Elastic Compute Cloud Research Articles

A proactive defense method for the stealthy EDoS attacks in a cloud environment

SummaryCloud computing technology provides flexibility to Cloud Service Provider (CSP) for providing the cloud resources based on the users' requirements. In on‐demand pricing model, the attackers exploit this feature and cause unwanted scaling‐up of the cloud resources without any intent to pay for them. The associated cost for the unpaid malicious usage burdens the CSP, and over a long period, economic losses occur at the CSP end. Thus, the resources and services offered by the CSP become unsustainable, and the attack is termed as Economic Denial‐of‐Sustainability (EDoS) attack. The existing defense approaches for EDoS attacks are reactive. Thus, the associated attack detection/mitigation cost is high; consequently, the approaches are not suitable for the Small and Medium Enterprises (SMEs). The aim of this paper is to detect and mitigate, internal and external, stealthy EDoS attacks proactively. The attack is detected using average CPU utilization threshold and utility function (in terms of cost for the utilized cloud computing resources) and mitigated using virtual firewalls. Amazon Elastic Compute Cloud (Amazon EC2) is used to evaluate the performance of the proposed approach. The proposed approach accurately detects the EDoS attack and mitigates its effect as well with reduced cost. It is observed that the approach provides competitive response time, victim service downtime, and attack reporting time. Thus, the overall performance is improved.

Smart Home Automation using ESP8266 and Internet of Things

By means of using the latest technologies such as cloud computing, Internet of Things, embedded systems etc. this paper proposes a working model for smart home automation. The advancement in field of information technology has led to numerous practical applications which can help the humans to lead a better life where regular chores can be handled via wireless communications instead of practical effort. The proposed model is projected as one of the cheapest ways available to automate a home and transform it into a smart home using ESP8266 microcontroller. The model provides a mechanism to remotely monitor and control the home devices by using a user interface which will be interlinked to Amazon Elastic Compute Cloud (EC2). The paper provides a background of the enabling technologies along with the proposed model. The paper concludes by providing the result of implementation and its challenges.

A Framework for Allocating Server Time to Spot and On-Demand Services in Cloud Computing

Cloud computing delivers value to users by facilitating their access to servers at any time period needed. An approach is to provide both on-demand and spot services on shared servers. The former allows users to access servers on demand at a fixed price and users occupy different time periods on servers. The latter allows users to bid for the remaining unoccupied time periods via dynamic pricing; however, without appropriate design, such time periods may be arbitrarily short since on-demand users arrive randomly. This is also the current service model adopted by Amazon Elastic Cloud Compute. In this article, we provide the first integral framework for sharing time on servers between on-demand and spot services while optimally pricing spot service. It guarantees that on-demand users can get served quickly while spot users can stably use servers for a properly long period once accepted, which is a key feature in making both on-demand and spot services accessible. Simulation results show that, by complementing the on-demand market with a spot market, a cloud provider can improve revenue by up to 461.5%. The framework is designed under assumptions that are met in real environments. It is a new tool that other cloud operators can use to quantify the advantage of a hybrid spot and on-demand service, making the case for eventually integrating this service model into their own infrastructures.

Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study.

BackgroundCloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis.ObjectiveThe goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis.MethodsA multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples.ResultsUsing AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools (ie, Quantitative Insights Into Microbial Ecology 2 and RStudio) were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team.ConclusionsBuilding a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis.

