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Heterogeneous servers are becoming prevalent in many high-performance computing environments, including clusters and data enters. In this paper, we consider multi-objective scheduling for heterogeneous server systems to optimize simultaneously the application performance, energy consumption and thermal imbalance. First, a greedy online framework is(More)
High response quality is critical for many best-effort interactive services, and at the same time, reducing energy consumption can directly reduce the operational cost of service providers. In this paper, we study the quality-energy tradeoff for such services by using a composite performance metric that captures their relative importance in practice:(More)
Microbial biomass phosphorus (MBP) is one of the most active forms of phosphorus (P) in soils. MBP plays an important role in the biogeochemical P cycle. To explore MBP distribution and its relationship with other factors, the MBP and rhizosphere soil P concentrations and fractions in six vegetation zones on the eastern slope of Gongga Mountain in SW China(More)
To service requests with high quality, interactive services such as web search, on-demand video and on line gaming keep average server utilization low. As servers become busy, queuing delays increase, and requests miss their deadlines, resulting in degraded quality of service with poor user experience and potential revenue loss. In this paper, we propose(More)
In this article, we combine the traditional checkpointing and rollback recovery strategies with verification mechanisms to cope with both fail-stop and silent errors. The objective is to minimize makespan and/or energy consumption. For divisible load applications, we use first-order approximations to find the optimal checkpointing period to minimize(More)
In order to improve processor utilizations on parallel systems, adaptive scheduling with parallelism feedback was recently proposed. A-Greedy, an existing adaptive scheduler, offers provably-good job execution time and processor utilization. Unfortunately, it suffers from unstable feedback and hence unnecessary processor reallocations even when the job has(More)
With proliferation of multicore computers and multiprocessor systems, an imminent challenge is to efficiently schedule parallel applications on these resources. In contrast to conventional static scheduling, adaptive schedulers that dynamically allocate processors to jobs possess good potential for improving processor utilization and speeding up job's(More)
Both fairness and efficiency are crucial measures for the performance of parallel applications on multiprocessor systems. In this paper, we study online adaptive scheduling for multiple sets of such applications, where each set may contain one or more jobs with time-varying parallelism profile. This scenario arises naturally when dealing with several(More)
This work focuses on resilience techniques at extreme scale. Many papers deal with fail-stop errors. Many others deal with silent errors (or silent data corruptions). But very few papers deal with fail-stop and silent errors simultaneously. However, HPC applications will obviously have to cope with both error sources. This paper presents a unified framework(More)
As multi-core processors proliferate, it has become more important than ever to ensure efficient execution of parallel jobs on multi-processor systems. In this paper, we study the problem of scheduling parallel jobs with arbitrary release time on multiprocessors while minimizing the jobs' mean response time. We focus on non-clairvoyant scheduling schemes(More)