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Datacenters today waste CPU and memory, as resources demanded by applications often fail to match the resources available on machines. This leads to stranded resources because one resource that runs out prevents placing additional applications that could consume the other resources. Unusable stranded resources result in reduced utilization of servers, and wasted money and energy. Quicksand is a new framework and runtime system that unstrands resources by providing developers with familiar, high-level abstractions (e.g., data structures, batch computing). Internally Quicksand decomposes them into resource proclets, granular units that each primarily consume resources of one type. Inspired by recent granular programming models, Quicksand decouples consumption of resources as much as possible. It splits, merges, and migrates resource proclets in milliseconds, so it can use resources on any machine, even if available only briefly. Evaluation of our prototype with four applications shows that Quicksand uses stranded resources effectively; that Quicksand reacts to changing resource availability and demand within milliseconds, increasing utilization; and that porting applications to Quicksand requires moderate effort. 

Maintaining low tail latency is critical for the efficiency and performance of large-scale datacenter systems. Software bugs that cause tail latency problems, however, are notoriously difficult to debug. We present LDB, a new latency profiling tool that aims to overcome this challenge by precisely identifying the specific functions that are responsible for tail latency anomalies. LDB observes the latency of all functions in a running program. It uses a novel, software-only technique called stack sampling, where a busy-spinning stack scanner thread polls lightweight metadata recorded in the call stack, shifting tracing costs away from program threads. In addition, LDB uses event tagging to record requests, inter-thread synchronization, and context switching. This can be used, for example, to generate per-request timelines and to find the root cause of complex tail latency problems such as lock contention in multi-threaded programs. We evaluate LDB with three datacenter applications, finding latency problems in each. Our results further show that LDB produces actionable insights, has low overhead, and can rapidly analyze recordings, making it feasible to use in production settings. 

Many applications can benefit from data that increases performance but is not required for correctness (commonly referred to as soft state). Examples include cached data from backend web servers and memoized computations in data analytics systems. Today's systems generally statically limit the amount of memory they use for storing soft state in order to prevent unbounded growth that could exhaust the server's memory. Static provisioning, however, makes it difficult to respond to shifts in application demand for soft state and can leave significant amounts of memory idle. Existing OS kernels can only spend idle memory on caching disk blocks—which may not have the most utility—because they do not provide the right abstractions to safely allow applications to store their own soft state. To effectively manage and dynamically scale soft state, we propose soft memory, an elastic virtual memory abstraction with unmap-and-reconstruct semantics that makes it possible for applications to use idle memory to store whatever soft state they choose while guaranteeing both safety and efficiency. We present Midas, a soft memory management system that contains (1) a runtime that is linked to each application to manage soft memory objects and (2) OS kernel support that coordinates soft memory allocation between applications to maximize their performance. Our experiments with four real-world applications show that Midas can efficiently and safely harvest idle memory to store applications' soft state, delivering near-optimal application performance and responding to extreme memory pressure without running out of memory. 

Kernel bypass systems have demonstrated order of magnitude improvements in throughput and tail latency for network-intensive applications relative to traditional operating systems (OSes). To achieve such excellent performance, however, they rely on dedicated resources (e.g., spinning cores, pinned memory) and require application rewriting. This is unattractive to cloud operators because they aim to densely pack applications, and rewriting cloud software requires a massive investment of valuable developer time. For both reasons, kernel bypass, as it exists, is impractical for the cloud. In this paper, we show these compromises are not necessary to unlock the full benefits of kernel bypass. We present Junction, the first kernel bypass system that can pack thousands of instances on a machine while providing compatibility with unmodified Linux applications. Junction achieves high density through several advanced NIC features that reduce pinned memory and the overhead of monitoring large numbers of queues. It maintains compatibility with minimal overhead through optimizations that exploit a shared address space with the application. Junction scales to 19–62× more instances than existing kernel bypass systems and can achieve similar or better performance without code changes. Furthermore, Junction delivers significant performance benefits to applications previously unsupported by kernel bypass, including those that depend on runtime systems like Go, Java, Node, and Python. In a comparison to native Linux, Junction increases throughput by 1.6–7.0× while using 1.2–3.8× less cores across seven applications. 

The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwide electricity use, with application demand scaling at a rapid rate. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with a software-centric approach: by making energy and carbon visible to application developers on a fine-grained basis, by modifying system APIs to make it possible to make informed trade offs between performance and carbon emissions, and by raising the level of application programming to allow for flexible use of more energy efficient means of compute and storage. We also lay out a research agenda for systems software to reduce the carbon footprint of datacenter computing.