Efficient Secure Outsourcing of Large-Scale Convex Separable Programming for Big Data

Big data has become a key basis of innovation and intelligence, potentially making our lives more convenient and bringing new opportunities to the modern society. Towards this goal, a critical underlying task is to solve a series of large-scale fundamental problems. Conducting such large-scale data analytics in a timely manner requires a large amount of computing resources, which may not be available for individuals and small companies in practice. By outsourcing their computations to the cloud, clients can solve such problems in a cost-effective way. However, confidential data stored at the cloud is vulnerable to cyber attacks, and thus needs to be protected. Previous works employ cryptographic techniques like homomorphic encryption, which significantly increase the computational complexity of solving a large-scale problem at the cloud and is impractical for big data applications. For the first time in the literature, we present an efficient secure outsourcing scheme for convex separable programming problems (CSPs). In particular, we first develop efficient matrix and vector transformation schemes only based on arithmetic operations that are computationally indistinguishable both in value and in structure under a chosen-plaintext attack (CPA). Then, we design a secure outsourcing scheme in which the client and the cloud collaboratively solve the transformed problems. The client can efficiently verify the correctness of returned results to prevent any malicious behavior of the cloud. Theoretical correctness and privacy analysis together show that the proposed scheme obtains optimal results and that the cloud cannot learn private information from the client's concealed data. We conduct extensive simulations on Amazon Elastic Cloud Computing (EC2) platform and find that our proposed scheme provides significant time savings to the clients.

Amazon Elastic Compute Cloud (EC2) versus In-House HPC Platform: A Cost Analysis

While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, we observe a wish for the convergence between Cloud Computing (CC) and High Performance Computing (HPC) platforms, with the commercial hope to see Cloud Computing (CC) infrastructures to eventually replace in-house facilities. If we exclude the performance point of view where many previous studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when running a HPC workload, the question of the real cost-effectiveness is often left aside with the intuition that, most probably, the instances offered by the Cloud providers are competitive from a cost point of view. In this article, we wanted to assert (or infirm) this intuition by analyzing what composes the Total Cost of Ownership (TCO) of an in-house HPC facility operated internally since 2007. This Total Cost of Ownership (TCO) model is then used to compare with the induced cost that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. Our approach to address this price comparison is three-fold. First we propose a theoretical price-performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2). Then, based on the HPC facility TCO analysis we propose a hourly price comparison between our in-house cluster and the equivalent EC2 instances. Finally, based on the experimental benchmarking on the local cluster and on the Cloud instances we propose an update of the former theoretical price model to reflect the real system performance. The results obtained advocate in general for the acquisition of an in-house HPC facility, which balances the common intuition in favor of Cloud Computing platforms, would they be provided by the reference Cloud provider worldwide.

SecFact: Secure Large-scale QR and LU Factorizations

We are now in the big data era. Due to the emerging various systems and applications, such as the Internet of Things, cyber-physical systems, smart cities, smart healthcare, we are able to collect more data than ever before. On the other hand, it makes it very difficult to analyze such massive data in order to advance our science and engineering fields. We note that QR and LU factorizations are two of the most fundamental mathematical tools for data analysis. However, conducting QR or LU factorization of an $m\times n$ m × n matrix requires computational complexity of $\mathcal{O}(m^2n)$ O ( m 2 n ) . This incurs a formidable challenge in efficiently analyzing large-scale data sets by normal users or small companies on traditional resource-limited computers. To overcome this limitation, industry and academia propose to employ cloud computing that can offer abundant computing resources. This, however, obviously raises security concerns and hence a lot of users are reluctant to reveal their data to the cloud. To this end, we propose two secure outsourcing algorithms for efficiently performing large-scale QR and LU factorizations, respectively. We implement the proposed algorithms on the Amazon Elastic Compute Cloud (EC2) platform and a laptop. The experiment results show significant time saving for the user.

