Modern datacenters run diverse workloads that increasingly comprise data-parallel computational jobs. There has been a steady rise in their demand leading to high-volume traffic. To meet these demands, datacenter providers operate their clusters at levels of high utilization. We show that under such conditions, existing schedulers impose large wait times on tail tasks, leading to long job completion time. We propose a new decentralized scheduler, Murmuration, that reduces the total wait time of tasks. It employs multiple communicating schedulers to schedule tasks of jobs such that their start times are as close together as possible, ensuring small tail task completion time and better average job completion time.

We present robust dynamic resource allocation mechanisms to allocate application resources meeting Service Level Objectives (SLOs) agreed between cloud providers and customers. In fact, two filter-based robust controllers, i.e.,$\mathcal {H}_{\infty }$filter and Maximum Correntropy Criterion Kalman filter (MCC-KF), are proposed. The controllers are self-adaptive, with process noise variances and covariances calculated using previous measurements within a time window. In the allocation process, a bounded client mean response time ($\mathop {\mathrm{mRT}}$) is maintained. Both controllers are deployed and evaluated on an experimental testbed hosting the RUBiS (Rice University Bidding System) auction benchmark web site. The proposed controllers offer improved performance under abrupt workload changes, shown via rigorous comparison with current state-of-the-art. On our experimental setup, the Single-Input-Single-Output (SISO) controllers can operate on the same server where the resource allocation is performed; while Multi-Input-Multi-Output (MIMO) controllers are on a separate server where all the data are collected for decision making. SISO controllers take decisions not dependent to other system states (servers), albeit MIMO controllers are characterized by increased communication overhead and potential delays. While SISO controllers offer improved performance over MIMO ones, the latter enable a more informed decision making framework for resource allocation problem of multi-tier applications.

Virtualized servers have been the key for the efficient deployment of cloud applications. As the application demand increases, it is important to dynamically adjust the CPU allocation of each component in order to save resources for other applications and keep performance high, e.g., the client mean response time (mRT) should be kept below a Quality of Service (QoS) target. In this work, a new form of Kalman filter, called the Maximum Correntropy Criterion Kalman Filter (MCC-KF), has been used in order to predict, and hence, adjust the CPU allocations of each component while the RUBiS auction site workload changes randomly as the number of clients varies. MCC-KF has shown high performance when the noise is non-Gaussian, as it is the case in the CPU usage. Numerical evaluations compare our designed framework with other current state-of-the-art using real-data via the RUBiS benchmark website deployed on a prototype Xen-virtualized cluster.

Real-time processing of big data has become a core operation in various areas of business, such as extracting value from real-time social network data. Big data workloads in the wild show a strong temporal variability that not only poses the risk of slow responsiveness in data analysis, but also leads to a high risk of service outage. The recent development of batch streaming systems based on the MapReduce framework is shown effective on non-overloaded systems. However, little is known on how to enhance the performance of the batch streaming systems for bursty workloads. In this paper, we propose a latency-driven data controller, Dslash, which aims to process as much data as possible, while processing these as fast as the application target latency and system capacity allow. In particular, we implement Dslash on Spark Streaming - an emerging and complex batch streaming system. Dslash features include (i) placing data in an augmented distributed memory, (ii) shedding out-of-date data, (iii) improving the processing locality of Map tasks, and (iv) delaying data processing in transient overloads. Extensive evaluations on a large number of workloads show that Dslash can ensure stable and fast responsiveness compared to vanilla Spark Streaming systems.

The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes. This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.

Peak loads are a challenge for streaming systems. Here we present Dslash, a latency-driven controller that keeps and processes as much data as the system resources allow in order to meet a target latency.

Federated stream processing systems, which utilise nodes from multiple independent domains, can be found increasingly in multi-provider cloud deployments, internet-of-things systems, collaborative sensing applications and large-scale grid systems. To pool resources from several sites and take advantage of local processing, submitted queries are split into query fragments, which are executed collaboratively by different sites. When supporting many concurrent users, however, queries may exhaust available processing resources, thus requiring constant load shedding. Given that individual sites have autonomy over how they allocate query fragments on their nodes, it is an open challenge how to ensure global fairness on processing quality experienced by queries in a federated scenario.

Resource management of virtualized servers in data centers has become a critical task, since it enables cost-effective consolidation of server applications. Resource management is an important and challenging task, especially for multitier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done toward adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high-intensity workload changes. In addition, our controllers are enhanced to deal with multitier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark website deployed on a prototype Xen-virtualized cluster.

As users of "big data" applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the "pay-as-you-go" model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs-systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results.

When users submit new queries to a distributed stream processing system (DSPS), a query planner must allocate physical resources, such as CPU cores, memory and network bandwidth, from a set of hosts to queries. Allocation decisions must provide the correct mix of resources required by queries, while achieving an efficient overall allocation to scale in the number of admitted queries. By exploiting overlap between queries and reusing partial results, a query planner can conserve resources but has to carry out more complex planning decisions. In this paper, we describe SQPR, a query planner that targets DSPSs in data centre environments with heterogeneous resources. SQPR models query admission, allocation and reuse as a single constrained optimisation problem and solves an approximate version to achieve scalability. It prevents individual resources from becoming bottlenecks by re-planning past allocation decisions and supports different allocation objectives. As our experimental evaluation in comparison with a state-of-the-art planner shows SQPR makes efficient resource allocation decisions, even with a high utilisation of resources, with acceptable overheads.

In this paper we propose Daedalus, a monitoring overlay network designed to compute approximate aggregate functions over multi-million of nodes, dynamic networks. Our design combines techniques from DBs and P2P systems. A small subset of related work is briefly described throughout the paper. Daedalus further extends these ideas to networks of much larger sizes, which we believe is a unique challenge.

Most peer-to-peer systems are built upon the assumption that their running environment is homogeneous. However, for Internet-scale applications this assumption could lead to performance limitations. We propose a framework for building peer-to-peer systems which use performance monitoring techniques to adapt to changes in their environment.

High-speed switches and routers internally operate using fixed-size cells or segments; variable-size packets are segmented and later reassembled. Connectionless ATM was proposed to quickly carry IP packets segmented into cells (AAL5) using a number of hardware-managed ATM VCs. We show that this is analogous to wormhole routing. We modify this architecture to make it applicable to existing ATM equipment: we propose a low-cost, single-input, single-output wormhole IP router that functions as a VP/VC translation filter between ATM subnetworks. When compared to IP routers, the proposed architecture features simpler hardware and lower latency. When compared to software-based IP-over-ATM techniques, the new architecture avoids the overheads of a large number of labels, or the delays of establishing new flows in software after the first few packets have suffered considerable latencies. We simulated a wormhole IP routing filter, showing that a few tens of hardware-managed VCs per outgoing VP usually suffice. We built and successfully tested a prototype, operating at 2/spl times/155 Mb/s, using one field programmable gate array (FPGA) and DRAM. Simple analysis shows that operation at 10 Gb/s and beyond is feasible today.