Amazon EC2 Service Research Articles

Contextual contracts for component‐oriented resource abstraction in a cloud of high performance computing services

SummaryEfforts to support high performance computing (HPC) applications' requirements in the context of cloud computing have motivated us to design HPC Shelf, a cloud computing services platform to build and deploy large‐scale parallel computing systems. We introduce Alite, the contextual contract system of HPC Shelf, to select component implementations according to requirements of the host application, target parallel computing platform characteristics (e.g., clusters and MPPs), quality of service (QoS) properties, and cost restrictions. It is evaluated through a small‐scale case study employing two complementary component‐based frameworks. The first one aims to represent components that implement linear algebra computations based on the BLAS interface. In turn, the second one aims to represent parallel computing platforms on the IaaS cloud offered by Amazon EC2 Service.

Anytime Monte Carlo

Abstract Monte Carlo algorithms simulates some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose a real-time budget on the computation, which results in the number of samples simulated being random. To complicate matters, the real time taken for each simulation may depend on the sample produced, so that the samples themselves are not independent of their number, and a length bias with respect to compute time is apparent. This is especially problematic when a Markov chain Monte Carlo (MCMC) algorithm is used and the final state of the Markov chain—rather than an average over all states—is required, which is the case in parallel tempering implementations of MCMC. The length bias does not diminish with the compute budget in this case. It also occurs in sequential Monte Carlo (SMC) algorithms, which is the focus of this paper. We propose an anytime framework to address the concern, using a continuous-time Markov jump process to study the progress of the computation in real time. We first show that for any MCMC algorithm, the length bias of the final state’s distribution due to the imposed real-time computing budget can be eliminated by using a multiple chain construction. The utility of this construction is then demonstrated on a large-scale SMC $ {}^2 $ implementation, using four billion particles distributed across a cluster of 128 graphics processing units on the Amazon EC2 service. The anytime framework imposes a real-time budget on the MCMC move steps within the SMC $ {}^2 $ algorithm, ensuring that all processors are simultaneously ready for the resampling step, demonstrably reducing idleness to due waiting times and providing substantial control over the total compute budget.

Game-Theoretic Resource Pricing and Provisioning Strategies in Cloud Systems

We consider several Software as a Service (SaaS) providers that offer services using the Cloud resources provided by an Infrastructure as a Service (IaaS) provider which adopts a pay-per-use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances. For this scenario, we study the virtual machine provisioning and spot pricing strategies. We consider a two-stage provisioning scheme. In the first stage, the SaaS providers determine the optimal number of required flat and on demand instances. Then, in the second stage, the IaaS provider sells its unused capacity as spot instances for which the SaaS providers compete by submitting a bid. We study two different IaaS provider pricing strategies: the first assumes the IaaS provider sets a unique price; in the second, instead, the IaaS provider can set different prices for different customers. We model the resulting problem as a Stackelberg game. For each pricing scheme, we show the existence of the game equilibrium and provide the solution algorithms. Through numerical evaluation we compare the provisioning and spot price under the two different pricing strategies as function of the system parameters.

Parasite cloud service providers: on-demand prices on top of spot prices

On-demand resource provisioning and elasticity are two of the main characteristics of the cloud computing paradigm. As a result, the load on a cloud service provider (CSP) is not fixed and almost always a number of its physical resources are not used, called spare resources. As the CSPs typically don't want to be overprovisioned at any time, they procure physical resources in accordance to a pessimistic forecast of their loads and this leads to a large amount of spare resources most of the time. Some CSPs rent their spare resources with a lower price called the spot price, which varies over time with respect to the market or the internal state of the CSP. In this paper, we assume the spot price to be a function of the CSP's load. We introduce the concept of a parasite CSP, which rents spare resources from several CSPs simultaneously with spot prices and rents them to its customers with an on-demand price lower than the host CSPs' on-demand prices. We propose the overall architecture and interaction model of the parasite CSP. Mathematical analysis has been made to calculate the amount of spare resources of the host CSPs, the amount of resources that the parasite CSP can rent (its virtual capacity) as well as the probability of SLA violations. We evaluate our analysis over pricing data gathered from Amazon EC2 services. The results show that if the parasite CSP relies on several host CSPs, its virtual capacity can be considerable and the expected penalty due to SLA violation is acceptably low.

