Thank you for downloading modern operating systems. As you may know, people have look hundreds times for their favorite readings like this modern operating systems, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful bugs inside their desktop computer. modern operating systems is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern operating systems is universally compatible with any devices to read.

Modern Operating Systems

In this paper, we propose an effortless way for disaggregating the CPU-memory couple, two of the most important resources in cloud computing. Instead of redesigning each resource board, the disaggregation is done at the power supply domain level. In other words, CPU and memory still share the same board, but their power supply domains are separated. Besides this disaggregation, we make the two following contributions: (1) the prototyping of a new ACPI sleep state (called zombie and noted Sz) which allows to suspend a server (thus save energy) while making its memory remotely accessible; and (2) the prototyping of a rack-level system software which allows the transparent utilization of the entire rack resources (avoiding resource waste). We experimentally evaluate the effectiveness of our solution and show that it can improve the energy efficiency of state-of-the-art consolidation techniques by up to 86%, with minimal additional complexity.

/pdf/welcome-to-zombieland-practical-and-energy-efficient-memory-39fnvbk0xt.pdf

Welcome to zombieland: practical and energy-efficient memory disaggregation in a datacenter

The in-memory cache system is a performance-critical layer in today's web server architecture. Memcached is one of the most effective, representative, and prevalent among such systems. An important problem is memory allocation. The default design does not make the best use of the memory. It fails to adapt when the demand changes, a problem known as slab calcification.

This paper introduces locality-aware memory allocation (LAMA), which solves the problem by first analyzing the locality of the Memcached requests and then repartitioning the memory to minimize the miss ratio and the average response time. By evaluating LAMA using various industry and academic workloads, the paper shows that LAMA outperforms existing techniques in the steady-state performance, the speed of convergence, and the ability to adapt to request pattern changes and overcome slab calcification. The new solution is close to optimal, achieving over 98% of the theoretical potential.

/pdf/lama-optimized-locality-aware-memory-allocation-for-key-2z5gznevkp.pdf

LAMA: optimized locality-aware memory allocation for key-value cache

The operating system is tasked with maintaining the coherency of per-core TLBs, necessitating costly synchronization operations, notably to invalidate stale mappings. As core-counts increase, the overhead of TLB synchronization likewise increases and hinders scalability, whereas existing software optimizations that attempt to alleviate the problem (like batching) are lacking. We address this problem by revising the TLB synchronization subsystem. We introduce several techniques that detect cases whereby soon-to-be invalidated mappings are cached by only one TLB or not cached at all, allowing us to entirely avoid the cost of synchronization. In contrast to existing optimizations, our approach leverages hardware page access tracking. We implement our techniques in Linux and find that they reduce the number of TLB invalidations by up to 98% on average and thus improve performance by up to 78%. Evaluations show that while our techniques may introduce overheads of up to 9% when memory mappings are never removed, these overheads can be avoided by simple hardware enhancements.

/pdf/optimizing-the-tlb-shootdown-algorithm-with-page-access-2s1g7rveoo.pdf

Optimizing the TLB Shootdown Algorithm with Page Access Tracking.

LAMA: Optimized Locality-aware MemoryAllocation for Key-value Cache

Data center servers are typically overprovisioned, leaving spare memory and CPU capacity idle to handle unpredictable workload bursts by the virtual machines running on them. While this allows for fast hotspot mitigation, it is also wasteful. Unfortunately, making use of spare capacity without impacting active applications is particularly difficult for memory since it typically must be allocated in coarse chunks over long timescales. In this work we propose re- purposing the poorly utilized memory in a data center to store a volatile data store that is managed by the hypervisor. We present two uses for our Mortar framework: as a cache for prefetching disk blocks, and as an application-level distributed cache that follows the memcached protocol. Both prototypes use the framework to ask the hypervisor to store useful, but recoverable data within its free memory pool. This allows the hypervisor to control eviction policies and prioritize access to the cache. We demonstrate the benefits of our prototypes using realistic web applications and disk benchmarks, as well as memory traces gathered from live servers in our university's IT department. By expanding and contracting the data store size based on the free memory available, Mortar improves average response time of a web application by up to 35% compared to a fixed size memcached deployment, and improves overall video streaming performance by 45% through prefetching.

