Radu Prodan

Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing

Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel production environments. However, the current commercial clouds have been built to support web and small database workloads, which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.

ASKALON: a tool set for cluster and Grid computing

Performance engineering of parallel and distributed applications is a complex task that iterates through various phases, ranging from modeling and prediction, to performance measurement, experiment management, data collection, and bottleneck analysis. There is no evidence so far that all of these phases should/can be integrated into a single monolithic tool. Moreover, the emergence of computational Grids as a common single wide‐area platform for high‐performance computing raises the idea to provide tools as interacting Grid services that share resources, support interoperability among different users and tools, and, most importantly, provide omnipresent services over the Grid. We have developed the ASKALON tool set to support performance‐oriented development of parallel and distributed (Grid) applications. ASKALON comprises four tools, coherently integrated into a service‐oriented architecture. SCALEA is a performance instrumentation, measurement, and analysis tool of parallel and distributed applications. ZENTURIO is a general purpose experiment management tool with advanced support for multi‐experiment performance analysis and parameter studies. AKSUM provides semi‐automatic high‐level performance bottleneck detection through a special‐purpose performance property specification language. The PerformanceProphet enables the user to model and predict the performance of parallel applications at the early stages of development. In this paper we describe the overall architecture of the ASKALON tool set and outline the basic functionality of the four constituent tools. The structure of each tool is based on the composition and sharing of remote Grid services, thus enabling tool interoperability. In addition, a data repository allows the tools to share the common application performance and output data that have been derived by the individual tools. A service repository is used to store common portable Grid service implementations. A general‐purpose Factory service is employed to create service instances on arbitrary remote Grid sites. Discovering and dynamically binding to existing remote services is achieved through registry services. The ASKALON visualization diagrams support both online and post‐mortem visualization of performance and output data. We demonstrate the usefulness and effectiveness of ASKALON by applying the tools to real‐world applications. Copyright

A survey and taxonomy of infrastructure as a service and web hosting cloud providers

With an increasing number of providers claiming to offer Cloud infrastructures, there is a lack in the community for a common terminology, accompanied by a clear definition and classification of Cloud features. We conduct in this paper a survey on a selection of Cloud providers, and propose a taxonomy of eight important Cloud computing elements covering service type, resource deployment, hardware, runtime tuning, business model, middleware, and performance. We conclude that the provisioning of Service Level Agreements as utilities, of open and interoperable middleware solutions, as well as of sustained performance metrics for high-performance computing applications are three elements with the highest need of further community research.

ASKALON: a Grid application development and computing environment

We present the ASKALON environment whose goal is to simplify the development and execution of workflow applications on the Grid. ASKALON is centered around a set of high-level services for transparent and effective Grid access, including a Scheduler for optimized mapping of workflows onto the Grid, an Enactment Engine for reliable application execution, a Resource Manager covering both computers and application components, and a Performance Prediction service based on training phase and statistical methods. A sophisticated XML-based programming interface that shields the user from the Grid middleware details allows the high-level composition of workflow applications. ASKALON is used to develop and port scientific applications as workflows in the Austrian Grid project. We present experimental results using two real-world scientific applications to demonstrate the effectiveness of our approach.

Towards a general model of the multi-criteria workflow scheduling on the grid

Workflow scheduling on the Grid becomes more challenging when multiple scheduling criteria are considered. Existing studies provide different approaches to the multi-criteria Grid workflow scheduling problem, and address different variants of the problem. A profound understanding of the problems nature can be an important step towards more generic scheduling approaches. Based on the related work and on our own experience, we propose several novel taxonomies of the problem, considering five facets: workflow model, scheduling criteria, scheduling process, resource model, and task model. We make a survey of the existing related work, and classify it according to the proposed taxonomies, identifying the most common use cases and the areas that have not been sufficiently explored yet.

ASKALON: A Development and Grid Computing Environment for Scientific Workflows

Most existing Grid application development environments provide the application developer with a nontransparent Grid. Commonly, application developers are explicitly involved in tedious tasks such as selecting software components deployed on specific sites, mapping applications onto the Grid, or selecting appropriate computers for their applications. Moreover, many programming interfaces are either implementation-technology-specific (e.g., based on Web services [24]) or force the application developer to program at a low-level middleware abstraction (e.g., start task, transfer data [22, 153]). While a variety of graphical workflow composition tools are currently being proposed, none of them is based on standard modeling techniques such as Unified Modeling Language (UML).

A Multi-objective Approach for Workflow Scheduling in Heterogeneous Environments

Traditional scheduling research usually targets make span as the only optimization goal, while several isolated efforts addressed the problem by considering at most two objectives. In this paper we propose a general framework and heuristic algorithm for multi-objective static scheduling of scientific workflows in heterogeneous computing environments. The algorithm uses constraints specified by the user for each objective and approximates the optimal solution by applying a double strategy: maximizing the distance to the constraint vector for dominant solutions and minimizing it otherwise. We analyze and classify different objectives with respect to their impact on the optimization process and present a four-objective case study comprising make span, economic cost, energy consumption, and reliability. We implemented the algorithm as part of the ASKALON environment for Grid and Cloud computing. Results for two real-world applications demonstrate that the solutions generated by our algorithm are superior to user-defined constraints most of the time. Moreover, the algorithm outperforms a related bi-criteria heuristic and a bi-criteria genetic algorithm.

Dynamic Resource Provisioning in Massively Multiplayer Online Games

Todays Massively Multiplayer Online Games (MMOGs) can include millions of concurrent players spread across the world and interacting with each other within a single session. Faced with high resource demand variability and with misfit resource renting policies, the current industry practice is to overprovision for each game tens of self-owned data centers, making the market entry affordable only for big companies. Focusing on the reduction of entry and operational costs, we investigate a new dynamic resource provisioning method for MMOG operation using external data centers as low-cost resource providers. First, we identify in the various types of player interaction a source of short-term load variability, which complements the long-term load variability due to the size of the player population. Then, we introduce a combined MMOG processor, network, and memory load model that takes into account both the player interaction type and the population size. Our model is best used for estimating the MMOG resource demand dynamically, and thus, for dynamic resource provisioning based on the game world entity distribution. We evaluate several classes of online predictors for MMOG entity distribution and propose and tune a neural network-based predictor to deliver good accuracy consistently under real-time performance constraints. We assess using trace-based simulation the impact of the data center policies on the quality of resource provisioning. We find that the dynamic resource provisioning can be much more efficient than its static alternative even when the external data centers are busy, and that data centers with policies unsuitable for MMOGs are penalized by our dynamic resource provisioning method. Finally, we present experimental results showing the real-time parallelization and load balancing of a real game prototype using data center resources provisioned using our method and show its advantage against a rudimentary client threshold approach.

Dynamic scheduling of scientific workflow applications on the grid: a case study

The existing Grid workflow scheduling projects do not handle recursive loops which are characteristic to many scientific problems. We propose a hybrid approach for scheduling Directed Graph (DG)-based workflows in a Grid environment with dynamically changing computational and network resources. Our dynamic scheduling algorithm is based on the iterative invocation of classical static Directed Acyclic Graphs (DAGs) scheduling heuristics generated using well-defined cycle elimination and task migration techniques. We approach the static scheduling problem as an application of a modular optimisation tool using genetic algorithms. We report successful implementation and experimental results on a pilot real-world material science workflow application.

GroudSim: an event-based simulation framework for computational grids and clouds

We present GroudSim, a Grid and Cloud simulation toolkit for scientific applications based on a scalable simulation-independent discrete-event core. GroudSim provides a comprehensive set of features for complex simulation scenarios from simple job executions on leased computing resources to calculation of costs, and background load on resources. Simulations can be parameterised and are easily extendable by probability distribution packages for failures which normally occur in complex environments. Experimental results demonstrate the improved scalability of GroudSim compared to a related process-based approach.