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1. CodedBulk: Inter-Datacenter Bulk Transfers using Network Coding

   Tseng, Shih-Hao ; Agarwal, Saksham ; Agarwal, Rachit ; Ballani, Hitesh ; Tang, Ao ( April 2021 , USENIX Symposium on Networked Systems Design and Implementation)

   null (Ed.)

   This paper presents CodedBulk, a system for high-throughput inter-datacenter bulk transfers. At its core, CodedBulk uses network coding, a technique from the coding theory community, that guarantees optimal throughput for individual bulk transfers. Prior attempts to using network coding in wired networks have faced several pragmatic and fundamental barriers. CodedBulk resolves these barriers by exploiting the unique properties of inter-datacenter networks, and by using a custom-designed hop-by-hop flow control mechanism that enables efficient realization of network coding atop existing transport protocols. An end-to end CodedBulk implementation running on a geo-distributed inter-datacenter network improves bulk transfer throughput by 1.2−2.5× compared to state-of-the-art mechanisms that do not use network coding. 

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2. Expanding across time to deliver bandwidth efficiency and low latency

   William M. Mellette, Rajdeep Das ( January 2020 , USENIX Symposium on Networked Systems Design and Implementation (NSDI))

   Datacenters need networks that support both low-latency and high-bandwidth packet delivery to meet the stringent requirements of modern applications. We present Opera, a dynamic network that delivers latency-sensitive traffic quickly by relying on multi-hop forwarding in the same way as expander-graph-based approaches, but provides near-optimal bandwidth for bulk flows through direct forwarding over time-varying source-to-destination circuits. Unlike prior approaches, Opera requires no separate electrical network and no active circuit scheduling. The key to Opera's design is the rapid and deterministic reconfiguration of the network, piece-by-piece, such that at any moment in time the network implements an expander graph, yet, integrated across time, the network provides bandwidth-efficient single-hop paths between all racks. We show that Opera supports low-latency traffic with flow completion times comparable to cost-equivalent static topologies, while delivering up to 4x the bandwidth for all-to-all traffic and supporting up to 60% higher load for published datacenter workloads. 

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3. Semeru: A Memory-Disaggregated Managed Runtime

   Chenxi Wang, Haoran Ma ; Zhenyuan Ruan, MIT ; Khanh Nguyen, Texas A&M ; Michael D. Bond, Ohio State ; Ravi Netravali, Miryung Kim ( November 2020 , 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020, Virtual Event, November 4-6, 2020)

   null (Ed.)

   Resource-disaggregated architectures have risen in popularity for large datacenters. However, prior disaggregation systems are designed for native applications; in addition, all of them require applications to possess excellent locality to be efficiently executed. In contrast, programs written in managed languages are subject to periodic garbage collection (GC), which is a typical graph workload with poor locality. Although most datacenter applications are written in managed languages, current systems are far from delivering acceptable performance for these applications. This paper presents Semeru, a distributed JVM that can dramatically improve the performance of managed cloud applications in a memory-disaggregated environment. Its design possesses three major innovations: (1) a universal Java heap, which provides a unified abstraction of virtual memory across CPU and memory servers and allows any legacy program to run without modifications; (2) a distributed GC, which offloads object tracing to memory servers so that tracing is performed closer to data; and (3) a swap system in the OS kernel that works with the runtime to swap page data efficiently. An evaluation of Semeru on a set of widely-deployed systems shows very promising results. 

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4. Grafs: declarative graph analytics

   https://doi.org/10.1145/3473588  

   Houshmand, Farzin ; Lesani, Mohsen ; Vora, Keval ( August 2021 , Proceedings of the ACM on Programming Languages)

   Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics error-prone and time-consuming. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks. It features novel semantics-preserving fusion transformations that optimize the specifications and reduce them to three primitives: reduction over paths, mapping over vertices and reduction over vertices. Reductions over paths are commonly calculated based on push or pull models that iteratively apply kernel functions at the vertices. This paper presents conditions, parametric in terms of the kernel functions, for the correctness and termination of the iterative models, and uses these conditions as specifications to automatically synthesize the kernel functions. Experimental results show that the generated code matches or outperforms handwritten code, and that fusion accelerates execution. 

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5. Scheduling Challenges for Variable Capacity Resources

   Zhang, Chaojie ; Chien, Andrew A. ( January 2021 , Proceedings of JSSPP21)

   Cirne, Walfredo ; Rodrigo, Gonzalo P. ; Klusáček, Dalibor (Ed.)

   Datacenter scheduling research often assumes resources as a constant quantity, but increasingly external factors shape capacity dynamically, and beyond the control of an operator. Based on emerging examples, we define a new, open research challenge: the variable capacity resource scheduling problem. The objective here is effective resource utilization despite sudden, perhaps large, changes in the available resources. We define the problem, key dimensions of resource capacity variation, and give specific examples that arise from the natural world (carbon- content, power price, datacenter cooling, and more). Key dimensions of the resource capacity variation include dynamic range, frequency, and structure. With these dimensions, an empirical trace can be character- ized, abstracting it from the many possible important real-world generators of variation. Resource capacity variation can arise from many causes including weather, market prices, renewable energy, carbon emission targets, and internal dynamic power management constraints. We give examples of three dif- ferent sources of variable capacity. Finally, we show variable resource capacity presents new scheduling challenges. We show how variation can cause significant performance degra- dation in existing schedulers, with up to 60% goodput reduction. Further, initial results also show intelligent scheduling techniques can be helpful. These insights show the promise and opportunity for future scheduling studies on resource volatility. 

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