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000-551 exam Dumps Source : IBM Optim Implementation for Distributed Systems (2009)
Test Code : 000-551
Test appellation : IBM Optim Implementation for Distributed Systems (2009)
Vendor appellation : IBM
: 122 actual Questions
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IBM IBM Optim Implementation for
IBM influence solutions and Agile strategy for a expeditiously SAP S/4HANA® deployment
GENEVA and ARMONK, N.Y., Feb. 20, 2019 /PRNewswire/ -- COFCO international (CIL) has chosen IBM (NYSE: IBM) to combine and digitally transform its company methods with the implementation of a modern SAP platform, including SAP S/4HANA®, SAP® Ariba®, SAP® SuccessFactors® and SAP grasp facts Governance.
COFCO international, the overseas agriculture enterprise platform for COFCO organisation, China's greatest food and agriculture enterprise, has selected IBM to combine and digitally seriously change its enterprise approaches with the implementation of a brand modern SAP platform.
CIL is the remote places agriculture commerce platform for COFCO agency, China's largest food and agriculture company. With $34B in annual income and over 100 million lots shipped in 2017, CIL has straight away become a leading, global agribusiness.
Standardizing processes throughout CIL's monetary, procurement, and construction actions will enable the company to integrate its enterprise operations with other notable activities similar to its core commodity buying and selling device. This digital transformation will deliver CIL with the platform it must additional develop its agribusiness.
Over a two-yr period, CIL and IBM will accomplice to design and test a worldwide template, pilot the answer, after which roll it out to over 30 international locations. IBM functions will set up IBM's impact solutions and Agile processes, utilising probably the most superior set of pre-configured options for SAP S/4HANA and SAP Ariba accessible in the trade to jumpstart the design side and accelerate implementation. With SAP Ariba, CIL will observe a expeditiously transition from legacy, analogue procurement, to digital procedures, more suitable stakeholder experiences, enhanced possibility administration, and decreased costs.
"IBM capabilities will advocate us hastily digitally transform their enterprise, standardize and simplify their processes, and vertically integrate their operations," referred to Mr. Andre Schneiter COFCO overseas CIO.
"IBM is extremely joyful to find a artery to advocate COFCO outlandish achieve their vision of standardized end-to-end procedures throughout their operations. With their possess an outcome on solution, they will birthright now advocate to construct a good, built-in provide chain and matching back wait enterprise strategies as a artery to give COFCO overseas's leadership visibility and handle throughout their complete supply chain," mentioned Cathy Rogerson, IBM functions vice president for the CPG, Agribusiness and Retail industries in Europe.
ABOUT COFCO international LTD.
With 12,000 people in 35 nations, COFCO overseas is the outlandish places agriculture enterprise platform for COFCO employer, China's largest meals and agriculture company. COFCO outlandish is focused on being a leader in the international grains, oilseeds and sugar provide chains, with assets across the Americas, Europe and Asia-Pacific. The commerce trades with over 50 international locations, while featuring farmers exciting direct access to the becoming chinese market. In 2017, COFCO international dealt with over one hundred million tonnes of connected commodities with revenues of $34bn. The commerce is accelerating its growth to create an international-class built-in global agriculture supply chain, anchored in China and competing globally.
For greater, discuss with: www.cofcointernational.com
For further tips on IBM gratify consult with www.ibm.com/capabilities
SAP, SAP S/4HANA and other SAP products and features mentioned herein as well as their respective trademarks are logos or registered emblems of SAP SE (or an SAP affiliate enterprise) in Germany and other international locations. sum other product and service names outlined are the trademarks of their respective organizations. gratify see http://www.sap.com/trademark for further trademark counsel and notices.
On the kisser of it the query would look clearly amenable to the adage that for any headline with a query outcome in it the reply is no. but a yoke of months ago at a conference i used to subsist speakme to somebody steeped on earth of open supply standardisation, having been a allotment of the Node.js ecosystem for years and he referred to:
“For the primary time in a long long term i am fearful of IBM.”
For these of you too puerile to abide in intellect the olden days IBM became the Google of its day, the Microsoft of its day, the AWS of its day. because the asserting went: “IBM isn’t the competition, it’s the atmosphere through which you compete.” IBM turned into completely preeminent in facts core computing over a *long* length of time, unless a combination of antitrust legislations, industry structural adjustments, and administration error pressured the commerce on to the back foot. The tenure of Louis Gerstner at the helm of IBM became slightly relish Satya Nadella’s circle at Microsoft – with a renaissance in keeping with open requirements, open source, and being an excellent group associate. by the point I entered the trade IBM was nevertheless a powerful player, however not the monster of historic. It nevertheless had great possess an impact on over enterprise choice making. If IBM mentioned a know-how became capable for the enterprise then by definition it was. IBM made Java an commercial enterprise normal. IBM made Linux an commerce ordinary. IBM made open supply a sensible commercial enterprise determination. IBM didn’t simply hearten clients to adopt these applied sciences although – it demanded impact, a seat on the requisites-atmosphere table, with the aid of contributing developer hours, cash, and possess an outcome on. The Apache utility groundwork. The Eclipse basis. Latterly the Linux foundation. IBM did greater than any individual to establish open source implementation as the modern trend of industry specifications setting. IBM’s strike continues to subsist very massive in sum of these open supply foundations, placing its weight at the back of the Linux basis’s evolution to additionally manage the Node.js foundation and The Cloud endemic Computing groundwork.
