Generalized ERSS tree model: Revisiting working sets

Abstract

Accurately characterizing the resource usage of an application at various levels in the memory hierarchy has been a long-standing research problem. Existing characterization studies are either motivated by specific allocation problems (e.g., memory page allocation) or they characterize a specific memory resource (e.g., L2 cache). The studies thus far have also implicitly assumed that there is no contention for the resource under consideration. The inevitable future of virtualization driven consolidation necessitates the sharing of physical resources at all levels of the memory hierarchy by multiple virtual machines (VMs). Given the lack of resource isolation mechanisms at several levels of the memory hierarchy within current commodity systems, provisioning resources for a virtualized application not only requires a precise characterization of its resource usage but must also account for the impact of resource contention due to other co-located applications during its lifetime. In this paper, we present a unifying Generalized ERSS Tree Model that characterizes the resource usage at all levels of the memory hierarchy during the entire lifetime of an application. Our model characterizes capacity requirements, the rate of use, and the impact of resource contention, at each level of memory. We present a methodology to build the model and demonstrate how it can be used for the accurate provisioning of the memory hierarchy in a consolidated environment. Empirical results suggest that the Generalized ERSS Tree Model is effective at characterizing applications with a wide variety of resource usage behaviors.

Centaur: Host-Side SSD Caching for Storage Performance Control

“…Hypervisors, upon the direction of the autonomic manager, control the storage performance of individual VMs by increasing or decreasing the cache space assigned to each VM to meet administrator goals. Further, unlike previous solutions for CPU caches that continuously repartition the cache [40], SSD cache partitions in Centaur are only periodically resized by the global scheduler to adapt to stable changes in the workloads and the storage system [21], [35]. Doing so ensures that stable workload characteristics are reflected post-repartitioning.…”

Workload Modeling for Computer Systems Performance Evaluation

“…This is related to the issue of program phases discussed later. The other is skewed popularity, in which some memory locations are much more popular than others, implying that they are reused much more often [380,217,410] (recall Figure 9.8).…”

“…Phases may actually be nested, with a major phase of the computation including several subphases, which may also be repeated [455,410]. In addition, one must distinguish between the reuse set, which is those elements that are indeed reused extensively (i, lenA, and lenB in the earlier example), and the other elements, which are transient in nature [216,410]. The reuse set provides a better approximation of the actual memory capacity needed by the application, because the memory used to store transient elements can be reused.…”

“…Thus, to enable modeling of the contention between applications that share memory resources, Koller at al. suggest the following three additional model parameters [410]:…”

Estimating Application Cache Requirement for Provisioning Caches in Virtualized Systems

“…In a companion paper [13], we propose a unified model called the Generalized ERSS Tree Model that comprehensively characterizes working sets across all phases of an application. The core of the model is a metric called effective reuse set size (ERSS) that accurately captures the amount of cache required by an application in a phase to avoid capacity misses.…”