Flexible User-Defined Availability in the Cloud
Total Page：16
File Type：pdf, Size：1020Kb
Availability Knob: Flexible User-Defined Availability in the Cloud Mohammad Shahrad David Wentzlaff Princeton University fmshahrad,[email protected] Abstract Categories and Subject Descriptors C.4 [Computer Sys- Failure is inevitable in cloud environments. Finding the root tems Organization]: PERFORMANCE OF SYSTEMS— cause of a failure can be very complex or at times nearly im- Reliability, availability, and serviceability; K.6.0 [MAN- possible. Different cloud customers have varying availability AGEMENT OF COMPUTING AND INFORMATION SYS- demands as well as a diverse willingness to pay for avail- TEMS]: General—Economics ability. In contrast to existing solutions that try to provide higher and higher availability in the cloud, we propose the Availability Knob (AK). AK provides flexible, user-defined, 1. Introduction availability in IaaS clouds, allowing the IaaS cloud customer to express their desire for availability to the cloud provider. Understanding and overcoming failures in computing sys- Complementary to existing high-reliability solutions and not tems has always been an arduous challenge. The emergence requiring hardware changes, AK enables more efficient mar- of billion-transistor designs along with smaller and less reli- kets. This leads to reduced provider costs, increased provider able transistors have led to an increasing rate of hardware profit, and improved user satisfaction when compared to an failures. Nevertheless, with all of its complexities, hard- IaaS cloud with no ability to convey availability needs. We ware is only one source of failure. The entire computing leverage game theory to derive incentive compatible pricing, stack, including hardware and software, has faced tremen- which not only enables AK to function with no knowledge dous growth in both scale and complexity, increasing the of the root cause of failure but also function under adver- probability that failure occurs in such large systems. More- sarial situations where users deliberately cause downtime. over, failure analysis in large-scale computing systems has We develop a high-level stochastic simulator to test AK in become extremely complicated due to the deployment of large-scale IaaS clouds over long time periods. We also pro- hundreds of thousands of machines with complex interac- totype AK in OpenStack to explore availability-API trade- tions. offs and to provide a grounded, real-world, implementation. Many researchers have studied failure along with its Our results show that deploying AK leads to more than 10% sources and consequences in large-scale computing infras- cost reduction for providers and improves user satisfaction. tructures [41, 57, 59] as well as cloud environments [19, It also enables providers to set variable profit margins based 22, 65]. Failure in the cloud can have major negative con- on the risk of not meeting availability guarantees and the dis- sequences such as propagated service disruptions [66], con- parity in availability supply/demand. Variable profit margins siderable energy waste [31], and more importantly negative enable cloud providers to improve their profit by as much as effects on provider’s reputation [33]. Cloud providers have 20%. used many effective techniques to increase the reliability of their infrastructures, but to date, failures are still frequent. Keywords flexible availability, cloud availability, failure- One of the main concerns of cloud customers is availabil- aware scheduling, cloud economics, SLA ity. According to Pan et al. [50], 73% of customer/provider service level agreement (SLA) negotiations included avail- ability concerns. Availability is considered to be both the Permission to make digital or hard copies of all or part of this work for personal or number one obstacle to the growth of cloud computing [16] classroom use is granted without fee provided that copies are not made or distributed and the most important opportunity for cloud providers [53]. for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the At the same time, surveys [35] illustrate that different cus- author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or tomers have different downtime demands, depending on republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. their application. For instance, a low-end, non-critical web SoCC ’16, October 05 - 07, 2016, Santa Clara, CA, USA. hosting service neither has the same availability demand nor c 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-4525-5/16/10. $15.00. the ability to pay for availability that a mission-critical bank- DOI: http://dx.doi.org/10.1145/2987550.2987556 ing service has. Infrastructure as a service (IaaS) cloud providers gener- Fixed Availability API ally use a diverse set of hardware components in their data centers. Different generations of one Intel processor fam- ily have different reliability, availability, and serviceability (RAS) features [11]. Undoubtedly, providers like Amazon Cloud Web Services that use different families and generations of Scheduler Intel processors [3] have considerable reliability heterogene- ity. Moreover, running different operating systems on the same processor can lead to various downtimes [11]. Like- wise, different IaaS components, such as memory DIMMs, disks, network switches, power supplies, etc., can also result (a) in varied reliability. The inevitability of failures, different market demands for availability based on application and IaaS customer needs, and the heterogeneous nature of component reliability, all require a shift from the conventional approach of maximiz- ing availability. Like many researchers have alluded to, there Cloud Scheduler is inefficiency in using fixed availability service level objec- tives (SLOs) [60, 63], and we believe that both providers and customers can benefit from flexible availability. In this paper, we propose the Availability Knob (AK) for IaaS clouds. AK enables cloud providers to serve customers (b) with various availability demands. On the customer side, AK allows customers to express their true availability needs and be charged accordingly. Providers benefit from the economic Figure 1. Top (a) shows how availability needs are not advantages of such flexibility and customers need only to conveyed to the cloud scheduler. Bottom (b) shows how pay for the minimum availability they require. We explore AK enables communicating availability information and can the implications of flexible availability on SLAs and derive schedule based on machine reliability. Different colors rep- economic incentives to prevent customers from gaming the resent different availability/reliability. system or providers from deliberately violating SLOs. We then explore and discuss how AK can make more profit for cloud providers. In order to evaluate AK and study different ments (SLAs) and the scheduler. We also mention the ex- design trade-offs, we developed a stochastic cloud simulator, isting availability monitoring tools/techniques which can be which enables simulating large-scale infrastructure for ex- utilized in Section 2.3. tended periods of time; something necessary for our failure- 2.1 SLA for Flexible Availability related system. We also implement an AK prototype using the OpenStack platform. In our evaluation, we show that AK Availability is a crucial metric for quality of service and has the potential to reduce the cost for the cloud provider, in- is included in almost all cloud SLAs. While availability crease provider profit, and improve user satisfaction (meet- is generally defined as the uptime of a service in a spe- ing user availability needs). cific time period, many assumptions can affect the way it is calculated. For instance, different cloud providers use dif- ferent service guarantee granularities, guarantee exclusions, 2. The Availability Knob time granularities, and measurement periods to measure the In contrast to typical IaaS clouds that only offer a fixed avail- availability [18]. According to a comparison [67] of pub- ability guarantee to customers, the Availability Knob (AK) lic cloud SLAs, a monthly measurement period is the most allows IaaS cloud customers to request their desired avail- common period, service granularities range from a single in- ability objectives and be charged accordingly. AK permits stance (VM) to all of a customer’s VMs in multiple avail- a provider to employ the failure history of its infrastructure ability zones (Amazon EC2), and time granularities differ to wisely serve different availability demands. It also allows from a minute to an hour. The way providers track avail- providers to exploit the usual heterogeneity of components ability can also be different. For instance, a service provider in a profitable manner. Figure 1 conceptually depicts how was reported to track only the internal system availability AK enables customers to address their availability demands rather than user accessibility [46]. Moreover, each provider and schedules them based on machine reliability. excludes certain events from availability measurement. For In this section, we discuss the two IaaS elements that example, under seven conditions, Amazon EC2 excludes should be changed to accomplish AK: service level agree- any downtime from service commitment calculations [2], Parameter Description