TU Delft
 
Alexandru IOSUP
Cloud Computing
Parallel and Distributed Systems
EWI PDS-A.Iosup-Research -Cloud Computing
 
 
 
 
 
 
 
 
 
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Cloud Computing Research (2008-ongoing) printer-friendly version: Cloud Computing Research by Alexandru Iosup, PDF [0.2MB]
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Rationale
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why and how is this work relevant?
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Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing hardware. Through the use of virtualization and resource time-sharing, clouds address with a single set of physical resources a large user base with different needs. Thus, clouds promise to enable for their owners the benefits of an economy of scale and, at the same time, reduce the operating costs for many applications. For example, clouds may become for scientists an alternative to clusters, grids, and parallel production environments.


People
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who is part of the group?
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  • Challent (Top Students) Program at TU Delft: Anand Sawant (Co-Scalability of Online Game Design and Infrastructure) and Ruben Verboon (Scheduling Policies for Scientific Workloads in Cloud-Based Environment), 2011; Jesse Donkervliet (Cloud-Based Procedural Content Generation for Puzzle Games), Tim Hegeman (Cloud-Based Big Data Processing), and Stefan Hugtenburg (Cloud Scheduling for Online Gaming), 2012.
  • Undergraduate Students: Adrian Lascateu (Politehnica University of Bucharest, Romania); Athanasios Antoniou, Marcin Biczak, and Lipu Fei (TU Delft).
  • Graduate Students: Nezih Yigitbasi (TU Delft), Vlad Nae (U. Innsbruck, Austria), David Villegas (Florida International University, US), Orna Agmon Ben-Yehuda (Technion, IL).
  • Collaborators: Dick Epema, Henk Sips (both TU Delft), Thomas Fahringer, Radu Prodan (both U. Innsbruck, Austria), Nicolae Tapus (Politehnica University of Bucharest, Romania), Seyed Masoud Sadjadi (Florida International University, US), Assaf Schuster, Mark Silberstein (both Technion, IL).
  • Yours truly: Alexandru Iosup.

 


Main Research Questions
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what do we try to achieve?
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  1. What is the actual performance of virtualized cloud resources? Many production clouds, including the largest publicly-accessible commercial clouds such as the Amazon Web Services and the Google App Engine, use virtualized resources to address diverse user requirements with the same set of physical resources. Virtualization can introduce performance penalties, either due of the additional middleware layer or to the interaction of workloads belonging to different virtual machines. Do virtualized resources deliver the same performance regardless of the application? In particular, are scientific applications affected by execution on virtualized resources?
     
  2. What guarantees do we have about the good performability of clouds over long periods of time? A major impediment to cloud adoption at large is their perceived instability, due, in lack of hard evidence, to novelty ("clouds are a technology too immature to be reliable"). Even if a cloud is avilable and works well today, it may well happen that it will not tomorrow. Does performance change over time (for the worse)? Are clouds really available all the time?
     
  3. Which new applications can make use of clouds? Commercial clouds are new to the public. What applications that we could not previously afford to run are now enabled by clouds? What applications can function well under the availability and performance profiles of the current production cloud services?
     

 


Main Achievements
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what did we do?
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  1. Evaluated the performance of resources from four production, commercial clouds. We have added to GrenchMark the C-Meter tool for evaluating the performance of cloud resources [3]. We have studied [2,6,9] the performance of resources from four production, commercial clouds: Amazon Elastic Compute Cloud (EC2), Mosso, Elastic Hosts, and GoGrid.
     
  2. Evaluated the variability of the performance delivered by production cloud services. We have collected year-long traces and, based on them, studied [8] the performance of over fifteen operations provided by nine services in two clouds, Amazon Web Services and Google App Engine.
     
  3. Designed CAMEO, a framework for continuous analytics for massively multiplayer online games (MMOGs) using cloud resources. We have provided [4] a first estimate of the feasibility and costs of performing continuous analytics for MMOGs on cloud resources.
     
  4. Within edutain@grid, analyzed the fesibility of running massively multiplayer online games (MMOGs) on cloud resources. We have analyzed [1,7] the potential gains of running MMOGs on cloud resources, based on an ideal cloud/data center model. We have started to investigate [5] the impact of virtualization on running MMOGs on cloud resources.
     

 


Main Findings
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what did we find?
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  1. "The performance of the resources leased by four production clouds, including Amazon EC2, for running scientific applications is at the moment below the theoretical peak and below the maximum efficiency obtained in other specialized environments." [2][6][9]
     
  2. "The costs of running grid workloads in clouds may be much lower than the costs of building and maintaining the grids that can run these workloads." [9]
     
  3. "The resource allocation strategy employed by the cloud user can be insignificant or can lead to 10-30% cost increase, when running long-term, grid-like workloads on clouds." [2][9]
     
  4. "The performance of the production cloud services offered by Amazon Web Services and Google App Engine varies over time, and can alter significantly the performance and cost profiles of large-scale applications such as job execution in scientific computing, virtual goods trading in social networks, and state management in social gaming." [8]
     
  5. "Continuous analytics for MMOGs on cloud resources is feasible and shows good promise of being cost-effective." [4][10]
     
  6. "Dynamic resource allocation from clouds can lead to a tenfold reduction of the platform operation costs for massively multiplayer online games (MMOGs)." [1][7]
     
  7. "Finding non-trivial, efficient strategies for scheduling bags-of-tasks on hybrid self-owned/cloud infrastructures is possible, even on-line; efficient means, for example, high performance, high reliability, and low cost." [12]
  8. "Naive static provisioning policies deliver stable performance, but incur up to 5 times higher cost." [13]
     
  9. "On-demand provisioning policies, and in particular policies that use job runtime information,are a good performance-cost trade-off among eight investigated provisioning policies." [13]
     

 


Publications
journals/conferences/workshops | all PDS group publications | my publications (with BibTeX) | my DBLP entry | my ACM DL entry
2014 click to toggle the display of all publications <-- click to see more details
[29] L. Fei, B. Ghit, A. Iosup, and D. Epema. KOALA-C: A Task Allocator for Integrated Multicluster and Multicloud Environments, In the IEEE (CLUSTER 2014) conference. Madrid, Spain, 21-26 Oct 2014. (accepted, acceptance ratio 24%=29/122)
keywords KOALA-C; multicluster scheduling; multicloud scheduling; TAGS-based cloud scheduling; empirical evaluation; experimental research; real-world experimentation; trace-based simulation.
   
KOALA-C: A Task Allocator for Integrated Multicluster and Multicloud Environments, PDF  
[28] A. Iosup, A. L. Varbanescu, M. Capota, T. Hegeman, Y. Guo, W.-L. Ngai, M. Verstraaten. Towards Benchmarking IaaS and PaaS Clouds for Graph Analytics, In the WBDB 2014 workshop. Potsdam, Germany, Aug 2014. (invited article)
keywords GRAPHALYTICS; benchmarking; graph processing; Big Data; cloud computing; benchmarking IaaS; benchmarking PaaS; distributed graph processing; empirical evaluation; experimental research; real-world experimentation.
   
Towards Benchmarking IaaS and PaaS Clouds for Graph Analytics, PDF  
[27] A. Iosup, S. Shen, Y. Guo, S. Hugtenburg, J. Donkervliet, and R. Prodan (U. Innsbruck, Austria). Massivizing Online Games Using Cloud Computing: A Vision, In the First International Workshop on Cloud Gaming Systems and Networks (C-Game 2014), held in conjunction with the IEEE International Conference on Multimedia and Expo (ICME 2014), Chengdu, China, Jul 2014.
keywords Massivizing online games; online social games; online games; virtual world management; big data processing; procedural content generation; PCG; PCG-G.
   
