Self-Adaptive Scheduler Parameterization

High-end parallel systems present a tremendous research challenge on how to best allocate their resources to match dynamic workload characteristics and user habits that are often unique to each system. Although thoroughly investigated, job scheduling for production systems remains an inexact science, requiring significant experience and intuition from system administrators to properly configure batch schedulers. State-of-the-art schedulers provide many parameters for their configuration, but tuning these to optimize performance and to appropriately respond to the continuously varying characteristics of the workloads can be very difficult — the effects of different parameters and their interactions are often unintuitive. In this paper, we introduce a new and general methodology for automating the difficult process of job scheduler parameterization. Our proposed methodology is based on online simulations of a model of the actual system to provide on-the-fly suggestions to the scheduler for automated parameter adjustment. Detailed performance comparisons via simulation using actual supercomputing traces from the Parallel Workloads Archive indicate that this self-adaptive parameterization via online simulation consistently outperforms other workload-aware methods for scheduler parameterization. This methodology is unique, flexible, and practical in that it requires no a priori knowledge of the workload, it works well even in the presence of poor user runtime estimates, and it can be used to address any system statistic of interest

DyScale: A MapReduce Job Scheduler for Heterogeneous Multicore Processors

The functionality of modern multi-core processors is often driven by a given power budget that requires designers to evaluate different decision trade-offs, e.g., to choose between many slow, power-efficient cores, or fewer faster, power-hungry cores, or a combination of them. Here, we prototype and evaluate a new Hadoop scheduler, called DyScale, that exploits capabilities offered by heterogeneous cores within a single multi-core processor for achieving a variety of performance objectives. A typical MapReduce workload contains jobs with different performance goals: large, batch jobs that are throughput oriented, and smaller interactive jobs that are response time sensitive. Heterogeneous multi-core processors enable creating virtual resource pools based on slow and fast cores for multi-class priority scheduling. Since the same data can be accessed with either slow or fast slots, spare resources (slots) can be shared between different resource pools. Using measurements on an actual experimental setting and via simulation, we argue in favor of heterogeneous multi-core processors as they achieve faster (up to 40 percent) processing of small, interactive MapReduce jobs, while offering improved throughput (up to 40 percent) for large, batch jobs. We evaluate the performance benefits of DyScale versus the FIFO and Capacity job schedulers that are broadly used in the Hadoop community

Virtualization in the Private Cloud: State of the Practice

Virtualization has become a mainstream technology that allows efficient and safe resource sharing in data centers. In this paper, we present a large scale workload characterization study of 90K virtual machines hosted on 8K physical servers, across several geographically distributed corporate data centers of a major service provider. The study focuses on 19 days of operation and focuses on the state of the practice, i. e., how virtual machines are deployed across different physical resources with an emphasis on processors and memory, focusing on resource sharing and usage of physical resources, virtual machine life cycles, and migration patterns and their frequencies. This paper illustrates that indeed there is a huge tendency in over-provisioning CPU and memory resources while certain virtualization features (e. g., migration and collocation) are used rather conservatively, showing that there is significant room for the development of policies that aim to reduce operational costs in data centers

PROST: Predicting Resource Usages with Spatial and Temporal Dependencies

We present a tool, PROST, which can achieve scalable and accurate prediction of server workload time series in data centers. As several virtual machines are typically co-located on physical servers, the CPU and RAM show strong temporal and spatial dependencies. PROST is able to leverage the spatial dependency among co-located VMs to improve the scalability of prediction models solely based on temporal features, such as neural network. We show the benefits of PROST in obtaining accurate prediction of resource usage series and designing effective VM sizing strategies for the private data centers

AWAIT: Efficient Overload Management for Busy Multi-tier Web Services under Bursty Workloads

The problem of service differentiation and admission control in web services that utilize a multi-tier architecture is more challenging than in a single-tiered one, especially in the presence of bursty conditions, i.e., when arrivals of user web sessions to the system are characterized by temporal surges in their arrival intensities and demands. We demonstrate that classic techniques for a session based admission control that are triggered by threshold violations are ineffective under bursty workload conditions, as user-perceived performance metrics rapidly and dramatically deteriorate, inadvertently leading the system to reject requests from already accepted user sessions, resulting in business loss. Here, as a solution for service differentiation of accepted user sessions we promote a methodology that is based on blocking, i.e., when the system operates in overload, requests from accepted sessions are not rejected but are instead stored in a blocking queue that effectively acts as a waiting room. The requests in the blocking queue implicitly become of higher priority and are served immediately after load subsides. Residence in the blocking queue comes with a performance cost as blocking time adds to the perceived end-to-end user response time. We present a novel autonomic session based admission control policy, called AWAIT, that adaptively adjusts the capacity of the blocking queue as a function of workload burstiness in order to meet predefined user service level objectives while keeping the portion of aborted accepted sessions to a minimum. Detailed simulations illustrate the effectiveness of AWAIT under different workload burstiness profiles and therefore strongly argue for its effectiveness