Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources

The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often centralized in the Cloud, the deployment of capable edge devices on the field motivates the need for cooperative event analytics that span Edge and Cloud computing. Here, we identify a novel problem of query placement on edge and Cloud resources for dynamically arriving and departing analytic dataflows. We define this as an optimization problem to minimize the total makespan for all event analytics, while meeting energy and compute constraints of the resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for such dynamic dataflows, and validate them using detailed simulations for 100 - 1000 edge devices and VMs. The results show that our heuristics offer O(seconds) planning time, give a valid and high quality solution in all cases, and reduce the number of query migrations. Furthermore, rebalance strategies when applied in these heuristics have significantly reduced the makespan by around 20 - 25%.Comment: 11 pages, 7 figure

Cost-Effective Resource Allocation and Throughput Maximization in Mobile Cloudlets and Distributed Clouds

With the advance in communication networks and the use explosion of mobile devices, distributed clouds consisting of many small and medium datacenters in geographical locations and cloudlets defined as "mini" datacenters are envisioned as the next-generation cloud computing platform. In particular, distributed clouds enable disaster-resilient and scalable services by scaling the services into multiple datacenters, while cloudlets allow pervasive and continuous services with low access delay by further enabling mobile users to access the services within their proximity. To realize the promises provided by distributed clouds and mobile cloudlets, it is urgently to optimize various system performance of distributed clouds and cloudlets, such as system throughput and operational cost by developing efficient solutions. In this thesis, we aim to devise novel solutions to maximize the system throughput of mobile cloudlets, and minimize the operational costs of distributed clouds, while meeting the resource capacity constraints and users' resource demands. This however poses great challenges, that is, (1) how to maximize the system throughput of a mobile cloudlet, considering that a mobile cloudlet has limited resources to serve energy-constrained mobile devices, (2) how to efficiently and effectively manage and evaluate big data in distributed clouds, and (3) how to efficiently allocate the resources of a distributed cloud to meet the resource demands of various users. Existing studies mainly focused on implementing systems and lacked systematic optimization methods to optimize the performance of distributed clouds and mobile cloudlets. Novel techniques and approaches for performance optimization of distributed clouds and mobile cloudlets are desperately needed. To address these challenges, this thesis makes the following contributions. We firstly study online request admissions in a cloudlet with the aim of maximizing the system throughput, assuming that future user requests are not known in advance. We propose a novel admission cost model to accurately model dynamic resource consumption, and devise efficient algorithms for online request admissions. We secondly study a novel collaboration- and fairness-aware big data management problem in a distributed cloud to maximize the system throughput, while minimizing the operational cost of service providers, subject to resource capacities and users' fairness constraints, for which, we propose a novel optimization framework and devise a fast yet scalable approximation algorithm with an approximation ratio. We thirdly investigate online query evaluation for big data analysis in a distributed cloud to maximize the query acceptance ratio, while minimizing the query evaluation cost. For this problem, we propose a novel metric to model the costs of different resource consumptions in datacenters, and devise efficient online algorithms under both unsplittable and splittable source data assumptions. We fourthly address the problem of community-aware data placement of online social networks into a distributed cloud, with the aim of minimizing the operational cost of the cloud service provider, and devise a fast yet scalable algorithm for the problem, by leveraging the close community concept that considers both user read rates and update rates. We also deal with social network evolutions, by developing a dynamic evaluation algorithm for the problem. We finally evaluate the performance of all proposed algorithms in this thesis through experimental simulations, using real and/or synthetic datasets. Simulation results show that the proposed algorithms significantly outperform existing algorithms

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Architecting Data Centers for High Efficiency and Low Latency

