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Using these methods, a demand planner can then view forecasts at a category level, store level, or regional level, for example. Typically, other dimensions used in demand forecasting include store location and customers, and these are commonly represented in a star schema data model see Figure 6.
Such a design that separates data can help with the performance of the analytics process used for generating forecasts. The method of striking a balance between all data stored together and separating data is referred to normalization and denormalization of a data model.
The data schema design has a pro- found impact on the analytic capabilities and the performance speed of completion of the computations. It is typical for individual analytical functions within supply chain optimization to have separate data marts. For example, data stored for demand forecasts can be stored in one data mart, whereas data related to inventory optimization or collaborative demand planning could each have separate data marts.
This single or integrated set of data marts for the supply chain analytics is also referred to as a demand signal repository DSR. See Figure 7. Slowly moving data e. Such slowly moving data is also referred to as cold or warm data.
Fast-moving data e. This faster-moving data is also referred to as hot data. Real-time analytics with ESP will become a vital component of a connected supply chain in the future. Organizations typically use a data mart purpose-built for analyt- ics such as demand forecasting.
Separation for a purpose generally increases performance e. Such isolation also allows data management processes to be tailored to the types of data, as well as to the speed of data ingestion. The data storage types can help with analytical loads; for example, OLAP systems are purpose-built for analytics e. In a digital supply chain, there are many different data sources, which generate different types of data e. The volume, variety, and velocity of data challenge traditional systems and methods for storing and analyzing data.
The data lake is a method to help with these challenges. Organizations can collect data from many sources and ingest these into their data lakes on premises or in the cloud. Traditional databases, data warehouses, and data marts follow a schema-on-write approach. The method of data ingestion, staging data importing into required formats, storing in a commonly accessible form and location , and ETL can take min- utes or hours depending on the complexity of tasks and the volume of data.
For example, forecasts at a weekly time granularity level often ingest incremental data at a weekly time interval. Forecasting systems often perform this data import process in batch jobs in nonbusiness hours e.
The forecasting process can also be automated, as can the sharing of data with downstream planning systems. If a demand planning process involves human review and col- laboration, then that process is included in a forecast cycle.
Depending on the forecast horizon e. A data lake follows a schema-on-read approach. In this case the data ingestion process extracts and loads data into a storage pool.
Data transformations are performed at a later stage if required ELT. The data remains in a data lake and can be accessed directly. It can also be transformed and copied to other data storage targets e. Such a data ingestion process via a data lake permits fast ingress of data and is typically aimed at fast-moving hot data.
Analytics on such fast-moving data can occur as quickly as data is ingested. This traditional method of ingesting, storing, and using data has a high consistency of data and is used by business professionals for deriving insights. A data lake, in contrast, can be used to ingest and store structured, semistructured, and raw data. A data lake follows a schema-on-read method, eliminating the need for data wrangling and molding at ingestion time.
A data lake is therefore well suited for fast ingestion of data from all types of sources e. Data lakes are designed to be hor- izontally scalable, with commodity hardware providing an excellent cost-to-performance ratio for organizations.
The maturity of data lake systems is steadily enhancing and, as the use by organizations worldwide and across industries increases, so do the solutions for easy access to data and analytics against such systems. This technology allows storing any data type on an interconnected grid of computer nodes, leveraging cheaper local storage present in each node.
This is a fundamental differ- ence from the traditional framework of vertically scaling servers increasing computing resources—central processing unit [CPU] and random-access memory [RAM].
The cost of vertically scaling is a lot higher, and, although advancements in computing are continuing, the vertical scale approach has a limit at some point, whereas in theory the horizontal scaling approach has no limit. A big analytical processing job is broken down into smaller segments referred to as the mapping phase and each node in a Hadoop server farm also called clusters analyzes segments of that job, based on data that is available to its local storage.
The results from each node are then consolidated into one result this step is the reduce phase. Once data has landed in a data lake, it can be analyzed, or pro- cessed further to be transferred into different formats and a data mart, for example.
Simple MapReduce methods enable data to be mined on a large scale and at faster speeds than were previously possible. See Figure 8. Typically, there are three copies of each data set, which are hosted on different nodes, assisting with disaster recovery goals. A user would like to report on the number of males per age group across all the data.
The technologies that enable a data lake, and a data lake itself, can help with the challenges of Big Data. The Big Data concept can be broken down into two interrelated concepts that need to be addressed if organizations can successfully leverage such technologies.
The second concept deals with a change in architecture to enable the 4-Vs of the data. The 4-Vs of Big Data 1. Volume i. Variety i. Velocity i. Variability i. The 4-Vs of Big Data have driven new architectural designs lever- aged by IT systems to meet these modern challenges.
A modern architecture referred to as the lambda architecture aims to separate functions and layers, enabling a scalable architecture with many com- ponents. Such components can perform tasks e. The building blocks of such an architecture depend on the software vendor and could be proprietary, open source, or a mixture of both. Extensive details of such architectures are beyond the scope for this book, but at an elevated level, the standard layers of an architecture design fol- lowing principles of the lambda architecture are depicted in Figure 9.
These layers enable ingestion of hot and cold fast- and slow-moving data. The processing of data can be sequential or in parallel. The analytics layer can also be sequential and in parallel, and handle hot or cold data. The results can be shared with other targets, such as near real-time decision-making processes, different storage platforms, and different systems, or presented as results.
This architecture is well suited for a hybrid approach of leveraging hot and cold in-streaming data, as well as already stored data. Analytical processes can combine newly ingested data with previously stored data to provide near real-time results or result sets for further analysis by other systems and processes.
This hybrid approach assists with the challenges of the 4-Vs of Big Data. Data ingestion can follow schema-on-write or schema-on-read, leverage different storage sys- tems and data types, and leverage distributed computational resources to provide results aiding data-driven insights promptly.
The logical building blocks of a lambda architecture are depicted in Figure 9. Data sources are examples only, based on demand-driven supply chain needs. Case Study: Leeds Teaching Hospital To improve its hospital services, it was necessary for Leeds Teaching Hospital to identify trends through vast amounts of data. The primary challenge was the enormous volume of structured and unstructured data.
One of the objectives of the health care provider was to detect possible outbreaks of infectious diseases as early as possible using data-driven insights and business intelligence. Previously such analysis relied on cold data that was already stored or archived, and hence out of date. Such data could help provide better data-driven insights near real time. The expected volume of data was half a million structured records and about one million unstructured data records.
Leveraging data from various sources would provide better insights but would require a lot of computing power. It was not feasible to provision a server farm lots of server computers to handle such analy- sis.
Costs, maintenance, and management of the computing environment would be too high a cost of ownership. Microsoft Azure cloud was chosen as it offered an integrated and seamless stack of solutions and components required e. This cloud environment enabled the on-demand processing of six years of data with millions of records. The integration of Microsoft business intelligence BI tools enabled a self-service approach to data-driven insights and evidence-based decisions.
The digitalization of processes e. Source: Microsoft September 7, In the context of demand-driven forecasting for a supply chain, a hybrid approach could help solve new challenges of the supply chain.
Such an approach could combine features of data processing hot, cold , data storage, analytics, and sending results to downstream systems for decisions near real time or at slower rates. See Figure Data virtualization is also a useful technology for abstracting data sources and layers. Details of statistics, forecast models, and modeling are beyond the scope for this book. The aim is to highlight enhancements and possibilities made possible with cloud computing, and how the combination of disciplines can enhance value further.
There are multiple challenges for the analytics of demand forecasting. First, there is the challenge of Big Data the volume of data that needs to be ingested and analyzed at various speeds. Third, there is the challenge of discovering patterns, trends, and links.
