Cloud Computing: Meet the Players. Performance Analysis of Cloud Providers

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1 BASEL UNIVERSITY COMPUTER SCIENCE DEPARTMENT Cloud Computing: Meet the Players. Performance Analysis of Cloud Providers Distributed Information Systems (CS341/HS2010) Report based on D.Kassman, T.Kraska, S.Loesing. An Evaluation of Alternative Architectures For Transaction Processing in The Cloud paper Written by: Advisor: Sergej Poltorak Christoph Langguth Basel 2011

2 Content Introduction Distributed database architectures in cloud-computing Classic Partitioning Replication Distributed Control Caching Cloud Services Amazon (AWS) AWS MySQL AWS MySQL/R AWS RDS AWS SimpleDB AWS S Google Microsoft TPC-W benchmark Metrics Results of experiment Overview Scale-out Cost analysis Scale-up Bulkloading Conclusion...10 Reference

3 Introduction Cloud computing is one of the most advertised technologies by software vendors. There has been a great deal of hype about cloud computing. Major software vendors claim that they are either cloudenabled or cloud-enabling. Cloud computing promises a number of advantages for the deployment of data-intensive applications. It s possible to distinguish several of them: Cost reduction: pay-as-you-go business model, operational costs. You pay exactly for what you use. Infinite scalability: unlimited throughput by adding servers if the workload increases (elasticity) A reduced operational cost by automating IT tasks such as security patches and different updates. A reduced time-to-market by removing or simplifying the time-consuming hardware provisioning, purchasing, and deployment process. And more In this report first two promises are explored, whereas two last are hard to measure. Two first promises address several basic questions: How well do the offerings scale with an increasing workload? Can indeed infinite throughput be achieved? How expensive are these offerings and how does their cost/performance ration compare? How predictable is the cost with regard to changes in the workload From the first look offers from cloud computing providers looks similar. But when it comes to the end-to-end performance, the services vary dramatically in price and scalability. Three big players of the IT industry are considered: Amazon, Google, Microsoft. To evaluate the current offering database and workload of TPC-W benchmark was used. Cloud computing providers use different architectures that mostly effect large-scale data management and transaction workload in the cloud. In the following chapter several existing architectures will be introduced. 1 Distributed database architectures in cloud-computing 1.1 Classic Classic architecture is used for most database applications today. Requests from clients are dispatched by a load balancer to an available machine which runs a web and application server. The web server handles the (HTTP) requests from clients and the application server executes the application logic specified (e.g., in Java with embedded SQL). The embedded SQL is shipped to the database server which interprets this request returns a result, and possibly updates the database. All data and logs for persistence purpose, the database server stores on storage device. Basically, database server interaction with storage system involves shipping physical blocks of data (e.g., 64K blocks) using get and put requests. Advantage for such architectures is possibility to choose the best component at every layer from a range of product on the market. Second, at the storage and web/application layers scalability and elasticity can be achieved. The potential bottleneck is database server. If the database server is overloaded, the only way is to buy a bigger machine. But these machines are quite expensive. All in all classic architecture has limitations in scalability and cost. 1.2 Partitioning Another approach to distributed database architecture is partitioning. It doesn t have separate database server, but database is combined together with storage system. The database is logically partitioned and each partition is controlled by a separate database server. The partitioning can be transparent or visible to the application programmer. The database servers can run on cheap machines, using 3

