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

Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions

Edge and Fog computing paradigms utilise distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of the fast-evolving IoT applications. Due to the fine-grained modularity of the microservices along with their independently deployable and scalable nature, MSA exhibits great potential in harnessing both Fog and Cloud resources to meet diverse QoS requirements of the IoT application services, thus giving rise to novel paradigms like Osmotic computing. However, efficient and scalable scheduling algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. To this end, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications scheduling in Edge and Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the scheduling problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey

Grid-centric scheduling strategies for workflow applications

Grid computing faces a great challenge because the resources are not localized, but distributed, heterogeneous and dynamic. Thus, it is essential to provide a set of programming tools that execute an application on the Grid resources with as little input from the user as possible. The thesis of this work is that Grid-centric scheduling techniques of workflow applications can provide good usability of the Grid environment by reliably executing the application on a large scale distributed system with good performance. We support our thesis with new and effective approaches in the following five aspects. First, we modeled the performance of the existing scheduling approaches in a multi-cluster Grid environment. We implemented several widely-used scheduling algorithms and identified the best candidate. The study further introduced a new measurement, based on our experiments, which can improve the schedule quality of some scheduling algorithms as much as 20 fold in a multi-cluster Grid environment. Second, we studied the scalability of the existing Grid scheduling algorithms. To deal with Grid systems consisting of hundreds of thousands of resources, we designed and implemented a novel approach that performs explicit resource selection decoupled from scheduling Our experimental evaluation confirmed that our decoupled approach can be scalable in such an environment without sacrificing the quality of the schedule by more than 10%. Third, we proposed solutions to address the dynamic nature of Grid computing with a new cluster-based hybrid scheduling mechanism. Our experimental results collected from real executions on production clusters demonstrated that this approach produces programs running 30% to 100% faster than the other scheduling approaches we implemented on both reserved and shared resources. Fourth, we improved the reliability of Grid computing by incorporating fault- tolerance and recovery mechanisms into the workow application execution. Our experiments on a simulated multi-cluster Grid environment demonstrated the effectiveness of our approach and also characterized the three-way trade-off between reliability, performance and resource usage when executing a workflow application. Finally, we improved the large batch-queue wait time often found in production Grid clusters. We developed a novel approach to partition the workow application and submit them judiciously to achieve less total batch-queue wait time. The experimental results derived from production site batch queue logs show that our approach can reduce total wait time by as much as 70%. Our approaches combined can greatly improve the usability of Grid computing while increasing the performance of workow applications on a multi-cluster Grid environment

Resource Provisioning Exploiting Cost and Performance Diversity within IaaS Cloud Providers

IaaS platforms such as Amazon EC2 allow clients access to massive computational power in the form of instances. Amazon hosts three different instance purchasing options, each with its own SLA covering pricing and availability. Amazon also offers access to a number of geographical regions, zones, and instance types to select from. In this thesis, the problem of utilizing Spot and On-Demand instances is analyzed and two approaches are presented in order to exploit the cost and performance diversity among different instance types and availability zones, and among the Spot markets they represent. We first develop RAMP, a framework designed to calculate the expected profit of using a specific Spot or On-Demand instance through an evaluation of instance reliability. RAMP is extended to develop RAMC-DC, a framework designed to allocate the most cost effective instance through strategies that facilitate interchangeability of instances among short jobs, reliability of instances among long jobs, and a comparison of the estimated costs of possible allocations. RAMC-DC achieves fault tolerance through comparisons of the price dynamics across instance types and availability zones, and through an examination of three basic checkpointing methods. Evaluations demonstrate that both frameworks take a large step toward low-volatility, high cost-efficiency resource provisioning. While achieving early-termination rates as low as 2.2%, RAMP can completely offset the total cost when charging the user just 17.5% of the On-Demand price. Moreover, the increases in profit resulting from relatively small additional charges to users are notably high, i.e., 100% profit compared to the resource provisioning cost with 35% of the equivalent On-Demand price. RAMC-DC can maintain deadline breaches below 1.8% of all jobs, achieve both early-termination and deadline breach rates as low as 0.5% of all jobs, and lowers total costs by between 80% and 87% compared to using only On-Demand instances