Such analysis is helpful for detecting changes in consumer behavior and tastes. It is also useful to new product forecasting that can leverage patterns, and information about similar products to assimilate the demand for a new product based on similar attributes and possible tastes. Finally, there is the challenge of automation and leveraging a vast repository of forecasting models and modeling techniques to increase accuracy and value of forecasts. This becomes even more important with multiple dimensions and the depth of those dimensions the depth of the hierarchy, e.
All these computations must also be time relevant. There are distinct phases of maturity when it comes to analytics, and the envisioned end state an organization wishes to be in will drive the state of advanced analytics leveraged. The four phases are depicted in Figure 11 and are also called the DDPP model, standing for descriptive, diagnostic, predictive, and prescriptive.
In the context of a demand-driven supply chain, this type of analytics focuses on reporting on what has happened. The second level is diagnostic analytics.
This type focuses on why something has happened. There are more interrelations between data, and reporting becomes more dynamic and interactive. Examples include interactive business intelligence dashboards with drill-down possibilities.
These dashboards are more sophisticated and allow more exploration and visualization of data. The business value shifts to the right, providing more insights from the data. Predictive analytics is the third level of maturity in the DDPP model. This type of analytics focuses on what could happen and leverages advanced analytics to provide possible outcomes. In the context of demand-driven forecasting, examples include forecasting demand for products based on demand signals.
Demand signals will vary for different products, and applying the same forecasting models e. Another example of utilizing ML and AI for demand forecasting could be clustering data from like products also referred to as surrogate products to help forecast demand for new products that may share similar traits or attributes.
Only a machine could digest such vast amounts of data and spot commonalities and trends that could be applied to forecast products with no historical data. The business value of this level of analytics shifts further to the right. This type of analytics focuses on providing decision sup- port or automating decisions and providing input into downstream decision and supply chain planning systems.
The sophistication of advanced analysis using the scale and depth of data available, increased automation, and timely decisions all increase the business value to the highest possible in the DDPP model. Such integration of components and technologies can lead to complex architecture designs and costs. Such a PaaS model makes it possible for organizations to extend on building blocks of software components and software stacks to address business challenges without having to worry about foundational elements.
This could mean an organization could merely spin up a data lake environment or a data warehouse, leverage data ingestion technologies to process hot and cold data, and utilize tools for advanced analytics and visual reporting without having to worry about deployment, management, maintenance, or development of such components.
An example of a software solution stack to help organizations solve demand forecasting challenges through a cloud computing environment is depicted in Figures 12, 13, and Azure is the name of the Microsoft cloud, which at the time of writing is one of the top two public cloud vendors in the world. At a high level, the AI services provide the advanced analytics and are the link between the data and presentation layer see Figure The underlying components required to ingest, store, analyze, and present data to decision makers or decision systems are included in the suite.
The design of the Microsoft AI suite has applied principles of the lambda architecture elaborated upon earlier. As depicted in all three diagrams Figures 12, 13, and 14 , data can be from a vast mixture of sources. There is support for hot fast-moving and cold slowly moving data.
Data ingestion is handled by components listed under the Ingest category see Figure Back-end computing nodes are automatically scaled to support the data workloads. The Microsoft Azure Data Factory is a visual design tool used to build a digital data pipeline between data sources and data storage.
Microsoft Azure Event Hubs is also a cloud-based data ingestion service that focuses on hot data. It enables organizations to stream in data and log millions of events per second in near real time. A key use case is that of telemetry data e. It is a cloud- managed service, meaning an organization does not have to worry about development, deployment, maintenance, and the like. These duties are the responsibility of the cloud vendor in this example it is Microsoft.
Event Hubs can be integrated with other cloud services of Microsoft, such as Stream Analytics. The Microsoft Azure Data Catalog is another example of a cloud service.
