Ziyang Li

FLOWPROPHET: Generic and Accurate Traffic Prediction for Data-Parallel Cluster Computing

Data-parallel computing frameworks (DCF) such as MapReduce, Spark, and Dryad etc. Have tremendous applications in big data and cloud computing, and throw tons of flows into data center networks. In this paper, we design and implement FLOWPROPHET, a general framework to predict traffic flows for DCFs. To this end, we analyze and summarize the common features of popular DCFs, and gain a key insight: since application logic in DCFs is naturally expressed by directed acyclic graphs (DAG), DAG contains necessary time and data dependencies for accurate flow prediction. Based on the insight, FLOWPROPHET extracts DAGs from user applications, and uses the time and data dependencies to calculate flow information 4-tuple, (source, destination, flow_size, establish_time), ahead-of-time for all flows. We also provide generic programming interface to FLOWPROPHET, so that current and future DCFs can deploy FLOWPROPHET readily. We implement FLOWPROPHET on both Spark and Hadoop, and perform extensive evaluations on a testbed with 37 physical servers. Our implementation and experiments demonstrate that, with time in advance and minimal cost, FLOWPROPHET can achieve almost 100% accuracy in source, destination, and flow size predictions. With accurate prediction from FLOWPROPHET, the job completion time of a Hadoop TeraSort benchmark is reduced by 12.52% on our cluster with a simple network scheduler.

An Integration Framework of Cloud Computing with Wireless Sensor Networks

Wireless sensor networks (WSN) is a key technology extensively applied in many fields, such as transportation, health-care and environment monitoring. Despite rapid development, the exponentially increasing data emanating from WSN is not efficiently stored and used. Besides, the data from multiple different types and locations of WSN needs to be well analyzed, fused and supplied to various types of clients, such as PC, workstation and smart phone. The emerging cloud computing technology provides scalable data process and storage power and some types of connectable services, which can helpfully utilize sensor data from WSN. In this paper, we propose an integration framework of cloud computing with WSN, in which sensor data is transmitted from WSN to cloud, and processed and stored in cloud, then mined and analyzed so as to be supplied to various clients. By applying virtualization and cloud storage technology, and Infrastructure as a Service (IaaS) and Software as a Service (SaaS) of cloud service model, the framework can fully process and store mass sensor data from multiple types of WSN. Besides, it efficiently mines and analyzes sensor data, based on which the data applications are well supplied to various types of clients in form of services.

OPTAS: Decentralized flow monitoring and scheduling for tiny tasks

Task-aware flow schedulers collect task information across the data center to optimize task-level performance. However, the majority of the tasks, which generate short flows and are called tiny tasks, have been largely overlooked by current schedulers. The large number of tiny tasks brings significant overhead to the centralized schedulers, while the existing decentralized schedulers are too complex to fit in commodity switches. In this paper we present OPTAS, a lightweight, commodity-switch-compatible scheduling solution that efficiently monitors and schedules flows for tiny tasks with low overhead. OPTAS monitors system calls and buffer footprints to recognize the tiny tasks, and assigns them with higher priorities than larger ones. The tiny tasks are then transferred in a FIFO manner by adjusting two attributes, namely, the window size and round trip time, of TCP. We have implemented OPTAS as a Linux kernel module, and experiments on our 37-server testbed show that OPTAS is at least 2.2× faster than fair sharing, and 1.2× faster than only assigning tiny tasks with the highest priority.

Rate-aware flow scheduling for commodity data center networks

Flow completion times (FCTs) are critical for many cloud applications. To minimize the average FCT, recent transport designs, such as pFabric, PASE, and PIAS, approximate the Shortest Remaining Time First (SRTF) scheduling. A common, implicit assumption of these solutions is that the remaining time is only determined by the remaining flow size. However, this assumption does not hold in many real-world scenarios where applications generate data at diverse rates that are smaller than the network capacity. In this paper, we look into this issue from system perspective and find that the operating system (OS) kernel can be exploited to better estimate the remaining time of a flow. In particular, we use the rate of copying data from user space to kernel space to measure the data generation rate. We design RAX, a rate aware flow scheduling method, that calculates the remaining time of a flow more accurately, based on not only the flow size but also the data generation rate. We have implemented a RAX prototype in Linux kernel and evaluated it through testbed experiments and ns-2 simulations. Our testbed results show that RAX reduces FCT by up to 14.9%/41.8% and 7.8%/22.9% over DCTCP and PIAS for all/medium flows respectively.

GraphF: An Efficient Fine-Grained Graph Processing System on Spark

This paper proposes the GraphF abstraction which exploits Adaptive Radix Tree for efficient graph indexing with lower storage cost. Leveraging the GraphF abstraction, we implement a separate graph computation engine on Spark. Experiments showed that on average GraphF outperforms GraphX and PowerGraph by up to 8.1X and 3.6X separately in execution time both for real world and for synthetic graphs. Moreover, GraphF is remarkably more efficient than GraphX in memory storage cost and saves up to sevenfold of space consumption.

Efficient Semantic-Aware Coflow Scheduling for Data-Parallel Jobs

This paper studies the communication pattern of data-parallel applications from the perspective of job execution, and discovers multiple inter-coflow dependencies. These inter-coflow dependencies, collectively named as semantic flow (seflow), can expose job-level semantics. It is observed that most distributed computing frameworks describe their job execution as directed acyclic graphs (DAG). So a seflow comprises not only all the coflows of a job but also the DAG-based relationship between them. Seflow, coflow and flow can be viewed as the top-down abstractions for communication of jobs.

Best Effort Task Scheduling for Data Parallel Jobs

The tasks of data-parallel computation jobs come up with diverse and time-varying resource requirements. The dynamic nature of task requirements brings challenges on making good scheduling decisions, due to it is hard to keep work-conserving. In this paper, we present BETS to cope with the requirement dynamics that aims at utilizing cluster resources fully. BETS employs a task model that represents for runtime task requirements, a coarse-grained task pipeline to make use of resources in a time-division multiplexing fashion, and fine-grained resource management to guarantee performance.

VirtMan: design and implementation of a fast booting system for homogeneous virtual machines in iVCE

Internet-based virtual computing environment (iVCE) has been proposed to combine data centers and other kinds of computing resources on the Internet to provide efficient and economical services. Virtual machines (VMs) have been widely used in iVCE to isolate different users/jobs and ensure trustworthiness, but traditionally VMs require a long period of time for booting, which cannot meet the requirement of iVCE’s large-scale and highly dynamic applications. To address this problem, in this paper we design and implement VirtMan, a fast booting system for a large number of virtual machines in iVCE. VirtMan uses the Linux Small Computer System Interface (SCSI) target to remotely mount to the source image in a scalable hierarchy, and leverages the homogeneity of a set of VMs to transfer only necessary image data at runtime. We have implemented VirtMan both as a standalone system and for OpenStack. In our 100-server testbed, VirtMan boots up 1000 VMs (with a 15 GB image of Windows Server 2008) on 100 physical servers in less than 120 s, which is three orders of magnitude lower than current public clouds.

Pallas: An Application-Driven Task and Network Simulation Framework

With the help of simulation tools, users can evaluate new proposals in cluster environment efficiently. However, current cloud simulators cannot meet the needs of application-driven simulation scenarios. In this paper, we propose Pallas, a task and network simulation framework that supports various cloud applications. Task-aware network scheduling and network-perceived task placement algorithms can be easily implemented in Pallas. We present the architecture and main components of Pallas and evaluate its effectiveness by comparing algorithm improvements to the actual results.

CoCache: Multi-layer Multi-path Cooperative Cache Accelerating the Deployment of Large Scale Virtual Machines

By analyzing the problems and challenges of virtual machine image store system in cloud computing environment, we present a cooperative persistent cache (CoCache) for virtual disks. CoCache takes advantage of the service ability of the cached nodes by providing virtual image data service for other nodes. CoCache can transfer data between nodes in a P2P pattern, for extending data service ability of the system. CoCache is realized in the kernel space of Linux, can support any kind of VMM. Experiments show that CoCache can effectively reduce the cost during virtual machines read data, and promote the service ability of virtual machine storage system. Layer-aware cache policy is proposed specially for improving cache hit rate in the multi-layer and multipath environment.