Chris Bunch

AppScale: Scalable and Open AppEngine Application Development and Deployment

We present the design and implementation of AppScale, an open source extension to the Google AppEngine (GAE) Platform-as- a-Service (PaaS) cloud technology. Our extensions build upon the GAE SDK to facilitate distributed execution of GAE applications over virtualized cluster resources, including Infrastructure-as-a-Service (IaaS) cloud systems such as Amazon’s AWS/EC2 and Epucalyptus. AppScale provides a framework with which researchers can investigate the interaction between PaaS and IaaS systems as well as the inner workings of, and new technologies for, PaaS cloud technologies using real GAE applications.

Eliminating navigation errors in web applications via model checking and runtime enforcement of navigation state machines

The enforcement of navigation constraints in web applications is challenging and error prone due to the unrestricted use of navigation functions in web browsers. This often leads to navigation errors, producing cryptic messages and exposing information that can be exploited by malicious users. We propose a runtime enforcement mechanism that restricts the control flow of a web application to a state machine model specified by the developer, and use model checking to verify temporal properties on these state machines. Our experiments, performed on three real-world applications, show that 1) our runtime enforcement mechanism incurs negligible overhead under normal circumstances, and can even reduce server processing time in handling unexpected requests; 2) by combining runtime enforcement with model checking, navigation correctness can be efficiently guaranteed in large web applications.

Neptune: a domain specific language for deploying hpc software on cloud platforms

In this paper, we present the design and implementation of Neptune, a domain specific language (DSL) that automates configuration and deployment of existing HPC software via cloud computing platforms. We integrate Neptune into a popular, open-source cloud platform, and extend the platform with support for user-level and automated placement of cloud services and HPC components. Such platform integration of Neptune facilitates hybrid-cloud application execution as well as portability across disparate cloud fabrics. Supporting additional cloud fabrics through a single interface enables high throughput computing (HTC) to be achieved by users who do not necessarily own grid-level resources but do have access to otherwise independent cloud technologies. We evaluate Neptune using different applications that employ a wide range of popular HPC packages for their implementation including MPI, X10, MapReduce, DFSP, and dwSSA. In addition, we show how Neptune can be extended to support other HPC software and application domains, and thus be used as a mechanism for many task computing (MTC).

A Pluggable Autoscaling Service for Open Cloud PaaS Systems

In this paper we present the design, implementation, and evaluation of a plug gable autoscaler within an open cloud platform-as-a-service (PaaS). We redefine high availability (HA) as the dynamic use of virtual machines to keep services available to users, making it a subset of elasticity (the dynamic use of virtual machines). This makes it possible to investigate autoscalers that simultaneously address HA and elasticity. We present and evaluate autoscalers within this plug gable system that are HA-aware and Quality-of-Service (QoS)-aware for web applications written in different programming languages. Hot spares can also be utilized to provide both HA and improve QoS to web users. Within the open source AppScale PaaS, hot spares can increase the amount of web traffic that the QoS-aware autoscaler serves to users by up to 32%. As this auto scaling system operates at the PaaS layer, it is able to control virtual machines and be cost-aware when addressing HA and QoS. This cost awareness uses Spot Instances within Amazon EC2 to reduce the cost of machines acquired by 91%, in exchange for increased startup time. This plug gable auto scaling system facilitates the investigation of new auto scaling algorithms by others that can take advantage of metrics provided by different levels of the cloud stack.

Language and Runtime Support for Automatic Configuration and Deployment of Scientific Computing Software over Cloud Fabrics

In this paper, we present the design and implementation of Neptune, a simple, domain-specific language based on the Ruby programming language. Neptune automates the configuration and deployment of scientific software frameworks over disparate cloud computing systems. Neptune integrates support for MPI, MapReduce, UPC, X10, StochKit, and others. We implement Neptune as a software overlay for the AppScale cloud platform and extend AppScale with support for elasticity and hybrid execution for scientific computing applications. Neptune imposes no overhead on application execution, yet significantly simplifies the application deployment process, enables portability across cloud systems, and promotes lock-in avoidance by specific cloud vendors.

Cloud Platform Datastore Support

There are many datastore systems to choose from that differ in many ways including public versus private cloud support, data management interfaces, programming languages, supported feature sets, fault tolerance, consistency guarantees, configuration, and their deployment processes. In this paper, we focus on technologies for structured data access (database/datastore systems) in cloud systems. Our goal is to simplify the use of datastore systems through automation and to facilitate their empirical evaluation using real world applications. To enable this, we provide a cloud platform abstraction layer that decouples a data access API from its implementation. Applications that use this API can use any datastore that “plugs into” our abstraction layer, thus enabling application portability. We use this layer to extend the functionality of multiple datastores without modifying the datastores directly. Specifically, we provide support for ACID transaction semantics for popular key-value stores (none of which provide this feature). We integrate this layer into the AppScale cloud platform—an open-source cloud platform that executes cloud applications written in Python, Java, and Go, over virtualized cluster resources and infrastructures-as-a-service (Eucalyptus, OpenStack, and Amazon EC2). We use this system to investigate the impact of extending disparate datastores via the application portability layer with distributed transaction support.

Enabling automated HPC / database deployment via the appscale hybrid cloud platform

In this paper, we discuss a prevalent issue facing the HPC community today: the lack of automation in the installation, deployment, and integration of HPC and database software. As a result, scientists today must play a dual role as researchers and as system administrators. The time required for scientists to become proficient with software stacks is significant and has increased with the complexity of modern systems such as cloud-based platforms and infrastructures. However, cloud computing offers many potential benefits to HPC software developers. It facilitates dynamic acquisition of computing and storage resources and access to scalable services. Moreover, cloud platforms such as AppScale abstract away the underlying system and automate deployment and control of supported software and services. As part of this project, we have extended AppScale with domain specific language support called Neptune that gives developers straightforward control over automatic configuration and deployment of cloud applications. Neptune also extends cloud support beyond web-services to HPC applications, components, and libraries. We discuss AppScale and Neptune, and how they can be extended via more intelligent database usage to provide a better solution for the next-generation of cloud-based HPC and data-intensive applications.

Supporting placement and data consistency strategies using hybrid clouds

In this paper, we investigate cloud platform support that provides distributed applications with automatic service placement across different cloud computing systems (hybrid clouds), and that enables application developers to investigate the impact of using different cloud-based data consistency models with their applications. By pursuing its implementation at the cloud runtime layer (platform), we are able to provide hybrid cloud support without requiring application modification or significant developer expertise. We investigate the efficacy of such portabililty for different application domains (web services and computationally-intensive HPC applications, via Monte Carlo simulations). We examine different hybrid cloud placement strategies based on cost, performance, and common use cases. We evaluate the performance of such placement strategies as well as different data consistency policies.