Data remains in its location, and data consumers can utilize tools they are familiar with to access and analyze the data. Data storage in the Microsoft Azure AI services follows principles of polyglot persistence, and an organization can leverage different types of storage types and technologies. The decoupling of the data layer makes it possible to store, process, and analyze hot and cold data in parallel utilizing the most suitable data storage technologies for each task.
These technologies are cloud services and managed by Microsoft. The analytics layers far right of Figure 13 are also cloud ser- vices.
Included in the Microsoft Azure AI services are open-source technologies e. The back-end architecture scales elastically depending on the demand of the analytics. Management of these components is the responsibility of Microsoft the public cloud vendor in this example. Spinning up and managing such environments on-premises would take days, if not weeks or even months, and would incur substantial up-front investments. Both hot and cold data can be analyzed via this platform.
The Microsoft Azure Data Lake Analytics and Azure HDInsight cloud services focus more on cold data and are well suited for investigating unstructured or semistructured data. Microsoft Azure Stream Analytics focuses on hot data.
The public cloud provider i. Applying such technologies and capabilities of these cloud services to the challenges of demand-driven forecasting can help organizations achieve improved forecast accuracy and business insights in less time, and more cost-effectively.
Organizations taking advantage of such pos- sibilities could improve their forecasts and demand planning process by sensing demand from downstream sources and adapting forecasts as new hot data is streamed and analyzed. Demand sensing uses gran- ular downstream sales data e. An organization could also leverage near real-time data e.
Data, analytics, and intelligence are combined to improve demand-driven forecasts or shape demand. Rolling forecasts could be updated based on newly arriving data. The time horizon of forecasts depends on each organization and the products sold. Perishable products, for example, would require a daily or even hourly forecast, whereas non- perishables i. Improving the forecasts based on near real-time demand signals could help prevent stock-outs and lost sales.
Improved forecast accuracy and timely insights could also assist with goals of preventing high inventory costs supply exceeding demand. The consumer is a returning buyer, and data from previous purchases is available via cold storage. Advanced analytics is used to identify current shopping behavior and compare it with past actions.
Analytics and intelligence e. Current inventory stock of the searched-for products as well as other related products is used as an input data variable for analysis. Such a suggestion could take advantage of price sensitivity insights, add sale promotions for related or complementary products, or help reduce inventory overstock by discounting associated products.
This provides a personalized shopping experience for consumers and can improve customer satisfaction, increase sale potential, and potentially lower inventory costs. New technologies such as bots can take advantage of ML and AI and could provide a simulated human personal shopper experience e.
These technologies should be complemented by a collaboration of demand planners and forecasters. Good practices of forecasting and demand planning should be applied to utilize data insights, yet also leverage domain experience and knowledge. Machine-generated fore- casts should not necessarily be overridden by human intervention, as the end result could be decreased forecast accuracy. The optimal approach would use a blend between statistical demand-driven forecasts and collaboration between demand plan- ners, as well as supply chain partners.
Statistical forecasts can provide valuable decision support, but should not be used to automate the demand planning system completely. Automated decision systems will not be avoidable, but the human factor must remain an independent part of the demand planning process Spitz , Organizations wishing to optimize their supply chain through digitalization combine disciplines e. It should not be used to automate the demand plan- ning process completely. As mentioned previously, automated decision systems will not be avoidable, but the human factor must remain an independent part of the process Spitz , Even though it is now possible to collect, store, and analyze vast amounts of data, companies must still formulate a strategy for data management e.
The Veritas Data Genomics Index highlights that over 40 percent of stored data has not been touched and leveraged in more than three years Veritas , 3.
As there is more and more data, organizations need a good data manage- ment strategy to identify useful data, which could then be leveraged in the analytics process and assist with goals of being a demand-driven supply chain. Such tests would probably be repeated over time to make sure an organization is aiming to be as data-smart as it can be. When organizations transition along maturity models of analytics, Big Data, and digitalized supply chain, their needs and capabilities change over time, so it is important to have a process in place that reevaluates data, analytics, and operations.
Demand planners have domain knowledge and can have an awareness of events outside of the digital network of data. For example, a purchaser has informed its supplying organization that it will place a large bulk order next month e. A purely machine-driven demand forecasting process could be underestimating the actual demand in this example.
A survey of forecasters highlighted that 55 percent of the respon- dents used a mixture of judgment and statistical forecasts, or a judgment-adjusted statistical forecast Kolassa and Siemsen ; Fildes and Petropoulos Domain knowledge and close collaboration across all functions are needed to make the most out of demand sensing and demand shaping Chase , A two-by-two matrix for forecasting Croxton et al.
Low demand variability and low or high demand volume could use data-driven forecasts. High demand variability and low demand volume could use a make-to-order forecast, and high demand variability and high demand volume could use people-driven forecasts Croxton et al.
The forecast value added FVA metric is a useful tool to help highlight whether or not human judgment and overriding the statistical forecast are adding value. The FVA metric is used to evaluate the performance positive or negative of each step and participants in the forecasting process.
In this evaluation process, a forecast performance metric is used as the baseline. Deviation from this baseline is then calculated as the forecast value added. The FVA analysis can thus be used to evaluate whether procedures and adjustments made to the statistical forecasts improved or decreased accuracy Gilliland , 14— One of the most common forecast performance metrics used is mean absolute percentage error MAPE.
The lower the MAPE value, the better is the forecast accuracy. These concepts are elaborated upon in the following example. A naive forecast is something that is simple to calculate, requires minimal effort, and is easy to understand. In this example, two frequently used naive models are used. This model uses the last actual value as the forecasted value. If actual units sold last week were 75, then the forecast for the following week would be 75 see Figure The second model used in this example is the seasonal random walk.
This model uses the actual values from the same period a year ago. If actual units sold in January, February, and March last year were 90, 60, and 42, then the forecasts for January, February, and March for next year would be 90, 60, and 42, respectively see Figure Using FVA, organi- zations can evaluate whether overriding statistical forecasts added any value.
Such analysis must also bear time pressures in mind, as consumers are becoming more demanding and stress the supply chain in unprecedented ways. Some software solutions for supply chain optimization include forecasting, inventory optimization, collaborative demand planning, and FVA functionality.
The results between business workbenches are shared seamlessly, and this fosters scenarios such as organizations performing demand-driven forecasting with collaborative demand planning, or forecasting with inventory replenishment and optimization, or a combination of all three disciplines forecasting, collaborative demand planning, and inventory optimization.
Cloud computing provides computing at scale and can do so elastically, scaling out or scaling back in increasing or decreasing computing power by adding or removing computer servers to process data in parallel.
Such possibilities and technologies are elaborated upon further in subsequent chapters. These are all options and possibil- ities made possible and viable through cloud platforms. One function alone is unlikely to help solve the challenges of a demand-driven supply chain, and it is the combination of all areas that creates expo- nential value.
Last, but not least, these technologies and platforms must be governed by organizational strategies. Data must be of value, and dark data should be avoided. Equally important are data governance and security.
Organizations must be aware of what is being captured, stored, shared, and acces- sible. GDPR does not differentiate between on-premises or a cloud environment, so clear structure, control, governance, and processes are needed. Organizations should select the right balance between statistically derived demand forecasts and collaborative demand planning. Such reviews should be repeated to allow organizations to adapt to changes.
Successful organizations keep trying to improve and follow the mantra of excellence being a continuous process and not an accident BS Reporter , 9. The combination of cloud computing, data, advanced analytics, business intelligence, people, and operations all combined would provide the most value to organizations. Business insights would be available at the right time to make or automate informed and data-driven decisions. Cloud concepts and advantages are explained in more detail in subsequent chapters.
The combined value of cloud computing and a demand-driven supply chain is elaborated on in the following. C loud or cloud computing is a term used to describe the use and outsourcing of information technology IT. In such a usage model, organizations shift away from constructing and utilizing their data centers to leveraging services of a public cloud provider. There are instances where organizations will build and consume a private cloud, which is where IT resources are deployed internally and are available to only that organization.
Organizations that are leveraging this wave of technological advance- ments will have competitive advantages over those companies that choose not to leverage cloud. There was a time when every household, town, or village had its own water well. Today, shared public utilities give us access to clean water by simply turning on the tap.
Cloud computing works a lot like our shared public utilities. However, instead of water coming from a tap, users access computing power from a pool of shared resources. Cloud computing is a new model for delivering computing resources—such as networks, servers, storage, or software applications. Kundra There are essential technologies that cloud computing builds on, and such technologies are highlighted throughout this chapter.
Some of the critical milestones and technologies along the journey to cloud computing are depicted as time lines in Figures 23 and These events are considered stepping-stones to cloud computing Daconta Licklider’s work on Arpanet is seen as predecesor to the internet.
IBM pioneers virtualization, virtual memory, and time s sharing of computer hardware. Computer era of mainframes, s minicomputers, and the start of personal computers. Chellapa VMware begins with making virtualization available for the masses.
Launches CRM software. Built to handle s failure, scale to thousands Hadoop – Big Data of machines, and handle vast volumes of data. Google, Yahoo, and the Cost-effective with scale Apache Software and commodity hardware. Foundation pioneer technologies for Big Data. The Big Data open-source framework and ecosystem emerges.
Several commercial vendors emerge. Storage S3 and compute EC2 cloud services launched. Microsoft Azure Microsoft announces cloud services. Sets vision for becoming the leader in Intelligent Cloud computing. While there are many events and technologies from the past that have led to cloud computing, some are more important than others, accelerating driving forces and possibilities of cloud models and services. Cloud service examples and offerings from top-performing public vendors are showcased in Chapter 4.
Virtualization 2. Big Data 1. This section focuses only on server virtualization. Even though the pioneering work for virtualization is attributed to IBM as far back as the s, it became a mainstream technology mainly through VMware see Figure Other software vendors such as Microsoft and Xen open-source soft- ware realized the market trend and adoption by organizations world- wide, and deliver virtualization software.
Virtualization is recognized as the pre-version of a cloud. Server virtualization is a way of abstracting physical hardware of a server machine and creating virtual machines VMs that are hosted on a physical server machine.
These virtual machines access only the abstracted hardware, and are unaware of other VMs running on the same physical computer referred to as the host machine. A software layer called the hypervisor performs the hardware abstraction. The hypervisor manages the access to this virtualized hardware and balances the load from multi- ple virtual machines that may reside also referred to as hosted on the same physical server machine.
Each VM has an operating system OS and can have applications within the virtual machine as if it were a regular operating system and computer. Hardware is presented virtually to the OS and applications within the VM, and instructions to this virtual hardware are as if it were native physical hardware. These abstraction features lead to broad compatibility of operating systems and applications within the VM. An operating system within a VM is referred to as a guest OS.
Depending on the vir- tualization vendor, this host OS can be thin e. There are limits for the virtual hardware sizes e. Should an application or operating system within a VM become rogue, then the other VMs on the host will still be unaffected, as the VMs are self-contained objects.
Encapsulation makes it easier to move VMs from host to host for performance balancing, for maintenance of hosts, or for disaster recovery with off-site storage. Through server virtualization, it is possible to host many vir- tual servers on fewer physical servers. The ratio of virtual machines to physical host machines is called the consolidation ratio.
For example, if there are 40 VMs on one physical host machine, then the consoli- dation ratio would be The virtualization software would increase costs, of course, but in general the total cost of ownership would decrease, as less physi- cal hardware would be needed, and consequently less data center space would be required.
Data center space and operation are very expensive e. A high consolidation ratio is also referred to as high density, meaning a higher number of VMs are squeezed onto a host machine.
