| 1. |
- Desmeurs, David, et al.
(författare)
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Event-Driven Application Brownout : Reconciling High Utilization and Low Tail Response Times
- 2015
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Ingår i: 2015 International Conference on Cloud and Autonomic Computing (ICCAC). - New York : IEEE Computer Society. - 9781467395663 ; , s. 1-12
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Konferensbidrag (refereegranskat)abstract
- Data centers currently waste a lot of energy, due to lack of energy proportionality and low resource utilization, the latter currently being necessary to ensure application responsiveness. To address the second concern we propose a novel application-level technique that we call event-driven Brownout. For each request, i.e., in an event-driven manner, the application can execute some optional code that is not required for correct operation but desirable for user experience, and does so only if the number of pending client requests is below a given threshold. We propose several autonomic algorithms, based on control theory and machine learning, to automatically tune this threshold based on measured application 95th percentile response times. We evaluate our approach using the RUBiS benchmark which shows a 11-fold improvement in maintaining response-time close to a set-point at high utilization compared to competing approaches. Our contribution is opening the path to more energy efficient data-centers, by allowing applications to keep response times close to a set-point even at high resource utilization.
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| 2. |
- Farokhi, Soodeh, et al.
(författare)
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Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints
- 2015
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Ingår i: 2015 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC). - 9781467395663 ; , s. 69-80
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Konferensbidrag (refereegranskat)abstract
- Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either CPU or memory, while an application may need arbitrary combinations of these resources at different stages of its execution. Nonetheless, the existing techniques cannot be readily used as-is without proper orchestration since they may lead to either under-or over-provisioning of resources and consequently result in undesirable behaviors such as performance disparity. The contribution of this paper is the design of an autonomic resource controller using a fuzzy control approach as a coordination technique. The novel controller dynamically adjusts the right amount of CPU and memory required to meet the performance objective of an application, namely its response time. We perform a thorough experimental evaluation using three different interactive benchmark applications, RUBiS, RUBBoS, and Olio, under workload traces generated based on open and closed system models. The results show that the coordination of memory and CPU elasticity controllers using the proposed fuzzy control provisions the right amount of resources to meet the response time target without over-committing any of the resource types. In contrast, with no coordinating between controllers, the behaviour of the system is unpredictable e.g., the application performance may be met but at the expense of over-provisioning of one of the resources, or application crashing due to severe resource shortage as a result of conflicting decisions.
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| 3. |
- Krzywda, Jakub, et al.
(författare)
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A Sensor-Actuator Model for Data Center Optimization
- 2015
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Ingår i: 2015 International Conference on Cloud and Autonomic Computing (ICCAC). - : IEEE Computer Society. - 9781467395663 ; , s. 192-195
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Konferensbidrag (refereegranskat)abstract
- Cloud data centers commonly use virtualization technologies to provision compute capacity with a level of indirection between virtual machines and physical resources. In this paper we explore the use of that level of indirection as a means for autonomic data center configuration optimization and propose a sensor-actuator model to capture optimization-relevant relationships between data center events, monitored metrics (sensors data), and management actions (actuators). The model characterizes a wide spectrum of actions to help identify the suitability of different actions in specific situations, and outlines what (and how often) data needs to be monitored to capture, classify, and respond to events that affect the performance of data center operations.
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| 4. |
- Tomas, Luis, et al.
(författare)
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Reducing Noisy-Neighbor Impact with a Fuzzy Affinity-Aware Scheduler
- 2015
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Ingår i: 2015 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC). - New York : IEEE Computer Society. - 9781467395663 ; , s. 33-44
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Konferensbidrag (refereegranskat)abstract
- Overbooking techniques have been proven efficientto increase overall utilization of cloud datacenters. However,overbooking may also degrade applications performance as (atleast) some applications need to share physical resources suchas CPU or memory. Consequently, interference may increaseamong the virtual machines that share resources, the so callednoisy neighbors effect. We present an affinity-aware schedulerto reduce the impact of such interference. A fuzzy logic engineaccounts for the uncertainty in these environments and estimateswhich CPU cores are currently more suitable for each incomingapplication. This helps the scheduler make virtual machine tophysical resource mapping decisions, also known as vcpu pinning.An experimental evaluation based on a combination of interactiveservices and batch applications confirms that our affinity-awarefuzzy scheduler reduces the interference among applications,enabling more predictable performance and consequently saferoverbooking.
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