Efficient data reconfiguration for today's cloud systems

Performance of big data systems largely relies on efficient data reconfiguration techniques. Data reconfiguration operations deal with changing configuration parameters that affect data layout in a system. They could be user-initiated like changing shard key, block size in NoSQL databases, or system-initiated like changing replication in distributed interactive analytics engine. Current data reconfiguration schemes are heuristics at best and often do not scale well as data volume grows. As a result, system performance suffers. In this thesis, we show that {\it data reconfiguration mechanisms can be done in the background by using new optimal or near-optimal algorithms coupling them with performant system designs}. We explore four different data reconfiguration operations affecting three popular types of systems -- storage, real-time analytics and batch analytics. In NoSQL databases (storage), we explore new strategies for changing table-level configuration and for compaction as they improve read/write latencies. In distributed interactive analytics engines, a good replication algorithm can save costs by judiciously using memory that is sufficient to provide the highest throughput and low latency for queries. Finally, in batch processing systems, we explore prefetching and caching strategies that can improve the number of production jobs meeting their SLOs. All these operations happen in the background without affecting the fast path. Our contributions in each of the problems are two-fold -- 1) we model the problem and design algorithms inspired from well-known theoretical abstractions, 2) we design and build a system on top of popular open source systems used in companies today. Finally, using real-life workloads, we evaluate the efficacy of our solutions. Morphus and Parqua provide several 9s of availability while changing table level configuration parameters in databases. By halving memory usage in distributed interactive analytics engine, Getafix reduces cost of deploying the system by 10 million dollars annually and improves query throughput. We are the first to model the problem of compaction and provide formal bounds on their runtime. Finally, NetCachier helps 30\% more production jobs to meet their SLOs compared to existing state-of-the-art

On Improving Efficiency of Data-Intensive Applications in Geo-Distributed Environments

Distributed systems are pervasively demanded and adopted in nowadays for processing data-intensive workloads since they greatly accelerate large-scale data processing with scalable parallelism and improved data locality. Traditional distributed systems initially targeted computing clusters but have since evolved to data centers with multiple clusters. These systems are mostly built on top of homogeneous, tightly integrated resources connected in high-speed local-area networks (LANs), and typically require data to be ingested to a central data center for processing. Today, with enormous volumes of data continuously generated from geographically distributed locations, direct adoption of such systems is prohibitively inefficient due to the limited system scalability and high cost for centralizing the geo-distributed data over the wide-area networks (WANs). More commonly, it becomes a trend to build geo-distributed systems wherein data processing jobs are performed on top of geo-distributed, heterogeneous resources in proximity to the data at vastly distributed geo-locations. However, critical challenges and mechanisms for efficient execution of data-intensive applications in such geo-distributed environments are unclear by far. The goal of this dissertation is to identify such challenges and mechanisms, by extensively using the research principles and methodology of conventional distributed systems to investigate the geo-distributed environment, and by developing new techniques to tackle these challenges and run data-intensive applications with efficiency at scale. The contributions of this dissertation are threefold. Firstly, the dissertation shows that the high level of resource heterogeneity exhibited in the geo-distributed environment undermines the scalability of geo-distributed systems. Virtualization-based resource abstraction mechanisms have been introduced to abstract the hardware, network, and OS resources throughout the system, to mitigate the underlying resource heterogeneity and enhance the system scalability. Secondly, the dissertation reveals the overwhelming performance and monetary cost incurred by indulgent data sharing over the WANs in geo-distributed systems. Network optimization approaches, including linear- programming-based global optimization, greedy bin-packing heuristics, and TCP enhancement, are developed to optimize the network resource utilization and circumvent unnecessary expenses imposed on data sharing in WANs. Lastly, the dissertation highlights the importance of data locality for data-intensive applications running in the geo-distributed environment. Novel data caching and locality-aware scheduling techniques are devised to improve the data locality.Doctor of Philosoph

Novel holistic architecture for analytical operation on sensory data relayed as cloud services

