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Multicore processors contain new hardware characteristics that are different from previous generation single-core systems or traditional SMP (symmetric multiprocessing) multiprocessor systems. These new characteristics provide new performance opportunities and challenges. In this paper, we show how hardware performance monitors can be used to provide a fine-grained, closely-coupled feedback loop to dynamic optimizations done by a multicore-aware operating system. These multicore optimizations are possible due to the advanced capabilities of hardware performance monitoring units currently found in commodity processors, such as execution pipeline stall breakdown and data address sampling. We demonstrate three case studies on how a multicore-aware operating system can use these online capabilities for (1) determining cache partition sizes, which helps reduce contention in the shared cache among applications, (2) detecting memory regions with bad cache usage, which helps in isolating these regions to reduce cache pollution, and (3) detecting sharing among threads, which helps in clustering threads to improve locality. Using realistic applications from standard benchmark suites, the following performance improvements were achieved: (1) up to 27% improvement in IPC (instructions-per-cycle) due to cache partition sizing; (2) up to 10% reduction in cache miss rates due to reduced cache pollution, resulting in up to 7% improvement in IPC; and (3) up to 70% reduction in remote cache accesses due to thread clustering, resulting in up to 7% application-level improvement.

Designing and implementing system software so that it scales well on shared-memory multiprocessors (SMMPs) has proven to be surprisingly challenging. To improve scalability, most designers to date have focused on concurrency by iteratively eliminating the need for locks and reducing lock contention. However, our experience indicates that locality is just as, if not more, important and that focusing on locality ultimately leads to a more scalable system.In this paper, we describe a methodology and a framework for constructing system software structured for locality, exploiting techniques similar to those used in distributed systems. Specifically, we found two techniques to be effective in improving scalability of SMMP operating systems: (i) an object-oriented structure that minimizes sharing by providing a natural mapping from independent requests to independent code paths and data structures, and (ii) the selective partitioning, distribution, and replication of object implementations in order to improve locality. We describe concrete examples of distributed objects and our experience implementing them. We demonstrate that the distributed implementations improve the scalability of operating-system-intensive parallel workloads.

Cloud-management stacks have become an increasingly important element in cloud computing, serving as the resource manager of cloud platforms. While the functionality of this emerging layer has been constantly expanding, its fault resilience remains under-studied. This paper presents a systematic study of the fault resilience of OpenStack-a popular open source cloud-management stack. We have built a prototype fault-injection framework targeting service communications during the processing of external requests, both among OpenStack services and between OpenStack and external services, and have thus far uncovered 23 bugs in two versions of OpenStack. Our findings shed light on defects in the design and implementation of state-of-the-art cloudmanagement stacks from a fault-resilience perspective.

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