So here they are in 2019, IBM nevertheless has that seat at the table, and now it’s acquiring red Hat, which furthermore had a great footprint in these communities. The deal to buy crimson Hat is exactly what made the adult above afraid. Java, Linux, Node.js and Kubernetes – IBM has a powerful play in sum of those communities. while Java and Linux are very plenty mature commodity technologies, Node continues to subsist establishing itself as an trade general. commercial enterprise adoption of Kubernetes, and associated application initiatives corresponding to Prometheus and Grafana, meanwhile, goes to silhouette the commerce over the next few years. IBM + pink Hat is an incredibly powerful possess an outcome on bloc.
my very own select is that, particularly as many in the industrial open source seller house worry the position of net infrastructure agencies, notably Amazon web functions, the influence of IBM may soundless doubtless subsist welcomed. recently at IBM’s suppose adventure my colleague Stephen O’Grady seemed on stage with IBM CEO and Chairman Ginny Rometty talking about the ongoing notable of open supply to IBM and the industry. The red Hat deal is profession defining for her, and should definitely subsist hugely influential in industry instructions for the near future. observe the chart above from a post by artery of Tomasz Tunguz at Redpoint Ventures for a artery of the scale of wager being made birthright here. As Tunguz argues:
purple Hat presents three corporations of utility items: working system (Linux and virtualization); utility construction (application server/JBoss); modern Infrastructure (OpenShift, OpenStack and Ansible). income contribution across these classes is sixty four%, 23% and 13%. The core infrastructure (working gadget and app construction) are actually awesome organizations, starting to subsist between 10-25% yearly. the brand modern Infrastructure is the quickest growing, doubling year over 12 months.
I respect purple Hat’s OpenShift platform as the chief in commercial enterprise platform-as-a-carrier. OpenShift offers a modern pile ambiance for software engineers. OpenShift simplifies the deployment and administration of advanced applied sciences relish Kubernetes for massive firms.
So IBM has a bizarre protecting/self-cannibalisation possibility round Websphere, a growth commerce in Linux, but the precise kicker for extend is red Hat OpenShift, as they paddle into what I actually possess portray as a secular shift round Kubernetes. actually every commerce they talk is looking at Kubernetes as the groundwork for a massive infrastructure overhaul – everybody runs K8s or goes to. Google remains the principal contributor to Kubernetes, but purple Hat has executed greater than any person else to even the load.
So execute individuals possess judgement to subsist anxious? i am relatively confident in the management of IBM individuals relish Todd Moore, VP of Open expertise, and their capability to execute the usurp issue for the trade as an entire, nonetheless it changed into nonetheless basically pleasing to hear somebody from the trade sing they were now terrified of IBM, on account of its dominance in a number of censorious industry necessities. It’s been a very long time given that that turned into the case.
additional analyzing – my boy Stephen wrote a extremely extremely considerable submit about the IBM pink Hat consolidation play.
full disclosure: AWS, IBM, red Hat, Microsoft and Google are sum purchasers.
publish Views: 744
(study this and other bizarre posts @ RedMonk)
See the total record of highest attribute server virtualization utility.
IBM PowerVM can virtualize AIX, IBM Linux, and IBM i shoppers working on its power server platform. certainly, it is without doubt one of the most complete featured virtulization courses in the marketplace – no surprise, given IBM's deep legacy in the facts core.
nevertheless it may now not subsist the simplest platform to do into effect. it is going to require consultants to installation it. consequently, mid-sized and massive companies should soundless execute first-class, however SMBs may subsist most reliable to steer transparent of it except they can find the money for outside assist. IBM PowerVM is geared notably for modern-day advanced facts facilities with disturbing application workloads.
IBM PowerVM can consolidate assorted workloads onto fewer programs, increasing server utilization and reducing cost. PowerVM gives a relaxed and scalable server virtualization ambiance for AIX, IBM i and Linux purposes built upon the RAS facets of the vigour programs platform. in brief, its hypervisor is rather bendy. it could assist provide services within the cloud faster by means of automating deployment of VMs and storage. it may well furthermore aid dispose of downtime via are alive mobility between servers.
PowerVM 2.2.6 delivers enterprise-grade virtualization, presenting the basis for cloud computing on IBM energy systems. it might probably efficaciously partake supplies among purposes, consolidate several workloads, and supply the utility mobility in a multi-cloud infrastructure. It is asserted to raise useful resource utilization, gash back operating charges, and provide a extra agile ambiance for IBM AIX, IBM i, and IBM Linux functions running on energy systems.
in the most contemporaneous free up, IBM has extra tightly built-in PowerVM with the vigour platform. every POWER9 server comes with POWERVM commerce version. there is additionally a typical version as well as an IBM PowerVM, Linux edition. PowerVM customary edition comprises here components:
N-Port identification Virtualization (NPIV)
Partition suspend and resume is supported on POWER8 processor-based mostly servers when the firmware is at degree eight.4.0, or later.
Shared processor pools
Shared storage pools
Single Root I/O Virtualization (SR-IOV)
digital I/O Server (VIOS)
digital community Interface Controller adapters
“It has been very legitimate with little to no downtime. we've been capable of stretch their IT greenbacks since the refresh rate on IBM energy can flee for years. additionally, they now possess been capable of add many more VMs to actual machines than other structures can run,” talked about a information hub manager in manufacturing.
AIX, Linux and IBM i shoppers
“Our commerce utilizes VMware and PowerVM. VMware is user pleasant and makes aiding windows OS simpler. PowerVM is relocating in that direction. PowerVM is stronger in that you should prioritize workloads throughout several VMs and subsist granular on your reservation of cores and digital CPUs. PowerVM permits you to regulate VM features whereas the VM is up and operating,” spoke of a materiel Admin in oil & fuel.
