Jai Dayal

Towards Thermal Aware Workload Scheduling in a Data Center

High density blade servers are a popular technology for data centers, however, the heat dissipation density of data centers increases exponentially. There is strong evidence to support that high temperatures of such data centers will lead to higher hardware failure rates and thus an increase in maintenance costs. Improperly designed or operated data centers may either suffer from overheated servers and potential system failures, or from overcooled systems, causing extraneous utilities cost. Minimizing the cost of operation (utilities, maintenance, device upgrade and replacement) of data centers is one of the key issues involved with both optimizing computing resources and maximizing business outcome. This paper proposes an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then a thermal aware task scheduling algorithm is presented which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing endurable decline in performance.

Thermal aware workload placement with task-temperature profiles in a data center

Data centers now play an important role in modern IT infrastructures. Related research shows that the energy consumption for data center cooling systems has recently increased significantly. There is also strong evidence to show that high temperatures in a data center will lead to higher hardware failure rates, and thus an increase in maintenance costs. This paper devotes itself in the field of thermal aware workload placement for data centers. In this paper, we propose an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then two thermal aware task scheduling algorithms, TASA and TASA-B, are presented which aim to reduce temperatures and cooling system power consumption in a data center. A simulation study is carried out to evaluate the performance of the proposed algorithms. Simulation results show that our algorithms can significantly reduce temperatures in data centers by introducing endurable decline in system performance.

FlexIO: I/O Middleware for Location-Flexible Scientific Data Analytics

Increasingly severe I/O bottlenecks on High-End Computing machines are prompting scientists to process simulation output data online while simulations are running and before storing data on disk. There are several options to place data analytics along the I/O path: on compute nodes, on separate nodes dedicated to analytics, or after data is stored on persistent storage. Since different placements have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. The FlexIO middleware described in this paper makes it easy for scientists to obtain such flexibility, by offering simple abstractions and diverse data movement methods to couple simulation with analytics. Various placement policies can be built on top of FlexIO to exploit the trade-offs in performing analytics at different levels of the I/O hierarchy. Experimental results demonstrate that FlexIO can support a variety of simulation and analytics workloads at large scale through flexible placement options, efficient data movement, and dynamic deployment of data manipulation functionalities.

Thermal aware workload scheduling with backfilling for green data centers

Data centers now play an important role in modern IT infrastructures. Related research has shown that the energy consumption for data center cooling systems has recently increased significantly. There is also strong evidence to show that high temperatures with in a data center will lead to higher hardware failure rates and thus an increase in maintenance costs. This paper devotes itself in the field of thermal aware resource management for data centers. This paper proposes an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then a thermal aware task scheduling algorithm with backfilling is presented which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing endurable decline in performance.

Task scheduling with ANN-based temperature prediction in a data center: a simulation-based study

High temperatures within a data center can cause a number of problems, such as increased cooling costs and increased hardware failure rates. To overcome this problem, researchers have shown that workload management, focused on a data center’s thermal properties, effectively reduces temperatures within a data center. In this paper, we propose a method to predict a workload’s thermal effect on a data center, which will be suitable for real-time scenarios. We use machine learning techniques, such as artificial neural networks (ANN) as our prediction methodology. We use real data taken from a data center’s normal operation to conduct our experiments. To reduce the data’s complexity, we introduce a thermal impact matrix to capture the spacial relationship between the data center’s heat sources, such as the compute nodes. Our results show that machine learning techniques can predict the workload’s thermal effects in a timely manner, thus making them well suited for real-time scenarios. Based on the temperature prediction techniques, we developed a thermal-aware workload scheduling algorithm for data centers, which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing an endurable decline in performance.