A One-Penny Imputed Genome from Next-Generation Reference Panels

Genotype imputation is commonly performed in genome-wide association studies because it greatly increases the number of markers that can be tested for association with a trait. In general, one should perform genotype imputation using the largest reference panel that is available because the number of accurately imputed variants increases with reference panel size. However, one impediment to using larger reference panels is the increased computational cost of imputation. We present a new genotype imputation method, Beagle 5.0, which greatly reduces the computational cost of imputation from large reference panels. We compare Beagle 5.0 with Beagle 4.1, Impute4, Minimac3, and Minimac4 using 1000 Genomes Project data, Haplotype Reference Consortium data, and simulated data for 10k, 100k, 1M, and 10M reference samples. All methods produce nearly identical accuracy, but Beagle 5.0 has the lowest computation time and the best scaling of computation time with increasing reference panel size. For 10k, 100k, 1M, and 10M reference samples and 1,000 phased target samples, Beagle 5.0's computation time is 3× (10k), 12× (100k), 43× (1M), and 533× (10M) faster than the fastest alternative method. Cost data from the Amazon Elastic Compute Cloud show that Beagle 5.0 can perform genome-wide imputation from 10M reference samples into 1,000 phased target samples at a cost of less than one US cent per sample.

A Local Approximation Approach for Processing Time-Evolving Graphs

To efficiently process time-evolving graphs where new vertices and edges are inserted over time, an incremental computing model, which processes the newly-constructed graph based on the results of the computation on the outdated graph, is widely adopted in distributed time-evolving graph computing systems. In this paper, we first experimentally study how the results of the graph computation on the local graph structure can approximate the results of the graph computation on the complete graph structure in distributed environments. Then, we develop an optimization approach to reduce the response time in bulk synchronous parallel (BSP)-based incremental computing systems by processing time-evolving graphs on the local graph structure instead of on the complete graph structure. We have evaluated our optimization approach using the graph algorithms single-source shortest path (SSSP) and PageRankon the Amazon Elastic Compute Cloud(EC2), a central part of Amazon.com’s cloud-computing platform, with different scales of graph datasets. The experimental results demonstrate that the local approximation approach can reduce the response time for the SSSP algorithm by 22% and reduce the response time for the PageRank algorithm by 7% on average compared to the existing incremental computing framework of GraphTau.

An MCDM method for cloud service selection using a Markov chain and the best-worst method

Due to the increasing number of cloud services, service selection has become a challenging decision for many organisations. It is even more complicated when cloud users change their preferences based on the requirements and the level of satisfaction of the experienced service. The purpose of this paper is to overcome this drawback and develop a cloud broker architecture for cloud service selection by finding a pattern of the changing priorities of User Preferences (UPs). To do that, a Markov chain is employed to find the pattern. The pattern is then connected to the Quality of Service (QoS) for the available services. A recently proposed Multi Criteria Decision Making (MCDM) method, Best Worst Method (BWM), is used to rank the services. We show that the method outperforms the Analytic Hierarchy Process (AHP). The proposed methodology provides a prioritized list of the services based on the pattern of changing UPs. The methodology is validated through a case study using real QoS performance data of Amazon Elastic Compute (Amazon EC2) cloud services.

Uncovering the Security Implications of Cloud Multi-Tenancy with Bolt

Cloud providers routinely schedule multiple applications per physical host to increase efficiency. The resulting interference on shared resources often leads to performance degradation and, more importantly, security vulnerabilities. Interference can leak important information about an application, ranging from a services placement to confidential data, such as private keys. We present Bolt, a practical system that accurately detects the type and characteristics of applications sharing a cloud platform based on the interference an adversary sees on shared resources. Bolt leverages online data mining techniques that only require 2-5 seconds for detection. In a multi-user study on Amazon Elastic Compute Cloud (EC2), Bolt correctly identifies the characteristics of 385 out of a set of 436 diverse workloads. Extracting this information enables a wide spectrum of previously impractical cloud attacks, including denial of service (DoS) attacks that increase tail latency by 140X, as well as resource freeing attacks (RFAs), and co-residency attacks. Finally, we show that, while advanced isolation mechanisms such as cache partitioning lower detection accuracy, they are insufficient to eliminate these vulnerabilities altogether. To do so, one must either disallow core sharing or allow it only between threads of the same application, leading to significant inefficiencies and performance penalties.