PowerVM is a application down load.
up to a thousand VMs on a separate server.
10% to fifteen%
administration materiel corresponding to Hardware administration Console (HMC), integrated Virtualization manager (IVM), and PowerVC aid to combination and manipulate elements by using a consolidated ratiocinative view. you could designate processors to partitions in increments of 0.01, which allows for dissimilar partitions to partake the processing vigour of the system. When the firmware is at stage 7.6, or later, micropartitions can subsist described as little as 0.05 of a processor and may subsist modified in increments as little as 0.01 of a processor. A maximum of 20 micropartitions can furthermore subsist created per core.
A operating AIX, Linux, or IBM i ratiocinative partition will furthermore subsist suspended together with its working gadget and functions. that you could partake recollection among partitions in a shared recollection pool, through the utilize of PowerVM active recollection Sharing. energy Virtualization performance (PowerVP) is a efficiency monitoring solution that gives specified and actual-time suggestions about virtualized workloads which are working on verve systems. you could utilize PowerVP to withhold in intellect how digital workloads utilize components, to research efficiency bottlenecks, and to obtain recommended decisions about aid allocation and virtualized computer placement.
provided through other IBM verve equipment.
that you could migrate an energetic or inert AIX, Linux, or IBM i ratiocinative partition from one device to an additional through the utilize of are alive Partition Mobility.
vigour programs supply a secured server platform. POWER9 hardware and firmware obtain it even more relaxed for cloud deployment with key facets for PowerVM servers. Implementation comprises:
A cozy IPL system or comfy Boot which most effective allows for platform company signed Hostboot and vigour Hypervisor (PHYP) linked firmware up via and together with Partition Firmware (PFW) to flee on the device.
A framework to aid far flung Attestation of the materiel firmware stack via a hardware depended on Platform Module (TPM).
Virtualization for AIX, Linux and IBM i customers running IBM vigour systems.
“It may well subsist over engineered for smaller applications. besides the fact that children, if the infrastructure is in location which you could utilize it to flee Linux VMs as smartly,” famed a device Admin in Oil & fuel
beginning at $590 per core, free with every other IBM products.
AIX, Linux and IBM i customers
one thousand VMs on a separate server
10 to 15
Virtualization for AIX, Linux and IBM i shoppers working IBM vigour platforms
$590 per core
flow active or inert VMs
most suitable for IBM environments
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March 31, 2009 10:02 ET
SYDNEY, AUSTRALIA--(Marketwire - March 31, 2009) - EnergyAustralia, Australia's largest electricity distribution network, today announced an agreement with IBM (NYSE: IBM) for the implementation of an energy network monitoring and control solution.
A key project within EnergyAustralia's overall bright network program, the Distribution Monitoring and Control (DM&C) project involves the roll-out of 12,000 sensing devices throughout the electricity distribution network, creating a smart grid. The project will enable EnergyAustralia to deliver energy more efficiently and reliably, and allow a greater number and compass of environmental solutions to subsist integrated into the electricity network such as renewable energy.
Under the agreement signed in the first quarter of this year, IBM will design and build the system IT architecture to advocate the project, in which sensing devices will connect with EnergyAustralia's operational systems using a combination of fourth generation and existing technologies. This world-class bright network will carry the necessary data for EnergyAustralia to reduce outages through faster foible location and preventative maintenance and, to drudgery towards managing distributed energy sources such as solar and storage devices.
EnergyAustralia's Managing Director George Maltabarow said the project was an notable allotment of the company's initial investment of $170 million in its smart network rollout.
"This project will abet us wait at the forefront of the global bright network transformation," Mr. Maltabarow said. "It will give us an instant picture of the electricity network which will abet truncate power interruptions by allowing us to quickly locate and repair faults.
"It will furthermore subsist of value preventative maintenance can subsist better targeted so they can avoid faults and outages in the first place."
In addition to consulting, systems integration and IT services expertise, IBM has invested heavily in solutions such as Tivoli Netcool and Websphere Datapower to assist utilities in realizing their bright network visions.
"IBM is delighted to bring its infrastructure, systems integration and project management expertise to abide on this ground-breaking transformation project," said David Murray, universal Manager, IBM Communications Sector, Australia.
"Only through the creation of a smart grid that can sense, communicate, anatomize and respond, can Australia build the energy infrastructure it needs to meet the challenges of climate change, globalization, and changing consumer demand."
IBM Smart Grid
IBM is working with clients in nearly 50 Smart Grid engagements across emerging and mature markets around the world. More about IBM's 'Smarter Planet' initiative: its vision to bring a modern level of intelligence to how the world works -- how every person, business, organization, government, natural system, and man-made system interacts, can subsist institute here: http://www-03.ibm.com/press/us/en/presskit/26094.wss
EnergyAustralia bright Networks
EnergyAustralia began its bright network program in 2006. It was the first utility in the world to build and operate a communications network using carrier grade Internet Protocol (IP) technology. EnergyAustralia has furthermore rolled out 800 kilometres of fibre optic cable to its 200 major substations and depots, installed hundreds of communications switches, and built a ordeal telecommunications network to allow two-way communication across its electricity network. For more information observe http://www.energy.com.au/energy/ea.nsf/Content/Splash
Although Spark has garnered a reputation as being a real-time analytics engine that is married to Hadoop, its life before being glued to that framework offers a different story.
At its inception in 2009, the Spark project was focused on sealing gaps in rudimentary machine learning at scale. The creators took a contemplate at the capabilities of the soundless developing Hadoop and NoSQL frameworks and realized that the stack for machine learning came up short—even though the bulk of emerging workloads required functionality that would quickly surpass what the Apache machine learning library (MLlib) was able to offer.
“But the require for machine learning keeps growing,” IBM’s data analytics lead Joel Horwitz explained. “And yes, for sum intents and purposes, we’re investing in Spark, but that’s really just the substrate we’re operating on. Machine learning is the actual killer app here—it will carry the insight economy over the next ten to twenty years. Their goal has been to build an engine that is based on an open measure so when machine learning continues to grow, there is a artery to scale with it. And if I am writing a machine learning algorithm and I want to utilize a different OS or architecture, I can. That’s the bigger vision here.”
Accordingly, IBM is looking beyond Hadoop for the future of its data analytics initiatives and while Hadoop platform is soundless a strong springboard for its future plans, substantial Blue is placing its bets on Spark, which is what Horwitz says is the “analytics operating system for both data science developers and data engineers.”
What is notable is that IBM went back to the original well to find the Spark source, partnering with the team at Databricks, which was founded by the original five members of the AMPLab team at UC Berkeley who developed Spark upon noticing those missing pieces for machine learning. Databricks has poured through the SystemML code, which is what IBM is calling its open source optimization toolset for Spark, and helped substantial Blue refine it so that it can drudgery seamlessly across different architectures—and for different users, including on Power Systems and System z mainframes (in conjunction with Hadoop) and as a discrete Spark as a Service offering that is offered via BigInsights Hadoop or on its BlueMix platform cloud implementation of Cloud Foundry.
The accent IBM has placed on Spark was the result of some initial efforts to attach greater machine learning capabilities to its BigInsights offering, of which Hadoop was the root. SystemML, which is a Java-based machine learning engine that IBM has contributed to the Apache Spark project, will provide what Horwitz calls an “optimizer” to allow for Spark to subsist distributed across great clusters—and to stick to a separate node when the dataset is smaller. In essence, it is an automatic parallelization implement for Spark, something Horwitz says has been missing in the market across the various machine learning libraries for Spark.
“With current approaches to writing, for example, a linear regression for Spark, the algorithm would not subsist distributed, and if it was, there would subsist nothing that thinks through how to flee the model,” explains Horwitz. “It wouldn’t recognize that for smaller datasets there would subsist no need to dole and leave it to flee on a separate node when it made sense. It wouldn’t recognize that if the model was asking for more iterations it should automatically extend the recollection allocated for the node.” This, as well as the declarative machine learning language IBM’s researchers possess layered on top that makes it easier to write machine learning code than writing in MapReduce or Java itself, is where the value is, says Horwitz.
“From the IBM perspective, Hadoop and Spark are indeed joined at the hip, but that’s really for now. The arresting thing is where Spark is going—Hadoop is heading in the direction of being a powerful data platform, but not Spark. Spark is elevated to something relish Linux—it literally becomes the operating system for not only one data system or server, but for many.”
This might sound chummy to those who possess seen IBM’s optimizers for its SQL engines, which allowed for more expressive queries. This is because both toolsets were developed at the Almaden Research Lab as well as the AMPLab project out of UC Berkeley—again, the birthplace of Spark.
IBM has had to drudgery to wait ahead of the Hadoop curve since it started late and with (yet another) several distribution. From the moves this week, it’s transparent IBM wants to wait in front of where the ease of the vendor world is heading with Spark. One could examine why IBM is putting so much pains into the Spark ecosystem and while obviously user require is a substantial allotment of it, the lessons from Hadoop are fresh in the minds of those who flee Software Group. When IBM rolled out its own Hadoop distribution, Horwitz said it was because at the time there was far too much variation in the ecosystem and too many disparate components to try to integrate. Having an approach to Spark select an angle of openness means IBM can set the measure early instead of being later to the game—and not possess to choose between picking a ally or being behind the curve.
Amazon, Microsoft, and other research-heavy webscale organizations possess furthermore been investing in machine learning code, but there is no attempt to set a measure for open architectural choices, Horowitz explained.
“We possess seen what happens when you close something off before. If you contemplate at something relish what happened with BigSQL—we close sourced that but what you saw then was fragmentation with a dozen or more different SQL variants on Hadoop. They don’t want to observe that happen with Spark.” And as he noted, there is soundless a transparent “what’s in for IBM” answer. “We are a services company, obtain no mistake about it. But we’ve seen what happens if you don’t provide an open platform for something that can change the world relish they believe Spark, and machine learning, more generally can.”
This article first appeared in IEEE Software magazine and is brought to you by InfoQ & IEEE Computer Society.
The growing popularity of cloud computing draws attention to its security challenges, which are particularly exacerbated due to resource sharing.1 Cloud computing’s multitenancy and virtualization features pose unique security and access control challenges due to sharing of physical resources among potential untrusted tenants, resulting in an increased risk of side-channel attacks.2 Additionally, the interference of multitenancy computation can result in unauthorized information flow. Heterogeneity of services in cloud computing environments demands varying degrees of granularity in access control mechanisms. Therefore, an inadequate or unreliable authorization mechanism can significantly extend the risk of unauthorized utilize of cloud resources and services. In addition to preventing such attacks, a fine-grained authorization mechanism can assist in implementing measure security measures. Such access control challenges and the complexities associated with their management summon for a sophisticated security architecture that not only adequately captures access management requirements but furthermore ensures secure interoperation across multiple clouds.
We present a distributed access control architecture for multitenant and virtualized environments. The design of this architecture is based on the principles from security management and software engineering. From a security management perspective, the goal is to meet cloud users’ access control requirements. From a software engineering perspective, the goal is to generate minute specifications of such requirements.
Several researchers possess previously addressed access control issues for cloud computing. Daniel Nurmi and his colleagues provided an authorization system to control the execution of virtual machines (VMs) to ensure that only administrators and owners could access them.3 Stefan Berger and his colleagues promoted an authorization model based on both role-based access control (RBAC) and security labels to control access to shared data, VMs, and network resources.4 Jose Alcaraz Calero and his colleagues presented a centralized authorization system that provides a federated path-based access control mechanism.5 What distinguishes their drudgery is that they present an architecture that can subsist implemented using an XML-based formalism.6 They furthermore address the problems of side-channel attacks and noninterference in the presence of multitenancy and resource virtualization. Accordingly, they present an access control architecture that addresses these challenges.
In order to build a secure and trusted distributed cloud computing infrastructure, the cloud architecture’s designer must address several authorization requirements.
Multitenancy and Virtualization
Side-channel attacks and interference among different policy domains pose daunting challenges in distributed clouds. Side-channel attacks are based on information obtained from physical implementation (for example, via time- or bandwidth-monitoring attacks). Side-channel attacks arise due to want of authorization mechanisms for sharing physical resources. The interference among tenants exists primarily because of covert channels with flawed access control policies that allow unauthorized information flow.7
Decentralized administration is characterized by the principle of local autonomy, which implies that each service model retains administrative control over its resources. This is in contrast to a centralized administration approach, which implies loss of autonomy in controlling resources; it’s not a desirable system feature when dealing with several independent clouds. Moreover, the need for a fine-grained access control can enact substantial requirements in designing an access control policy employing a great number of authorization rules. These rules can grow significantly with an extend in the granularity of resources, as well as with the number of users and services supported by the cloud. A centralized design based on the integration of sum global rules can pose significant challenges.
Secure Distributed Collaboration
To advocate a decentralized environment, the cloud infrastructure should allow both horizontal and plumb policy interoperation for service delivery. Due to the heterogeneous nature of the cloud, resource and service policies might utilize different models requiring seamless interoperation among policies. These policies must subsist correctly specified, verified, and enforced. A service-level agreement (SLA) can provide secure collaboration and assure that services are provided according to pre-established rules.
Because a user might invoke services across multiple clouds, access control policies must advocate a mechanism to transfer a customer’s credentials across layers to access services and resources. This requirement includes a provision for a decentralized single-sign-on mechanism within the authorization model, which can enable persistent authorization for customers in terms of their identity and entitlement across multiple clouds.6
The collaborative nature of a cloud computing environment requires the specification of semantic and contextual constraints to ensure adequate protection of services and resources, especially for mobile services. Semantic constraints (for example, separation of duties) and contextual constraints (such as temporal or environmental constraints included in an access request) must subsist evaluated when determining access to services and resources.8 Semantic and contextual constraints are specified in the access control policy.
Designing a Distributed Cloud Architecture
The nature of assuring resource sharing across multiple clouds depends on the collaborative environment. figure 1 shows three types of collaborations (federated, loosely coupled, and ad hoc) that can fulfill the aforementioned authorization requirements.
Federated collaboration is characterized by a towering degree of mutual dependence and dependence among collaborating clouds and supports a long-term interoperation. To subsist secure, this collaboration requires a global metapolicy that’s consistent with local policies of the collaborating clouds. A policy-composition framework (top secrete of figure 1) is necessary if a global metapolicy needs to subsist generated by integrating the policies of individual clouds.8
Loosely Coupled Collaboration
In a loosely coupled collaborative environment, local policies govern interactions among multiple clouds. In contrast to a federated collaboration, this collaboration is more flexible and autonomous in terms of access policies and resource management. Two collaborating clouds can virtualize their resources and allow autonomous sharing of resources. The information about the virtualized shareable resources and services of each cloud is stored in a virtual global directory service (VGDS), which is manifested across service-level agreement (SLAs). The middle secrete of figure 1 shows the verification for conformance of individual clouds’ security and privacy policies for loosely coupled collaboration.
Ad Hoc Collaboration
In ad hoc collaboration, a user is only awake of a few remote sharable services. Because a priori information about an application’s overall service requirements might not subsist available to the user or cloud at the start of a session, a cloud might contravene access to its resources. To ensure secure interoperation via discovered resources and services in a dynamic interoperation environment where clouds can link and leave in an ad hoc manner, usurp authentication and authorization mechanisms need to subsist developed.
Several metrics can subsist used to evaluate these collaborations, including
degree of interoperation, which indicates the level of service and resource sharing among multiple clouds;
autonomy, which refers to a cloud’s aptitude to perform its local operations without any interference from cross-cloud accesses;
degree of privacy, which specifies the extent of information a cloud provider discloses about its internal policies and local constraints; and
verification complexity, which quantifies the complexity associated with verifying the correctness of the overall constraints while integrating multiple policies.
Figure 1 shows the tradeoffs among collaboration types and these metrics; the collaboration metrics’ arrows point toward higher values. For example, ad hoc collaboration supports a higher level of privacy than federated or loosely coupled collaborations do.
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FIGURE 1. Characterization of collaboration in a mutlicloud environment. In a distributed environment, they can build a security architecture based on the design of these collaborations. Their comparison is based on degree of interoperation, autonomy, privacy, and verification complexity. The architecture they present in this article is based on federated and loosely coupled collaborations.
A Distributed Cloud Security Architecture
The proposed distributed architecture that addresses and incorporates the aforementioned authorization requirements can subsist built using three types of components: a virtual resource manager (VRM), a distributed access control module (ACM; figure 2), and an SLA (Figure 3). The proposed architecture (Figure 4) uses the RBAC model, which is recognized for its advocate for simplified administration and scalability.6 However, the design of this architecture is generic enough to advocate other access control policies, such as discretionary access control and multilevel security.
FIGURE 2. Access control module architecture. This component can subsist used to build the proposed distributed architecture.
FIGURE 3. Service-level agreement (SLA) architecture. This component can subsist used to build the proposed distributed architecture.
FIGURE 4. Intercloud and intracloud interoperations for the distributed security architecture. Shaded SLAs correspond to alternate architectures involving peer-to-peer interoperation.
VRM Design Specification
The heterogeneity and granularity of virtual resources in a cloud environment summon for a VRM at each layer of the cloud, as depicted in figure 4. The VRM is answerable for providing and deploying virtual resources. Consequently, it maintains a list of required virtual resources with their configuration, including both local and remote resources through VGDS-the one shown in figure 1. SLAs provide access to remote resources, whereas the VRM is answerable for monitoring deployed resources and might designate or release them to ensure SLA compliance, including guarantees for attribute of service. To manage the scalability issue in cloud computing in term of users and resources, the VRM uses a distributed architecture.3
ACM Design Specification
An ACM resides at each layer to implement the access control policy at its resident layer. As shown in figure 2, the main components of an ACM include
a policy determination point,
a policy enforcement point (PEP),and
a policy base.
The authorization request (Figure 2, step 1) submitted to the PEP includes the requesting subject, the requested service or resource, and the nature of permissions requested for that service or resource (such as read or write privileges). The request might furthermore involve the credentials needed for authentication and authorization. The PEP extracts the authentication credentials and the context information from the authorization request and forwards them to the credential evaluator and context evaluator (Figure 2, step 2). The PEP receives the determination about granting the request (Figure 2, step 3) and either grants or denies the user’s authorization request.
If the request contains an authenticating credential, the credential evaluator assigns a user a local role based on the user-to-role assignment rules stored in the RBAC policy base. The process of user-to-role assignment requires input from the context evaluator regarding contextual constraints. If the request contains an authorization credential, the credential evaluator assesses if the role corresponds to a local role. If not, the implication is that this is a single-sign-on request and requires role mapping by a material SLA. Subsequently, the user acquires the privileges of the locally assigned role or of a mapped role in a remote cloud.6
To allow interoperation among autonomous policies manifested through ACMs, an SLA implements a mediated policy. For this purpose, an SLA performs role mapping, specifies isolation constraints for resource sharing to forestall side-channel attacks, and presents a virtualized view of resources at the levels for which the SLA is negotiated. In addition, an SLA usually includes quality-of-service parameters, as well as billing and auditing functions. figure 3 depicts the authorization flux within an SLA.
Role mapping is a function that maps a local role to a role in a remote cloud and grants access to sum the mapped role’s permissions. The mutually agreed upon mediated policy, which is generally a subset of the policies of the participating ACMs, enforces access control for distributed services or resources through this mapping. In addition, the SLA physically isolates resources to forestall side-channel attacks at the remote cloud.2 Such isolation can forestall multiple VMs from residing on the same physical machine. Physical isolation can subsist explicitly enforced in the figure of cardinality constraint rules in the RBAC policy.6 By setting the cardinality constraint parameter to one, they can implement such isolation.
RBAC Policy Specification for Proposed Architecture
We adopted an XML-based specification due to its compatibility with the emerging standards for cloud systems and security protocols, with the ultimate goal being that the proposed architecture should subsist interoperable with complementary security protocols for cloud systems. Figures 5a and 5b exhibit the XML-based specifications of ACMs and SLAs, respectively. (The complete details of RBAC XML declaration loom elsewhere.6)
The ACM’s XML user sheet defines the authenticating credentials and the XML role sheet defines the authorization credentials. The XML user-to-role assignment sheet defines user-to-role assignment rules, which can subsist based on attributes associated with users’ credentials as defined in the XML user sheet. XML permission-to-role assignment sheets define permission-to-role assignment rules. Permission-to-role constraints can subsist based on attributes associated with a role’s credential or the resource nature as defined in XML virtual resource sheets (see figure 5c). The constraints can subsist semantic (for instance, separation of duty) or temporal. To portray authorization requirements as a set of predicates, predicate function definitions sheets define the formal notion of predicate expression. A predicate function definition sheet can involve mediated rules for intercloud resource sharing; a predicate expression can abet evaluate sets of temporal or non-temporal constraints.6
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FIGURE 5. High-level XML declaration: (a) access control module, (b) mediated service-level agreement policy, and (c) virtual resource definition and sharing constraint (local and remote).
A authorization defined in the XML authorization sheet comprises a specified operation on a given resource type. Thus, a role assigned a authorization defined on a given resource nature receives access to sum instances of that resource type. XML allows access granularity at individual levels within a resource nature to provide advocate for individual virtual resources-for example, as mentioned earlier, they can specify the physical isolation ascribe of a virtual resource at the individual resource level in the figure of a cardinality constraint to forestall side-channel attacks in the local cloud. Note that depending on if the requested resources are local or remote, the ACM decides whether or not to invoke SLA. The XML specification of the SLA depicted in figure 5b provides a limited view of advertised virtual resources, role mapping, and cardinality constraints.