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system

Optimizing Resource Management in Cloud Analytics Services

The fundamental challenge in the cloud today is how to build and optimize machine learning and data analytical services. Machine learning and data analytical platforms are changing computing infrastructure from expensive private data centers to easily accessible online services. These services pack user requests as jobs and run them on thousands of machines in parallel in geo-distributed clusters. The scale and the complexity of emerging jobs lead to increasing challenges for the clusters at all levels, from power infrastructure to system architecture and corresponding software framework design. These challenges come in many forms. Today's clusters are built on commodity hardware and hardware failures are unavoidable. Resource competition, network congestion, and mixed generations of hardware make the hardware environment complex and hard to model and predict. Such heterogeneity becomes a crucial roadblock for efficient parallelization on both the task level and job level. Another challenge comes from the increasing complexity of the applications. For example, machine learning services run jobs made up of multiple tasks with complex dependency structures. This complexity leads to difficulties in framework designs. The scale, especially when services span geo-distributed clusters, leads to another important hurdle for cluster design. Challenges also come from the power infrastructure. Power infrastructure is very expensive and accounts for more than 20% of the total costs to build a cluster. Power sharing optimization to maximize the facility utilization and smooth peak hour usages is another roadblock for cluster design. In this thesis, we focus on solutions for these challenges at the task level, on the job level, with respect to the geo-distributed data cloud design and for power management in colocation data centers. At the task level, a crucial hurdle to achieving predictable performance is stragglers, i.e., tasks that take significantly longer than expected to run. At this point, speculative execution has been widely adopted to mitigate the impact of stragglers in simple workloads. We apply straggler mitigation for approximation jobs for the first time. We present GRASS, which carefully uses speculation to mitigate the impact of stragglers in approximation jobs. GRASS's design is based on the analysis of a model we develop to capture the optimal speculation levels for approximation jobs. Evaluations with production workloads from Facebook and Microsoft Bing in an EC2 cluster of 200 nodes show that GRASS increases accuracy of deadline-bound jobs by 47% and speeds up error-bound jobs by 38%. Moving from task level to job level, task level speculation mechanisms are designed and operated independently of job scheduling when, in fact, scheduling a speculative copy of a task has a direct impact on the resources available for other jobs. Thus, we present Hopper, a job-level speculation-aware scheduler that integrates the tradeoffs associated with speculation into job scheduling decisions based on a model generalized from the task-level speculation model. We implement both centralized and decentralized prototypes of the Hopper scheduler and show that 50% (66%) improvements over state-of-the-art centralized (decentralized) schedulers and speculation strategies can be achieved through the coordination of scheduling and speculation. As computing resources move from local clusters to geo-distributed cloud services, we are expecting the same transformation for data storage. We study two crucial pieces of a geo-distributed data cloud system: data acquisition and data placement. Starting from developing the optimal algorithm for the case of a data cloud made up of a single data center, we propose a near-optimal, polynomial-time algorithm for a geo-distributed data cloud in general. We show, via a case study, that the resulting design, Datum, is near-optimal (within 1.6%) in practical settings. Efficient power management is a fundamental challenge for data centers when providing reliable services. Power oversubscription in data centers is very common and may occasionally trigger an emergency when the aggregate power demand exceeds the capacity. We study power capping solutions for handling such emergencies in a colocation data center, where the operator supplies power to multiple tenants. We propose a novel market mechanism based on supply function bidding, called COOP, to financially incentivize and coordinate tenants' power reduction for minimizing total performance loss while satisfying multiple power capping constraints. We demonstrate that COOP is "win-win", increasing the operator's profit (through oversubscription) and reducing tenants' costs (through financial compensation for their power reduction during emergencies).</p

Network flow optimization for distributed clouds

Internet applications, which rely on large-scale networked environments such as data centers for their back-end support, are often geo-distributed and typically have stringent performance constraints. The interconnecting networks, within and across data centers, are critical in determining these applications' performance. Data centers can be viewed as composed of three layers: physical infrastructure consisting of servers, switches, and links, control platforms that manage the underlying resources, and applications that run on the infrastructure. This dissertation shows that network flow optimization can improve performance of distributed applications in the cloud by designing high-throughput schemes spanning all three layers. At the physical infrastructure layer, we devise a framework for measuring and understanding throughput of network topologies. We develop a heuristic for estimating the worst-case performance of any topology and propose a systematic methodology for comparing performance of networks built with different equipment. At the control layer, we put forward a source-routed data center fabric which can achieve near-optimal throughput performance by leveraging a large number of available paths while using limited memory in switches. At the application layer, we show that current Application Network Interfaces (ANIs), abstractions that translate an application's performance goals to actionable network objectives, fail to capture the requirements of many emerging applications. We put forward a novel ANI that can capture application intent more effectively and quantify performance gains achievable with it. We also tackle resource optimization in the inter-data center context of cellular providers. In this emerging environment, a large amount of resources are geographically fragmented across thousands of micro data centers, each with a limited share of resources, necessitating cross-application optimization to satisfy diverse performance requirements and improve network and server utilization. Our solution, Patronus, employs hierarchical optimization for handling multiple performance requirements and temporally partitioned scheduling for scalability

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study