4 many machines that each operate on fairly small data set in order to sustain the load. On the other hand partitioning has scalability limitation while dealing with changing workload: adding or removing machines in order to deal with higher query/update workload involves repartitioning the data and therefore moving data between machines. 1.3 Replication Third approach is replication architecture. Replication could be used to increase scalability and reliability of the system. It also contains several database servers and each database server controls a copy of the whole database (or partition of the database). The replication could be transparent (or visible) and the storage is associated to the database servers. In order to keep replicas consistent the ROWA (read-once, write all) protocol is used. This protocol is based on Master copy. If replication is not transparent, applications direct all update requests to the database server which controls the Master copy. After that Master server propagates all committed updates to the satellites when these updates have been successfully committed. If replication is transparent, then requests are routed automatically to the Master or a satellite. Cheap hardware can be used to run database servers. The satellites can run on cheap machines. Such architecture can scale-out and down nicely with the workload, if the workload is read-mostly. A satellite servers can be easily added or dropped depending on workload. Adding new satellite involves Master to copy database on new server. Potentially it means that for update-intensive workloads, the Master can become the bottleneck. 1.4 Distributed Control Distributed Control architecture models the database system as a distributed system. Architecture looks similar to previous, replication and partitioning. In this architecture the storage system is separated from the database servers and the database servers access concurrently and autonomously the shared data from the storage system. That have a huge impact on the implementation, performance and cost of a system. In order to synchronize read and write access to the shared data, distributed protocols which guarantee different levels of consistency can be applied. The database tier is merged with the web and application server tier, which means that database access if affected as library as part of the application server, but not providing separate database server processes. This architecture is potentially best match for cloud computing. It provides full scalability and elasticity at all tiers. Each HTTP request can be routed to any server so that full scalability can be achieved at that level. At storage layer, data can be partitioned or replicated, therefore it s possible to achieve scalability at this layer too. And cheap hardware at all layers can be used. It s worth to mention, that because of CAP theorem, it s not possible to achieve consistency, availability and resilience to network partitioning at the same time. For example, Amazon S3 service, which will be introduced later, can achieve eventual consistency. 4

5 1.5 Caching And the last architecture is caching. It can be combined with any other architecture. Caching can be integrated at the database server layer. The results of database queries are stored by dedicated cache servers. These servers keep the query results in their main memory so that accessing the cache is as fast as possible. Memcached is the most widely used opensource software to support such distributed main-memory caches. To keep cache memory consistent there are many different schemes for replication. In our case, the application controls cache consistency. Such approach is adopted by Google AppEngine. Cheap machines can be used for caching. Adding and dropping MemCache machines is trivial at any point at time. In the next chapter several services offered by three of the big cloud computing players will be described: Amazon, Google and Microsoft. 2 Cloud Services Services have difference in business model they follow, software components used at all tiers and the programming model. For performance experiments the architecture is most relevant. Another important aspect is HW configuration: for several services, the user must configure how many virtual machines are deployed. Only Google AppEngine allocates fully automatically hardware resources for all tiers depending on workload. SimpleDB and Azure automatically provision and adopt resources at the DB server and storage layers, but require manual configuration of the HW resources at the web/app server layer. 2.1 Amazon (AWS) Amazon is infrastructure as a service (Iaas) provider. Amazon provides a set of basic services to use computing infrastructure (CPU cycles, storage and network), install a platform (e.g., Tomcat web/app server and MySQL database server) and run applications on that platform. The basic Amazon services used in the experiments are EC2 (for CPU cycles), EBS (for storage service) and S3 (storage as keyvalue store). Only SimpleDB and RDS services are Amazon specific, others can be easily provided by other cloud providers and are declared as flexible AWS MySQL The first variant of Amazon services AWS MySQL follows classic, non cloud-enabled model to deploy enterprise web application. Tomcat v and MySQL v5.051 running on Ubuntu 8.04 software were used. In implementation, it was used varying number of EC2 machines in order to run the web/app servers and execute the (TPC-W benchmark) application logic. The number of EC2 machines varied depending on the workload. The MySQL server was run on a separate EC2 machine. As a storage system, EBS was used for both the database and the logs. EBS guarantees persistence (the EBS data is replicated) AWS MySQL/R AWS MySQL/R represents replication architecture. MySQL Replication v was used on a set of EC2 machines. In this case EC2 machines cheap local disks can be used for storing the database because the durability of the data was guaranteed by the Replication architecture. EBS was used for the logs of the Master copy. The replication is not transparent. Each application server maintains a 5

