In multicore systems, shared resources such as caches or the memory subsystem can lead to contention between applications running on different cores, entailing reduced performance and poor energy efficiency. The characteristics of individual applications, the assignment of applications to machines and execution contexts, and the selection of processor frequencies have a dramatic impact on resource contention, performance, and energy efficiency.We employ the concept of task activity vectors for characterizing applications by resource utilization. Based on this characterization, we apply migration and co-scheduling policies that improve performance and energy efficiency by combining applications that use complementary resources, and use frequency scaling when scheduling cannot avoid contention owing to inauspicious workloads.We integrate the policies into an operating system scheduler and into a virtualization system, allowing placement decisions to be made both within and across physical nodes, and reducing contention both for individual tasks and complete applications. Our evaluation based on the Linux operating system kernel and the KVM virtualization environment shows that resource-conscious scheduling reduces the energy delay product considerably.

Actions usually taken to prevent processors from overheating, such as decreasing the frequency or stopping the execution flow, also degrade performance. Multiprocessor systems, however, offer the possibility of moving the task that caused a CPU to overheat away to some other, cooler CPU, so throttling becomes only a last resort taken if all of a system's processors are hot. Additionally, the scheduler can take advantage of the energy characteristics of individual tasks, and distribute hot tasks as well as cool tasks evenly among all CPUs.This work presents a mechanism for determining the energy characteristics of tasks by means of event monitoring counters, and an energy-aware scheduling policy that strives to assign tasks to CPUs in a way that avoids overheating individual CPUs. Our evaluations show that the benefit of avoiding throttling outweighs the overhead of additional task migrations, and that energy-aware scheduling in many cases increases the system's throughput.

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