Resource Provisioning Exploiting Cost and Performance Diversity within IaaS Cloud Providers

IaaS platforms such as Amazon EC2 allow clients access to massive computational power in the form of instances. Amazon hosts three different instance purchasing options, each with its own SLA covering pricing and availability. Amazon also offers access to a number of geographical regions, zones, and instance types to select from. In this thesis, the problem of utilizing Spot and On-Demand instances is analyzed and two approaches are presented in order to exploit the cost and performance diversity among different instance types and availability zones, and among the Spot markets they represent. We first develop RAMP, a framework designed to calculate the expected profit of using a specific Spot or On-Demand instance through an evaluation of instance reliability. RAMP is extended to develop RAMC-DC, a framework designed to allocate the most cost effective instance through strategies that facilitate interchangeability of instances among short jobs, reliability of instances among long jobs, and a comparison of the estimated costs of possible allocations. RAMC-DC achieves fault tolerance through comparisons of the price dynamics across instance types and availability zones, and through an examination of three basic checkpointing methods. Evaluations demonstrate that both frameworks take a large step toward low-volatility, high cost-efficiency resource provisioning. While achieving early-termination rates as low as 2.2%, RAMP can completely offset the total cost when charging the user just 17.5% of the On-Demand price. Moreover, the increases in profit resulting from relatively small additional charges to users are notably high, i.e., 100% profit compared to the resource provisioning cost with 35% of the equivalent On-Demand price. RAMC-DC can maintain deadline breaches below 1.8% of all jobs, achieve both early-termination and deadline breach rates as low as 0.5% of all jobs, and lowers total costs by between 80% and 87% compared to using only On-Demand instances

Reliable and energy efficient resource provisioning in cloud computing systems

Cloud Computing has revolutionized the Information Technology sector by giving computing a perspective of service. The services of cloud computing can be accessed by users not knowing about the underlying system with easy-to-use portals. To provide such an abstract view, cloud computing systems have to perform many complex operations besides managing a large underlying infrastructure. Such complex operations confront service providers with many challenges such as security, sustainability, reliability, energy consumption and resource management. Among all the challenges, reliability and energy consumption are two key challenges focused on in this thesis because of their conflicting nature. Current solutions either focused on reliability techniques or energy efficiency methods. But it has been observed that mechanisms providing reliability in cloud computing systems can deteriorate the energy consumption. Adding backup resources and running replicated systems provide strong fault tolerance but also increase energy consumption. Reducing energy consumption by running resources on low power scaling levels or by reducing the number of active but idle sitting resources such as backup resources reduces the system reliability. This creates a critical trade-off between these two metrics that are investigated in this thesis. To address this problem, this thesis presents novel resource management policies which target the provisioning of best resources in terms of reliability and energy efficiency and allocate them to suitable virtual machines. A mathematical framework showing interplay between reliability and energy consumption is also proposed in this thesis. A formal method to calculate the finishing time of tasks running in a cloud computing environment impacted with independent and correlated failures is also provided. The proposed policies adopted various fault tolerance mechanisms while satisfying the constraints such as task deadlines and utility values. This thesis also provides a novel failure-aware VM consolidation method, which takes the failure characteristics of resources into consideration before performing VM consolidation. All the proposed resource management methods are evaluated by using real failure traces collected from various distributed computing sites. In order to perform the evaluation, a cloud computing framework, 'ReliableCloudSim' capable of simulating failure-prone cloud computing systems is developed. The key research findings and contributions of this thesis are: 1. If the emphasis is given only to energy optimization without considering reliability in a failure prone cloud computing environment, the results can be contrary to the intuitive expectations. Rather than reducing energy consumption, a system ends up consuming more energy due to the energy losses incurred because of failure overheads. 2. While performing VM consolidation in a failure prone cloud computing environment, a significant improvement in terms of energy efficiency and reliability can be achieved by considering failure characteristics of physical resources. 3. By considering correlated occurrence of failures during resource provisioning and VM allocation, the service downtime or interruption is reduced significantly by 34% in comparison to the environments with the assumption of independent occurrence of failures. Moreover, measured by our mathematical model, the ratio of reliability and energy consumption is improved by 14%

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS