Daniel G. Waddington

Load Balancing Aware Real-Time Task Partitioning in Multicore Systems

Real-time applications of future IT will continue to drive the demand for performance scaling in devices ranging from sensors to servers. Parallel processing in the form of mul-ticore and manycore architectures will also continue to be the principal route to unleashing next generation performance capabilities. To fully exploit multicore processors, real-time applications are expected to provide a large degree of parallel-ism, where real-time tasks can utilize multiple cores at the same time. Guaranteeing real-time performance, while making efficient use of multicore resources, requires a scheduling method that offers both high schedulability and effective load balancing. Many existing real-time scheduling methods for multicore systems focus on schedulability or load balancing, but not both -- each coming at the expense of the other. In this work we develop an efficient scheduling algorithm that not only guarantees real-time performance but also demonstrates effective distribution of tasks across cores. Experimental re-sults show that our method significantly outperforms state-of-the-art approaches in terms of load balancing while still providing good schedulability. We also show the benefits with respect to energy reduction that result from balanced load.

Implementing a high-performance recommendation system using Phoenix++

Recommendation systems are important big data applications that are used in many business sectors of the global economy. While many users utilize Hadoop-like MapReduce systems to implement recommendation systems, we utilize the high-performance shared-memory MapReduce system Phoenix++ [1] to design a faster recommendation engine. In this paper, we design a distributed out-of-core recommendation algorithm to maximize the usage of main memory, and devise a framework that invokes Phoenix++ as a sub-module to achieve high performance. The design of the framework can be extended to support different types of big data applications. The experiments on Amazon Elastic Compute Cloud (Amazon EC2) demonstrate that our new recommendation system can be faster than its Hadoop counterpart by up to 225% without losing recommendation quality.

A Scalable Physical Memory Allocation Scheme for L4 Microkernel

L4 microkernel family has become very successful on mobile devices. However, with the rapid shift from uniprocessor to multicore and manycore processor, many critical OS functions including physical memory allocator (PMA) must be re-designed in order to achieve better system throughput. While research and engineering efforts have been made for PMA in monolithic kernels such as Linux, not much work can be found for L4 microkernels. Due to the the design difference, the PMA in L4 microkernels is part of user level page fault handler (a.k.a. pager), which is executed as a stand-alone server in the least privilege mode. Memory allocation and free requests are handled through inter-process communication (IPC) rather than normal system or kernel function calls. In this work, we first study the scalability issue of the PMA implementation in L4 microkernels, and propose our solution in the context of Fiasco.OC, a state-of-the-art L4 microkernel implementation. We also discuss how to leverage the L4 microkernel design advantages to implement a PMA with more advanced features, such as load balancing, customizability and NUMA-awareness. Finally, we conduct experiments to verify the scalability result of our solution. The experiment is conducted on a 48-core AMD magny-cours server.