JSSPP 2017 - Workshops on Job Scheduling Strategies for Parallel Processing

Large parallel systems have been in production for about 20 years, creating the need of scheduling for such systems. This workshop was created in 1995 to provide a forum for the research and engineering community working in the area. Initially, parallel systems were very static. Machines were built in fixed configurations, which would be wholesale replaced every few years. Much of the workload consisted of parallel scientific jobs. These jobs were static, running on a fixed number of nodes. Systems were primarily managed via batch queues. The user experience was far from interactive; jobs could wait in queues for days or even weeks.
A little over 10 years ago, the emergence of large scale, interactive, web applications began to drive the development of a new class of systems and schedulers. These systems would run “services”, which would essentially never terminate (unlike scientific jobs). This created systems and schedulers with vastly different properties. Moreover, this created an enormous demand for computing resources, resulting in a commercial market of competing providers. At the same time, the increasing demands for more power and interactivity have driven scientific platforms in a similar direction, causing the lines between these platforms to blur.
Nowadays, parallel processing is much more dynamic and connected. Many workloads are interactive and make use of variable resources over time. Complex parallel infrastructures can now be built on the fly, using resources from different sources, provided with different prices and quality of services. Capacity planning became more proactive, where resources are acquired continuously, with the goal of staying ahead of demand. The interaction model between job and resource manager is shifting to one of negotiation, where they agree on resources, price, and quality of service. These are just a few examples of the open issues facing our field.
JSSPP solicits papers that address any of the challenges in parallel scheduling, including:
Design and evaluation of new scheduling approaches.
Performance evaluation of scheduling approaches, including methodology, benchmarks, and metrics.
Workloads, including characterization, classification, and modeling.
Consideration of additional constraints in scheduling systems, like job priorities, price, accounting, load estimation, and quality of service guarantees.
Impact of scheduling strategies on application performance, user friendliness, cost efficiency, and energy efficiency.
Scaling and composition of very large scheduling systems.
Cloud provider issues: capacity planning, service level assurance, reliability.
Interaction between schedulers on different levels, like processor level as well as whole single- or even multi-owner systems.
Experience reports from production systems.
Experience reports from large scale compute campaigns.