We consider a common setting where storage is disaggregated from the compute in data-parallel systems. Colocating caching tiers with the compute machines can reduce load on the interconnect but doing so leads to new resource management challenges. We design a system Netco, which prefetches data into the cache (based on workload predictability), and appropriately divides the cache space and network bandwidth between the prefetches and serving ongoing jobs. Netco makes various decisions (what content to cache, when to cache and how to apportion bandwidth) to support end-to-end optimization goals such as maximizing the number of jobs that meet their service-level objectives (e.g., deadlines). Our implementation of these ideas is available within the open-source Apache HDFS project. Experiments on a public cloud, with production-trace inspired workloads, show that Netco uses up to 5x less remote I/O compared to existing techniques and increases the number of jobs that meet their deadlines up to 80%.

Neither traffic engineering nor fixed prices (e.g., \$/GB) alone fully address the challenges of highly utilized inter-datacenter WANs. The former offers more service to users who overstate their demands and poor service overall. The latter offers no service guarantees to customers, and providers have no lever to steer customer demand to lightly loaded paths/times. To address these issues, we design and evaluate Pretium -- a framework that combines dynamic pricing with traffic engineering for inter-datacenter bandwidth. In Pretium, users specify their required rates or transfer sizes with deadlines, and a price module generates a price quote for different guarantees (promises) on these requests. The price quote is generated using internal prices (which can vary over time and links) which are maintained and periodically updated by Pretium based on history. A supplementary schedule adjustment module gears the agreed-upon network transfers towards an efficient operating point by optimizing time-varying operation costs. Experiments using traces from a large production WAN show that Pretium improves total system efficiency (value of routed transfers minus operation costs) by more than 3.5X relative to current usage-based pricing schemes, while increasing the provider profits by 2X.

To reduce the impact of network congestion on big data jobs, cluster management frameworks use various heuristics to schedule compute tasks and/or network flows. Most of these schedulers consider the job input data fixed and greedily schedule the tasks and flows that are ready to run. However, a large fraction of production jobs are recurring with predictable characteristics, which allows us to plan ahead for them. Coordinating the placement of data and tasks of these jobs allows for significantly improving their network locality and freeing up bandwidth, which can be used by other jobs running on the cluster. With this intuition, we develop Corral, a scheduling framework that uses characteristics of future workloads to determine an offline schedule which (i) jointly places data and compute to achieve better data locality, and (ii) isolates jobs both spatially (by scheduling them in different parts of the cluster) and temporally, improving their performance. We implement Corral on Apache Yarn, and evaluate it on a 210 machine cluster using production workloads. Compared to Yarn's capacity scheduler, Corral reduces the makespan of these workloads up to 33% and the median completion time up to 56%, with 20-90% reduction in data transferred across racks.

We found that interactive services at Bing have highly variable datacenter-side processing latencies because their processing consists of many sequential stages, parallelization across 10s-1000s of servers and aggregation of responses across the network. To improve the tail latency of such services, we use a few building blocks: reissuing laggards elsewhere in the cluster, new policies to return incomplete results and speeding up laggards by giving them more resources. Combining these building blocks to reduce the overall latency is non-trivial because for the same amount of resource (e.g., number of reissues), different stages improve their latency by different amounts. We present Kwiken, a framework that takes an end-to-end view of latency improvements and costs. It decomposes the problem of minimizing latency over a general processing DAG into a manageable optimization over individual stages. Through simulations with production traces, we show sizable gains; the 99th percentile of latency improves by over 50% when just 0.1% of the responses are allowed to have partial results and by over 40% for 25% of the services when just 5% extra resources are used for reissues.

The disconnect between the resource-centric interface exposed by today's cloud providers and tenant goals hurts both entities. Tenants are encumbered by having to translate their performance and cost goals into the corresponding resource requirements, while providers suffer revenue loss due to un-informed resource selection by tenants. Instead, we argue for a "job-centric" cloud whereby tenants only specify high-level goals regarding their jobs and applications. To illustrate our ideas, we present Bazaar, a cloud framework offering a job-centric interface for data analytics applications.

Policy autonomy exercised by Autonomous Systems (ASes) on the Internet can result in persistent oscillations in Border Gateway Protocol, the Internet's inter-domain routing protocol. Current solutions either rely on globally consistent policy assignments, or require significant deviations from locally assigned policies, resulting in significant loss of autonomy of ASes. In this paper, we take a different approach that guarantees stability with less restrictive policies. Namely, we propose multipath routing to find a better trade-off between AS policy autonomy and system stability. We design an algorithm, STABLE PATH(S) ASSIGNMENT (SPA), that provably detects persistent oscillations and eliminates these oscillations by assigning multiple paths to some ASes in the network. Such an assignment allows each AS to use its most-preferred available path, while requiring very few ASes to carry transit traffic along additional paths in order to break oscillations. We design a distributed protocol for SPA and present tight bounds on the number of paths assigned to the ASes in the network. Using simulations on the AS graph, we show that in presence of oscillations, SPA assigns at most two paths to any AS in the network (in 99.9% of the instances), with an extremely small fraction of ASes assigned the extra path.