Mia Primorac

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Corrigendum to “The IX Operating System: Combining Low Latency, High Throughput and Efficiency in a Protected Dataplane”

ACM Transactions on Computer Systems (TOCS), 2017

On page 21 of “The IX Operating System: Combining Low Latency, High Throughput and Efficiency in ... more On page 21 of “The IX Operating System: Combining Low Latency, High Throughput and Efficiency in a Protected Dataplane” we describe our use of the tool mutilate to evaluate the latency and throughput of memcached. We discovered an error in our setup: we did not load the initial key-value state into memcached before the start of the experiment. Instead, memcached started in an empty state, causing some GET requests to require less computation than intended. Table 1 shows the performance differences between our original and corrected memcached results.

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How to Measure the Killer Microsecond

Computer Communication Review, 2017

Datacenter-networking research requires tools to both generate traffic and accurately measure lat... more Datacenter-networking research requires tools to both generate traffic and accurately measure latency and throughput. While hardware-based tools have long existed commercially, they are primarily u...

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When to Hedge in Interactive Services

In online data-intensive (OLDI) services, each client request typically executes on multiple serv... more In online data-intensive (OLDI) services, each client request typically executes on multiple servers in parallel; as a result, “system hiccups”, although rare within a single server, can interfere with many client requests and cause violations of service-level objectives. Service providers have long been fighting this “tail at scale” problem through “hedging”, i.e., issuing redundant queries to mask system hiccups. This, however, can potentially cause congestion that is more detrimental to tail latency than the hiccups themselves. This paper asks: when does it make sense to hedge in OLDI services, and how can we hedge enough to mask system hiccups but not as much as to cause congestion? First, we show that there are many realistic scenarios where hedging can have no benefit—where any hedging-based scheduling policy, including the state-of-the-art, yields no latency reduction compared to optimal load balancing without hedging. Second, we propose LÆDGE, a scheduling policy that combin...

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FCR: Fast and Consistent Controller-Replication in Software Defined Networking

We consider the problem of coordination among replicated SDN controllers, where the challenge is ... more We consider the problem of coordination among replicated SDN controllers, where the challenge is to ensure a consistent view of the network while reacting to network events in a prompt manner. Existing solutions are either consensus-based, which achieve consistency at the expense of high latency; or eventual-consistency-based, which have low latency at the expense of severe limitations on the types of applications and policies implementable by the controller. We propose the Fast and Consistent Controller-Replication (FCR) scheme. FCR is based on a deterministic agreement mechanism that performs agreement on the input of controllers, instead of agreement on the output as done in consensus mechanisms. We formally prove that FCR provides the same guarantees in terms of implementable applications and network policies, as any deterministic single-image controller. Through simulation and implementation, we show that these guarantees can be implemented with little latency overhead, compare...

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Energy proportionality and workload consolidation for latency-critical applications

Proceedings of the Sixth ACM Symposium on Cloud Computing - SoCC '15, 2015

Energy proportionality and workload consolidation are important objectives towards increasing eff... more Energy proportionality and workload consolidation are important objectives towards increasing efficiency in largescale datacenters. Our work focuses on achieving these goals in the presence of applications with µs-scale tail latency requirements. Such applications represent a growing subset of datacenter workloads and are typically deployed on dedicated servers, which is the simplest way to ensure low tail latency across all loads. Unfortunately, it also leads to low energy efficiency and low resource utilization during the frequent periods of medium or low load. We present the OS mechanisms and dynamic control needed to adjust core allocation and voltage/frequency settings based on the measured delays for latency-critical workloads. This allows for energy proportionality and frees the maximum amount of resources per server for other background applications, while respecting service-level objectives. Monitoring hardware queue depths allows us to detect increases in queuing latencies. Carefully coordinated adjustments to the NIC's packet redirection table enable us to reassign flow groups between the threads of a latency-critical application in milliseconds without dropping or reordering packets. We compare the efficiency of our solution to the Pareto-optimal frontier of 224 distinct static configurations. Dynamic resource control saves 44%-54% of processor energy, which corresponds to 85%-93% of the Pareto-optimal upper bound. Dynamic resource control also allows background jobs to run at 32%-46% of their standalone throughput, which corresponds to 82%-92% of the Pareto bound.

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The IX Operating System

ACM Transactions on Computer Systems, 2016

The conventional wisdom is that aggressive networking requirements, such as high packet rates for... more The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and μs-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. We present ix , a dataplane operating system that provides high I/O performance and high resource efficiency while maintaining the protection and isolation benefits of existing kernels. ix uses hardware virtualization to separate management and scheduling functions of the kernel (control plane) from network processing (dataplane). The dataplane architecture builds upon a native, zero-copy API and optimizes for both bandwidth and latency by dedicating hardware threads and networking queues to dataplane instances, processing bounded batches of packets to completion, and eliminating coherence traffic and multicore synchronization. The control plane dynamically adjusts core allocations and voltage/frequency settings to meet service-level objectives. We demonstrate that ix outpe...

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