Miss Ratio Curve Research Articles

Performance of Memory Virtualization Using Global Memory Resource Balancing

Virtualization has become a universal generalization layer in contemporary data centers. By multiplexing hardware resources into multiple virtual machines and facilitating several operating systems to run on the same physical platform at the same time, it can effectively decrease power consumption and building size or improve security by isolating virtual machines. In a virtualized system, memory resource supervision acts as a decisive task in achieving high resource employment and performance. Insufficient memory allocation to a virtual machine will degrade its performance drastically. On the contrasting, over allocation reasons ravage of memory resources. In the meantime, a virtual machine's memory stipulates may differ drastically. As a consequence, effective memory resource management calls for a dynamic memory balancer, which, preferably, can alter memory allocation in a timely mode for each virtual machine-based on their present memory stipulate and therefore realize the preeminent memory utilization and the best possible overall performance. Migrating operating system instances across discrete physical hosts is a helpful tool for administrators of data centers and clusters: It permits a clean separation among hardware and software, and make easy fault management. In order to approximate the memory, the stipulate of each virtual machine and to adjudicate probable memory resource disagreement, an extensively planned approach is to build an Least Recently Used based miss ratio curve which provides not only the current working set size but also the correlation between performance and the target memory allocation size. In this paper, the authors initially present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL based Least Recently Used association, dynamic hot set sizing. This assessment outcome confirms that, for the complete SPEC CPU 2006 benchmark set, subsequent to pertaining the 3 optimizing techniques, the mean overhead of MRC construction are lowered from 173% to only 2%. Based on current WSS, the authors then predict its trend in the near future and take different tactics for different forecast results. When there is an adequate amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. These experimental results show that this design achieves 49% center-wide speedup.

MPart: miss-ratio curve guided partitioning in key-value stores

Web applications employ key-value stores to cache the data that is most commonly accessed. The cache improves an web application's performance by serving its requests from memory, avoiding fetching them from the backend database. Since the memory space is limited, maximizing the memory utilization is a key to delivering the best performance possible. This has lead to the use of multi-tenant systems, allowing applications to share cache space. In addition, application data access patterns change over time, so the system should be adaptive in its memory allocation. In this work, we address both multi-tenancy (where a single cache is used for multiple applications) and dynamic workloads (changing access patterns) using a model that relates the cache size to the application miss ratio, known as a miss ratio curve. Intuitively, the larger the cache, the less likely the system will need to fetch the data from the database. Our efficient, online construction of the miss ratio curve allows us to determine a near optimal memory allocation given the available system memory, while adapting to changing data access patterns. We show that our model outperforms an existing state-of-the-art sharing model, Memshare, in terms of overall cache hit ratio and does so at a lower time cost. We show that for a typical system, overall hit ratio is consistently 1 percentage point greater and 99.9th percentile latency is reduced by as much as 2.9% under standard web application workloads containing millions of requests.

Fast Miss Ratio Curve Modeling for Storage Cache

The reuse distance (least recently used (LRU) stack distance) is an essential metric for performance prediction and optimization of storage cache. Over the past four decades, there have been steady improvements in the algorithmic efficiency of reuse distance measurement. This progress is accelerating in recent years, both in theory and practical implementation. In this article, we present a kinetic model of LRU cache memory, based on the average eviction time (AET) of the cached data. The AET model enables fast measurement and use of low-cost sampling. It can produce the miss ratio curve in linear time with extremely low space costs. On storage trace benchmarks, AET reduces the time and space costs compared to former techniques. Furthermore, AET is a composable model that can characterize shared cache behavior through sampling and modeling individual programs or traces.

Dynamic Memory Balancing for Virtualization

Allocating memory dynamically for virtual machines (VMs) according to their demands provides significant benefits as well as great challenges. Efficient memory resource management requires knowledge of the memory demands of applications or systems at runtime. A widely proposed approach is to construct a miss ratio curve (MRC) for a VM, which not only summarizes the current working set size (WSS) of the VM but also models the relationship between its performance and the target memory allocation size. Unfortunately, the cost of monitoring and maintaining the MRC structures is nontrivial. This article first introduces a low-cost WSS tracking system with effective optimizations on data structures, as well as an efficient mechanism to decrease the frequency of monitoring. We also propose a Memory Balancer (MEB), which dynamically reallocates guest memory based on the predicted WSS. Our experimental results show that our prediction schemes yield a high accuracy of 95.2% and low overhead of 2%. Furthermore, the overall system throughput can be significantly improved with MEB, which brings a speedup up to 7.4 for two to four VMs and 4.54 for an overcommitted system with 16 VMs.

Dynamic tracking of page miss ratio curve for memory management

Memory can be efficiently utilized if the dynamic memory demands of applications can be determined and analyzed at run-time. The page miss ratio curve(MRC), i.e. page miss rate vs. memory size curve, is a good performance-directed metric to serve this purpose. However, dynamically tracking MRC at run time is challenging in systems with virtual memory because not every memory reference passes through the operating system (OS).This paper proposes two methods to dynamically track MRC of applications at run time. The first method is using a hardware MRC monitor that can track MRC at fine time granularity. Our simulation results show that this monitor has negligible performance and energy overheads. The second method is an OS-only implementation that can track MRC at coarse time granularity. Our implementation results on Linux show that it adds only 7--10% overhead.We have also used the dynamic MRC to guide both memory allocation for multiprogramming systems and memory energy management. Our real system experiments on Linux with applications including Apache Web Server show that the MRC-directed memory allocation can speed up the applications' execution/response time by up to a factor of 5.86 and reduce the number of page faults by up to 63.1%. Our execution-driven simulation results with SPEC2000 benchmarks show that the MRC-directed memory energy management can improve the Energy * Delay metric by 27--58% over previously proposed static and dynamic schemes.

Dynamic tracking of page miss ratio curve for memory management

Memory can be efficiently utilized if the dynamic memory demands of applications can be determined and analyzed at run-time. The page miss ratio curve(MRC), i.e. page miss rate vs. memory size curve, is a good performance-directed metric to serve this purpose. However, dynamically tracking MRC at run time is challenging in systems with virtual memory because not every memory reference passes through the operating system (OS).This paper proposes two methods to dynamically track MRC of applications at run time. The first method is using a hardware MRC monitor that can track MRC at fine time granularity. Our simulation results show that this monitor has negligible performance and energy overheads. The second method is an OS-only implementation that can track MRC at coarse time granularity. Our implementation results on Linux show that it adds only 7--10% overhead.We have also used the dynamic MRC to guide both memory allocation for multiprogramming systems and memory energy management. Our real system experiments on Linux with applications including Apache Web Server show that the MRC-directed memory allocation can speed up the applications' execution/response time by up to a factor of 5.86 and reduce the number of page faults by up to 63.1%. Our execution-driven simulation results with SPEC2000 benchmarks show that the MRC-directed memory energy management can improve the Energy * Delay metric by 27--58% over previously proposed static and dynamic schemes.

Dynamic tracking of page miss ratio curve for memory management

Memory can be efficiently utilized if the dynamic memory demands of applications can be determined and analyzed at run-time. The page miss ratio curve(MRC), i.e. page miss rate vs. memory size curve, is a good performance-directed metric to serve this purpose. However, dynamically tracking MRC at run time is challenging in systems with virtual memory because not every memory reference passes through the operating system (OS).This paper proposes two methods to dynamically track MRC of applications at run time. The first method is using a hardware MRC monitor that can track MRC at fine time granularity. Our simulation results show that this monitor has negligible performance and energy overheads. The second method is an OS-only implementation that can track MRC at coarse time granularity. Our implementation results on Linux show that it adds only 7--10% overhead.We have also used the dynamic MRC to guide both memory allocation for multiprogramming systems and memory energy management. Our real system experiments on Linux with applications including Apache Web Server show that the MRC-directed memory allocation can speed up the applications' execution/response time by up to a factor of 5.86 and reduce the number of page faults by up to 63.1%. Our execution-driven simulation results with SPEC2000 benchmarks show that the MRC-directed memory energy management can improve the Energy * Delay metric by 27--58% over previously proposed static and dynamic schemes.

On the fractal dimension of computer programs and its application to the prediction of the cache miss ratio

Fractal geometry is proposed as a powerful measure of program behavior, and its application to the prediction of the miss ratio of programs in fully associative caches is presented. Programs are modeled as one-dimensional fractal random-walks. The fractal cache model is based on the parameterization of a program trace by a small number of constants, one of which is the fractal dimension of the program. The model is validated by trace-driven simulations of several program traces. With this model, it is possible to read the trace of a program once, and then predict the behavior of the miss ratio curve of that program in fully associative caches of varying sizes. >

On the self contained modelling of DB/DC systems

This paper discusses the modelling of DB/DC systems operating in a demand paging environment. As an extension of an earlier definition of Denning a class of systems is defined, those operating with "working set integrity", for which one can obtain system response parameters solely from the resource requirements of the input processes when running in isolation. No external information such as a miss ratio curve from another systems environment is required. The interdependence between the resource requirements of processes when running in a systems environment and the system performance parameters is faced directly and expressed as a set of simultaneous equations. The emphasis is on the effect of the sharing of a common set of pages among the active set of processes. Both unrestricted shared usage and usage with locking are treated.