Commercial Cloud Research Articles

Dynamic provisioning with structure inspired selection and limitation of VMs based cost-time efficient workflow scheduling in the cloud

Workflow scheduling in cloud computing environments is nowadays a hot topic as scientific workflows application are gradually taking advantage of commercial cloud assets. Common users’ quality of service (QoS) requirements are the respect of defined budget and deadline when executing their workflow job. Since execution cost minimization and completion time minimization are contradictory objectives, addressing such issue through trade-off function approaches have proved to be an efficient way. This paper presents the Cost-Time Trade-off efficient Workflow Scheduling with Dynamic provisioning (CTTWSDP) algorithm. CTTWSDP relies on dynamic VMs provisioning with a limited number of leased VMs, and a cost-time trade-off function over heterogeneous instances to determine the most viable schedule. CTTWSDP also proposed an improved Implicit Requested Instance Types Range (IRITR) evaluation, which is a scheduling concept introduced in our previous work. The IRITR evaluation aims at determining a range of VMs instance types that best suits the workflow execution, in order to avoid overbidding and underbidding that may lead to budget and deadline violation respectively. The results of simulations prove the effectiveness of the proposal. CTTWSDP achieves a 17.09–76.06% higher success rate when compared to four state-of-the-art algorithms. Furthermore, ANOVA along with Tukey–Kramer post-hoc tests have been conducted, revealing the effectiveness of CTTWSDP over three of the baseline algorithm, while for the fourth one the outperformance of CTTWSDP is not statistically significant. An analysis of the standard deviation of the success rate proves that CTTWSDP is more stable in its performance no matter the type and the workload of the workflow. With a standard deviation of 6.73, smaller than the ones obtained by the other algorithms from 18.66 to 34.10.

Towards Deadline Guaranteed Cloud Storage Services

More and more organizations move their data and workload to commercial cloud storage systems. However, the multiplexing and sharing of the resources in a cloud storage system present unpredictable data access latency to tenants, which may make online data-intensive applications unable to satisfy their deadline requirements. Thus, it is important for cloud storage systems to provide deadline guaranteed services. In this paper, to meet a current form of service level objective (SLO) that constrains the percentage of each tenant’s data access requests failing to meet its required deadline below a given threshold, we build a mathematical model to derive the upper bound of acceptable request arrival rate on each server. We then propose a Deadline Guaranteed storage service (called DGCloud ) that incorporates three basic algorithms. Its deadline-aware load balancing scheme redirects requests and creates replicas to release the excess load of each server beyond the derived upper bound. Its workload consolidation algorithm tries to maximally reduce servers while still satisfying the SLO to maximize the resource utilization. Its data placement optimization algorithm re-schedules the data placement to minimize the transmission cost of data replication. We further propose three enhancement methods to further improve the performance of DGCloud . A dynamic load balancing method allows an overloaded server to quickly offload its excess workload. A data request queue improvement method sets different priorities to the data responses in a server’s queue so that more requests can satisfy the SLO requirement. A wakeup server selection method selects a sleeping server that stores more popular data to wake up, which allows it to handle more data requests. Our trace-driven experiments in simulation and Amazon EC2 show the superior performance of DGCloud compared with previous methods in terms of deadline guarantees and system resource utilization, and the effectiveness of its individual algorithms.

Surviving switch failures in cloud datacenters

Switch failures can hamper access to client services, cause link congestion and blackhole network traffic. In this study, we examine the nature of switch failures in the datacenters of a large commercial cloud provider through the lens of survival theory. We study a cohort of over 180,000 switches with a variety of hardware and software configurations and find that datacenter switches have a 98% likelihood of functioning uninterrupted for over 3 months since deployment in production. However, there is significant heterogeneity in switch survival rates with respect to their hardware and software: the switches of one vendor are twice as likely to fail compared to the others. We attribute the majority of switch failures to hardware impairments and unplanned power losses. We find that the in-house switch operating system, SONiC, boosts the survival likelihood of switches in datacenters by 1% by eliminating switch failures caused by software bugs in vendor switch OSes.

Commercial Cloud Services for a Robust Mobile Application Backend Data Storage

Rapid advancements in the infrastructure of Information and Communication Technology (ICT) have led to radically new but ubiquitous technology; cloud computing. Cloud computing has gracefully emerged offering services that possess on-demand scalability, huge computing power, and a utility-like availability, all at a relatively low cost. It has unsurprisingly become a paradigm shift in ICT, gaining adoptions in all forms of application i.e., personal, academic, business, or government. Not only for its cost-effectiveness but also for its inherent ability to meet business goals and provide strategic ICT resources. More recently there have been advances in cloud computing leading to the evolution of newer commercial cloud services, one of which is the Mobile backend as a Service (MBaaS). The MBaaS is important and required for a robust mobile application back-end data storage and management. Its wide adoption and importance stem from its ability to simplify application development and deployment. Also, MBaaS is robust, with the ability to cope with errors by providing nifty tools and other features. These enable rapid scaffolding of mobile applications. This paper reviews Mobile backend as a Service (MBaaS) and provides required background knowledge on some cloud services and their providers to enable stakeholders to make informed decisions and appropriate choices.

Modeling and verifying a resource allocation algorithm for secure service migration for commercial cloud systems

AbstractCloud computing is the delivery of on‐demand computing resources. It shares the resources or provides vir‐utilization that enables single user to access various Cloud services such as CPU, memory, storage devices, network, and so on. However, more commercial cloud services offered by several cloud service providers (CSPs) are available in the market place. Most CSPs must, therefore, deal with the dynamic resource allocation where the mobile services are migrating from one cloud to another cloud environment to provide heterogeneous resources based on user needs. There is still a lack of heuristics that are able to check requested resources and available resources to allocate and deallocate before it begins the secure service migration. We proposed a resource allocation security protocol that allows resources to be allocated and migrated efficiently in a secure service migration between cloud infrastructures. Furthermore, formal methods can be used for protocols to verify the desired properties, detecting attacks and producing accurate outcomes. This article presents formal modeling and verification of this abstract protocol using ProVerif cryptographic tool to validate the security properties such as secrecy of resources, authentication from both parties and key exchange in order to securely migrate resources in commercial cloud environments.

ParaX

Despite the fact that GPUs and accelerators are more efficient in deep learning (DL), commercial clouds like Facebook and Amazon now heavily use CPUs in DL computation because there are large numbers of CPUs which would otherwise sit idle during off-peak periods. Following the trend, CPU vendors have not only released high-performance many-core CPUs but also developed efficient math kernel libraries. However, current DL platforms cannot scale well to a large number of CPU cores, making many-core CPUs inefficient in DL computation. We analyze the memory access patterns of various layers and identify the root cause of the low scalability, i.e., the per-layer barriers that are implicitly imposed by current platforms which assign one single instance (i.e., one batch of input data) to a CPU. The barriers cause severe memory bandwidth contention and CPU starvation in the access-intensive layers (like activation and BN). This paper presents a novel approach called ParaX, which boosts the performance of DL on many-core CPUs by effectively alleviating bandwidth contention and CPU starvation. Our key idea is to assign one instance to each CPU core instead of to the entire CPU, so as to remove the per-layer barriers on the executions of the many cores. ParaX designs an ultralight scheduling policy which sufficiently overlaps the access-intensive layers with the compute-intensive ones to avoid contention, and proposes a NUMA-aware gradient server mechanism for training which leverages shared memory to substantially reduce the overhead of per-iteration parameter synchronization. We have implemented ParaX on MXNet. Extensive evaluation on a two-NUMA Intel 8280 CPU shows that ParaX significantly improves the training/inference throughput for all tested models (for image recognition and natural language processing) by 1.73X ~ 2.93X.

Transcriptome annotation in the cloud: complexity, best practices, and cost.

The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative provides NIH-funded researchers cost-effective access to commercial cloud providers, such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). These cloud providers represent an alternative for the execution of large computational biology experiments like transcriptome annotation, which is a complex analytical process that requires the interrogation of multiple biological databases with several advanced computational tools. The core components of annotation pipelines published since 2012 are BLAST sequence alignments using annotated databases of both nucleotide or protein sequences almost exclusively with networked on-premises compute systems. We compare multiple BLAST sequence alignments using AWS and GCP. We prepared several Jupyter Notebooks with all the code required to submit computing jobs to the batch system on each cloud provider. We consider the consequence of the number of query transcripts in input files and the effect on cost and processing time. We tested compute instances with 16, 32, and 64 vCPUs on each cloud provider. Four classes of timing results were collected: the total run time, the time for transferring the BLAST databases to the instance local solid-state disk drive, the time to execute the CWL script, and the time for the creation, set-up, and release of an instance. This study aims to establish an estimate of the cost and compute time needed for the execution of multiple BLAST runs in a cloud environment. We demonstrate that public cloud providers are a practical alternative for the execution of advanced computational biology experiments at low cost. Using our cloud recipes, the BLAST alignments required to annotate a transcriptome with ∼500,000 transcripts can be processed in <2 hours with a compute cost of ∼$200-$250. In our opinion, for BLAST-based workflows, the choice of cloud platform is not dependent on the workflow but, rather, on the specific details and requirements of the cloud provider. These choices include the accessibility for institutional use, the technical knowledge required for effective use of the platform services, and the availability of open source frameworks such as APIs to deploy the workflow.

Seamless integration of commercial Clouds with ATLAS Distributed Computing

The CERN ATLAS Experiment successfully uses a worldwide distributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700,000 concurrently running production and analysis jobs to process simulation and detector data. In total more than 500 PB of data are distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing data rate in future LHC runs new developments are underway to embrace industry accepted protocols and technologies, and utilize opportunistic resources in a standard way. This paper reviews how the Google and Amazon Cloud computing services have been seamlessly integrated as a Grid site within PanDA and Rucio. Performance and brief cost evaluations will be discussed. Such setups could offer advanced Cloud tool-sets and provide added value for analysis facilities that are under discussions for LHC Run-4.

CloudBank for Europe

The vast amounts of data generated by scientific research pose enormous challenges for capturing, managing and processing this data. Many trials have been made in different projects (such as HNSciCloud and OCRE), but today, commercial cloud services do not yet play a major role in the production computing environments of the publicly funded research sector in Europe. Funded by the Next Generation Internet programme (NGI-Atlantic) from the EC, in partnership with the University California San Diego (UCSD), CERN is piloting the use of CloudBank in Europe. CloudBank has been developed by the UCSD, University of Washington and University of California, Berkeley with NSF grant support, to provide a set of managed services simplifying access to public cloud for research and education, via a cloud procurement partnership with Strategic Blue, a financial broker SME, specialised in cost management and optimisation. The European NGI experiment is provisioning cloud services from multiple vendors and deploying a series of use-cases in the domain of Machine Learning and HPCaaS, contributing to the scientific programme of the Large Hadron Collider. The main objective is to address technical, financial and legal challenges to determine whether CloudBank can be successfully used by Europe’s research community as part of its global research activity.

A Lightweight Policy Update Scheme for Outsourced Personal Health Records Sharing

With high flexibility and accessibility of data outsourcing environment such as cloud computing environment, several healthcare providers implement electronic personal health records (PHRs) to enable individual patients to manage their own health data in such resilient and scalable environment. However, PHRs contain highly sensitive information of which the security and privacy issues are the critical concern. Besides, PHRs owners should be capable to flexibly and securely define their own access policy for their outsourced data. In addition to the basic authentication feature, existing commercial cloud platforms usually provide symmetric or public key encryption as an optional feature to support data confidentiality for their tenants. However, such traditional encryption schemes are not suitable for data outsourcing environment because of high key management overhead of symmetric encryption and high maintenance cost for handling multiple copies of ciphertext for public key encryption solution. In this paper, we design and develop a secure and fine-grained access control scheme with lightweight access policy update for outsourced PHRs. Our proposed scheme is based on the ciphertext policy attribute-based encryption (CP-ABE) and proxy re-encryption (PRE). In addition, we introduce a policy versioning technique to support the full traceability of policy changes. Finally, we conducted the performance evaluation to demonstrate the efficiency of the proposed scheme.

Cost-enabled QoS aware task scheduling in the cloud management system

Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms.

Commercial Cloud Computing for Connected Vehicle Applications in Transportation Cyberphysical Systems: A Case Study

This article focuses on the feasibility of commercial cloud services for connected vehicle (CV) applications in a transportation cyberphysical systems (TCPS) environment. TCPS implies that CVs, in addition to being connected with each other, communicate with the transportation and computing infrastructure to fulfill application requirements. The motivation of this study is to accelerate commercial cloud-based CV application development by presenting the lessons learned from implementing a CV mobility application using Amazon Web Services (AWS). The feasibility of our cloud-based CV application is assessed at three levels: 1) the development of a cloud-based TCPS architecture, 2) the deployment of a cloud-based CV application using AWS, and 3) the evaluation of the cloud-based CV application. We implemented this CV mobility application using a serverless cloud architecture and found that such a cloud-based TCPS environment could meet the permissible delay limits of CV mobility applications. As the CV penetration levels on the surface transportation systems increase significantly over the next several years, this article illustrates how the commercial cloud could automatically scale the backend infrastructure to meet the rapidly increasing demand of real-world CV applications. Through real-world experiments, we demonstrated how commercial cloud services, along with a serverless cloud architecture, can advance the transportation digital infrastructure for supporting CV mobility applications in a TCPS environment. In addition, to enable other researchers to recreate the serverless cloud computing environment in AWS efficiently and to further contribute to commercial cloud utilization for developing CV applications, an AWS CloudFormation template related to our CV mobility application is published in an open source data management platform and is referred to in this article.

Identifiers.org: Compact Identifier services in the cloud.

MotivationSince its launch in 2010, Identifiers.org has become an important tool for the annotation and cross-referencing of Life Science data. In 2016, we established the Compact Identifier (CID) scheme (prefix: accession) to generate globally unique identifiers for data resources using their locally assigned accession identifiers. Since then, we have developed and improved services to support the growing need to create, reference and resolve CIDs, in systems ranging from human readable text to cloud-based e-infrastructures, by providing high availability and low-latency cloud-based services, backed by a high-quality, manually curated resource.ResultsWe describe a set of services that can be used to construct and resolve CIDs in Life Sciences and beyond. We have developed a new front end for accessing the Identifiers.org registry data and APIs to simplify integration of Identifiers.org CID services with third-party applications. We have also deployed the new Identifiers.org infrastructure in a commercial cloud environment, bringing our services closer to the data.Availabilityand implementationhttps://identifiers.org.

Hierarchical Coded Matrix Multiplication

In distributed computing systems slow working nodes, known as stragglers, can greatly extend finishing times. Coded computing is a technique that enables straggler-resistant computation. Most coded computing techniques presented to date provide robustness by ensuring that the time to finish depends only on a set of the fastest nodes. However, while stragglers do compute less work than non-stragglers, in real-world commercial cloud computing systems (e.g., Amazon’s Elastic Compute Cloud (EC2)) the distinction is often a soft one. In this paper, we develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hierarchical</i> coded computing that exploits the work completed by all nodes, both fast and slow, automatically integrating the potential contribution of each. We first present a conceptual framework to represent the division of work amongst nodes in coded matrix multiplication as a cuboid partitioning problem. This framework allows us to unify existing methods and motivates new techniques. We then develop three methods of hierarchical coded computing that we term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bit-interleaved</i> coded computation (BICC), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multilevel</i> coded computation (MLCC), and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hybrid</i> hierarchical coded computation (HHCC). In this paradigm, each worker is tasked with completing a sequence (a hierarchy) of ordered subtasks. The sequence of subtasks, and the complexity of each, is designed so that partial work completed by stragglers can be used, rather than ignored. We note that our methods can be used in conjunction with any coded computing method. We illustrate this by showing how we can use our methods to accelerate all previously developed coded computing techniques by enabling them to exploit stragglers. Under a widely studied statistical model of completion time, our approach realizes a 66% improvement in the expected finishing time. On Amazon EC2, the gain was 27% when stragglers are simulated.