Mortar: filling the gaps in data center memory

While hard drives hold on to the capacity advantage, flash-based solid-state drives (SSD) with high bandwidth and low latency have become good alternatives for I/O-intensive applications. Traditional data prefetching has been primarily designed to improve I/O performance on hard drives. The same techniques, if applied unchanged on flash drives, are likely to either fail to fully utilize SSDs, or interfere with application I/O requests, both of which could result in undesirable application performance. In this work, we demonstrate that data prefetching, when effectively harnessing the high performance of SSDs, can provide significant performance benefits for a wide range of data-intensive applications. The new technique, flashy prefetching, consists of accurate prediction of application needs in runtime and adaptive feedback-directed prefetching that scales with application needs, while being considerate to underlying storage devices. We have implemented a real system in Linux and evaluated it on four different SSDs. The results show 65–70% prefetching accuracy and an average 20% speedup on LFS, web search engine traces, BLAST, and TPC-H like benchmarks across various storage drives.

https://storageconference.us/2012/Papers/03.Flash.1.FlashyPrefetching.pdf

Flashy prefetching for high-performance flash drives

The processing of graph data at large scale, though important and useful for real-world applications, continues to be challenging, particularly for problems such as graph partitioning. Optimal graph partitioning is NP-hard, but several methods provide approximate solutions in reasonable time. Yet scaling these approximate algorithms is also challenging. In this paper, we describe our efforts towards improving the scalability of one such technique, stochastic block partition, which is the baseline algorithm for the IEEE HPEC Graph Challenge [1]. Our key contributions are: improvements to the parallelization of the baseline bottom-up algorithm, especially the Markov Chain Monte Carlo (MCMC) nodal updates for Bayesian inference; a new top-down divide and conquer algorithm capable of reducing the algorithmic complexity of static partitioning and also suitable for streaming partitioning; a parallel single-node multi-CPU implementation and a parallel multi-node MPI implementation. Although our focus is on algorithmic scalability, our Python implementation obtains a speedup of 1.65× over the fastest baseline parallel C++ run at a graph size of 100k vertices divided into 8 subgraphs on a multi-CPU single node machine. It achieves a speedup of 61× over itself on a cluster of 4 machines with 256 CPUs for a 20k node graph divided into 4 subgraphs, and 441× speedup over itself on a 50k node graph divided into 8 subgraphs on a multi-CPU single node machine.

Scalable stochastic block partition

Cloud and data center applications often make heavy use of virtualized servers, where flash-based solid-state drives (SSDs) have become popular alternatives over hard drives for data-intensive applications. Traditional data prefetching focuses on applications running on bare metal systems using hard drives. In contrast, virtualized systems using SSDs present different challenges for data prefetching. Most existing prefetching techniques, if applied unchanged in such environments, are likely to either fail to fully utilize SSDs, interfere with virtual machine I/O requests, or cause too much overhead if run in every virtualized instance.In this work, we demonstrate that data prefetching, when run in a virtualization-friendly manner can provide significant performance benefits for a wide range of data-intensive applications. We have designed and developed VIO-prefetching , consisting of accurate prediction of application needs in runtime and adaptive feedback-directed prefetching that scales with application needs, while being considerate to underlying storage devices and host systems. We have implemented a real system in Linux and evaluated it on different storage devices with the virtualization layer.Our comprehensive study provides insights of VIO-prefetching’s behavior at various virtualization system configurations, e.g., the number of VMs, in-guest processes, application types, etc. The proposed method improves virtual I/O performance up to 43 percent with the average of 14 percent for 1 to 12 VMs while running various applications on a Xen virtualization system.

An Adaptive IO Prefetching Approach for Virtualized Data Centers

Systems, apparatuses, and methods for implementing a memory sampling based migrating page cache are disclosed. In one embodiment, a system includes one or more processors and a multi-level memory hierarchy. The system is configured to record metadata associated with a portion of memory access instructions executed by one or more processors in one or more sampling intervals. The system generates predictions on which memory pages will be accessed in a subsequent sampling interval based on the recorded metadata. The system migrates one or more memory pages to a first memory level from a second memory level responsive to predicting that the one or more memory pages will be accessed in the subsequent sampling interval. The system also adjusts a duration of the sampling interval based on the number of memory accesses or a number of page faults per interval.

Ahsen J. Uppal

Papers

Mortar: filling the gaps in data center memory

Flashy prefetching for high-performance flash drives

Scalable stochastic block partition

An Adaptive IO Prefetching Approach for Virtualized Data Centers

Memory-sampling based migrating page cache