Modern data centers, housing remarkably powerful computational capacity, are built in massive scales and consume a huge amount of energy. The energy consumption of data centers has mushroomed from virtually nothing to about three percent of the global electricity supply in the last decade, and will continuously grow. Unfortunately, a significant fraction of this energy consumption is wasted due to the inefficiency of current data center architectures, and one of the key reasons behind this inefficiency is the stringent response latency requirements of the user-facing services hosted in these data centers such as web search and social networks. To deliver such low response latency, data center operators often have to overprovision resources to handle high peaks in user load and unexpected load spikes, resulting in low efficiency. This dissertation investigates data center architecture designs that reconcile high system efficiency and low response latency. To increase the efficiency, we propose techniques that understand both microarchitectural-level resource sharing and system-level resource usage dynamics to enable highly efficient co-locations of latency-critical services and low-priority batch workloads. We investigate the resource sharing on real-system simultaneous multithreading (SMT) processors to enable SMT co-locations by precisely predicting the performance interference. We then leverage historical resource usage patterns to further optimize the task scheduling algorithm and data placement policy to improve the efficiency of workload co-locations. Moreover, we introduce methodologies to better manage the response latency by automatically attributing the source of tail latency to low-level architectural and system configurations in both offline load testing environment and online production environment. We design and develop a response latency evaluation framework at microsecond-level precision for data center applications, with which we construct statistical inference procedures to attribute the source of tail latency. Finally, we present an approach that proactively enacts carefully designed causal inference micro-experiments to diagnose the root causes of response latency anomalies, and automatically correct them to reduce the response latency.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144144/1/yunqi_1.pd

A Survey on the Evolution of Stream Processing Systems

Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. This survey provides a comprehensive overview of fundamental aspects of stream processing systems and their evolution in the functional areas of out-of-order data management, state management, fault tolerance, high availability, load management, elasticity, and reconfiguration. We review noteworthy past research findings, outline the similarities and differences between early ('00-'10) and modern ('11-'18) streaming systems, and discuss recent trends and open problems.Comment: 34 pages, 15 figures, 5 table

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Stealth databases : ensuring user-controlled queries in untrusted cloud environments

Sensitive data is increasingly being hosted online in ubiquitous cloud storage services. Recent advances in multi-cloud service integration through provider multiplexing and data dispersion have alleviated most of the associated risks for hosting files which are retrieved by users for further processing. However, for structured data managed in databases, many issues remain, including the need to perform operations directly on the remote data to avoid costly transfers. In this paper, we motivate the need for distributed stealth databases which combine properties from structure-preserving dispersed file storage for capacity-saving increased availability with emerging work on structure-preserving encryption for on-demand increased confidentiality with controllable performance degradation. We contribute an analysis of operators executing in map-reduce or map-carry-reduce phases and derive performance statistics. Our prototype, StealthDB, demonstrates that for typical amounts of personal structured data, stealth databases are a convincing concept for taming untrusted and unsafe cloud environments

Data Replication and Its Alignment with Fault Management in the Cloud Environment

Nowadays, the exponential data growth becomes one of the major challenges all over the world. It may cause a series of negative impacts such as network overloading, high system complexity, and inadequate data security, etc. Cloud computing is developed to construct a novel paradigm to alleviate massive data processing challenges with its on-demand services and distributed architecture. Data replication has been proposed to strategically distribute the data access load to multiple cloud data centres by creating multiple data copies at multiple cloud data centres. A replica-applied cloud environment not only achieves a decrease in response time, an increase in data availability, and more balanced resource load but also protects the cloud environment against the upcoming faults. The reactive fault tolerance strategy is also required to handle the faults when the faults already occurred. As a result, the data replication strategies should be aligned with the reactive fault tolerance strategies to achieve a complete management chain in the cloud environment. In this thesis, a data replication and fault management framework is proposed to establish a decentralised overarching management to the cloud environment. Three data replication strategies are firstly proposed based on this framework. A replica creation strategy is proposed to reduce the total cost by jointly considering the data dependency and the access frequency in the replica creation decision making process. Besides, a cloud map oriented and cost efficiency driven replica creation strategy is proposed to achieve the optimal cost reduction per replica in the cloud environment. The local data relationship and the remote data relationship are further analysed by creating two novel data dependency types, Within-DataCentre Data Dependency and Between-DataCentre Data Dependency, according to the data location. Furthermore, a network performance based replica selection strategy is proposed to avoid potential network overloading problems and to increase the number of concurrent-running instances at the same time

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications