Execution time is no longer the only metric by which computational systems are judged. In fact, explicitly sacrificing raw performance in exchange for energy savings is becoming a common trend in environments ranging from large server farms attempting to minimize cooling costs to mobile devices trying to prolong battery life. Hardware designers, well aware of these trends, include capabilities like DVFS (to throttle core frequency) into almost all modern systems. However, hardware capabilities on their own are insufficient and must be paired with other logic to decide if, when, and by how much to apply energy-minimizing techniques while still meeting performance goals. One obvious choice is to place this logic into the OS scheduler. This choice is particularly attractive due to the relative simplicity, low cost, and low risk associated with modifying only the scheduler part of the OS. Herein we survey the vast field of research on energy-cognizant schedulers. We discuss scheduling techniques to perform energy-efficient computation. We further explore how the energy-cognizant scheduler's role has been extended beyond simple energy minimization to also include related issues like the avoidance of negative thermal effects as well as addressing asymmetric multicore architectures.

On multicore systems contention for shared resources occurs when memory-intensive threads are co-scheduled on cores that share parts of the memory hierarchy, such as last-level caches and memory controllers. Previous work investigated how contention could be addressed via scheduling. A contention-aware scheduler separates competing threads onto separate memory hierarchy domains to eliminate resource sharing and, as a consequence, mitigate contention. However, all previous work on contention-aware scheduling assumed that the underlying system is UMA (uniform memory access latencies, single memory controller). Modern multicore systems, however, are NUMA, which means that they feature non-uniform memory access latencies and multiple memory controllers. We discovered that contention management is a lot more difficult on NUMA systems, because the scheduler must not only consider the placement of threads, but also the placement of their memory. This is mostly required to eliminate contention for memory controllers contrary to the popular belief that remote access latency is the dominant concern. In this work we quantify the effects on performance imposed by resource contention and remote access latency. This analysis inspires the design of a contention-aware scheduling algorithm for NUMA systems. This algorithm significantly outperforms a NUMA-unaware algorithm proposed before as well as the default Linux scheduler. We also investigate memory migration strategies, which are the necessary part of the NUMA contention-aware scheduling algorithm. Finally, we propose and evaluate a new contention management algorithm that is priority-aware.

Multicore processors have become commonplace in both desk-top and servers. A serious challenge with multicore processors is that cores share on and o chip resources such as caches, memory buses, and memory controllers. Competition for these shared resources between threads running on different cores can result in severe and unpredictable performance degradations. It has been shown in previous work that the OS scheduler can be made shared-resource-aware and can greatly reduce the negative e ects of resource contention. The search space of potential scheduling algorithms is huge considering the diversity of available multicore architectures, an almost infinite set of potential workloads, and a variety of conflicting performance goals. We believe the two biggest obstacles to developing new scheduling algorithms are the difficulty of implementation and the duration of testing. We address both of these challenges with our toolset AKULA which we introduce in this paper. AKULA provides an API that allows developers to implement and debug scheduling algorithms easily and quickly without the need to modify the kernel or use system calls. AKULA also provides a rapid evaluation module, based on a novel evaluation technique also introduced in this paper, which allows the created scheduling algorithm to be tested on a wide variety of work-loads in just a fraction of the time testing on real hardware would take. AKULA also facilitates running scheduling algorithms created with its API on real machines without the need for additional modifications. We use AKULA to develop and evaluate a variety of different contention-aware scheduling algorithms. We use the rapid evaluation module to test our algorithms on thousands of workloads and assess their scalability to futuristic massively multicore machines.

Contention for shared resources on multicore processors remains an unsolved problem in existing systems despite significant research efforts dedicated to this problem in the past. Previous solutions focused primarily on hardware techniques and software page coloring to mitigate this problem. Our goal is to investigate how and to what extent contention for shared resource can be mitigated via thread scheduling. Scheduling is an attractive tool, because it does not require extra hardware and is relatively easy to integrate into the system. Our study is the first to provide a comprehensive analysis of contention-mitigating techniques that use only scheduling. The most difficult part of the problem is to find a classification scheme for threads, which would determine how they affect each other when competing for shared resources. We provide a comprehensive analysis of such classification schemes using a newly proposed methodology that enables to evaluate these schemes separately from the scheduling algorithm itself and to compare them to the optimal. As a result of this analysis we discovered a classification scheme that addresses not only contention for cache space, but contention for other shared resources, such as the memory controller, memory bus and prefetching hardware. To show the applicability of our analysis we design a new scheduling algorithm, which we prototype at user level, and demonstrate that it performs within 2\% of the optimal. We also conclude that the highest impact of contention-aware scheduling techniques is not in improving performance of a workload as a whole but in improving quality of service or performance isolation for individual applications.

Contention for shared resources on multicore processors remains an unsolved problem in existing systems despite significant research efforts dedicated to this problem in the past. Previous solutions focused primarily on hardware techniques and software page coloring to mitigate this problem. Our goal is to investigate how and to what extent contention for shared resource can be mitigated via thread scheduling. Scheduling is an attractive tool, because it does not require extra hardware and is relatively easy to integrate into the system. Our study is the first to provide a comprehensive analysis of contention-mitigating techniques that use only scheduling. The most difficult part of the problem is to find a classification scheme for threads, which would determine how they affect each other when competing for shared resources. We provide a comprehensive analysis of such classification schemes using a newly proposed methodology that enables to evaluate these schemes separately from the scheduling algorithm itself and to compare them to the optimal. As a result of this analysis we discovered a classification scheme that addresses not only contention for cache space, but contention for other shared resources, such as the memory controller, memory bus and prefetching hardware. To show the applicability of our analysis we design a new scheduling algorithm, which we prototype at user level, and demonstrate that it performs within 2% of the optimal. We also conclude that the highest impact of contention-aware scheduling techniques is not in improving performance of a workload as a whole but in improving quality of service or performance isolation for individual applications and in optimizing system energy consumption.