Active job monitoring in pilots

Recent developments in high energy physics (HEP) including multi-core jobs and multi-core pilots require data centres to gain a deep understanding of the system to monitor, design, and upgrade computing clusters. Networking is a critical component. Especially the increased usage of data federations, for example in diskless computing centres or as a fall-back solution, relies on WAN connectivity and availability. The specific demands of different experiments and communities, but also the need for identification of misbehaving batch jobs, requires an active monitoring. Existing monitoring tools are not capable of measuring fine-grained information at batch job level. This complicates network-aware scheduling and optimisations. In addition, pilots add another layer of abstraction. They behave like batch systems themselves by managing and executing payloads of jobs internally. The number of real jobs being executed is unknown, as the original batch system has no access to internal information about the scheduling process inside the pilots. Therefore, the comparability of jobs and pilots for predicting run-time behaviour or network performance cannot be ensured. Hence, identifying the actual payload is important. At the GridKa Tier 1 centre a specific tool is in use that allows the monitoring of network traffc information at batch job level. This contribution presents the current monitoring approach and discusses recent e_orts and importance to identify pilots and their substructures inside the batch system. It will also show how to determine monitoring data of specific jobs from identified pilots. Finally, the approach is evaluated

Provisioning of data locality for HEP analysis workflows

The heavily increasing amount of data produced by current experiments in high energy particle physics challenge both end users and providers of computing resources. The boosted data rates and the complexity of analyses require huge datasets being processed in short turnaround cycles. Usually, data storages and computing farms are deployed by different providers, which leads to data delocalization and a strong influence of the interconnection transfer rates. The CMS collaboration at KIT has developed a prototype enabling data locality for HEP analysis processing via two concepts. A coordinated and distributed caching approach that reduce the limiting factor of data transfers by joining local high performance devices with large background storages were tested. Thereby, a throughput optimization was reached by selecting and allocating critical data within user work-flows. A highly performant setup using these caching solutions enables fast processing of throughput dependent analysis workflows

Boosting Performance of Data-intensive Analysis Workflows with Distributed Coordinated Caching

Data-intensive end-user analyses in high energy physics require high data throughput to reach short turnaround cycles. This leads to enormous challenges for storage and network infrastructure, especially when facing the tremendously increasing amount of data to be processed during High-Luminosity LHC runs. Including opportunistic resources with volatile storage systems into the traditional HEP computing facilities makes this situation more complex. Bringing data close to the computing units is a promising approach to solve throughput limitations and improve the overall performance. We focus on coordinated distributed caching by coordinating workows to the most suitable hosts in terms of cached files. This allows optimizing overall processing efficiency of data-intensive workows and efficiently use limited cache volume by reducing replication of data on distributed caches. We developed a NaviX coordination service at KIT that realizes coordinated distributed caching using XRootD cache proxy server infrastructure and HTCondor batch system. In this paper, we present the experience gained in operating coordinated distributed caches on cloud and HPC resources. Furthermore, we show benchmarks of a dedicated high throughput cluster, the Throughput-Optimized Analysis-System (TOpAS), which is based on the above-mentioned concept

High performance data analysis via coordinated caches

With the second run period of the LHC, high energy physics collaborations will have to face increasing computing infrastructural needs. Opportunistic resources are expected to absorb many computationally expensive tasks, such as Monte Carlo event simulation. This leaves dedicated HEP infrastructure with an increased load of analysis tasks that in turn will need to process an increased volume of data. In addition to storage capacities, a key factor for future computing infrastructure is therefore input bandwidth available per core. Modern data analysis infrastructure relies on one of two paradigms: data is kept on dedicated storage and accessed via network or distributed over all compute nodes and accessed locally. Dedicated storage allows data volume to grow independently of processing capacities, whereas local access allows processing capacities to scale linearly. However, with the growing data volume and processing requirements, HEP will require both of these features. For enabling adequate user analyses in the future, the KIT CMS group is merging both paradigms: popular data is spread over a local disk layer on compute nodes, while any data is available from an arbitrarily sized background storage. This concept is implemented as a pool of distributed caches, which are loosely coordinated by a central service. A Tier 3 prototype cluster is currently being set up for performant user analyses of both local and remote data

Federation of compute resources available to the German CMS community

The German CMS community (DCMS) as a whole can benefit from the various compute resources, available to its different institutes. While Grid-enabled and National Analysis Facility resources are usually shared within the community, local and recently enabled opportunistic resources like HPC centers and cloud resources are not. Furthermore, there is no shared submission infrastructure available. Via HTCondor\u27s [1] mechanisms to connect resource pools, several remote pools can be connected transparently to the users and therefore used more efficiently by a multitude of user groups. In addition to the statically provisioned resources, also dynamically allocated resources from external cloud providers as well as HPC centers can be integrated. However, the usage of such dynamically allocated resources gives rise to additional complexity. Constraints on access policies of the resources, as well as workflow necessities have to be taken care of. To maintain a well-defined and reliable runtime environment on each resource, virtualization and containerization technologies such as virtual machines, Docker, and Singularity, are used

Mastering Opportunistic Computing Resources for HEP

As results of the excellent LHC performance in 2016, more data than expected has been recorded leading to a higher demand for computing resources. It is already foreseeable that for the current and upcoming run periods a flat computing budget and the expected technology advance will not be sufficient to meet the future requirements. This results in a growing gap between supplied and demanded resources. One option to reduce the emerging lack of computing resources is the utilization of opportunistic resources such as local university clusters, public and commercial cloud providers, HPC centers and volunteer computing. However, to use opportunistic resources additional challenges have to be tackled. At the Karlsruhe Institute of Technology (KIT) an infrastructure to dynamically use opportunistic resources is built up. In this paper tools, experiences, future plans and possible improvements are discussed

HEP Analyses on Dynamically Allocated Opportunistic Computing Resources

The current experiments in high energy physics (HEP) have a huge data rate. To convert the measured data, an enormous number of computing resources is needed and will further increase with upgraded and newer experiments. To fulfill the ever-growing demand the allocation of additional, potentially only temporary available non-HEP dedicated resources is important. These so-called opportunistic resources cannot only be used for analyses in general but are also well-suited to cover the typical unpredictable peak demands for computing resources. For both use cases, the temporary availability of the opportunistic resources requires a dynamic allocation, integration, and management, while their heterogeneity requires optimization to maintain high resource utilization by allocating best matching resources. To find the best matching resources which should be allocated is challenging due to the unpredictable submission behavior as well as an ever-changing mixture of workflows with different requirements. Instead of predicting the best matching resource, we base our decisions on the utilization of resources. For this reason, we are developing the resource manager TARDIS (Transparent Adaptive Resource Dynamic Integration System) which manages and dynamically requests or releases resources. The decision of how many resources TARDIS has to request is implemented in COBalD (COBald - The Opportunistic Balancing Daemon) to ensure further allocation of well-used resources while reducing the amount of insufficiently used ones. TARDIS allocates and manages resources from various resource providers such as HPC centers or commercial and public clouds while ensuring a dynamic allocation and efficient utilization of these heterogeneous opportunistic resources. Furthermore, TARDIS integrates the allocated opportunistic resources into one overlay batch system which provides a single point of entry for all users. In order to provide the dedicated HEP software environment, we use virtualization and container technologies. In this contribution, we give an overview of the dynamic integration of opportunistic resources via TARDIS/COBalD in our HEP institute as well as how user analyses benefit from these additional resources

A scalable architecture for online anomaly detection of WLCG batch jobs

For data centres it is increasingly important to monitor the network usage, and learn from network usage patterns. Especially con_guration issues or misbehaving batch jobs preventing a smooth operation need to be detected as early as possible. At the GridKa data and computing centre we therefore operate a tool BPNetMon for monitoring tra_c data and characteristics of WLCG batch jobs and pilots locally on di_erent worker nodes. On the one hand local information itself are not su_cient to detect anomalies for several reasons, e.g. the underlying job distribution on a single worker node might change or there might be a local miscon_guration. On the other hand a centralised anomaly detection approach does not scale regarding network communication as well as computational costs. We therefore propose a scalable architecture based on concepts of a super-peer network

Tier 3 batch system data locality via managed caches

Modern data processing increasingly relies on data locality for performance and scalability, whereas the common HEP approaches aim for uniform resource pools with minimal locality, recently even across site boundaries. To combine advantages of both, the High-Performance Data Analysis (HPDA) Tier 3 concept opportunistically establishes data locality via coordinated caches. In accordance with HEP Tier 3 activities, the design incorporates two major assumptions: First, only a fraction of data is accessed regularly and thus the deciding factor for overall throughput. Second, data access may fallback to non-local, making permanent local data availability an ineficient resource usage strategy. Based on this, the HPDA design generically extends available storage hierarchies into the batch system. Using the batch system itself for scheduling file locality, an array of independent caches on the worker nodes is dynamically populated with high-profile data. Cache state information is exposed to the batch system both for managing caches and scheduling jobs. As a result, users directly work with a regular, adequately sized storage system. However, their automated batch processes are presented with local replications of data whenever possible