More AWS and Cloud-based Research at Mobile & Cloud Lab

Transcription

1 Basics of Cloud Computing Lecture 7 More AWS and Cloud-based Research at Mobile & Cloud Lab Satish Srirama

2 Outline More Amazon Web Services How we are using cloud Cloud based Mobile & Cloud Lab 4/21/2015 Satish Srirama 2/41

3 Cloud Providers and Services we Amazon Web Services Amazon EC2 Amazon S3 Amazon EBS Amazon Elastic Load Balancing Amazon Auto Scale Amazon CloudWatch Eucalyptus OpenStack SciCloud Management providers ElasticFox RightScale PaaS Google AppEngine Windows Azure already discussed 4/21/2015 Satish Srirama 3/41

4 MORE AWS 4/21/2015 Satish Srirama 4

5 AWS we discuss AWS Management Console AWS Identity and Access Management AWS Elastic Beanstalk AWS CloudFormation Amazon Simple Workflow Service Amazon Elastic MapReduce 4/21/2015 Satish Srirama 5/41

6 AWS Management Console Hope some of you have started using Amazon accounts You can manage your complete Amazon account with management console (Similar to Hybridfox) AMI Management Instance Management Security Group Management Elastic IP Management Elastic Block Store Key Pair management etc. Have different panes for different services 4/21/2015 Satish Srirama 6/41

7 AWS Management Console -screenshot 4/21/2015 Satish Srirama 7

8 AWS Identity and Access Management (IAM) How can an enterprise or group of people use a single credit card? Manage IAM users Create new users and manage them Create groups Manage permissions Creating policies Manage credentials Create and assign temporary security credentials 4/21/2015 Satish Srirama 8/41

9 IAM policy Example policy giving access to complete EC2 4/21/2015 Satish Srirama 9/41

10 AWS Elastic Beanstalk Enables to easily deploy and manage applications in the AWS cloud Simply upload a bundle of the applications built using.net, PHP and Java technologies Automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring Something similar to PaaS One retains full control over the AWS resources powering the application You can access the underlying resources at any time 4/21/2015 Satish Srirama 10/41

11 AWS Elastic Beanstalk AWS EBis built using familiar software stacks such as the Apache HTTP Server for PHP, IIS 7.5 for.net, and Apache Tomcat for Java There is no additional charge for Elastic Beanstalk Only the underlying AWS resources (e.g. Amazon EC2, Amazon S3) are charged Leverages AWS services such as Amazon EC2, S3, SNS, ELB, and Auto Scaling to deliver the same highly reliable, scalable, and cost-effective infrastructure 4/21/2015 Satish Srirama 11/41

12 AWS CloudFormation Provides an easy way to create and manage a collection of related AWS resources, provisioning and updating them in an orderly and predictable fashion It is based on templates model Templates describe the AWS resources, the associated dependencies, and runtime parameters to run an app. The templates describe stacks, which are set of software and hardware resources. Something similar to CloudML and RightScale server templates Hides several details How the AWS services need to be provisioned Subtleties of how to make those dependencies work. 4/21/2015 Satish Srirama 12/41

13 AWS CloudFormation Amazon provides several pre-built templates to start common apps as: WordPress(blog) LAMP stack Gollum (wiki used by GitHub) There is no additional charge for AWS CloudFormation. You pay for AWS resources (e.g. EC2 instances, Elastic Load Balancers, etc.) 4/21/2015 Satish Srirama 13/41

14 Amazon Simple Workflow Service A workflow service for building scalable, resilient applications Reliably coordinates all of the processing steps within applications such as business processes, sophisticated data analytics applications, or managing cloud infrastructure services Manages task execution dependencies, scheduling, and concurrency Provides simple API calls from code written in any language Capable to run on EC2 instances, or any of the customer s machines located anywhere in the world 4/21/2015 Satish Srirama 14/41

15 Amazon Simple Workflow Service Maintains application state Tracks workflow executions and logs their progress Holds and dispatches tasks Controls which tasks each of the application hosts will be assigned to execute 4/21/2015 Satish Srirama 15/41