Security and Safety in Amazon EC2 Service – A Research on EC2 Service AMIs

There are some multinational companies available in the market to provide cloud services such as Amazon Web Services, Microsoft Azure, and IBM Smart Cloud and so on. Nowadays an organization need to work on different technologies, it need not to install the technologies, it can simply acquire the technology available in online as a service.It is the best practice in the cloud based services that it allows the users to make their own exceptional unprecedented virtual images and share them to with various customers in a comparative cloud. Close to these customer shared virtual pictures, the cloud serviceproviders will in like manner give the virtual pictures that have been preconfigured with open source database and web server to orchestrate our stray pieces. In this paper, we had made an examination to check the general security risks related with the usage of virtual machine pictures from the uninhibitedly available inventories of cloud master affiliations. In adjusted, we had managed the open standard virtual pictures that are existed on the Amazon EC2 association. We analyzed the security issues of the virtual pictures which are available on the Amazon EC2 Cluster as the open AMI (Amazon Machine Images).

Cost-Performance Modeling with Automated Benchmarking on Elastic Computing Clouds

The importance of evaluating performance of cloud systems has been increasing with the rapid growing market demands for cloud computing. However, the performance testers often have to go through the hassle of tedious manual operations when interacting with the cloud. A cloud performance evaluation framework is designed for both broad cloud support and good workload extensibility, which provides an automatic interface to monitor the capability and scalability of Infrastructure-as-a-Service cloud systems. Cloud API modules are implemented for Amazon EC2 service and OpenStack. It can achieve flexible control workflows for multiple of different workloads and user customization to test scenarios. With several built-in workloads and metric aggregation methods, a series of tests is performed on our private clouds to compare the performance and scalability from multiple aspects. A methodology is also proposed to build a cost-performance model to better understand and analyze the efficiency of different types of cloud systems. Based on the results of the experiments, the model indicates a polynomial relation between performance per instance and the overall cost.

The Feasibility of Amazon's Cloud Computing Platform for Parallel, GPU-Accelerated, Multiphase-Flow Simulations

The feasibility of running MPI-parallel, GPU-accelerated, multiphase flow simulations on Amazon's Elastic Compute Cloud (EC2) service is evaluated as an alternative computational resource. A cloud cluster on Amazon EC2 is compared to a conventional local high-performance computing cluster in terms of performance and cost. The steps necessary to set up a cloud cluster and acquire the appropriate hardware and software stacks are outlined. The incompressible multiphase flow solver is benchmarked on both cloud and local clusters by performing strong and weak scaling analyses. Amazon's EC2 service is competitive with the local cluster in a certain range of simulations, but there are some performance limitations, particularly in its GPU card performance and cluster network connection, which negatively impact the parallel simulations presented herein. Finally, Amazon's EC2 service is studied from an economic perspective and compared with a conventional local cluster.

Software security requirements management as an emerging cloud computing service

Emerging cloud applications are growing rapidly and the need for identifying and managing service requirements is also highly important and critical at present. Software Engineering and Information Systems has established techniques, methods and technology over two decades to help achieve cloud service requirements, design, development, and testing. However, due to the lack of understanding of software security vulnerabilities that should have been identified and managed during the requirements engineering phase, we have not been so successful in applying software engineering, information management, and requirements management principles that have been established for the past at least 25 years, when developing secure software systems. Therefore, software security cannot just be added after a system has been built and delivered to customers as seen in today’s software applications. This paper provides concise methods, techniques, and best practice requirements engineering and management as an emerging cloud service (SSREMaaES) and also provides guidelines on software security as a service. This paper also discusses an Integrated-Secure SDLC model (IS-SDLC), which will benefit practitioners, researchers, learners, and educators. This paper illustrates our approach for a large cloud system Amazon EC2 service.