Paul Townend

Analysis, Modeling and Simulation of Workload Patterns in a Large-Scale Utility Cloud

Understanding the characteristics and patterns of workloads within a Cloud computing environment is critical in order to improve resource management and operational conditions while Quality of Service (QoS) guarantees are maintained. Simulation models based on realistic parameters are also urgently needed for investigating the impact of these workload characteristics on new system designs and operation policies. Unfortunately there is a lack of analyses to support the development of workload models that capture the inherent diversity of users and tasks, largely due to the limited availability of Cloud tracelogs as well as the complexity in analyzing such systems. In this paper we present a comprehensive analysis of the workload characteristics derived from a production Cloud data center that features over 900 users submitting approximately 25 million tasks over a time period of a month. Our analysis focuses on exposing and quantifying the diversity of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload created by such components. Our derived model is implemented by extending the capabilities of the CloudSim framework and is further validated through empirical comparison and statistical hypothesis tests. We illustrate several examples of this works practical applicability in the domain of resource management and energy-efficiency.

An Approach for Characterizing Workloads in Google Cloud to Derive Realistic Resource Utilization Models

Analyzing behavioral patterns of workloads is critical to understanding Cloud computing environments. However, until now only a limited number of real-world Cloud data center trace logs have been available for analysis. This has led to a lack of methodologies to capture the diversity of patterns that exist in such datasets. This paper presents the first large-scale analysis of real-world Cloud data, using a recently released dataset that features traces from over 12,000 servers over the period of a month. Based on this analysis, we develop a novel approach for characterizing workloads that for the first time considers Cloud workload in the context of both user and task in order to derive a model to capture resource estimation and utilization patterns. The derived model assists in understanding the relationship between users and tasks within workload, and enables further work such as resource optimization, energy-efficiency improvements, and failure correlation. Additionally, it provides a mechanism to create patterns that randomly fluctuate based on realistic parameters. This is critical to emulating dynamic environments instead of statically replaying records in the trace log. Our approach is evaluated by contrasting the logged data against simulation experiments, and our results show that the derived model parameters correctly describe the operational environment within a 5% of error margin, confirming the great variability of patterns that exist in Cloud computing.

A novel intrusion severity analysis approach for Clouds

Cloud computing presents exciting opportunities to foster research for scientific communities; virtual machine technology has a profound role in this. Among other benefits, virtual machine technology enables Clouds to offer large scale and flexible computing infrastructures that are available on demand to address the diverse requirements of scientific research. However, Clouds introduce novel security challenges which need to be addressed to facilitate widespread adoption. This paper is focused on one such challenge-intrusion severity analysis. In particular, we highlight the significance of intrusion severity analysis for the overall security of Clouds. Additionally, we present a novel method to address this challenge in accordance with the specific requirements of Clouds for intrusion severity analysis. We also present rigorous evaluation to assess the effectiveness and feasibility of the proposed method to address this challenge for Clouds. HighlightsSignificance of intrusion severity analysis for Clouds has been highlighted. ? Parameters for intrusion severity analysis along with their significance have been highlighted. ? A machine learning based approach is presented to address intrusion severity analysis. ? Rigorous evaluation demonstrated success rates of above 90% for the proposed method.

A provenance-aware weighted fault tolerance scheme for service-based applications

Service-orientation has been proposed as a way of facilitating the development and integration of increasingly complex and heterogeneous system components. However, there are many new challenges to the dependability community in this new paradigm, such as how individual channels within fault-tolerant systems may invoke common services as part of their workflow, thus increasing the potential for common-mode failure. We propose a scheme that - for the first time - links the technique of provenance with that of multi-version fault tolerance. We implement a large test system and perform experiments with a single-version system, a traditional MVD system, and a provenance-aware MVD system, and compare their results. We show that for this experiment, our provenance-aware scheme results in a much more dependable system than either of the other systems tested, whilst imposing a negligible timing overhead.

An Analysis of Failure-Related Energy Waste in a Large-Scale Cloud Environment

Cloud computing providers are under great pressure to reduce operational costs through improved energy utilization while provisioning dependable service to customers; it is therefore extremely important to understand and quantify the explicit impact of failures within a system in terms of energy costs. This paper presents the first comprehensive analysis of the impact of failures on energy consumption in a real-world large-scale cloud system (comprising over 12 500 servers), including the study of failure and energy trends of the spatial and temporal environmental characteristics. Our results show that 88% of task failure events occur in lower priority tasks producing 13% of total energy waste, and 1% of failure events occur in higher priority tasks due to server failures producing 8% of total energy waste. These results highlight an unintuitive but significant impact on energy consumption due to failures, providing a strong foundation for research into dependable energy-aware cloud computing.

An Empirical Failure-Analysis of a Large-Scale Cloud Computing Environment

Cloud computing research is in great need of statistical parameters derived from the analysis of real-world systems. One aspect of this is the failure characteristics of Cloud environments composed of workloads and servers, currently, few metrics are available that quantify failure and repair times of workloads and servers at a large-scale. Workload metrics in particular are critical for characterizing and modeling accurate workload behavior, enabling more realistic workload simulation and failure scenarios of systems. This paper presents the analysis of failure data of a large-scale production Cloud environment (consisting of over 12,500 servers), and includes a study of failure and repair times and characteristics for both Cloud workloads and servers. Our results show that failure characteristics for workload and servers are highly variable and that production Cloud workloads can be accurately modeled by a Gamma distribution. Repair times range between 30 seconds to 4 days, and 25 minutes to 8 days, for workloads and servers respectively.

An Analysis of the Server Characteristics and Resource Utilization in Google Cloud

Understanding the resource utilization and server characteristics of large-scale systems is crucial if service providers are to optimize their operations whilst maintaining Quality of Service. For large-scale data enters, identifying the characteristics of resource demand and the current availability of such resources, allows system managers to design and deploy mechanisms to improve data enter utilization and meet Service Level Agreements with their customers, as well as facilitating business expansion. In this paper, we present a large-scale analysis of server resource utilization and a characterization of a production Cloud data enter using the most recent data enter trace logs made available by Google. We present their statistical properties, and a comprehensive coarse-grain analysis of the data, including submission rates, server classification, and server resource utilization. Additionally, we perform a fine-grained analysis to quantify the resource utilization of servers wasted due to the early termination of tasks. Our results show that data enter resource utilization remains relatively stable at between 40 - 60%, that the degree of correlation between server utilization and Cloud workload environment varies by server architecture, and that the amount of resource utilization wasted varies between 4.53 - 14.22% for different server architectures. This provides invaluable real-world empirical data for Cloud researchers in many subject areas.

Quantification of Security for Compute Intensive Workloads in Clouds

Cloud Computing is a promising technology to facilitate development of large-scale, on-demand, flexible computing infrastructures. However, improving dependability of Cloud Computing is critical for realization of its potential. In this paper, we describe our efforts to quantify security for Clouds to facilitate provision of assurance for quality of service, one of the factors contributing to dependability. This has profound implications for delivering customized security solutions such as effective intrusion prevention and detection which is the overall objective of our research. In order to demonstrate the applicability of our research, we have incorporated these requirements in the resource acquisition phase for Clouds. We also present experiments to demonstrate the effectiveness of our approach to address the Random Migration Problem for virtualized computing environments.

Multi-tenancy in Cloud Computing

As Cloud Computing becomes the trend of information technology computational model, the Cloud security is becoming a major issue in adopting the Cloud where security is considered one of the most critical concerns for the large customers of Cloud (i.e. governments and enterprises). Such valid concern is mainly driven by the Multi-Tenancy situation which refers to resource sharing in Cloud Computing and its associated risks where confidentiality and/or integrity could be violated. As a result, security concerns may harness the advancement of Cloud Computing in the market. So, in order to propose effective security solutions and strategies a good knowledge of the current Cloud implementations and practices, especially the public Clouds, must be understood by professionals. Such understanding is needed in order to recognize attack vectors and attack surfaces. In this paper we will propose an attack model based on a threat model designed to take advantage of Multi-Tenancy situation only. Before that, a clear understanding of Multi-Tenancy, its origin and its benefits will be demonstrated. Also, a novel way on how to approach Multi-Tenancy will be illustrated. Finally, we will try to sense any suspicious behavior that may indicate to a possible attack where we will try to recognize the proposed attack model empirically from Google trace logs. Google trace logs are a 29-day worth of data released by Google. The data set was utilized in reliability and power consumption studies, but not been utilized in any security study to the extent of our knowledge.

Moses: A grid-enabled spatial decision support system

The authors present an architecture for simulation modeling using the resources of grid computing. The use of the grid provides access to the substantial data storage and processing power, which are necessary to translate such models from computational tools into genuine planning aids. As well as providing access to virtualized compute resources, the architecture allows customized applications to meet the needs of an array of potential user organizations. A number of key obstacles in the deployment and integration of e-Science services are identified. These include the high computational costs of simulation modeling at the microscale for typical ‘‘what if’’ scenario questions in research and policy settings; the management and technical issues relating to security in licensing common data sources; sociocultural, legal, and administrative restrictions on the privacy of individual-level response data; and the slow development and lack of uptake of agreed standards such as JSR-168 compliant portlets in the construction of useable applications.