Reducing and Analyzing the PHAT Survey with the Cloud

Abstract We discuss the technical challenges we faced and the techniques we used to overcome them when reducing the Panchromatic Hubble Andromeda Treasury (PHAT) photometric data set on the Amazon Elastic Compute Cloud (EC2). We first describe the architecture of our photometry pipeline, which we found particularly efficient for reducing the data in multiple ways for different purposes. We then describe the features of EC2 that make this architecture both efficient to use and challenging to implement. We describe the techniques we adopted to process our data, and suggest ways these techniques may be improved for those interested in trying such reductions in the future. Finally, we summarize the output photometry data products, which are now hosted publicly in two places in two formats. They are in simple fits tables in the high-level science products on MAST, and on a queryable database available through the NOAO Data Lab.

Advancement in GSM Network to Access Cloud Services

Cloud services are offering a large number of utilities to the mobile users. Mobile users can share, store, develop, compute and many other services on the cloud. Due to extensive utilization of cloud services by the mobile users, security concerns are also evolving with the same pace. Among different security problems, secure access to the cloud services (cloud data utilization, data storage) is also a difficult and challenging task. This paper highlights the security concerns, particularly addresses the issue of secure access to cloud infrastructure, Such as access the cloud services securely by the mobile users. As Elastic Cloud Computing is valid only for Amazon, MLaden model is theoretical based model and not implemented practically, Wayne model enhances the end user security but not proposed tool for practical implementation. Intention of this paper is to propose mechanism to securely access the cloud services using GSM band. Only defined frequency band of GSM will provide the access to the cloud services. Users will be restricted to use the particular frequency to access the cloud services.

Applying big data paradigms to a large scale scientific workflow: Lessons learned and future directions

The increasing amounts of data related to the execution of scientific workflows has raised awareness of their shift towards parallel data-intensive problems. In this paper, we deliver our experience combining the traditional high-performance computing and grid-based approaches with Big Data analytics paradigms, in the context of scientific ensemble workflows. Our goal was to assess and discuss the suitability of such data-oriented mechanisms for production-ready workflows, especially in terms of scalability. We focused on two key elements in the Big Data ecosystem: the data-centric programming model, and the underlying infrastructure that integrates storage and computation in each node. We experimented with a representative MPI-based iterative workflow from the hydrology domain, EnKF-HGS, which we re-implemented using the Spark data analysis framework. We conducted experiments on a local cluster, a private cloud running OpenNebula, and the Amazon Elastic Compute Cloud (AmazonEC2). The results we obtained were analysed to synthesize the lessons we learned from this experience, while discussing promising directions for further research.

Probabilistic Optimization of Resource Distribution and Encryption for Data Storage in the Cloud

In this paper, we develop a decentralized probabilistic method for performance optimization of cloud services. We focus on Infrastructure-as-a-Service where the user is provided with the ability of configuring virtual resources on demand in order to satisfy specific computational requirements. This novel approach is strongly supported by a theoretical framework based on tail probabilities and sample complexity analysis. It allows not only the inclusion of performance metrics for the cloud but the incorporation of security metrics based on cryptographic algorithms for data storage. To the best of the authors’ knowledge this is the first unified approach to provision performance and security on demand subject to the Service Level Agreement between the client and the cloud service provider. The quality of the service is guaranteed given certain values of accuracy and confidence. We present some experimental results using the Amazon Web Services, Amazon Elastic Compute Cloud service to validate our probabilistic optimization method.

Efficient Secure Outsourcing of Large-Scale Sparse Linear Systems of Equations

Solving large-scale sparse linear systems of equations (SLSEs) is one of the most common and fundamental problems in big data, but it is very challenging for resource-limited users. Cloud computing has been proposed as a timely, efficient, and cost-effective way of solving such expensive computing tasks. Nevertheless, one critical concern in cloud computing is data privacy. Specifically, clients’ SLSEs usually contain private information that should remain hidden from the cloud for ethical, legal, or security reasons. Many previous works on secure outsourcing of linear systems of equations (LSEs) have high computational complexity, and do not exploit the sparsity in the LSEs. More importantly, they share a common serious problem, i.e., a huge number of memory I/O operations. This problem has been largely neglected in the past, but in fact is of particular importance and may eventually render those outsourcing schemes impractical. In this paper, we develop an efficient and practical secure outsourcing algorithm for solving large-scale SLSEs, which has low computational and memory I/O complexities and can protect clients’ privacy well. We implement our algorithm on Amazon Elastic Compute Cloud, and find that the proposed algorithm offers significant time savings for the client (up to 74 percent) compared to previous algorithms.