By virtualizing a data cen- ter, organizations can reduce IT costs tremendously. Processes such as procurement and deployment of servers are also transformed, as these would occur virtually. A VM can be deployed within minutes com- pared to procurement cycles and deployment processes that typically require several weeks. The capital and operational expenses that can be assigned to a virtual machine are much lower than similar costs for a physical server.
Virtual infrastructure e. The hypervisor software layer pro- vides hardware abstraction, but there is a degradation in performance compared to native e. Container technologies e. Applications are containerized virtual sandbox and leverage the same host OS see Figure 27 for a graphical differentiation.
There can only be one host OS, and applications must be supported by that host OS. Containers can be deployed on physical server machines, within VMs, or in a cloud. Such a containment technology further increases the density of physical hosts and improves agility. Applications are contained in virtual containers i. VMs hosted on the same physical server machine do not affect each other. It is possible to take a snapshot in time before an event e.
Developers can create and deploy a VM, undertake testing, and then undeploy the VM, making the entire process more cost-effective. It is possible to move a VM while it is running live to another physical host machine.
Such a move is also referred to as a hot migration. A cold migration is performed when the VM is not running not switched on and is possible due to the encapsulation of the VM. Both forms of movements provide possibilities that would be far harder to achieve with pure physical servers. VMs can be migrated to other host machines for performance reasons or maintenance reasons e. VMs may also be migrated to other data centers, and virtualization enables such possibilities.
In Facebook migrated VMs from one data center to another without affecting the million users of its Instagram service Metz, , June.
It was an enormous undertaking, migrating the services and more than 20 billion photos, and this was made possible through virtualization technologies and more. Virtualization makes it easier to deploy VMs in parallel for service availability, or rapidly deploy a VM if needed. VMs can be migrated or stored at another location providing improved disaster recovery options and lower costs.
Cloud computing is more extensive than virtualization and is the evolutionary result of virtualization. The top-performing ven- dors of clouds are showcased later in this chapter. Microsoft can provide a hybrid option as the virtualiza- tion technologies, and cloud services are both owned and operated by the same vendor, making integration or migration seamless.
There are three main challenges of this massive data increase. SoftLayer provides native IPv6 support for its publicly available services eliminating the need to tunnel to carry IPv6 over IPv4 networks, which in turn means that the networks are not limited by the diminishing pool of IPv4 addresses.
Page 4 of 7. The data center facilities are included within the scope of the report, however, other aspects of the services including the FedIMS system and its processes, are not included within the scope of this report. SoftLayer also provides co-location services, security protection services, encryption certificates, virtualization services network, security, server, and storage , managed hosting solutions, backup and storage solutions.
The accompanying description includes only those controls directly impacting SoftLayer s Platform Services and customers hosting environments utilizing SoftLayer s Platform Services, and does not include controls over other services.
SoftLayer also provides enterprise-class tools to help mitigate potential security risks and ensure availability. Tools provided by SoftLayer include, but are not limited to, load balancing, intrusion detection and prevention, standard and dedicated hardware firewalls, anti-virus, anti-spyware, anti-malware, VeriSign and GeoTrust SSL Certificates.
SoftLayer s Platform provides infrastructure as a service for customers with bare metal servers, or a hybrid environment that encompasses bare metal servers and virtual servers. Future references to customers environments refers to customers bare metal, virtual servers, or hybrid set ups that include both bare metal and virtual servers.
The SoftLayer Platform is a modular platform that leverages a proprietary automated provisioning system using bare metal servers, virtual servers, storage, and networking services to integrate and provide highly-scalable private and hybrid Infrastructure as a Service IaaS. SoftLayer s Platform Services include the activities that are performed by SoftLayer for these customers, the physical and environmental security of the hardware, the network, the logical access restrictions, and customer support.