16 Amazon Elastic MapReduce Web interface and command-line tools for running Hadoop jobs on EC2 Data stored in Amazon S3 Monitors job and shuts machines after use Running a job Upload job jar & input data to S3 Create the cluster Create a Job Flow as steps Wait for the completion and examine the results 4/21/2015 Satish Srirama 16/41

17 Other interesting AWS Amazon Relational Database Service Provides access to the capabilities of familiar database engines MySQL, Oracle or Microsoft SQL Server NoSQL databases Simple DB DynamoDB 4/21/2015 Satish Srirama 17/41

18 CLOUD BASED MOBILE & CLOUD LAB 4/21/2015 Satish Srirama 18

19 Scientific Computing on the Cloud Public clouds provide very convenient access to computing resources On-demand and in real-time As long as you can afford them High performance computing (HPC) on cloud Virtualization and communication latencies are major hindrances [Srirama et al, SPJ 2011; Batrashev et al, HPCS 2011] Things have improved significantly over the years Research at scale Cost-to-value of experiments 4/21/2015 Satish Srirama 19/41

20 Adapting Computing Problems to Cloud Reducing the algorithms to cloud computing frameworks like MapReduce [Srirama et al, FGCS 2012] Designed a classification on how the algorithms can be adapted to MR Algorithm single MapReduce job Monte Carlo, RSA breaking Algorithm nmapreduce jobs CLARA (Clustering), Matrix Multiplication Each iteration in algorithm single MapReduce job PAM (Clustering) Each iteration in algorithm nmapreduce jobs Conjugate Gradient Applicable especially for Hadoop MapReduce 4/21/2015 Satish Srirama 20/41

21 Issues with Hadoop MapReduce It is designed and suitable for: Data processing tasks Embarrassingly parallel tasks Has serious issues with iterative algorithms Long start up and clean up times ~17 seconds No way to keep important data in memory between MapReduce job executions At each iteration, all data is read again from HDFS and written back there at theend Results in a significant overhead in every iteration 4/21/2015 Satish Srirama 21/41

22 Alternative Approaches Restructuring algorithms into non-iterative versions CLARA instead of PAM [Jakovits & Srirama, Nordicloud 2013] Alternative MapReduce implementations that are designed to handle iterative algorithms [Jakovits and Srirama, HPCS 2014] E.g. Twister, HaLoop, Spark Alternative distributed computing models Bulk Synchronous Parallel model [Valiant, 1990][Jakovits et al, HPCS 2013] Building a fault-tolerant BSP framework (NEWT) [Kromonov et al, HPCS 2014] 4/21/2015 Satish Srirama 22/41

23 Remodeling Enterprise Applications for the Cloud Remodeling workflow based applications for the cloud To reduce communication latencies among the components Intuition: Reduce inter-node communication and to increase the intra-node communication Auto-scale them based on optimization model and CloudML 4/21/2015 [Srirama and Viil, HPCC 2014] Satish Srirama 23/41

24 Migrating Scientific Workflows to the Cloud Workflow can be represented as weighted directed acyclic graph (DAG) Partitioning the workflow into groups with graph partitioning techniques [Srirama and Viil, HPCC 2014] Such that the sum of the weights of the edges connecting to vertices in different groups is minimized Utilized Metis multilevel k-way partitioning Scheduling the workflows with tools like Pegasus Considered peer-to-peer file manager (Mule) for Pegasus 4/21/2015 Satish Srirama 24/41

25 [Tomi T Ahonen] 4/21/2015 Satish Srirama 25

26 Mobile Applications One can do interesting things on mobiles directly Today s mobiles are far more capable Location-based services (LBSs), mobile social networking, mobile commerce, context-aware services etc. It is also possible to make the mobile a service provider Mobile web service provisioning [Srirama et al, ICIW 2006; Srirama and Paniagua, MS 2013] Challenges in security, scalability, discovery and middleware are studied [Srirama, PhD 2008] Mobile Social Network in Proximity [Chang et al, ICSOC 2012; PMC 2014] 4/21/2015 Satish Srirama 26/41

27 However, we still have not achieved Longer battery life Battery lasts only for 1-2 hours for continuous computing Same quality of experience as on desktops Weaker CPU and memory Storage capacity Still it is a good idea to take the support of external resources for building resource intensive mobile applications 4/21/2015 Satish Srirama 27/41

28 Mobile Cloud Applications Bring the cloud infrastructure to the proximity of the mobile user Mobile has significant advantage by going cloud-aware Increased data storage capacity Availability of unlimited processing power PC-like functionality for mobile applications Extended battery life (energy efficiency) 4/21/2015 Satish Srirama 28/41

29 Mobile Cloud Our interpretation We do not see Mobile Cloud to be just a scenario where mobile is taking the help of a much powerful machine!!! We do not see cloud as just a pool of virtual machines Mobile Cloud based system should take advantage of some of the key intrinsic characteristics of cloud efficiently Elasticity & AutoScaling Utility computing models Parallelization (e.g., using MapReduce) 4/21/2015 Satish Srirama 29/41

30 Mobile Cloud Binding Models [Flores et al, MoMM 2011] [Flores and Srirama, MCS 2013] Mobile Cloud 4/21/2015 Task Delegation [Flores & Srirama, JSS 2014] Code Offloading Satish Srirama 30/41

31 [Flores et al, MoMM 2011] MCM enables Interoperability between different Cloud Services (IaaS, SaaS, PaaS) and Providers (Amazon, Eucalyptus, etc) Provides an abstraction layer on top of API Composition of different Cloud Services Asynchronous communication between the device and MCM [Warrenet al, IEEE PC 2014] Means to parallelize the tasks and take advantage of Cloud s intrinsic characteristics 4/21/2015 Satish Srirama 31/41

32 MCM applications CroudSTag [Srirama et al, MobiWIS 2011] Social group formation with people identified in Pictures/Videos Zompopo [Srirama et al, NGMAST 2011] Intelligent calendar, by mining accelerometer sensor data Bakabs [Paniagua et al, iiwas-2011] Managing the Cloud resources from mobile Sensor data analysis Human activity recognition Context aware gaming MapReduce based sensor data analysis [Paniagua et al, MobiWIS 2012] SPiCa: A Social Private Cloud Computing Application Framework [Chang et al, MUM 2014] 4/21/2015 Satish Srirama 32/41

33 Code Offloading-Major Components Major research challenges What, when, where and how to offload? Mobile Code profiler System profilers Decision engine Cloud based surrogate platform 4/21/2015 [Flores and Srirama, MCS 2013] Satish Srirama 33/41

34 Challenges and technical problems Inaccurate code profiling Code has non-deterministic behaviour during runtime Based on factors such as input, type of device, execution environment, CPU, memory etc. Some code cannot be profiled (e.g. REST) Integration complexity Dynamic behaviour vs Static annotations E.g. Static annotations cause unnecessary offloading Dynamic configuration of the system Offloading scalability and offloading as a service Surrogate should have similar execution environment Should also consider about resource availability of Cloud [Flores et al, IEEE Communications Mag 2015] 4/21/2015 Satish Srirama 34/41

35 Practical adaptability of offloading 4/21/2015 Applications that can benefit became Satish limited Srirama with increase in device capacities 35/41

36 Way to proceed? Code offloading is not yet a reality!!! Take advantage of crowdsourcing Computational offloading customized by data analytics By analysing how a particular app behaves in a community of devices E.g. Carat detects energy anomalies [Oliner et al, 2013] By studying over ~328,000 apps gets an idea on what is resource intensive app Determines energy drain distribution of an app Decision models can also benefit from crowdsourcing Analysis of code offloading traces [Flores and Srirama, MCS 2013] [Flores et al, IEEE Communications Mag 2015] 4/21/2015 Satish Srirama 36/41

