Scheduling Storms and Streams in the Cloud

Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. Each graph is a job, where nodes represent compute tasks and edges indicate data-flows between these compute tasks. Jobs (graphs) arrive randomly over time, and upon completion, leave the system. When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. Specifically, neighboring compute tasks in the graph that are mapped to different servers incur load on the network; thus a mapping of the jobs among the servers incurs a cost that is proportional to the number of "broken edges". We propose a low complexity randomized scheduling algorithm that, without service preemptions, stabilizes the system with graph arrivals/departures; more importantly, it allows a smooth trade-off between minimizing average partitioning cost and average queue lengths. Interestingly, to avoid service preemptions, our approach does not rely on a Gibbs sampler; instead, we show that the corresponding limiting invariant measure has an interpretation stemming from a loss system.Comment: 14 page

Smart technologies for effective reconfiguration: the FASTER approach

Current and future computing systems increasingly require that their functionality stays flexible after the system is operational, in order to cope with changing user requirements and improvements in system features, i.e. changing protocols and data-coding standards, evolving demands for support of different user applications, and newly emerging applications in communication, computing and consumer electronics. Therefore, extending the functionality and the lifetime of products requires the addition of new functionality to track and satisfy the customers needs and market and technology trends. Many contemporary products along with the software part incorporate hardware accelerators for reasons of performance and power efficiency. While adaptivity of software is straightforward, adaptation of the hardware to changing requirements constitutes a challenging problem requiring delicate solutions. The FASTER (Facilitating Analysis and Synthesis Technologies for Effective Reconfiguration) project aims at introducing a complete methodology to allow designers to easily implement a system specification on a platform which includes a general purpose processor combined with multiple accelerators running on an FPGA, taking as input a high-level description and fully exploiting, both at design time and at run time, the capabilities of partial dynamic reconfiguration. The goal is that for selected application domains, the FASTER toolchain will be able to reduce the design and verification time of complex reconfigurable systems providing additional novel verification features that are not available in existing tool flows

Collector Managed Inventory, A Proactive Planning Approach to the Collection of Liquids coming from End-of-life Vehicles

In this article we introduce Collector Managed Inventory (CMI) as the reverse logistics counter part of Vendor Managed Inventory (VMI).The collection company takes responsibility for the inventories of cores or materials to be recycled.Experience in forward supply chain management has shown the potential of VMI by bringing the coordination of transportation and inventory decisions to the same supply chain entity.Using information technology called telemetry, we are able to monitor inventory levels at distance.We introduce a proactive planning methodology based on two types of collection orders: must- and can-orders.Every collection period all must-orders have to be collected, while canorders are only collected if they can be combined beneficially with the must-orders.The routing problem is solved by a combination of route generation and set partitioning.The system is illustrated in a real-life case study for Auto Recycling Nederland on the collection of liquids coming from end-oflife vehicles, such as coolant and oil.In several scenarios the old reactive approach is compared to the proactive approach resulting in cost savings up to about 19%.supply;logistics;vehicle routine;distribution;information technology;inventory