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[26] B. Ghit, A. Iosup, N. Yigitbasi (Intel Labs, Portland, OR, USA), and D. Epema. Balanced Resource Allocations Across Multiple Dynamic MapReduce Clusters, In the ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems (SIGMETRICS 2014). Austin, TX, USA, 16-20 Jun 2014. (accepted, acceptance ratio 17%=40/238)
keywords Fawkes; elastic MapReduce; performance evaluation; big data processing; MapReduce; Hadoop; YARN; empirical evaluation; experimental research.
   
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[25] Y. Guo, M. Biczak, A. L. Varbanescu, A. Iosup, C. Martella, and T. L. Willke. How Well do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis, In the IEEE International Parallel and Distributed Processing Symposium (IPDPS 2014). Phoenix, AZ, USA, 19-23 May 2014. (accepted, acceptance ratio 21%=114/541)
keywords graph processing; performance evaluation; big data processing; benchmarking; MapReduce; Hadoop; YARN; Giraph; GraphLab; Neo4J;graph-processing algorithms; graph-processing datasets; empirical evaluation; experimental research.
 
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2013 click to toggle the display of all publications <-- click to see more details
[24] Kefeng Deng, Junqiang Song, and Kaijun Ren (NUDT, CN), and Alexandru Iosup (TUD, NL). Exploring Portfolio Scheduling for Long-Term Execution of Scientific Workloads in IaaS Clouds, In the ACM/IEEE Conference on High Performance Computing (SC|13). Denver, Colorado, USA, 17-22 Nov 2013. (accepted, acceptance ratio 20%=92/457)
keywords portfolio scheduler; portfolio scheduling; data center; data centre; scheduling policies; Scheduling; IaaS clouds; Cloud computing; simulation; performance evaluation; Scheduling; Quality of service and service-level agreement management.
 
- | Exploring Portfolio Scheduling for Long-Term Execution of Scientific Workloads in IaaS Clouds, PDF [1.75 MB] (Update June 2013)  
[23] A. L. Varbanescu and A. Iosup. On Many-Task Big Data Processing: from GPUs to Clouds. In the 6th Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS) held in conjunction with SC|13. Denver, Colorado, USA, 17 Nov 2013.
keywords Large-scale big-data processing; Execution engine; Predictability; Elasticity; High-performance; portfolio scheduling; socially aware scheduling; cloud computing; cloud-based big data.
 
- | On Many-Task Big Data Processing: From GPUs to Clouds, PDF [2.0 MB]  
invited
[22] T. Hegeman, B. Ghit, M. Capotã, J. Hidders, D. Epema, and A. Iosup. The BTWorld Use Case for Big Data Analytics: Description, MapReduce Logical Workflow, and Empirical Evaluation, In the IEEE International Conference on Big Data (Big Data 2013). Santa Clara, CA, USA, 6-9 Oct 2013. (accepted, acceptance ratio 17%=45/259)
keywords BTWorld; big data processing; use case; MapReduce logical workflow; benchmarking; performance evaluation; empirical evaluation; experimental research.
 
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[21] Aleksandar Milenkoski (KIT, DE), Alexandru Iosup (TUD, NL), Samuel Kounev (KIT, DE), Kai Sachs (SAP AG, DE), Piotr Rygielski (KIT, DE), Jason Ding (Salesforce, CA, USA), Walfredo Cirne (Google Inc., CA, USA), Florian Rosenberg (IBM T.J. Watson, NY, USA). Cloud Usage Patterns: A Formalism for the Description of Cloud Usage Scenarios, SPEC Research Group's Technical Report SPEC-RG-2013-001 (Version: 1.0). April 25, 2013.
keywords cloud usage patterns; formalism; cloud computing; IaaS; PaaS; SaaS; software engineering.
 