Cost-driven provisioning and execution of a computing-intensive service on the Amazon EC2

The decision of migrating a service to a cloud-based system must take into consideration many different aspects. Among them, economical costs is one of the most important. This paper describes how a computing-intensive service, based on a bag-of-tasks approach, has been migrated from a grid infrastructure to the Amazon Elastic Compute Cloud (EC2) infrastructure. A cost-based model for the evaluation of the economic costs of providing the service on the cloud has also been proposed, which considers computing costs as well as storage and platform deployment costs. The model includes a wide range of different instance types and purchasing policies provided by the Amazon EC2, as well as the deadline and problem size provided by the service user. The paper also shows how the proposed cost-based model is integrated into the framework used for the service deployment and execution, making possible the interaction with the Amazon Web services for hiring the required cloud resources and efficiently use them for the execution of the service requests.

Parallel Secure Outsourcing of Large-scale Nonlinearly Constrained Nonlinear Programming Problems

Nonlinearly constrained nonlinear programming (NLC-NLP) problems arise in various optimal decision-making fields, such as financial engineering, urban planning, supply chain management, and power system control. These real-world NLC-NLP problems are usually large-scale because they need to take into account massive variables and constraints. Solving NLC-NLP problems with common algorithms, e.g., the gradient projection method (GPM), generally incurs very high time complexity. This results in the inevitable inability of solving large-scale NLC-NLP problems by using general-purpose computers within a feasible time. To address this challenge, a cost-effective solution is to adopt cloud computing, but this raises security concerns since real-world NLC-NLP problems always carry sensitive information. Previous secure outsourcing algorithms try to protect sensitive information, but still let cloud service tenants bear heavy computation workload. To fill the gap, we develop a practical secure outsourcing algorithm for solving large-scale NLC-NLP problems with the GPM. To be more prominent, we also parallelize the secure outsourcing algorithm for accelerating computations and avoiding possible memory overflowing. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop, and notice that it can significantly reduce the tenant's computing time even for large-scale NLC-NLP problems.

A New E-learning Model Based on Elastic Cloud Computing for Distance Education

The development of information technology has a positive influence over distance education. For instance, it gives birth to E-learning, a novel and convenient mode of online learning. However, the existing E-learning platforms are often in need of heavy infrastructural investment. To solve the problem, this paper introduces the architecture of elastic cloud computing to E-learning, and proposes a new load balancing algorithm based on elastic cloud computing. Then, it establishes a five-layer elastic cloud-based E-learning model for distance education. The subsequent evaluation demonstrates that the model can effectively respond to the dynamic load imposed on by numerous users, and maximize the revenue of the E-learning platform.

The Great Automatic Grammatizator: writing, labour, computers

What does it mean when we say that computers can ‘write’ and how are recent developments in neural networks and machine learning changing this capacity? This article examines the long-standing literary fear of authorship being replaced by machines while also interrogating the labour and credit implications that sit behind widely used structures of authorship in a technological age. The argument makes reference to one work of computer-generated writing – Johannes Helden & Hakan Jonson’s Evolution [2014] – and to one software paradigm (a character-based recurrent neural networks for language acquisition trained on the corpus of the journal Textual Practice). I here argue that unless we conceive more broadly of the criteria for ‘authorship’ as a labour function, and unless we take seriously the need to see textual production as social production, hybridized (but predominantly) machine identities will come to dominate a literary landscape.