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Taming Performance Variability caused by Client-Side Hardware Configuration

Georgia Antoniou, Haris Volos, Yiannakis Sazeides

TL;DR

This paper identifies the hardware configuration of the client side as an important source of performance variation that can affect the accuracy and the correctness of the conclusions of a study that analyzes the performance of microservices.

Abstract

Many online services running in datacenters are implemented using a microservice software architecture characterized by strict latency requirements. Consequently, this popular software paradigm is increasingly used for the performance evaluation of server systems. Due to the scale and complexity of datacenters, the evaluation of server optimization techniques is usually done on a smaller scale using a client-server model. Although the experimental details of the server side are excessively described in most publications, the client side is often ignored. This paper identifies the hardware configuration of the client side as an important source of performance variation that can affect the accuracy and the correctness of the conclusions of a study that analyzes the performance of microservices. This is partially attributed to the strict latency requirements of microservices and the small scale of the experimental environment. In this work we present, using a widely used online-service, several examples where the accuracy and the trends of the conclusions differ based on the configuration of the client-side. At the same time we show that the experimental evaluation time can be significantly affected by the hardware configuration of the client. All these provoke the discussion of the right way to configure the experimental environment for assessing the performance of microservices.

Taming Performance Variability caused by Client-Side Hardware Configuration

TL;DR

This paper identifies the hardware configuration of the client side as an important source of performance variation that can affect the accuracy and the correctness of the conclusions of a study that analyzes the performance of microservices.

Abstract

Many online services running in datacenters are implemented using a microservice software architecture characterized by strict latency requirements. Consequently, this popular software paradigm is increasingly used for the performance evaluation of server systems. Due to the scale and complexity of datacenters, the evaluation of server optimization techniques is usually done on a smaller scale using a client-server model. Although the experimental details of the server side are excessively described in most publications, the client side is often ignored. This paper identifies the hardware configuration of the client side as an important source of performance variation that can affect the accuracy and the correctness of the conclusions of a study that analyzes the performance of microservices. This is partially attributed to the strict latency requirements of microservices and the small scale of the experimental environment. In this work we present, using a widely used online-service, several examples where the accuracy and the trends of the conclusions differ based on the configuration of the client-side. At the same time we show that the experimental evaluation time can be significantly affected by the hardware configuration of the client. All these provoke the discussion of the right way to configure the experimental environment for assessing the performance of microservices.

Paper Structure

This paper contains 18 sections, 3 equations, 9 figures, 4 tables.

Figures (9)

  • Figure 1: Typical experimental methodology.
  • Figure 2: Performance evaluation of SMT impact on Memcached service latency with LP and HP clients. (a) Average Response Time (median) for HP/LP client and SMT ON/OFF server, (b) 99th Percentile Latency (median) for HP/LP client and SMT ON/OFF server, (c) Slowdown (avg) caused by disabling SMT on the Average Response Time for HP and LP client and (d) Slowdown (avg) caused by disabling SMT on the 99th Percentile Latency for HP and LP client.
  • Figure 3: Performance evaluation of C1E impact on Memcached service latency with LP and HP clients. (a) Average Response Time (median) for HP/LP client and C1E ON/OFF server, (b) 99th Percentile Latency (median) for HP/LP client and C1E ON/OFF server, (c) Slowdown (avg) caused by enabling C1E on the Average Response Time for HP and LP client and (d) Slowdown (avg) caused by enabling C1E on the 99th Percentile Latency for HP and LP client.
  • Figure 4: Performance evaluation of SMT and C1E impact on HDSearch service latency with LP and HP clients. (a) Average Response Time (median) for HP/LP client and SMT ON/OFF server, (b) 99th Percentile Latency (median) for HP/LP client and SMT ON/OFF server, (c) Average Response Time (median) for HP/LP client and C1E ON/OFF server and (d) 99th Percentile Latency (median) for HP/LP client and C1E ON/OFF server.
  • Figure 5: (a) Standard Deviation of Memcached for the Average Response Time with LP/HP client configuration and SMT ON/OFF server configuration, (b) Standard Deviation of HDSearch for the Average Response Time with LP/HP client configuration and SMT ON/OFF server configuration.
  • ...and 4 more figures