37 Data Analytics on the Cloud Cloud scale data storage solutions Cloud scale data analytics Pig & Hive NoSQL Implementing graph algorithms on graph databases Large-scale Data Processing on the Cloud - MTAT (Fall 2015) 4/21/2015 Satish Srirama 37/41

38 WE ALWAYS WELCOME NEW IDEAS! 4/21/2015 Satish Srirama 38

39 This week in lab Advanced Google AppEngine You will try accessing DB 4/21/2015 Satish Srirama 39/41

40 Next Week Summarize what we have learnt How to prepare for the examination 4/21/2015 Satish Srirama 40/41

41 References Check Amazon videos and webinars at List of Publications - Satish Narayana Srirama - [Flores et al, IEEE Communications Mag2015] H. Flores, P. Hui, S. Tarkoma, Y. Li, S. N. Srirama, R. Buyya: Mobile Code Offloading: From Concept to Practice and Beyond, IEEE Communications Magazine, ISSN: , 53(3):80-88, IEEE. DOI: /MCOM [Flores and Srirama, JSS 2014] H. Flores, S. N. Srirama: Mobile Cloud Middleware, Journal of Systems and Software, ISSN: , 92(1):82-94, Elsevier. DOI: /j.jss [Chang et al, PMC 2014] C. Chang, S. N. Srirama, S. Ling: Towards an Adaptive Mediation Framework for Mobile Social Network in Proximity, Pervasive and Mobile Computing Journal, MUCS Fast track, ISSN: , 12: , Elsevier. DOI: /j.pmcj [Warren et al, IEEE PC 2014] I. Warren, A. Meads, S. N. Srirama, T. Weerasinghe, C. Paniagua: Push Notification Mechanisms for Pervasive Smartphone Applications, IEEE Pervasive Computing, ISSN: , 13(2):61-71, IEEE. DOI: /MPRV [Chang et al, MUM 2014] C. Chang, S. N. Srirama, S. Ling: SPiCa: A Social Private Cloud Computing Application Framework, The 13th International Conference on Mobile and Ubiquitous Multimedia (MUM 2014), November 25-28, 2014, pp ACM. [Jakovits and Srirama, HPCS 2014] P. Jakovits, S. N. Srirama: Evaluating MapReduceFrameworks for Iterative Scientific Computing Applications, The 2014 (12th) International Conference on High Performance Computing & Simulation (HPCS 2014), July 21-25, 2014, pp IEEE. [Kromonov et al, HPCS 2014] I. Kromonov, P. Jakovits, S. N. Srirama: NEWT -A resilient BSP framework for iterative algorithms on Hadoop YARN, The 2014 (12th) International Conference on High Performance Computing & Simulation (HPCS 2014), July 21-25, 2014, pp IEEE. [Srirama and Viil, HPCC 2014] S. N. Srirama, J. Viil: Migrating Scientific Workflows to the Cloud: Through Graph-partitioning, Scheduling and Peer-to-Peer Data Sharing, 16th Int. Conf. on High Performance and Communications (HPCC 2014) workshops, August 20-22, 2014, pp IEEE. [Jakovits and Srirama, Nordicloud 2013] P. Jakovits, S. N. Srirama: Clustering on the Cloud: Reducing CLARA to MapReduce, 2nd Nordic Symposium on Cloud Computing& Internet Technologies (NordiCloud2013), September 02-03, 2013, pp ACM. [Jakovits et al, HPCS 2013] P. Jakovits, S. N. Srirama, I. Kromonov: Viability of the Bulk Synchronous Parallel Model for Science on Cloud, The 2013 (11th) International Conference on High Performance Computing & Simulation (HPCS 2013), July 01-05, 2013, pp IEEE. [Srirama and Paniagua, MS 2013] S. N. Srirama, C. Paniagua: Mobile Web Service Provisioning and Discovery in Android Days, The 2013 IEEE International Conference on Mobile Services (MS 2013), June 27 -July 02, 2013, pp IEEE. [Flores and Srirama, MCS 2013] H. Flores, S. N. Srirama: Adaptive Code Offloading for Mobile Cloud Applications: Exploiting Fuzzy Sets and Evidence-based Learning, The Fourth ACM Workshop on Mobile Cloud Computing