Estimating Application Cache Requirement for Provisioning Caches in Virtualized Systems

Abstract

Abstract-Miss rate curves (MRCs) are a fundamental concept in determining the impact of caches on an application's performance. In our research, we use MRCs to provision caches for applications in a consolidated environment. Current techniques for building MRCs at the CPU caches level require changes to the applications and are restricted to a few processor architectures [7], [22]. In this work, we investigate two techniques to partition shared L2 and L3 caches in a server and build MRCs for the VMs. These techniques make different trade-offs across accuracy, flexibility, and intrusiveness dimensions. The first technique is based on operating system (OS) page coloring and does not require change in commodity hardware or application. We improve upon existing page-coloring based approaches by identifying and overcoming a subtle but real problem of unequal associative cache sets loading to implement accurate cache allocation. Our second technique called CacheGrabber is even less intrusive and requires no changes in hardware, OS, or application. We present a comprehensive evaluation of the relative merits of these and other techniques to estimate MRCs. Our evaluation study enables a data center administrator to select the technique most suitable to his (her) specific data center to provision caches for consolidated applications.

Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

“…Of particular interest is the cache, because current virtualization technologies do not ensure isolation of the cache usage of individual VMs accommodated by the same PM, leading to contention between them [58,100]. Thus, it is important to model and predict the performance interference that can be expected when co-locating a pair of VMs [56].…”

“…A related approach, taken by several researchers, was to use a web application with real web traces: for example, RUBiS, a web application for online auctions [25], has been used my multiple researchers with various web server traces [51,95]; Wikipedia traces were also used [37]. Other benchmark applications used include the NAS Parallel Benchmarks (http://www.nas.nasa.gov/publications/ npb.html) [58,73,96,103], the BLAS linear algebra package (http://www.netlib.org/blas/) [103] and the related Linpack benchmark (http://netlib.org/benchmark/hpl/) [31,99].…”

Intelligent Management and Efficient Operation of Big Data

“…Here, using Big Data analytics is possible to process data from multiple sources and on-the-fly extract relevant knowledge to drive the strategy or business of either enterprises or other organizations. Consequently to obtain a good performance in the supporting infrastructure for processing big quantities of data, such as low latency and high throughput, some management enhancements are needed to operate more intelligently the available computing resources in each data center, such as virtual machines (Dai et al, 2013), memory (Zhou & Li, 2013), CPU scheduling (Bae et al, 2012), and cache (Koller et al, 2011), I/O (Ram et al, 2013). Other very important functional aspect to be aware in data centers is to enhance the network performance (Marx, 2013;Lange et al, 2011;Saleem, Hassan, & Asirvadam, 2011).…”

Gem5v: a modified gem5 for simulating virtualized systems

“…They may use it to fairly partition shared caches [10,22,26], optimize routing or cache coherency protocol in their network [17,19,23,24] or they can provide quality of service (QoS) in memory accesses [21,30]. These are just simple ideas but one could go beyond them to design a virtualization-capable processor.…”