6 connection to the Master copy and connections to one satellite. Requests of updating transactions are handled by the Master, requests of read-only transactions are directed to the satellite associated to the application server AWS RDS RDS is a relational database service. It implements the same architecture as AWS MySQL, that is why we can expect both approaches to perform and cost similarly. The difference is that in RDS case user doesn t need to be worry about managing the deployment, patches, upgrades and backups. RDS comes in five sizes ranging from small to quadruple extra large database servers. That is the way to implement scale-up on the database tier AWS SimpleDB AWS SimpleDB service uses it own database service SimpleDB. It provides a simple interface which allows to insert, update and delete records. Additionally, it allows to retrieve records based on their key/values or based on ranges on primary and secondary key. It supports a low level of consistency (eventual) and does not synchronize concurrent read and write accesses. Since SimpleDB doesn t support SQL, SQL operator (i.e., joins) had to be implemented at the application level. Java library with these SQL operations was implemented additionally. As a result all the relevant base data from SimpleDB to the application servers was shipping and that resulted in poorer performance AWS S3 The last service offered by Amazon as an example of fourth architecture called Distributed Control is S3. It only provides a low level put/get interface, higher-level services (SQL query processing, indexing) had to be implemented as part of the application. Library provided the basic database features for implementing the TPC-W benchmark was implemented. Furthermore, the library implemented a protocol in order to synchronize concurrent accesses from multiple application servers to the same S3 objects. This protocol only supports eventual consistency. To improve performance, caching of S3 objects was carried out in the application servers. The integrated web/app/db server made use of Tomcat and the library to implement basic SQL constructs and consistency. 2.2 Google The second cloud computing big player is Google. It follows a platform as a service (PaaS) approach. Google AppEngine doesn t provide control over the computing resources for user. It automatically scales the resources consumed by an application out and down. Google supports simplified SQL dialect, referred to as GQL, which doesn t implement group by, joins, LIKE predicates. The missing functionality was implemented in Java as part of library in the same way as for the AWS S3 and AWS SimpleDB. As it was mentioned before Google AppEngine supports a MemCache. 2.3 Microsoft The last big player in market of cloud computing is Microsoft. It s based on Windows, SQL Server and.net. To experiment with Azure, the TPC-W benchmark in C# with embedded SQL was implemented. Like Amazon and Google, Microsoft has not yet published full details on the implementation of Azure. All three cloud providers charge for storage, network traffic and CPU hours. What is more, Azure charges monthly flat fee depending on the database size with unlimited database connectivity. 3 TPC-W benchmark TPC-W benchmark is suitable benchmark for investigating end-to-end performance of enterprise web applications that involve transaction processing. Benchmark models an online bookstore with a mix of fourteen different kinds of requests: searching for products, displaying products and update functions. What is more, benchmark specifies three workload mixes. In all experiments ordering mix was used because it is the most update-intensive mix. Finally, TPC-W allows to study different workloads with regard to the request throughput. For this purpose TPC-W benchmark models emulated browsers (EB). Each EB simulates one user who issues a request, waits for the answer, and then issues the next request 6

7 after a specified waiting time. EB is varying from 1 (500 request/h) to 9000 (1250 request/s). One benchmark request is 6.6 HTTP requests. 3.1 Metrics Original TPC-W benchmark has two metrics (throughput metric and cost per request) and several other metrics were introduced. The list of metrics used: WIPS(EB) - throughput metric, number of valid TPC-W requests/s. Request is valid in case if user got answer of the request in appreciate time range depending on query (from 3s to 20s). Benchmark results may only be reported if 90% of all requests of every category are valid Cost/WIPS(EB) relates the performance (WIPS) to the total cost of ownership of computer system. In experiments, cost was computed by considering the bills had to pay. CostPerDay(EB) the total cost of running the benchmark with certain number of EBs for 24 hours. S(Cost/WIPS) standard deviation of the Cost/WIPS for a set of different EB settings. The goal of performance evaluation was to study the scalability and cost of alternative cloud service offerings under different workloads. TPC-W benchmark was implemented on all services. In all experiments, the emulated browsers (EBs) of the TPC-W benchmark were run on EC2 machines in the Amazon cloud. 4 Results of experiment 4.1 Overview The table summarize the overall results of the study. The first column gives the maximum throughput (WIPS) that could be achieved for each variant. The second and third columns list the COST/WIPS for low (EB=1) and high workloads (EB = max). The fourth column gives the mean and variance of the cost for the whole range of workloads. Only S3 and Azure are able to sustain high workloads of 9000EBs. Authors of paper believe that S3 variant with Distributed Control architecture is able to scale even beyond 9000EBs. This architecture is the only architecture which has no potential bottleneck. Since Azure based on Replication architecture, it reaches limits as soon as the Master database server is overloaded. But it seems, that Microsoft use high-end machines for the SQL Azure database layer. Speaking about cost, all Amazon variants need to pay for at least one EC2 instance in order to be able to respond to client requests, even if there is no load at all. SQL Azure has a monthly flat fee and at least one machine for a web/app server must be paid in order to keep a web application online in the Azure cloud. Google AppEngine is the only variant that does not have any fixed cost and is free if there is no load. Obviously, fixed costs are not compliant with the pay-as-you-go paradigm promised by cloud computing. Fourth column shows that with the exception of Google, all services have a high variance which means that the cost for the service is highly dependent on the load and becomes unpredictable. 7

