Measuring interference between live datacenter applications

Kambadur, Melanie; Moseley, Tipp; Hank, Rick; Kim, Martha A.

doi:10.1109/sc.2012.78

Cited by 76 publications

(50 citation statements)

References 49 publications

(70 reference statements)

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“…To minimize the interference of VMs colocated on the same physical server [31], we assume that dynamic VM placement is carried out so as to place on each server only VMs exhibiting as little interference as possible. These mechanisms, however, are outside the scope of this paper, and we assume that existing techniques are used (e.g., [32][33][34][35][36][37][38][39] for dynamic and contention-aware VM placement and [40,41] for admission control).…”

Section: System Model and Problem Definitionmentioning

confidence: 99%

FC2Q: exploiting fuzzy control in server consolidation for cloud applications with SLA constraints

Anglano

Canonico

Guazzone

2014

Concurrency and Computation

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SUMMARYModern cloud data centers rely on server consolidation (the allocation of several virtual machines on the same physical host) to minimize their costs. Choosing the right consolidation level (how many and which virtual machines are assigned to a physical server) is a challenging problem, because contemporary multitier cloud applications must meet service level agreements in face of highly dynamic, nonstationary, and bursty workloads. In this paper, we deal with the problem of achieving the best consolidation level that can be attained without violating application service level agreements. We tackle this problem by devising fuzzy controller for consolidation and QoS (FC2Q), a resource management framework exploiting feedback fuzzy logic control, that is able to dynamically adapt the physical CPU capacity allocated to the tiers of an application in order to precisely match the needs induced by the intensity of its current workload. We implement FC2Q on a real testbed and use this implementation to demonstrate its ability of meeting the aforementioned goals by means of a thorough experimental evaluation, carried out with real-world cloud applications and workloads. Furthermore, we compare the performance achieved by FC2Q against those attained by existing state-of-the-art alternative solutions, and we show that FC2Q works better than them in all the considered experimental scenarios.

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Section: System Model and Problem Definitionmentioning

confidence: 99%

FC2Q: exploiting fuzzy control in server consolidation for cloud applications with SLA constraints

Anglano

Canonico

Guazzone

2014

Concurrency and Computation

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“…Kambadur et al [35] propose using remote machines to analyze profile data obtained from live datacenter applications using Google Wide Profiler [36]. This technique has a very low overhead; however, since they collect profiles on live applications, but process them on remote machines, the round-times are too large to make phase-sensitive analysis/scheduling.…”

Section: Related Workmentioning

confidence: 99%

Quality Time: A simple online technique for quantifying multicore execution efficiency

Gupta

Sampson

Taylor

2014

2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

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Abstract-In order to increase utilization, multicore processors share memory resources among an increasing number of cores. This sharing leads to memory interference, which in turn leads to a non-uniform degradation in the execution of concurrent applications, even in the presence of fairness mechanisms. Many utilities rely on application CPU Time both for measuring resource usage and inferring application progress. These utilities are therefore directly affected by the distorting effects of multicore interference on the representativeness of CPU Time as a proxy for progress. This makes reasoning about myriad properties from fairness, to QoS, to throughput optimality very difficult in consolidated environments, such as IaaS.We introduce the notion of Quality Time, which provides a measure of application progress analogous to CPU Time's measure of resource usage, and we propose a simple online sampling-based technique to approximate Quality Time with high accuracy. We have implemented three user-space tools called Qtime, Qtop, and Qplacer. Qtime can attach to an application to calculate its Quality Time online, Qtop is a dashboard that monitors the Quality Times of all applications on the system, and Qplacer leverages Quality Time information to find better application placements and improve overall system quality. With Quality Time, we are able to reduce the error in inferring execution efficiency from 150.3% to 25.1% in the worst case and from 30.0% to 7.5% on average. Qplacer can increase average system throughput by 3.2% when compared to static application placement.

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“…The tail latency of these key-value accesses decides the response time of the end-user request, which is directly associated with the user experience and the revenue [4], [6]. Nevertheless, because the performance of servers is time-varying [5], [16], the tail latency is hard to be guaranteed, and may become long beyond expectation in certain condition. Recent study shows that the 99 th percentile latency can be one order of magnitude larger than the median latency [5], indicating that there is a large space to cut the tail latency of key-value accesses.…”

Section: Introductionmentioning

confidence: 99%

“…One reason is that the service time of keys are time varying, as the performance of server is influenced by many factors [5], [16]. The other reason is that the size of the waiting keys at server is unknown, due to the large degree of concurrency in key-value access.…”

Section: Introductionmentioning

confidence: 99%

Tars: Timeliness-Aware Adaptive Replica Selection for Key-Value Stores

Jiang

Fang

Xie

et al. 2017

2017 26th International Conference on Computer Communication and Networks (ICCCN)

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Abstract-In current large-scale distributed key-value stores, a single end-user request may lead to key-value access across tens or hundreds of servers. The tail latency of these key-value accesses is crucial to the user experience and greatly impacts the revenue. To cut the tail latency, it is crucial for clients to choose the fastest replica server as much as possible for the service of each key-value access. Aware of the challenges on the time varying performance across servers and the herd behaviors, an adaptive replica selection scheme C3 is proposed recently. In C3, feedback from individual servers is brought into replica ranking to reflect the time-varying performance of servers, and the distributed rate control and backpressure mechanism is invented. Despite of C3's good performance, we reveal the timeliness issue of C3, which has large impacts on both the replica ranking and the rate control, and propose the Tars (timeliness-aware adaptive replica selection) scheme. Following the same framework as C3, Tars improves the replica ranking by taking the timeliness of the feedback information into consideration, as well as revises the rate control of C3. Simulation results confirm that Tars outperforms C3.

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Measuring interference between live datacenter applications

Cited by 76 publications

References 49 publications

FC2Q: exploiting fuzzy control in server consolidation for cloud applications with SLA constraints

FC2Q: exploiting fuzzy control in server consolidation for cloud applications with SLA constraints

Quality Time: A simple online technique for quantifying multicore execution efficiency

Tars: Timeliness-Aware Adaptive Replica Selection for Key-Value Stores

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