Adaptive Dispatching of Tasks in the Cloud

The increasingly wide application of Cloud Computing enables the consolidation of tens of thousands of applications in shared infrastructures. Thus, meeting the quality of service requirements of so many diverse applications in such shared resource environments has become a real challenge, especially since the characteristics and workload of applications differ widely and may change over time. This paper presents an experimental system that can exploit a variety of online quality of service aware adaptive task allocation schemes, and three such schemes are designed and compared. These are a measurement driven algorithm that uses reinforcement learning, secondly a "sensible" allocation algorithm that assigns jobs to sub-systems that are observed to provide a lower response time, and then an algorithm that splits the job arrival stream into sub-streams at rates computed from the hosts' processing capabilities. All of these schemes are compared via measurements among themselves and with a simple round-robin scheduler, on two experimental test-beds with homogeneous and heterogeneous hosts having different processing capacities.Comment: 10 pages, 9 figure

Developing Resource Usage Service in WLCG

According to the Memorandum of Understanding (MoU) of the World-wide LHC Computing Grid (WLCG) project, participating sites are required to provide resource usage or accounting data to the Grid Operational Centre (GOC) to enrich the understanding of how shared resources are used, and to provide information for improving the effectiveness of resource allocation. As a multi-grid environment, the accounting process of WLCG is currently enabled by four accounting systems, each of which was developed independently by constituent grid projects. These accounting systems were designed and implemented based on project-specific local understanding of requirements, and therefore lack interoperability. In order to automate the accounting process in WLCG, three transportation methods are being introduced for streaming accounting data metered by heterogeneous accounting systems into GOC at Rutherford Appleton Laboratory (RAL) in the UK, where accounting data are aggregated and accumulated throughout the year. These transportation methods, however, were introduced on a per accounting-system basis, i.e. targeting at a particular accounting system, making them hard to reuse and customize to new requirements. This paper presents the design of WLCG-RUS system, a standards-compatible solution providing a consistent process for streaming resource usage data across various accounting systems, while ensuring interoperability, portability, and customization

A Meta-Brokering Framework for Science Gateways

Recently scientific communities produce a growing number of computation-intensive applications, which calls for the interoperation of distributed infrastructures including Clouds, Grids and private clusters. The European SHIWA and ER-flow projects have enabled the combination of heterogeneous scientific workflows, and their execution in a large-scale system consisting of multiple Distributed Computing Infrastructures. One of the resource management challenges of these projects is called parameter study job scheduling. A parameter study job of a workflow generally has a large number of input files to be consumed by independent job instances. In this paper we propose a meta-brokering framework for science gateways to support the execution of such workflows. In order to cope with the high uncertainty and unpredictable load of the utilized distributed infrastructures, we introduce the so called resource priority services. These tools are capable of determining and dynamically updating priorities of the available infrastructures to be selected for job instances. Our evaluations show that this approach implies an efficient distribution of job instances among the available computing resources resulting in shorter makespan for parameter study workflows

Resource Pricing In A Dynamic Multi-Commodity Market For Computational Resources

The adoption of market-based principles in resource management systems for computational infrastructures such as grids and clusters allows for matching demand and supply for resources in a utility maximizing manner. As such, they offer a promise of producing more efficient resource allocations, compared to traditional system-centric approaches that do not allow consumers and providers to express their valuations for computational resources. In this paper, we investigate the pricing of resources in grids through the use of a computational commodity market of CPU resources, where resource prices are determined through the computation of a supply-and-demand equilibrium. In particular, we introduce several categories of CPUs characterized by their execution speed. These differ in cost and performance but may be used interchangeably in executing jobs and thus represent so-called substitutable resources. We investigate the performance of the algorithms for computing the supply-and-demand equilibrium in this multi-commodity setting under dynamically varying consumer and provider populations.Comment: 14 Pages, IJCNC Journa

A combined computing framework for load balancing in multi-tenant cloud eco-system

Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required

Traffic and task allocation in networks and the cloud

Communication services such as telephony, broadband and TV are increasingly migrating into Internet Protocol(IP) based networks because of the consolidation of telephone and data networks. Meanwhile, the increasingly wide application of Cloud Computing enables the accommodation of tens of thousands of applications from the general public or enterprise users which make use of Cloud services on-demand through IP networks such as the Internet. Real-Time services over IP (RTIP) have also been increasingly significant due to the convergence of network services, and the real-time needs of the Internet of Things (IoT) will strengthen this trend. Such Real-Time applications have strict Quality of Service (QoS) constraints, posing a major challenge for IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement. Thus in this thesis we first describe our design for a novel ``Real-Time (RT) traffic over CPN'' protocol which uses QoS goals that match the needs of voice packet delivery in the presence of other background traffic under varied traffic conditions; we present its experimental evaluation via measurements of key QoS metrics such as packet delay, delay variation (jitter) and packet loss ratio. Pursuing our investigation of packet routing in the Internet, we then propose a novel Big Data and Machine Learning approach for real-time Internet scale Route Optimisation based on Quality-of-Service using an overlay network, and evaluate is performance. Based on the collection of data sampled each $2$ minutes over a large number of source-destinations pairs, we observe that intercontinental Internet Protocol (IP) paths are far from optimal with respect to metrics such as end-to-end round-trip delay. On the other hand, our machine learning based overlay network routing scheme exploits large scale data collected from communicating node pairs to select overlay paths, while it uses IP between neighbouring overlay nodes. We report measurements over a week long experiment with several million data points shows substantially better end-to-end QoS than is observed with pure IP routing. Pursuing the machine learning approach, we then address the challenging problem of dispatching incoming tasks to servers in Cloud systems so as to offer the best QoS and reliable job execution; an experimental system (the Task Allocation Platform) that we have developed is presented and used to compare several task allocation schemes, including a model driven algorithm, a reinforcement learning based scheme, and a ``sensible’’ allocation algorithm that assigns tasks to sub-systems that are observed to provide lower response time. These schemes are compared via measurements both among themselves and against a standard round-robin scheduler, with two architectures (with homogenous and heterogenous hosts having different processing capacities) and the conditions under which the different schemes offer better QoS are discussed. Since Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet, we also describe a smart distributed system that combines local and remote Cloud facilities, allocating tasks dynamically to the service that offers the best overall QoS, and it includes a routing overlay which minimizes network delay for data transfer between Clouds. Internet-scale experiments that we report exhibit the effectiveness of our approach in adaptively distributing workload across multiple Clouds.Open Acces