8 4.2 Scale-out Figure shows the WIPS achieved by each variant as a function of EB. Figure shows only two variants, that scale S3 and Azure. Azure scales well for using powerful machines to run the database servers. Scale almost ideally up to 9000EBs and achieve the maximum throughput at 9000EBs. The ideal throughput of a perfect system is shown as dotted line. All other variants have scalability limits and cannot sustain the load after a certain number of EBS. The figure show the ideal (dotted), WIPS (green) and the number of issued requests that were submitted (yellow). If a system cannot sustain the load and does not produce responses anymore then the number of issued requests is lower than in an ideal system. The throughput of RDS after 3500EBs stays constant. All requests return answers, but growing percentage of requests are no answered within the response time constraints. 4.3 Cost analysis Table details the Cost/WIPS for the alternative variants with varying EBs. Google AppEngine is the cheapest for low workloads. It is the only variant that has no fixed costs. There is only negligible monthly fee to store database. Azure and MySQL is cheapest for medium to large workloads (more than 100EBs), because all these approaches can amortize their fixed cost for these workloads. For Azure SQL server has a fixed cost per month 100$ for a database of up to 10Gb, for MySQL- EC2 instance must be rented in order to keep the database online. RDS involves an hourly fixed fee so that the cost per WIPS 8

9 decreases in load situation. It s worth to mention, that for Google traffic is cheaper, than for Amazon and Microsoft. The cost analysis indicates for which kind of workload a service is oriented. Google is targeting the low end market, Microsoft seems to be focusing on enterprise customers. All in all, cost analysis is more an artifact of the business models used by Amazon, Google, Microsoft. Table shows the total cost per day for the alternative approaches and varying load (EBs). It s not surprising, that Google wins for small workloads and Azure for medium and large workloads. SimpleDB is an exceptionally expensive service. Users must pay even though SimpleDB drops many requests and is not able to sustain the workload. As for S3, this service exactly demonstrates what we expect from payas-you-go business model. Total cost grows linearly with the workload. Figure shows the cost per day spent on network traffic, CPUs and storage. Network traffic costs are purely variable costs which entirely depend on the workload. The CPU cost has a variable and fixed components. Fixed CPU costs are required to reserve machines and variable costs are incurred by surcharges of actual usage. For example, the cost of an EC2 instance depends on the usage. The fixed storage sots are monthly costs for storing the database. The variable costs are the costs per request to fetch and put data into the database. In the diagram we can see fixed+variables storage costs. Cost factors showed on the figure are taken with respect of EBs=250. This figure visualizes the percentage of each cost factor in each variant, depending on the workload. The cost of the SimpleDb variant is dominated by the cost of querying and updating the SimpleDB service. 9

10 4.4 Scale-up Amazon RDS provides a scale-up option. However, even the biggest RDS machine is not able to sustain 9000EBs and throughput of more than 1000WIPS. Database server can become the bottleneck and scaling-up the database server can become the only way to achieve higher throughput. 4.5 Bulkloading The table shows bulkloading times and costs as well as the database size and monthly storage costs for the alternative variants. However, storage costs are negligible and are not an important factor for the overall cost. 5 Conclusion It became clear that the alternative providers have different business models and target different kind of applications. The more fundamental question of what is the right data management architecture for cloud computing could not be answered. Although, it s worth to mention that this paper was one of the first attempt to establish framework that allows vendors to gradually improve their services and allows users to compare products. Reference for more details 1. D.Kassman, T.Kraska, S.Loesing. Systems Group, Department of Computer Science, ETH Zurich, Switzerland. An Evaluation of Alternative Architectures for Transaction Processing in the Cloud. kossmann.pdf?key1= &key2= &coll=guide&dl=guide&cfid= &c FTOKEN= Amazon. Amazon WebServices Danga. MemCached TPC. TPC-W 1.8. TPC Council, A. Tanenbaum and M. van Steen. Distributed Systems: Principles and Paradigms. Prentice Hall,

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