Are Federated Cloud Sharing Systems Sustainable?: On Dynamic Sharing Markets and Their Stability

The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem faced by the SCs due to their relatively scarce resources might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the federated sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise. Based on this idea, a recent effort proposed SC-Share, a performance-driven static market model for competitive small cloud environments that results in an efficient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important question with a non-obvious answer still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run - is it still possible to achieve a stable market efficient state when the supply of SC resources is dynamic in nature?. In this article, we take a first step to addressing the problem of efficient market design for single SC resource sharing in dynamic environments. We answer our previous question in the affirmative through the use of Arrow and Hurwicz's disequilibrium process in economics, and the gradient play technique in game theory that allows us to iteratively converge upon efficient and stable market equilibria.

Abstract IA24: A modern data commons approach to advance population science

Abstract Since 2014, NCI has been building data science platforms to integrate genetic information about tumors with data on how patients respond to therapy. This work advances the 2011 Institute of Medicine recommendation to build a unified system to collect, integrate, and share cancer data from the widest possible set of research studies. NCI is working to fulfill this recommendation by establishing an NCI Cancer Research Data Commons (CRDC), virtual, expandable informatics infrastructure and data repositories, which are designed to support the work of the NCI intramural and extramural cancer research communities. The CRDC is a crucial component of a broader National Cancer Data Ecosystem that spans discovery research, patient participation, and population surveillance. The development of such an ecosystem was a key recommendation of the Cancer Moonshot Blue Ribbon Panel. NCI has been developing the Cancer Research Data Commons based on the understanding that big data emerging from research programs, precision medicine trials, and surveillance can be used to develop and inform models that predict how patients will response to treatment. Numerous NCI-funded programs will provide key data for this ecosystem, including a variety of discovery programs, clinical trials, cohort studies, and NCI’s Surveillance, Epidemiology, and End Results program (SEER), which is the most comprehensive source of population-based information in the country that includes stage of cancer, detailed characterization of the cancer including important biomarkers at the time of diagnosis, and performs active follow-up to determine patient survival data. The CRDC is designed to be as flexible as possible so that many types and sources of data can be accessed in a unified, interoperable manner. It is this data interoperability that facilitates novel insights into cancer biology and treatment. During the past several years, NCI has been developing key building blocks that comprise the CRDC. These building blocks include (i) Data Commons Nodes, which are repositories that house harmonized data from multiple programs; (ii) Cloud Resources, which provide workspaces, analysis capabilities, and access to elastic computing resources in commercial cloud environments; (iii) a Data Commons Framework (DCF), which is a set of modular, reusable services that maximize interoperability across cancer datatypes and repositories, increase sustainability, and reduce future resource cost; and (iv) semantics services to support data models, vocabularies, ontologies, data submission, and cross-domain queries. The modular design of the CRDC means that new services and components can be added flexibly, which allows for new data sources and new technologies to become integrated as they become available. The NCI Cancer Research Data Commons will serve as a platform for understanding fundamental cancer biology questions, including cancer initiation, progression, and recurrence, in ways that support new prevention, treatment, and surveillance strategies. Citation Format: Anthony Kerlavage. A modern data commons approach to advance population science [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA24.