Flexpath: Type-Based Publish/Subscribe System for Large-Scale Science Analytics

As high-end systems move toward exascale sizes, a new model of scientific inquiry being developed is one in which online data analytics run concurrently with the high end simulations producing data outputs. Goals are to gain rapid insights into the ongoing scientific processes, assess their scientific validity, and/or initiate corrective or supplementary actions by launching additional computations when needed. The Flexpath system presented in this paper addresses the fundamental problem of how to structure and efficiently implement the communications between high end simulations and concurrently running online data analytics, the latter comprised of componentized dynamic services and service pipelines. Using a type-based publish/subscribe approach, Flexpath encourages diversity by permitting analytics services to differ in their computational and scaling characteristics and even in their internal execution models. Flex path uses direct and MxN connections between interacting services to reduce data movements, to allow for runtime connectivity changes to accommodate component arrivals/departures, and to support the multiple underlying communication protocols used for analytics workflows in which simulation outputs are processed by analytics services residing on the same nodes where they are generated, on the same machine, and/or on attached or remote analytics engines. This paper describes the design and implementation of Flexpath, and evaluates it with two widely used scientific applications and their associated data analytics methods.

D2T: Doubly Distributed Transactions for High Performance and Distributed Computing

Current exascale computing projections suggest rather than a monolithic simulation running for the majority of the machine, a collection of components comprising the scientific discovery process will be employed in an online workflow. This move to an online workflow scenario requires knowledge that inter-step operations are completed and correct before the next phase begins. Further, dynamic load balancing or fault tolerance techniques may dynamically deploy or redeploy resources for optimal use of computing resources. These newly configured resources should only be used if they are successfully deployed. Our D2T system offers a mechanism to support these kinds of operations by providing database-like transactions with distributed servers and clients. Ultimately, with adequate hardware support, full ACID compliance is possible for the transactions. To prove the viability of this approach, we show that the D2T protocol has less than 1.2 seconds of overhead using 4096 clients and 32 servers with good scaling characteristics using this initial prototype implementation.

eMOLST: a documentation flow for distributed health informatics

Electronic Health Records (EHRs) have many potential advantages over traditional paper records, such as wide scale access, error checking, and protection from physical damage to a record. As with any medical record, paper or electronic, both the patients privacy and the documents integrity must be guaranteed. With initiatives such as Integrating the Healthcare Enterprise (IHE), computerized healthcare systems are able to share EHRs on a large scale, while protecting the patients privacy rights. However, IHE does not yet meet the needs of all healthcare systems, as we will show with the eMOLST project. The eMOLST project delivers software in support of Medical Order for Life Sustaining Treatment (MOLST) forms and uses IHE specifications for cross enterprise document storage and sharing, patient identification, and user authentication and authorization. The Web‐based system provides secure access to electronic MOLST documents regardless of the patients or healthcare providers location. The eMOLST project allows a user to have Single Sign On (SSO) access to the system from either the users associated enterprise, or through a Web portal shared amongst all users across all enterprises. In this paper, we show a security solution to allow SSO from multiple access points for IHE compliant systems. Copyright

High end scientific codes with computational I/O pipelines: improving their end-to-end performance

This paper uses computational I/O pipelines to integrate computations into the I/O path that perform data analytics on the data generated by scientific simulations. A novel attribute is the use of a pluggable execution environment in which analysis tools can be orchestrated into a multi-stage pipeline for processing simulation output data. Performance considerations are addressed through the use of a high performance data transport. The approach is evaluated with the end-to-end performance of a Magnetohydrodynamics application at large scale.

SODA: Science-Driven Orchestration of Data Analytics

As scientific simulation applications evolve on the path towards exascale, a new model of scientific inquiry is required where concurrently with the running simulation, online analytics operate on the data it produces. By avoiding offline data storage except when absoluately necessary, it enables speeding up the scientific discovery process by providing rapid insights into the simulated science phenomena and affording more frequent, detailed data analytics than is possible with the traditional purely offline approach of using disk for intermediate data storage. However, a challenge for online analytics is to respond to behavior dynamics caused by changing simulation outputs and by unforeseen events on the underlying hardware/software platforms. This paper presents SODA, a set of run-time abstractions for online orchestration of data analytics, realized by embedding analytics tasks into workstations that monitor component behavior and enable responses to run-time changes in their resource demands and in the platforms resource availability. For high end simulations running on a leadership class machine, experimental evaluations show SODA can invoke efficient orchestration operations responding to a diverse set of run-time dynamics at different granularities to meet end-user and analysis specific requirements.