Performance Modeling and Design of Computer Systems

Harchol-Balter, Mor

doi:10.1017/cbo9781139226424

Cited by 472 publications

(138 citation statements)

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“…Fig. 8 also shows the predicted 95 th percentile latency using an M/G/n queuing model [22] (where n = number of threads). The latencies predicted by the queuing model would be realized if there were no overhead to adding threads (i.e., if service times stayed constant).…”

Section: Case Studymentioning

confidence: 99%

Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

Kasture

Sánchez

2016

2016 IEEE International Symposium on Workload Characterization (IISWC)

143

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Abstract-Latency-critical applications, common in datacenters, must achieve small and predictable tail (e.g., 95 th or 99 th percentile) latencies. Their strict performance requirements limit utilization and efficiency in current datacenters. These problems have sparked research in hardware and software techniques that target tail latency. However, research in this area is hampered by the lack of a comprehensive suite of latency-critical benchmarks.We present TailBench, a benchmark suite and evaluation methodology that makes latency-critical workloads as easy to run and characterize as conventional, throughput-oriented ones. TailBench includes eight applications that span a wide range of latency requirements and domains, and a harness that implements a robust and statistically sound load-testing methodology. The modular design of the TailBench harness facilitates multiple load-testing scenarios, ranging from multi-node configurations that capture network overheads, to simplified single-node configurations that allow measuring tail latency in simulation. Validation results show that the simplified configurations are accurate for most applications. This flexibility enables rapid prototyping of hardware and software techniques for latency-critical workloads.

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Section: Case Studymentioning

confidence: 99%

Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

Kasture

Sánchez

2016

2016 IEEE International Symposium on Workload Characterization (IISWC)

143

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“…The Application and Replication service traffic streams generally have time-varying rates at which data need to be sent. In this paper, we assume the traffic streams to be non-homogeneous Poisson processes [7]. The VM image flow is also exogenous to the traffic management, but for reasons outlined earlier it can be assumed to instead arrive in bulks at given time intervals.…”

Section: B Flows Differentiation and Traffic Modelmentioning

confidence: 99%

A control theoretical approach to non-intrusive geo-replication for cloud services

Dürango

Tärneberg

Tomás

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-Complete data center failures may occur due to disastrous events such as earthquakes or fires. To attain robustness against such failures and reduce the probability of data loss, data must be replicated in another data center sufficiently geographically separated from the original data center. Implementing geo-replication is expensive as every data update operation in the original data center must be replicated in the backup. Running the application and the replication service in parallel is cost effective but creates a trade-off between potential replication consistency and data loss and reduced application performance due to network resource contention. We model this trade-off and provide a control-theoretical solution based on Model Predictive Control to dynamically allocate network bandwidth to accommodate the objectives of both replication and application data streams. We evaluate our control solution through simulations emulating the individual services, their traffic flows, and the shared network resource. The MPC solution is able to maintain a consistent performance over periods of persistent overload, and is quickly able to indiscriminately recover once the system return to a stable state. Additionally, the MPC balances the two objectives of consistency and performance according to the proportions specified in the objective function.

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“…Quantity ρ = λ/nµ denotes the server farm load. It is known [9] that, for system stability, the condition 0 ≤ρ<1 is to be satisfied, in other words the condition 0 ≤λ< nµ. We shall use the following notation to define the Sf parameters: T setup = server mean setup time, P on = busy-server power absorption in the on state, P idle = idle-server power absorption, P off = off-server absorption, and S = average job service time.…”

Section: Power and Energy Systems IIImentioning

confidence: 99%

“…It is known [9,10] that,in queueing systems, the SPTF minimizes the system mean response time. So, we conjecture that by using the SPTF discipline in server farm systems, the Sfdebtto pay inresponse time is smaller than with abstract disciplines.…”

Section: Server Farm Power and Qos Evaluation Indicesmentioning

confidence: 99%

Power Management of Server Farms

Iazeolla¹,

Pieroni²

2014

AMM

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Keywords: server farm, server farm modeling, server farm QoS, server farm power management, server farm performance management, server farm power and performance management. AbstractThe power management of server farms (Sf) is becoming a relevant problem in economical terms. Server farms totalize millions of servers all over the world that need to be electrically powered. Research is thus expected to investigate into methods to reduce Sf power consumption. However, saving power may turn into waste of performance (high response_times), in other words, into waste of Sf Quality of Service(QoS). By use of a Sfmodel, this paper investigates Sf power management strategies that look at compromises between power-saving and QoS. Various optimizing Sf power management policies are studied combined with the effects of job queueing disciplines. The (policy, discipline) pairs, or strategies, that optimize the Sf power consumption (minimum absorbed Watts), the Sf performance (minimum response_time), and the Sf performance-per-Watt (minimum response_time-per-Watt) are identified. By use of the model, the work the server-manager has to do to direct hisSf is greatly simplified, since the universe of all possible (π, δ) strategies he needs to choose from is drastically reduced to a very small set of most significant strategies.

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Performance Modeling and Design of Computer Systems

Cited by 472 publications

References 0 publications

Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

A control theoretical approach to non-intrusive geo-replication for cloud services

Power Management of Server Farms

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