FUNNEL：评估网络服务软件变更的影响
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"FUNNEL: Assessing Software Changes in Web-based Services"
这篇研究论文“FUNNEL: Assessing Software Changes in Web-based Services”聚焦于在基于互联网的服务中评估软件变更的影响，特别是针对性能变化的检测。这对于运维团队至关重要，因为它使得当软件更新导致性能意外下降时，可以及时回滚更改。然而，手动检查数百万次的性能测量数据是不现实的。
FUNNEL（可能是“快速且鲁棒的性能评估工具”的缩写）是作者提出的一种自动化工具，旨在快速、稳健地识别软件发布后可能引发的性能问题。该工具的设计目标是解决大规模服务中的性能监控挑战，通过自动化手段提高问题发现的效率和准确性。
论文的主要贡献可能包括以下几点：
1. **自动化性能分析**：FUNNEL提供了一种自动化的解决方案，能够处理大量性能数据，从而减少了人工干预的需求，提高了评估软件变更性能影响的速度。
2. **异常检测算法**：FUNNEL可能包含了一种或多种异常检测算法，用于识别性能指标的异常变化，这些变化可能是由于软件变更导致的。
3. **实时监控与反馈**：鉴于Web服务的实时性，FUNNEL可能会实时监测性能指标，并在检测到性能下降时立即触发警报或自动响应机制。
4. **鲁棒性与适应性**：由于Web服务的复杂性和动态性，FUNNEL设计为对各种类型和规模的软件变更具有鲁棒性，能适应不断变化的环境。
5. **决策支持**：FUNNEL可能还提供了决策支持功能，帮助运维团队判断是否需要回滚变更，以及何时进行回滚，从而减少服务中断和用户满意度下降的风险。
6. **实验验证与评估**：论文可能包括了使用真实世界的数据集进行的实验，以证明FUNNEL的有效性和效率，并与其他性能监控工具进行了对比。
这篇研究论文为互联网服务的性能管理提供了一个创新的工具，通过自动化和智能化的方式提升了运维团队的响应能力，有助于维护服务的稳定性和用户体验。其方法和成果对于从事云服务、大数据处理和分布式系统运维的IT专业人士具有很高的参考价值。
1939-1374 (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSC.2016.2539945, IEEE
Transactions on Services Computing
JOURNAL OF IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL. X, NO. X, X 2016 3
2.1 Scope of Studied Software Changes
In this paper, we focus on two types of software changes on servers
in large Web-based services, software upgrades and configuration
changes, for the following three reasons. (1) The operations
team typically care about the unexpected consequences that are
potentially due to these planned changes; (2) These changes are
controllable by the operations team via command line interfaces
and observable in logs; (3) We have observed that these two types
constitute more than 90% of the tens of thousands of software
changes in our data.
Software Upgrades. With the current rapid evolution of
the Internet, new features are continuously being deployed with
software upgrades. The operations team also conducts software
upgrades to fix bugs or improve service performance. In a large
service, it is often the case that one software upgrade implements
multiple features or bug fixes, and FUNNEL considers such a
software upgrade as a whole. FUNNEL decides whether the
whole software upgrade introduces any KPI change but does
not attempt to distinguish which individual feature or bug fix
introduces KPI changes. In addition, the web services in our
scenario usually trigger a software upgrade when operators try
to patch the software, and thus we classify the patches as software
upgrades.
Configuration Changes. Using command line interfaces, the
operations team can change the configurations by using specific
commands. The configuration change can be in the operating sys-
tem (OS) or infrastructure software (e.g., a configuration change
in Apache), service configuration (e.g., an increase in the number
of threads in a service process by command lines), deployment
scale (e.g., an increase in the number of servers where a service
is deployed), or data source (e.g., an update to the strategy that
calculates the valid page view counts).
With above focuses, the following perspectives are out of
scope for this paper.
1) We do not consider the software changes on the network
devices such as routers and switches, which have been
already studied in depth in [6], [7], [14];
2) We do not consider software changes that were external
to the company, e.g., a change in a peer company, since
these changes might be invisible to the studied company’s
operations team.
3) We do not explicitly consider the interactions across
multiple concurrent or consecutive software changes on
a same server, which can be considered as one combined
change as a straw man approach.
More detailed studies along the last three directions are left as
future work.
2.2 KPI
In the studied Web-based services, there are hundreds of thousands
of servers providing various types of services. Each service (e.g.,
search, web mail, social networking) runs on one or more servers
with a specific process on each server. An instance denotes a
process of a specific service on a specific server. A KPI is a
performance metric of a given server/service/process. There are
three types of KPIs that need to be monitored for software changes
assessment: server KPIs, instance KPIs and service KPIs. The
operations team deploys an agent on each server to monitor the
status of each instance and collect the KPIs of all instances
Search engine service
Server 1 Server 2 Server n
Instance 1 Instance n
Instance
PVC
...
...
Instance 2
Instance
PVRD
Instance
AFC
...
...
Instance
PVC
Instance
PVRD
Instance
AFC
...
CPU
utilization
Memory
utilization
NIC
throughput
Service
PVC
Service
PVRD
Service
AFC
...
PVC = Page view count
PVRD = Page view response delay
ACF = Access failure count
KPI
Fig. 1. The relationship among service, server, instance and KPI
continuously. For example, immediately after the process serves
a customer with some Web page view, the page view count is
incremented and a new page view response delay is recorded.
In addition, by analyzing server log files that record the system
status, the agent is able to periodically collect server KPIs, such
as CPU utilization, memory utilization, and NIC throughput. A
service KPI is an aggregation of all instance KPIs in the service.
Fig. 1 shows an example of the relationship among service (search
engine service), server (server 1, server 2, ..., server n), instance
(instance 1, instance 2, ..., instance n) and KPI (page view count,
page view response delay, access failure count, CPU utilization,
memory utilization, and NIC throughput).
After collecting the measurements of KPIs of servers and
instances, the agent on each server delivers the measurements
via datacenter networks to a centralized Hadoop-based database,
which also stores the service KPIs aggregated based on the KPIs
of the instances. The database also provides a subscription tool for
other systems, such as FUNNEL, to periodically receive the sub-
scribed measurements based on the server, instance, and service.
The data collection interval at the servers is typically 1 minute,
and thus the time granularity of the input KPI measurements
of FUNNEL is 1 minute. If we set the time granularity of the
KPI measurements larger, the detection delay of FUNNEL will
be larger. Although the accuracy of FUNNEL may be impacted
by the time granularity of the measurements [18], the accuracy
of FUNNEL is relatively good (see Section 4.2) with the time
granularity of 1 minute, we set the time granularity of the KPI
measurements as 1 minute. Within one second, the measurements
subscribed by FUNNEL are pushed to FUNNEL via datacenter
networks.
In large services, there might exist some KPIs of dubious
quality. To the best of our knowledge, there is no previous work on
eliminating low-quality KPIs in Web-based services. In this paper,
we do not focus on eliminating low-quality KPIs either. FUNNEL
detects all KPI changes in the impact set regardless of the quality
of the KPI, and delivers the results to the operations team. The
operations team will then determine whether the performance
changes in the low-quality KPIs are induced by the software
change or not.
As described in [19], even the operations team does not know
exactly what is a “good” threshold for a specific KPI, e.g., due to
seasonal variation. Therefore, it is difficult to obtain the expected
values from the operations team for FUNNEL, and we do not give
a threshold for any KPI in this paper.
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