FECBench: A Holistic Interference-aware Approach for Application Performance Modeling

Abstract

Services hosted in multi-tenant cloud platforms often encounter performance interference due to contention for non-partitionable resources, which in turn causes unpredictable behavior and degradation in application performance. To grapple with these problems and to define effective resource management solutions for their services, providers often must expend significant efforts and incur prohibitive costs in developing performance models of their services under a variety of interference scenarios on different hardware. This is a hard problem due to the wide range of possible co-located services and their workloads, and the growing heterogeneity in the runtime platforms including the use of fog and edge-based resources, not to mention the accidental complexity in performing application profiling under a variety of scenarios. To address these challenges, we present FECBench (Fog/Edge/Cloud Benchmarking), an open source framework comprising a set of 106 applications covering a wide range of application classes to guide providers in building performance interference prediction models for their services without incurring undue costs and efforts. Through the design of FECBench, we make the following contributions. First, we develop a technique to build resource stressors that can stress multiple system resources all at once in a controlled manner, which helps to gain insights into the impact of interference on an application's performance. Second, to overcome the need for exhaustive application profiling, FECBench intelligently uses the design of experiments (DoE) approach to enable users to build surrogate performance models of their services. Third, FECBench maintains an extensible knowledge base of application combinations that create resource stresses across the multidimensional resource design space. Empirical results using realworld scenarios to validate the efficacy of FECBench show that the predicted application performance has a median error of only 7.6% across all test cases, with 5.4% in the best case and 13.5% in the worst case.

Linearize, predict and place

“…Other resources, such as memory and I/O, also need to be accounted for as has been presented in earlier studies [9]. Additionally, the linearization transformation for join operators currently assumes interleaving, i.e., OR semantics.…”

A Comprehensive Study on Design and Implementation of Bigdata-As-A-Service Stratum of Lifecycle Management of Smart Internet of Things Applications

“…We determined what number of more machines to arrangement dependent on the distinction between the present status and wanted state. The Stratum Resource Manager conveys the ML model on these machines and starts the expectation administration consequently to deal with dynamic remaining burden proactively [16].…”

“…IV-C2, we profile the forecast administration on the specific equipment before sending it on the group of machines. In light of the quantity of approaching solicitations (dynamic outstanding burden), we scale here and there the quantity of ML model containers to ensure the pre-characterized QoS in an occasion driven way utilizing the Docker swarm group management instrument, as showed in our ongoing work [16].…”

Be Good Neighbors: A Novel Application Isolation Metric Used to Optimize the Initial Container Placement in CaaS

“…However, few studies in the literature take both the two objectives into consideration for initial container placement (ICP) in CaaS. On the other hand, the underlying performance modeling methods adopted by most related works [11]- [17] usually establish a relationship function between performance and resource requirements. Due to the required massive tests and complex computations, these methods are always blocked in a large-scale application scenario.…”

“…So an application when being deployed is often "excluded" by others co-located in the same PM due to resource competitions; a servere Interference means an intensified competition. As such, an Interference often has an adverse impact on the service performance, e.g., lengthening the application response time [17]- [22]. As noted in [23], the best way to prevent Interference is "don't let such quarreling people be neighbors" in the initial stages.…”