|
| 2. |
- 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.
|
|
| 3. |
- 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.
|
|
| 4. |
- 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.
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- 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.
|
|
| 8. |
- Dackland, Krister, et al.
(författare)
-
A ring-oriented approach for block matrix factorizations on shared and distributed memory architectures
- 1993
-
Ingår i: Proceedings of the Sixth SIAM Conference on Parallel Processing for Scientific Computing. - Norfolk : SIAM Publications. - 0898713153 ; , s. 330-338
-
Konferensbidrag (refereegranskat)abstract
- A block (column) wrap-mapping approach for design of parallel block matrix factorization algorithms that are (trans)portable over and between shared memory multiprocessors (SMM) and distributed memory multicomputers (DMM) is presented. By reorganizing the matrix on the SMM architecture, the same ring-oriented algorithms can be used on both SMM and DMM systems with all machine dependencies comprised to a small set of communication routines. The algorithms are described on high level with focus on portability and scalability aspects. Implementation aspects of the LU , Cholesky, and QR factorizations and machine specific communication routines for some SMM and DMM systems are discussed. Timing results show that our portable algorithms have similar performance as machine specific implementations. 1 Introduction With the introduction of advanced parallel computer architectures a demand for efficient and portable algorithms has emerged. Several attempts to design algorithms and implementat.
|
|
| 9. |
|
|
| 10. |
- Dackland, Krister, et al.
(författare)
-
Design and performance modeling of parallel block matrix factorizations for distributed memory multicomputers
- 1992
-
Ingår i: Proceedings of the Industrial Mathematics Week. ; , s. 102-116
-
Konferensbidrag (refereegranskat)abstract
- Efficient and scalable parallel block algorithms for the LU factorization with partial pivoting, the Cholesky, and QR factorizations in a distributed memory multicomputer environment are presented. The distributed system is viewed as a ring of processors and the algorithms correspond to shared memory algorithms parallelized on block level (explicit parallelism). Performance of the algorithms are analyzed theoretically and illustrated empirically by implementations on the Intel iPSC/2 hypercube. A model predicting performance and optimal block size is presented.
|
|
