| 1. |
- Dürango, Jonas, et al.
(författare)
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Control-theoretical load-balancing for cloud applications with brownout
- 2014
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Ingår i: 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - 9781467360906 - 9781479977468 ; , s. 5320-5327
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Konferensbidrag (refereegranskat)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 self-adaptive application simulator, together with some state-of-the-art solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.
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| 2. |
- Kihl, Maria, et al.
(författare)
-
The Challenge of Cloud Control
- 2013
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Ingår i: The 8th International Workshop on Feedback Computing (Feedback Computing '13).
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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.
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| 3. |
- Klein, Cristian, 1985-, et al.
(författare)
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Improving Cloud Service Resilience using Brownout-Aware Load-Balancing
- 2014
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Ingår i: 2014 IEEE 33rd International Symposium On Reliable Distributed Systems (SRDS). - : IEEE Computer Society. - 9781479955848 ; , s. 31-40, s. 31-40
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Konferensbidrag (refereegranskat)abstract
- We focus on improving resilience of cloud services (e.g., e-commerce website), when correlated or cascading failures lead to computing capacity shortage. We study how to extend the classical cloud service architecture composed of a load-balancer and replicas with a recently proposed self-adaptive paradigm called brownout. Such services are able to reduce their capacity requirements by degrading user experience (e.g., disabling recommendations).Combining resilience with the brownout paradigm is to date an open practical problem. The issue is to ensure that replica self-adaptivity would not confuse the load-balancing algorithm, overloading replicas that are already struggling with capacity shortage. For example, load-balancing strategies based on response times are not able to decide which replicas should be selected, since the response times are already controlled by the brownout paradigm.In this paper we propose two novel brownout-aware load-balancing algorithms. To test their practical applicability, we extended the popular lighttpd web server and load-balancer, thus obtaining a production-ready implementation. Experimental evaluation shows that the approach enables cloud services to remain responsive despite cascading failures. Moreover, when compared to Shortest Queue First (SQF), believed to be near-optimal in the non-adaptive case, our algorithms improve user experience by 5%, with high statistical significance, while preserving response time predictability.
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| 4. |
- Papadopoulos, Alessandro, et al.
(författare)
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Control-based load-balancing techniques : Analysis and performance evaluation via a randomized optimization approach
- 2016
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Ingår i: Control Engineering Practice. - : Elsevier. - 0967-0661 .- 1873-6939. ; 52, s. 24-34
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Tidskriftsartikel (refereegranskat)abstract
- Cloud applications are often subject to unexpected events like flashcrowds and hardware failures. Users that expect a predictable behavior may abandon an unresponsive application when these events occur. Researchers and engineers addressed this problem on two separate fronts: first, they introduced replicas - copies of the application with the same functionality - for redundancy and scalability; second, they added a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience. The presence of multiple replicas requires a dedicated component to direct incoming traffic: a load-balancer. Existing load-balancing strategies based on response times interfere with the response time controller developed for brownout-compliant applications. In fact, the brownout approach bounds response times using a control action. Hence, the response time, that was used to aid load-balancing decision, is not a good indicator of how well a replica is performing. To fix this issue, this paper reviews some proposal for brownout-aware load-balancing and provides a comprehensive experimental evaluation that compares them. To provide formal guarantees on the load balancing performance, we use a randomized optimization approach and apply the scenario theory. We perform an extensive set of experiments on a real machine, extending the popular lighttpd web server and load-balancer, and obtaining a production-ready implementation. Experimental results show an improvement of the user experience over Shortest Queue First (SQF)-believed to be near-optimal in the non-adaptive case. The improved user experience is obtained preserving the response time predictability.
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| 5. |
- Papadopoulos, Alessandro, et al.
(författare)
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PEAS : A Performance Evaluation framework for Auto-Scaling strategies in cloud applications
- 2016
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Ingår i: ACM Transactions on Modeling and Performance Evaluation of Computing Systems. - United States : Association for Computing Machinery (ACM). - 2376-3639 .- 2376-3647. ; :4
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Tidskriftsartikel (refereegranskat)abstract
- Numerous auto-scaling strategies have been proposed in the last few years for improving various Quality of Service (QoS)indicators of cloud applications, e.g., response time and throughput, by adapting the amount of resources assigned to theapplication to meet the workload demand. However, the evaluation of a proposed auto-scaler is usually achieved throughexperiments under specific conditions, and seldom includes extensive testing to account for uncertainties in the workloads, andunexpected behaviors of the system. These tests by no means can provide guarantees about the behavior of the system in generalconditions. In this paper, we present PEAS, a Performance Evaluation framework for Auto-Scaling strategies in the presenceof uncertainties. The evaluation is formulated as a chance constrained optimization problem, which is solved using scenariotheory. The adoption of such a technique allows one to give probabilistic guarantees of the obtainable performance. Six differentauto-scaling strategies have been selected from the literature for extensive test evaluation, and compared using the proposedframework. We build a discrete event simulator and parameterize it based on real experiments. Using the simulator, each auto-scaler’s performance is evaluated using 796 distinct real workload traces from projects hosted on the Wikimedia foundations’servers, and their performance is compared using PEAS. The evaluation is carried out using different performance metrics,highlighting the flexibility of the framework, while providing probabilistic bounds on the evaluation and the performance of thealgorithms. Our results highlight the problem of generalizing the conclusions of the original published studies and show thatbased on the evaluation criteria, a controller can be shown to be better than other controllers.