and Services (MCS MobiSys 2013, June 25-28, 2013, pp ACM. [Olineret al, 2013] Oliner, Adam J., AnandP. Iyer, Ion Stoica, EemilLagerspetz, and SasuTarkoma. "Carat: Collaborative energy diagnosis for mobile devices." In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, p. 10. ACM, [Srirama et al, SOCA 2012] S. N. Srirama, C. Paniagua, H. Flores: Social Group Formation with Mobile Cloud Services, Service Oriented Computing and Applications Journal, ISSN: , 6(4): , Springer. DOI: /s [Srirama et al, FGCS 2012] S. N. Srirama, P. Jakovits, E. Vainikko: Adapting Scientific Computing Problems to Clouds using MapReduce, Future Generation Computer Systems Journal, 28(1): , Elsevier press. DOI /j.future [Chang et al, ICSOC 2012] C. Chang, S. N. Srirama, S. Ling: An Adaptive Mediation Framework for Mobile P2P Social Content Sharing, 10th International Conference on Service Oriented Computing (ICSOC 2012), November 12-16, 2012, pp Springer LNCS. [Paniagua et al, MobiWIS2012] C. Paniagua, H. Flores, S. N. Srirama: Mobile Sensor Data Classification for Human Activity Recognition using MapReduce on Cloud, The 9th Int. Conf. on Mobile Web Information Systems (MobiWIS 2012), August 27-29, 2012, pp Elsevier. [Srirama et al, SPJ 2011] S. N. Srirama, O. Batrashev, P. Jakovits, E. Vainikko: Scalability of Parallel Scientific Applications on the Cloud, Scientific Programming Journal, Special Issue on Science-driven Cloud Computing, 19(2-3):91-105, IOS Press. DOI /SPR [Flores et al, MoMM2011] H. Flores, S. N. Srirama, C. Paniagua: A Generic Middleware Framework for Handling Process Intensive Hybrid Cloud Services from Mobiles, The 9th International Conference on Advances in Mobile Computing & Multimedia (MoMM-2011), December 5-7, 2011, pp ACM. [Paniagua et al, iiwas2011] C. Paniagua, S. N. Srirama, H. Flores: Bakabs: Managing Load of Cloud-based Web Applications from Mobiles, The 13th International Conference on Information Integration and Web-based Applications & Services (iiwas-2011), December 5-7, 2011, pp ACM. [Srirama et al, MobiWIS 2011] S. N. Srirama, C. Paniagua, H. Flores: CroudSTag: Social Group Formation with Facial Recognition and Mobile Cloud Services, The 8th International Conference on Mobile Web Information Systems (MobiWIS 2011), September 19-21, 2011, v. 5 of Procedia Computer Science, pp Elsevier. doi: /j.procs [Srirama et al, NGMAST 2011] S. N. Srirama, H. Flores, C. Paniagua: Zompopo: Mobile Calendar Prediction based on Human Activities Recognition using the Accelerometer and Cloud Services, 5th International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST 2011), September 14-16, 2011, pp IEEE. [Batrashevet al, HPCS 2011] O. Batrashev, S. N. Srirama, E. Vainikko: Benchmarking DOUG on the Cloud, The 2011 International Conference on High Performance Computing& Simulation (HPCS 2011), July 4-8, 2011, pp IEEE. [Srirama, PhD 2008] S. N. Srirama: Mobile Hosts in Enterprise Service Integration, PhD thesis, RWTH Aachen University, September, [Srirama et al, ICIW 2006] S. N. Srirama, M. Jarke, W. Prinz: Mobile Web Service Provisioning, Proceedings of the Advanced International Conference on Telecommunications and International Conference on Internet and Web Applications and Services (AICT-ICIW 2006), February 23-25, 2006, pp IEEE Computer Society Press. [Valiant, 1990] L. G. Valiant: A bridging model for parallel computation, Commun. ACM, vol. 33, no. 8, pp , Aug /21/2015 Satish Srirama 41/41