Cloud Usage Patterns: A Formalism for the Description of Cloud Usage Scenarios, PDF [3.5MB] (Update April 2013)  
[20] K. Deng, R. Verboon, and A. Iosup A Periodic Portfolio Scheduler for Scientific Computing in the Data Center, In the 17th Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP'13). Held in conjunction with the IEEE International Parallel and Distributed Processing Symposium (IPDPS). Boston, Massachusetts, USA, 24 May 2013.
keywords portfolio scheduler; portfolio scheduling; data center; data centre; scheduling policies; Scheduling; IaaS clouds; Cloud computing; simulation; performance evaluation.
 
- | A Periodic Portfolio Scheduler for Scientific Computing in the Data Center, PDF [0.7 MB] (Update May 2013)  
[19] A.-C. Olteanu, N. Tapus, and A. Iosup Extending the Capabilities of Mobile Devices for Online Social Applications through Cloud Offloading, In IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). Doctoral Symposium. Delft, the Netherlands, 20-24 May 2013.
keywords mobile applications; cloud computing; social applications; online gaming; workload characterization; workload modeling; offloading mechanisms; communication offloading; lossy performance offloading; lossless performance offloading; experimental research; empirical research.
 
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[18] S. Shen, A. Iosup, and D. Epema Massivizing Multi-Player Online Games on Clouds, In IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). Doctoral Symposium. Delft, the Netherlands, 20-24 May 2013.
keywords online gaming; cloud computing; workload characterization; workload modeling; scalability; elasticity; reliability; performance evaluation; experimental research; empirical research.
 
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[17] B. Ghit, A. Iosup, and D. Epema Towards an Optimized Big Data Processing System, In IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). Doctoral Symposium. Delft, the Netherlands, 20-24 May 2013.
keywords Big Data; elastic MapReduce; cloud computing; MapReduce; Hadoop; workload characterization; workload modeling; scalability; elasticity; performance evaluation; benchmarking; experimental research; empirical research.
 
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2012 click to toggle the display of all publications <-- click to see more details
[16] A. Iosup, R. Prodan, and D. Epema, IaaS Cloud Benchmarking: Approaches, Challenges, and Experience. In 5th Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS 2012), held in conjunction with SC, Salt Lake City, Utah, USA, Nov 2012.
keywords Benchmarking; challenges; IaaS clouds; Cloud computing; performance evaluation; concepts; discussion.
 
- | IaaS Cloud Benchmarking: Approach, Challenges, and Experience, PDF [9.5MB]  
invited
[15] V. Nae, L. Kopfle, R. Prodan, and A. Iosup, Massively Multiplayer Online Games on Unreliable Resources, In International Workshop on Network and Systems Support for Games (NetGames 2012), Venice, Italy, November 22-23, 2012 (accepted as short paper)
keywords MMOG, online gaming, fault-tolerance, cloud computing, reliability, QoS, monitoring.

 
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[14] E. Folkerts, A. Alexandrov, K. Sachs, A. Iosup, V. Markl, and C. Tosun, Benchmarking in the Cloud: What it Should, Can, and Cannot Be. In TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC 2012), held in conjunction with VLDB 2012, Istanbul, Turkey, Aug 2012.
keywords Benchmarking; Cloud computing; performance evaluation; concepts; discussion.
 
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[13] D. Villegas, A. Antoniou, S. M. Sadjadi, and A. Iosup, An Analysis of Provisioning and Allocation Policies for Infrastructure-as-a-Service Clouds, In IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May 13-16, 2012, Ottawa, Canada. (accepted; acceptance ratio 27%=83/302)
keywords Scheduling; Cloud computing; Provisioning policies; Allocation policies; Empirical performance analysis.
 
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[12] O. Agmon Ben-Yehuda, A. Schuster, A. Sharov, M. Silberstein, and A. Iosup, ExPERT: Pareto-Efficient Task Replication on Grids and a Cloud, IEEE International Parallel and Distributed Processing Symposium (IPDPS). (accepted; acceptance ratio 21%=118/569) Extended version as Technical Report Technion CS-2011-03
keywords Scheduling; Cloud computing; Provisioning policies; Pareto scheduling; bags of tasks; Empirical performance analysis; Simulation-based scheduling.
 
