| 1. |
- Bayuh Lakew, Ewnetu, et al.
(författare)
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SmallTail : Scaling Cores and Probabilistic Cloning Requests for Web Systems
- 2018
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Ingår i: 15TH IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC 2018). - : IEEE. - 9781538651391 ; , s. 31-40
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Konferensbidrag (refereegranskat)abstract
- Users quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the performance variability is higher. The prior art shows the prominence of increasing the request redundancy to curtail the latency either in the off-line setting or without scaling-in cores of virtual machines. In this paper, we propose an opportunistic scaler, termed SmallTail, which aims to achieve stringent targets of tail latency while provisioning a minimum amount of resources and keeping them well utilized. Against dynamic workloads, SmallTail simultaneously adjusts the core provisioning per VM and probabilistically replicates requests so as to achieve the tail latency target. The core of SmallTail is a two level controller, where the outer loops controls the core provision per distributed VMs and the inner loop controls the clones in a finer granularity. We also provide theoretical analysis on the steady-state latency for a given probabilistic replication that clones one out of N arriving requests. We extensively evaluate SmallTail on three different web systems, namely web commerce, web searching, and web bulletin board. Our testbed results show that SmallTail can ensure the 95th latency below 1000 ms using up to 53% less cores compared to the strategy of constant cloning, whereas scaling-core only solution exceeds the latency target by up to 70%.
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| 2. |
- Krzywda, Jakub, 1989-, et al.
(författare)
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ALPACA : Application Performance Aware Server Power Capping
- 2018
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Ingår i: ICAC 2018. - : IEEE Computer Society. - 9781538651391 ; , s. 41-50
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Konferensbidrag (refereegranskat)abstract
- Server power capping limits the power consumption of a server to not exceed a specific power budget. This allows data center operators to reduce the peak power consumption at the cost of performance degradation of hosted applications. Previous work on server power capping rarely considers Quality-of-Service (QoS) requirements of consolidated services when enforcing the power budget. In this paper, we introduce ALPACA, a framework to reduce QoS violations and overall application performance degradation for consolidated services. ALPACA reduces unnecessary high power consumption when there is no performance gain, and divides the power among the running services in a way that reduces the overall QoS degradation when the power is scarce. We evaluate ALPACA using four applications: MediaWiki, SysBench, Sock Shop, and CloudSuite’s Web Search benchmark. Our experiments show that ALPACA reduces the operational costs of QoS penalties and electricity by up to 40% compared to a non optimized system.
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| 3. |
- Mehta, Amardeep, 1985-, et al.
(författare)
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Distributed Cost-Optimized Placement for Latency-Critical Applications in Heterogeneous Environments
- 2018
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Ingår i: Proceedings of the IEEE 15th International Conference on Autonomic Computing (ICAC). - : IEEE Computer Society. - 9781538651391 ; , s. 121-130
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Konferensbidrag (refereegranskat)abstract
- Mobile Edge Clouds (MECs) with 5G will create new opportunities to develop latency-critical applications in domains such as intelligent transportation systems, process automation, and smart grids. However, it is not clear how one can costefficiently deploy and manage a large number of such applications given the heterogeneity of devices, application performance requirements, and workloads. This work explores cost and performance dynamics for IoT applications, and proposes distributed algorithms for automatic deployment of IoT applications in heterogeneous environments. Placement algorithms were evaluated with respect to metrics including number of required runtimes, applications’ slowdown, and the number of iterations used to place an application. Iterative search-based distributed algorithms such as Size Interval Actor Assignment in Groups (SIAA G) outperformed random and bin packing algorithms, and are therefore recommended for this purpose. Size Interval Actor Assignment in Groups at Least Utilized Runtime (SIAA G LUR) algorithm is also recommended when minimizing the number of iterations is important. The tradeoff of using SIAA G algorithms is a few extra runtimes compared to bin packing algorithms.
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| 4. |
- Nylander, Tommi, et al.
(författare)
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Cloud Application Predictability through Integrated Load-Balancing and Service Time Control
- 2018
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Ingår i: Proceedings of the 15th IEEE International Conference on Autonomic Computing. - 9781538651391
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Konferensbidrag (refereegranskat)abstract
- Cloud computing provides the illusion of infinite capacity to application developers. However, data center provisioning is complex and it is still necessary to handle the risk of capacity shortages. To handle capacity shortages, graceful degradation techniques sacrifice user experience for predictability. In all these cases, the decision making policy that determines the degradation interferes with other decisions happening at the infrastructure level, like load-balancing choices. Here, we reconcile the two approaches, developing a load-balancing strategy that also handles capacity shortages and graceful degradation when necessary. The proposal is based on a sound control-theoretical approach. The design of the approach avoids the pitfalls of interfering control decisions. We describe the technique and provide evidence that it allows us to achieve higher performance in terms of emergency management and user experience.
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