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| 6. |
- Tärneberg, William, et al.
(författare)
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The 6G Computing Continuum (6GCC) : Meeting the 6G computing challenges
- 2022
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Ingår i: 2022 1st International Conference on 6G Networking, 6GNet 2022. - : IEEE Computer Society. - 9781665467636
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Konferensbidrag (refereegranskat)abstract
- 6G systems, such as Large Intelligent Surfaces, will require distributed, complex, and coordinated decisions through-out a very heterogeneous and cell free infrastructure. This will require a fundamentally redesigned software infrastructure accompanied by massively distributed and heterogeneous computing resources, vastly different from current wireless networks. To address these challenges, in this paper, we propose and motivate the concept of a 6G Computing Continuum (6GCC) and two research testbeds, to advance the rate and quality of research. 6G Computing Continuum is an end-to-end compute and software platform for realizing large intelligent surfaces and its tenant users and applications. One for addressing the challenges or orchestrating shared computational resources in the wireless domain, implemented on a Large Intelligent Surfaces testbed. Another simulation-based testbed is intended to address scalability and global-scale orchestration challenges.
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| 7. |
- Ali-Eldin, Ahmed, et al.
(författare)
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An adaptive hybrid elasticity controller for cloud infrastructures
- 2012
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Ingår i: 2012 IEEE Network operations and managent symposium (NOMS). - : IEEE Communications Society. - 9781467302685 ; , s. 204-212
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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.
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| 8. |
- Ali-Eldin, Ahmed, 1985-, et al.
(författare)
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Analysis and characterization of a Video-on-Demand service workload
- 2015
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Ingår i: Proceedings of the 6th ACM Multimedia Systems Conference, MMSys 2015. - New York, NY, USA : ACM Digital Library. - 9781450333511 ; , s. 189-200
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Konferensbidrag (refereegranskat)abstract
- Video-on-Demand (VoD) and video sharing services accountfor a large percentage of the total downstream Internet traf-fic. In order to provide a better understanding of the loadon these services, we analyze and model a workload tracefrom a VoD service provided by a major Swedish TV broad-caster. The trace contains over half a million requests gener-ated by more than 20000 unique users. Among other things,we study the request arrival rate, the inter-arrival time, thespikes in the workload, the video popularity distribution, thestreaming bit-rate distribution and the video duration distri-bution. Our results show that the user and the session ar-rival rates for the TV4 workload does not follow a Poissonprocess. The arrival rate distribution is modeled using a log-normal distribution while the inter-arrival time distributionis modeled using a stretched exponential distribution. Weobserve the “impatient user” behavior where users abandonstreaming sessions after minutes or even seconds of startingthem. Both very popular videos and non-popular videos areparticularly affected by impatient users. We investigate ifthis behavior is an invariant for VoD workloads.
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| 9. |
- Ali-Eldin, Ahmed, 1985-
(författare)
-
Capacity Scaling for Elastic Compute Clouds
- 2013
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- AbstractCloud computing is a computing model that allows better management, higher utiliza-tion and reduced operating costs for datacenters while providing on demand resourceprovisioning for different customers. Data centers are often enormous in size andcomplexity. In order to fully realize the cloud computing model, efficient cloud man-agement software systems that can deal with the datacenter size and complexity needto be designed and built.This thesis studies automated cloud elasticity management, one of the main andcrucial datacenter management capabilities. Elasticity can be defined as the abilityof cloud infrastructures to rapidly change the amount of resources allocated to anapplication in the cloud according to its demand. This work introduces algorithms,techniques and tools that a cloud provider can use to automate dynamic resource pro-visioning allowing the provider to better manage the datacenter resources. We designtwo automated elasticity algorithms for cloud infrastructures that predict the futureload for an application running on the cloud. It is assumed that a request is either ser-viced or dropped after one time unit, that all requests are homogeneous and that it takesone time unit to add or remove resources. We discuss the different design approachesfor elasticity controllers and evaluate our algorithms using real workload traces. Wecompare the performance of our algorithms with a state-of-the-art controller. We ex-tend on the design of the best performing controller out of our two controllers anddrop the assumptions made during the first design. The controller is evaluated with aset of different real workloads.All controllers are designed using certain assumptions on the underlying systemmodel and operating conditions. This limits a controller’s performance if the modelor operating conditions change. With this as a starting point, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components,an analyzer and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.
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| 10. |
- Ali-Eldin, Ahmed, et al.
(författare)
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Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
- 2012
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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
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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.
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