To avoid security risk due to potential interference as a result of multitenancy, they must abstract policies by participating ACMs and SLAs as an information flux model. Subsequently, this model can subsist verified to ensure the property of noninterference.7 Such verification ensures that each domain remains unaffected by the actions of other domains. As side-channel attacks can subsist managed through cardinality constraints, unauthorized information flux can only occur when there’s conflict among cloud policies. In conjunction with the data model, verification models8 or verification tools (such as Alloy9) can detect conflicts among policies, which causes unauthorized information flow.
Distributed Authorization Process and utilize Cases
Three types of interoperations related to authorization flux can occur at various layers of the distributed architecture, as illustrated in figure 4. nature 1 depicts a horizontal (peer-to-peer) interoperation between the same levels of different cloud providers; nature 2 represents a plumb interoperation between layers within the same cloud; and nature 3 indicates a cross-layered interoperation between different clouds at different layers. Both nature 1 and 3 interoperations require SLAs among the participating clouds. These three types of interoperation furthermore establish distributed authorization mechanisms among ACMs.
For distributed authorization, VRMs utilize their peer-to-peer or cross- layered interoperations through VGDSs in order to provide the required re- sources. VGDSs possess both the local virtual resource IDs and the paths of the physical resources they map to, as well as remote virtual resource IDs consistent with the SLAs that advertise these resources. Therefore, a VGDS can manifest either through peer-to-peer or cross-layered SLAs (shown in dotted SLA blocks at the PaaS and IaaS levels of figure 4). Assessment of these architectural choices is an open problem.
For interoperations among ACMs, they envision loosely coupled collaboration consistent with nature 1 and nature 3 interoperations because individual clouds need to reveal only limited in- formation about their services and policies. Federated cloud collaboration requires an extensive analysis prior to generating the global metapolicy, which can result in a towering degree of complexity and rule explosion. Therefore, this approach isn’t scalable for distributed collaboration. Also, generating a consistent global metapolicy could require extensive mediation to resolve conflicts among heterogeneous policies.8 Similarly, ad hoc collaboration doesn’t federate credentials across clouds because it lacks SLA support.
For nature 2 interoperation, federated collaboration can subsist an usurp approach because it requires only plumb integration of polices. Therefore, the towering complexity for generating a global metapolicy within a cloud is justified because the cloud provider has access to sum its local policies belonging to the three service models. However, the provider must address the challenge of conflict resolution and mediation for generating such a metapolicy. figure 5a shows an case of a high-level metapolicy specification; further details loom elsewhere.6
When a customer requests a service or virtual resource, the request goes to the local ACM (Figure 6, step 1). If the ACM grants this request, it routes the request to the local VRM (step 2). If the requested resources reside in the local cloud, the VRM (after consulting the VGDS) forwards the request to the local ACM of the lower level-for example, from SaaS to PaaS (step 3). Ultimately, the request goes to the infrastructure as a service (IaaS)-level VRM in order to deploy the required physical resources. If the required resources are in a remote cloud, the local VRM, after consulting the VGDS, issues a remote request to the usurp SLA (step 3). The SLA, after performing its functions involving role mapping and evaluating the policy constraints, forwards the request to the remote ACM (step 4). After verifying its own constraints (including cardinality constraints), the ACM informs its local VRM to designate the desired resources (step 5). Finally, the VRM identifies and configures the local physical resources (step 6).
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FIGURE 6. flux of request via the access control module and virtual resource manager across multiple clouds.
This authorization process is a generic representation of a set of utilize cases. To specify these cases, they adopt Alcaraz Calero and colleagues’ authorization model4 by extending it to advocate multitenancy and virtualization in a distributed environment. figure 7 illustrates two classes of scenarios covering sum feasible interactions within and across multiple clouds. These scenarios involve the three types of interoperations discussed earlier in this article. Assuming an RBAC model, the authorization request can subsist represented using a four-tuple expression (subject, permission, interface, demur [attributes]), which can subsist interpreted in the following way: the subject (as a role) asking for a authorization to subsist performed over the demur (virtual resource or service) with its attributes (such as isolation constraint) and that object’s interface type. They assume the authorization request is time stamped to accommodate temporal contextual constraint. From an RBAC perspective, the subject is represented as a role. In addition, users of the XML user sheet specified in figure 5a, which identifies user-to-role assignments, can assume their respective roles. Along with this assignment, the proposed four-tuple can fully specify an authorization request.
When user X initiates the authorization process to access an application (app) at the SaaS level of its local cloud (SaaSCP1), the corresponding ACM’s PEP needs to authenticate the user prior to assigning a local role (for example, Rx) based on its credentials. If X requires a remote resource, the participating SLA assigns it a mapped role (say, Ry).
The local SaaS verifi es this request, represented as Rx, execute, SaaSCP1, app, for authorization. Consequently, one of the following scenarios can occur.
Scenario A. figure 7a depicts this scenario. They assume the requested resources are locally available, resulting in nature 2 collaboration within the local cloud. Accordingly, the SaaS’s local VRM identifies virtual resources-for example, computation instance (CompInstx) and storage (Storex). Assuming that the local policy verifi es the authorization request, the VRM, after consulting with the VGDS, requests the two desired resources through the following two authorization requests: Rx, execute, PaaSCP1, CompInstx(isolation=1) and Rx, execute, IaaSCP1, StoreX. Here, they assume X is requesting fully isolated computation resources to avoid side-channel attacks.
Scenario B. figure 7b shows four scenarios depicting ACM interaction across multiple clouds at different levels:
The service requested (app) by X consists of two services, app1 and app2 (local and remote, respectively), causing interoperation between SaaS ACMs in different clouds. In this case, they assume a peer-to-peer interoperation (type 1). Consequently, the VRM in the local SaaS of CP1 forwards the request Ry, execute, SaaSCP2, app2 to the remote SaaS’s ACM of CP2 through the material SLA (depicted in figure 6). Because app1 and app2 utilize virtualized resources in their local clouds, the remaining authorization process within each cloud is similar to scenario A.
In scenario B.2, the local SaaS needs to access virtual resources managed by CP2’s PaaS and IaaS. Assuming cross-layered SLA architecture, the local SaaS’s VRM generates the authorization request Ry, execute, PaaSCP2, CompInstx(isolation = 1), which is then forwarded to CP2’s PaaS’s ACM through the SLA. The remaining authorization process for acquiring virtualized resources within the remote cloud is similar to scenario A.
Scenario B.3 is identical to scenario B.2, except the local cloud needs virtual resources, which are maintained by a remote IaaS. Accordingly, the local PaaS’s VRM generates the authorization request Ry, execute, IaaSCP2, VMx(isolation = 1) and forwards it to the remote IaaS’s ACM through a cross-layered SLA.
In scenario B.4, an intermediate cloud must process the authorization request with further rerouting to a remote cloud (CP3) where the physical infrastructure is located.1 In this case, SaaS, PaaS, and IaaS belong to several clouds. The authorization requests Ry, execute, PaaSCP2,CompInstX(isolation = 1) and Rz, execute, IaaSCP3,VMX( isolation = 1) are generated in succession to the corresponding ACMs after the VRMs invoke the SLAs.
FIGURE 7. Scenario-based policy interoperation. (a) Secure interoperation within a local cloud to acquire resources that are locally available. (b) Secure interoperation involving SLAs at different levels to acquire resources among multiple clouds.
These utilize cases portray high-level design requirements for the proposed architecture and cover sum feasible authorization flux processes that can subsist used to design and develop the distributed architecture. Currently, progress for a prototype of this architecture is underway; it uses the Microsoft Azure platform to develop a health surveillance and rapid response infrastructure with the capability of collecting and analyzing real-time epidemic data from various hospitals. This cloud computing environment consists of compute clusters, reliable data storage, and software services. The stakeholders involve researchers, physicians, and government public health management personnel in the chain of reporting. The services provided to stakeholders involve visual analytics, statistical data analysis, and scenario simulations.10
The architecture they present in this article represents a precise but comprehensive authorization design for access management. Using an XML-based declaration of the access control policy for this architecture is a step toward its implementation. However, they must address several open challenges in order to implement a fully secure and trusted cloud environment. These involve design of an authentication mechanism, cryptography and key management, mediation for conflict resolution of heterogeneous policies, software design for virtualized resources, integrating information flux verification tools to ensure noninterference, and architectural choices for SLAs. They procedure to address these challenges in their future work.
The research in this article is partially funded by the US National Science Foundation under vouchsafe IIS-0964639.
1. H. Takabi, J.B.D. Joshi, and G.-J. Ahn, "Security and Privacy Challenges in Cloud Computing Environments," IEEE Security & Privacy, vol. 8, no. 6, 2010, pp. 24-31.2. T. Ristenpart et al., "Hey, You, win off of My Cloud: Exploring Information Leakage in Third-Party Compute Clouds," Proc. 16th ACM Conf. Computer and Communications Security (CCS 09), ACM, 2009, pp. 199-212.3. D. Nurmi et al., "The Eucalyptus Open- Source Cloud-Computing System," Proc. 9th IEEE/ACM Int’l Symp. Cluster Computing and the Grid (CCGRID 09), IEEE CS, 2009, pp. 124-131.4. S. Berger et al., "Security for the Cloud Infrastructure: Trusted Virtual Data hub Implementation," IBM J. Research and Development, vol. 53, no. 4, 2009, pp. 560-571.5. J.M. Alcaraz Calero et al., "Toward a Multitenancy Authorization System for Cloud Services," IEEE Security & Privacy, vol. 8, no. 6, 2010, pp. 48-55.6. R. Bhatti, E. Bertino, and A. Ghafoor, "X- Federate: A Policy Engineering Framework for Federated Access Management," IEEE Trans. Software Eng., vol. 32, no. 5, 2006, pp. 330-346.7. J. Rushby, Noninterference, Transitivity, and Channel-Control Security Policies, tech. report CSL-92-02, Computer Science Lab, SRI Int’l, 1992.8. B. Shafi q et al., "Secure Interoperation in a Multidomain Environment Employing RBAC Policies," IEEE Trans. information and Data Eng., vol. 17, no. 11, 2005, pp. 1557-1577.9. D. Jackson, I. Schechter, and I. Shlyakhter, "ALCOA: The Alloy Constraint Analyzer," Proc. 22nd Int’l Conf. Software Eng., ACM, 2000, pp. 730-733.10. S. Afzal, R. Maciejewski, and D.S. Ebert, "Visual Analytics determination advocate Environment for Epidemic Modeling and Response Evaluation," IEEE Conf. Visual Analytics Science and Technology (VAST 11), IEEE CS, 2011, pp. 191-200.
About the Authors
This article first appeared in IEEE Software magazine. IEEE Software's mission is to build the community of leading and future software practitioners. The magazine delivers reliable, useful, leading-edge software progress information to withhold engineers and managers abreast of rapid technology change.