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2011 click to toggle the display of all publications <-- click to see more details
[11] V. Nae, R. Prodan, T. Fahringer, and A. Iosup, A new business model for massively multiplayer online games, 2nd ACM/SPEC International Conference of Performance Engineering (ICPE 2011), March 2011. (accepted)
keywords cloud computing, economic model, massively multiplayer online games, MMOG, cloud-based gaming.
 
 
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2010  click to toggle the display of all publications <-- click to see more details
[10 A. Iosup, A. Lascateu, and N. Tapus, CAMEO: Enabling Social Networks for Massively Multiplayer Online Games through Continuous Analytics and Cloud Computing, In ACM/IEEE Symposium on Network and Systems Support for Games (NetGames 2010), Taipei, Taiwan, November 16-17, 2010 (accepted, acceptance ratio 30%=9/27)
keywords CAMEO, game analytics, cloud computing, massively social gaming, data acquisition, data mining, data visualization.
 
Article CAMEO: Enabling Social Networks for Massively Multiplayer Online
Games through Continuous Analytics and Cloud Computing in ACM/IEEE NetGames 2010, PDF [0.3MB]
[9 A. Iosup, S. Ostermann, N. Yigitbasi, R. Prodan, Th. Fahringer, and D. Epema, Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing, IEEE Trans. on Parallel and Distributed Systems (TPDS) (accepted, in print).
keywords cloud computing, scientific computing, performance evaluation.
 
Article Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing in IEEE TPDS 2010, PDF [0.5MB]
journal  
  info Evaluated the performance of four cloud computing providers (Amazon EC2, Mosso, Elastic Hosts, and GoGrid) when executing scientific computing micro-benchmarks and applications (HPCC, lmbench, Bonnie, CacheBench). Investigated through trace-based simulations the use of clouds vs. other scientific computing infrastructures such as grids and parallel production environments.
 
 
[8 A. Iosup, N. Yigitbasi, and D. Epema, On the Performance Variability of Production Cloud Services, IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid 2011). (accepted; acceptance ratio 29%=55/189).
keywords cloud computing, performance variability, social applications, Amazon Web Services, Google App Engine.
 
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  info Evaluated the performance variability of over ten cloud service operations from two major production clouds (Amazon Web Services and Google App Engine). Investigated through trace-based simulations the impact of the variability observed for the studied cloud services on three large-scale applications, job execution in scientific computing, virtual goods trading in social networks, and state management in social gaming.
 
 
[7 R. Prodan, V. Nae, and A. Iosup, Dynamic Resource Provisioning in Massively Multiplayer Online Games, In IEEE Transactions on Parallel and Distributed Systems (TPDS), 2010 (accepted, in print).
keywords massively multiplayer online games, platform, virtualization, cloud computing.
 
Article Dynamic Resource Provisioning in Massively Multiplayer Online Games at IEEE TPDS 2010, PDF [1.0MB]Electronic Edition
journal  
  info (Improved version of our SC|08 article) We have investigated a new dynamic resource provisioning method for MMOG operation using external data centres as low-cost resource providers. We have introduced a comprehensive MMOG load model that takes into account both the player interaction type and the population size. We have assessed using trace-based simulation the impact of the data centre policies on the quality of resource provisioning. Last, we have presented experimental results showing the real-time parallelization and load balancing of a real game prototype using data centre resources provisioned using our method and show its advantage against a typical client threshold approach.
 
 
[6 S. Ostermann, A. Iosup, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema, A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, In D.R. Avresky et al. (Eds.): Cloudcomp 2009, LNICST 34, pp. 115?131, 2010.
keywords cloud computing, scientific computing, performance evaluation.
 
Article A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing at CloudComp 2009, PDF [0.5MB]  
  info (Cleaned version of TU Delft Technical Report PDS-2008-006) Evaluated the performance of one cloud computing provider (Amazon EC2) when executing scientific computing micro-benchmarks and applications (HPCC, lmbench, Bonnie, CacheBench). Identified improvements for scientific computing on cloud resources.
 
