Fahad R. Dogar

Decentralized task-aware scheduling for data center networks

Many data center applications perform rich and complex tasks (e.g., executing a search query or generating a users news-feed). From a network perspective, these tasks typically comprise multiple flows, which traverse different parts of the network at potentially different times. Most network resource allocation schemes, however, treat all these flows in isolation -- rather than as part of a task -- and therefore only optimize flow-level metrics. In this paper, we show that task-aware network scheduling, which groups flows of a task and schedules them together, can reduce both the average as well as tail completion time for typical data center applications. To achieve these benefits in practice, we design and implement Baraat, a decentralized task-aware scheduling system. Baraat schedules tasks in a FIFO order but avoids head-of-line blocking by dynamically changing the level of multiplexing in the network. Through experiments with Memcached on a small testbed and large-scale simulations, we show that Baraat outperforms state-of-the-art decentralized schemes (e.g., pFabric) as well as centralized schedulers (e.g., Orchestra) for a wide range of workloads (e.g., search, analytics, etc).

Friends, not foes: synthesizing existing transport strategies for data center networks

Many data center transports have been proposed in recent times (e.g., DCTCP, PDQ, pFabric, etc). Contrary to the common perception that they are competitors (i.e., protocol A vs. protocol B), we claim that the underlying strategies used in these protocols are, in fact, complementary. Based on this insight, we design PASE, a transport framework that synthesizes existing transport strategies, namely, self-adjusting endpoints (used in TCP style protocols), innetwork prioritization (used in pFabric), and arbitration (used in PDQ). PASE is deployment friendly: it does not require any changes to the network fabric; yet, its performance is comparable to, or better than, the state-of-the-art protocols that require changes to network elements (e.g., pFabric). We evaluate PASE using simulations and testbed experiments. Our results show that PASE performs well for a wide range of application workloads and network settings.

Architecting for edge diversity: supporting rich services over an unbundled transport

The end-to-end nature of todays transport protocols is increasingly being questioned by the growing heterogeneity of networks and devices, and the need to support in-network services. To address these challenges, we present Tapa, a transport architecture that systematically combines two concepts. First, it unbundles todays transport such that network specific functions (e.g., congestion control) are implemented on a per-segment basis, where a segment spans a part of the end-to-end path that is homogeneous (e.g., wired Internet or an access network) while functions that relate to application semantics (e.g., data ordering) are still implemented end-to-end. Second, it has an explicit notion of in-network services (e.g., caching, opportunistic content retrieval, etc) that can be supported while maintaining precise end-to-end application semantics. In this paper, we present the basic design, implementation and evaluation of Tapa. We also present diverse case studies that show how Tapa can easily support opportunistic content retrieval in online social networks, various mobile and wireless optimizations, and an in-network energy saving service that improves battery life of mobile devices.

Towards a Redundancy-Aware Network Stack for Data Centers

In this paper, we make a case for a redundancy-aware network stack (RANS) for data centers. In RANS, applications expose information about replicas to the network, which in turn, uses duplicate requests to improve performance of typical applications by enabling them to effectively avoid stragglers. At the heart of RANS is the use of duplicate-aware scheduling, which ensures that duplicate-requests do not overload the system and disturb any primary requests. We highlight the challenges and opportunities present at different layers of RANS, from new interfaces that capture replicas and their semantics, to in-network mechanisms that deal with duplicates. Our preliminary evaluation shows the promise of duplicate-aware scheduling in improving performance of typical data center applications.

PASE: Synthesizing Existing Transport Strategies for Near-Optimal Data Center Transport

Several data center transport protocols have been proposed in recent years (e.g., DCTCP, PDQ, and pFabric). In this paper, we first identify the underlying strategies used by the existing data center transports, namely, in-network Prioritization (used in pFabric), Arbitration (used in PDQ), and Self-adjusting at Endpoints (PASE) (used in DCTCP). We show that these strategies are complimentary to each other, rather than substitutes, as they have different strengths and can address each other’s limitations. Unfortunately, prior data center transports use only one of these strategies. As a result, they either achieve near-optimal performance or deployment friendliness (i.e., require no changes to the data plane) but not both. Based on this insight, we design a data center transport protocol called PASE, which carefully synthesizes these strategies by assigning different transport responsibilities to each strategy. The key advantage of PASE over prior art is that it achieves both near-optimal performance as well as deployment friendliness. PASE does not require any changes in network switches (hardware or software); yet, it achieves comparable, or even better, performance than the state-of-the-art protocols (such as pFabric) that require changes to network elements. Our evaluation results show that the PASE performs well for a wide range of application workloads and network settings.

Towards Slack-Aware Networking

We are moving towards an Internet where most of the packets may be consumed by \emph{machines} -- set-top-boxes or smart-phone apps prefetching content, Internet of Things (IoT) devices uploading their data to the cloud, or data centers doing geo-distributed replication. We observe that such machine centric communication can afford to have \emph{slack} built into it: every packet can be marked as to when it will be consumed in future. Slack could be anywhere from seconds to hours or even days. In this paper, we make a case for slack-aware networking by illustrating slack opportunities that arise for a wide range of applications as they interact with the cloud and its pricing models (e.g., spot pricing). We also sketch the design of SlackStack, a network stack with explicit support for slack at multiple levels of the stack, from a slack-based interface to slack-aware optimizations at the transport and network layers.

Efficient load balancing over asymmetric datacenter topologies

Abstract Datacenter networks are often structured as multi-rooted trees to provide high bisection bandwidth at low cost. To utilize the available bisection bandwidth, an efficient load balancing algorithm is required. Under symmetric network conditions, packet spraying is known to perform well, as it enables fine-grained (packet-level) load balancing over equal cost paths. However, packet spraying performs poorly in asymmetric topologies. To make packet spraying effective under asymmetry while retaining its simplicity, we propose SAPS, “Symmetric Adaptive Packet Spraying”, a Software-Defined Networking (SDN) based scheme that uses packet spraying over symmetric virtual topologies. SAPS is based on the key insight that if we provide each flow with a symmetric view of the network fabric, then packet spraying can produce near-optimal performance. Through simulations and testbed experiments, we evaluate SAPS. Over a variety of application workloads and asymmetric network scenarios, including single and multiple link failures, results indicate that SAPS performs well, e.g., under single link failure, outperforming state-of-the-art load balancing schemes by up to 61% for average flow completion times.

Load balancing over symmetric virtual topologies

Datacenter networks are often structured as multi-rooted trees to provide high bisection bandwidth at low cost. To utilize the available bisection bandwidth, an efficient load balancing algorithm is required. Packet Spraying is known to perform well in symmetric topologies as it provides per-packet load balancing over equal cost paths. However, packet spraying performs poorly in asymmetric topologies. In this paper we ask, “How can we make packet spraying effective in asymmetric topologies while retaining its simplicity?” Towards this end, we propose SAPS, “Symmetric Adaptive Packet Spraying”, an SDN-based scheme that uses packet spraying over symmetric virtual topologies. SAPS is based on the key insight that if we provide each flow with a symmetric view of the network fabric, then packet spraying can produce near-optimal performance. We evaluate SAPS using simulations and testbed experiments. Our results indicate that SAPS performs well for a variety of application workloads and asymmetric network scenarios.

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