PODPAC: open-source Python software for enabling harmonized, plug-and-play processing of disparate earth observation data sets and seamless transition onto the serverless cloud by earth scientists

In this paper, we present the Pipeline for Observational Data Processing, Analysis, and Collaboration (PODPAC) software. PODPAC is an open-source Python library designed to enable widespread exploitation of NASA earth science data by enabling multi-scale and multi-windowed access, exploration, and integration of available earth science datasets to support analysis and analytics; automatic accounting for geospatial data formats, projections, and resolutions; simplified implementation and parallelization of geospatial data processing routines; standardized sharing of data and algorithms; and seamless transition of algorithms and data products from local development to distributed, serverless processing on commercial cloud computing environments. We describe the key elements of PODPAC’s architecture, including Nodes for unified encapsulation of disparate scientific data sources; Algorithms for plug-and-play processing and harmonization of multiple data source Nodes; and Lambda functions for serverless execution and sharing of new data products via the cloud. We provide an overview of our open-source code implementation and testing process for development and deployment of PODPAC. We describe our interactive, JupyterLab-based end-user documentation including quick-start examples and detailed use case studies. We conclude with examples of PODPAC’s application to: encapsulate data sources available on Amazon Web Services (AWS) Open Data repository; harmonize processing of multiple earth science data sets for downscaling of NASA Soil Moisture Active Passive (SMAP) soil moisture data; and deploy a serverless SMAP-based drought monitoring application for use access from mobile devices. We postulate that PODPAC will also be an effective tool for wrangling and standardizing massive earth science data sets for use in model training for machine learning applications.

Blockchain-based public auditing and secure deduplication with fair arbitration

Data auditing enables data owners to verify the integrity of their sensitive data stored at an untrusted cloud without retrieving them. This feature has been widely adopted by commercial cloud storage. However, the existing approaches still have some drawbacks. On the one hand, the existing schemes have a defect of fair arbitration, i.e., existing auditing schemes lack an effective method to punish the malicious cloud service provider (CSP) and compensate users whose data integrity is destroyed. On the other hand, a CSP may store redundant and repetitive data. These redundant data inevitably increase management overhead and computational cost during the whole data life cycle. To address these challenges, we propose a blockchain-based public auditing and secure deduplication scheme with fair arbitration. By using a smart contract, our scheme supports automatic penalization of the malicious CSP and compensates users whose data integrity is damaged. Moreover, our scheme introduces a message-locked encryption algorithm and removes the random masking in data auditing. Compared with the existing schemes, our scheme can effectively reduce the computational cost of tag verification and data storage costs. We give a comprehensive analysis to demonstrate the correctness of the proposed scheme in terms of storage, batch auditing, and data consistency. Also, extensive experiments conducted on the platform of Ethereum blockchain demonstrate the efficiency and effectiveness of our scheme.

An Observable Network Route Support on Interpretation of Cloud Computing

The Commercial cloud computing is appropriate conventional and funding agencies beyond prototyping, and initiated fund Production exercise. An important feature of any technical computing Program is moving production data, inward and outward. By means of the virtual machine performance and cost relatively glowing assumed, Network performance and cost is not. This article provides an authentication in the regions of Amazon Web Services, Microsoft Azure network and between Google clouds platforms, cloud both resources and major DTNs In research platform in the Pacific, including the Federation of OSG data cache Network backbone, cloud inside their own. This article contains both qualitative results of the analysis, as well as latency and throughput measuring. It also includes analysis of the cost of contribution Cloud Based on the network.

고속 디지털 시스템 채널의 신호 무결성 성능 분석을 위한 웹기반 서비스 플랫폼

In general, frequency domain and time domain signal integrity characteristics include frequency domain S-parameters, time domain reflectometry(TDR), time domain transmission(TDT) and Eye-Diagram. In this paper, an web-based simulation environment(WSE) which is implemented for analyzing frequency domain and time domain signal integrity characteristics of high-speed digital system channel is introduced. The WSE can be utilized for channel simulation in low cost and reduced time, and in this platform users can also share the engineering web report page link which shows the performance results calculated by the computing machine on the commercial cloud service. By using this WSE, users can communicate with the design engineers and share the design comments verifying the standard channel’s electric specifications.