 
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2009  click to toggle the display of all publications <-- click to see more details
[5 V. Nae, A. Iosup, R. Prodan, and T. Fahringer, The Impact of Virtualization on the Performance of Massively Multiplayer Online Games, In ACM/IEEE Symposium on Network and Systems Support for Games (NetGames 2009), Paris, France, November 23-24, 2009 (accepted, acceptance ratio 27%=10/37).
keywords massively multiplayer online games, platform, virtualization, cloud computing.
 
The Impact of Virtualization on the Performance of Massively Multiplayer Online Games at NetGames 2009, PDF [0.5MB]  
  info We have proposed a new hybrid resource provisioning model that uses a smaller and less expensive set of self-owned data centers, complemented by virtualized cloud computing resources during peak hours. Using real traces from RuneScape, one of the most successful contemporary MMOGs, we have evaluated with simulations the effectiveness of the on-demand cloud resource provisioning strategy for MMOGs.
 
 
[4 A. Iosup, CAMEO: Continuous Analytics for Massively Multiplayer Online Games on Cloud Resources, In 2nd International Workshop on Real Time Online Interactive Applications on the Grid (ROIA 2009), Delft, the Netherlands, August 24, 2009, Springer, LNCS vol.?, p.1--10 (accepted, in print).
keywords massively multiplayer online games, analytics, cloud computing.
 
CAMEO: Continuous Analytics for Massively Multiplayer Online Games on Cloud Resources at ROIA 2009, PDF [0.5MB]  
  info We have introduced CAMEO, an architecture for Continuous Analytics for Massively multiplayEr Online games on cloud resources.
 
 
[3 N. Yigitbasi, A. Iosup, S. Ostermann, and D.H.J. Epema, C-Meter: A Framework for Performance Analysis of Computing Clouds, In the International Workshop on Cloud Computing (Cloud 2009), May 18-21, 2009, in conjunction with CCGrid'09 (accepted).
keywords cloud computing, GrenchMark, performance evaluation, C-Meter.
 
Article C-Meter: A Framework for Performance Analysis of Computing Clouds at Cloud'09, PDF [0.3MB]
Electronic EditionDBLP BiBTeX EntryDBLP Conference Entry
  info We extend GrenchMark, our framework for performance evaluation in large-scale distributed systems, with C-Meter, a component to assess the performance of computing clouds.
 
 
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[2 S. Ostermann, A. Iosup, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema, A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, TU Delft Technical Report PDS-2008-006, Dec 2008.
keywords cloud computing, performance evaluation, performance analysis, benchmarking, Bonnie, LMbench, CacheBench.
 
Article A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing at CloudComp 2009, PDF [0.5MB]  
  info Evaluated the performance of one cloud computing provider (Amazon EC2) when executing scientific computing micro-benchmarks and applications (HPCC, lmbench, Bonnie, CacheBench). Identified improvements for scientific computing on cloud resources.
 
 
[1 V. Nae, A. Iosup, S. Podlipnig, R. Prodan, D.H.J.Epema, T. Fahringer, Efficient Management of Data Center Resources for Massively Multiplayer Online Games, In the ACM/IEEE SuperComputing Conference on High Performance Networking and Computing (SC'08), Nov 10-16, 2008 (accepted; acceptance ratio 20%).
keywords massively multiplyer online games (MMOG), cloud computing, data centers, virtual environments, resource provisioning, games.
 
Article Efficient Use of Data Center Resources for Massively Multiplayer Online Games at SuperComputing 2008, PDF [0.6MB]
Electronic EditionDBLP BiBTeX EntryDBLP Conference Entry
 
  info We have analyzed through trace-based simulations the potential of running MMOGs entirely in clouds and data centers. We modeled ideal clouds and data centers, and found a theoretically achievable ten-fold reduction in the resource consumption.
 
 
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Last modified: Fri, 12 December, 2013 01:05 PM
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