Many of the activities for SoftLayer s bare metal or virtual server are the same, and as such, the processes and controls that are included within the scope of this report are the same; and where there are differences in execution of the control, such notations are referenced. Data Center Facilities Each data center building may contain multiple server rooms SR , which are designated as separate areas of the data center, whether separated by a cage or through a room enclosure.
All facilities are secured from public access and protected collectively by redundancy in power, cooling, fire suppression, network connectivity and security. Each server room is typically made up of one pod the initial go live date and modernization status of the server room determines whether it follows a pod structure or a legacy structure. Each pod is built to the same specifications to support up to 5, servers. Leveraging this standardization across all geographic locations, SoftLayer optimizes key data center performance variables including: space, power, network, personnel, and internal infrastructure.
SoftLayer provides Platform Services out of 17 locations, as of October 31, and uses multiple telecom service providers for backbone connectivity and multiple co-location management providers for data center facility management.
Page 5 of 7. Production Network The production network encompasses the public network, the private network, and the management network described above. Master Backend Routers MBR are the most critical network devices for managing the security and availability of the production network. Redundant Master Backend Routers are installed in each data center as part of the production network infrastructure.
Significant changes made to these devices have the potential to affect both security and availability of the Platform Services provided by SoftLayer. Other network devices exist within the production network, such as the Frontend Customer Router, the Backend Customer Router, and the production network switches. These devices have not been included within this assessment based on management s determination that failures of these network devices would have a much lower impact on the security and availability of the production network.
This system description does not include the SoftLayer Corporate network. Page 6 of 7. It is based on SoftLayer s robust Open API Library and allows customers to fully manage their environments through discrete services, and other automated functions. Management capabilities include system and network management, account management, ordering and deployment, and customer support.
The Customer Portal allows customers to: Create and manage tickets for incident response and resolution Review account information View information and some configuration data regarding their purchased solutions Perform functions such as OS reloads, and access RescueLayer Maintain firewall and DNS configurations that affect their bare metal servers Purchase or upgrade services which initiates the automated provisioning process for new systems SoftLayer personnel also have access to IMS to set up and configure purchased solutions, assist in troubleshooting technical issues, and responding to customer requests.
Ticket queues are predefined in IMS, and as ticket requests are received and prioritized, the tickets are routed to the specified team to resolve. Page 7 of 7. Webtrends Inc. The platform built to perform. No two clouds are built the same way. SoftLayer gives you the highest performing. Bookmark not defined. Major differentiators This document will outline your first few hours as a customer and hopefully answer all initial questions.
Server administration is a challenging task. All Rights Reserved. Executive Summary Hosting and datacenter services provider. Problems: Client operations are unique and require more than a one-size-fits-all solution they need hybrid hosting to virtualization,. This document will outline your first 48 hours as a customer and hopefully answer all initial questions.
Dedicated server administration is a challenging task. Infrastructure on the Cloud Faster, Easier, Economical Global cloud leader Formed by 10 industry veterans in Model predicated on software-driven infrastructure Unencumbered by early-industry legacy. We are a security company that protects businesses. Architecture and operational responsibilities. The company s flagship product, the YubiKey, uniquely.
Ayla Networks, Inc. CenturyLink Cloud Infrastructure, application services, and managed services – all in a single, integrated platform Businesses like yours are moving their apps to CenturyLink Cloud. All signs point to. Proposed Interconnection Capacity Collocation Equipment Requirements YubiCloud Service Version 1. The company s flagship product, the YubiKey, uniquely combines.
Endpoint Protection Software. SIEM Tools. Featured Categories. All Categories. OPNsense vs. Cisco Secure Firewall vs. Fortinet FortiGate. Jira vs. Microsoft Azure DevOps. Checkmarx vs. Cloudflare DNS vs. Microsoft Power Automate vs. Aruba Wireless vs. Microsoft Intune vs. KVM vs. Oracle VM VirtualBox. More Comparisons. Learn More. Sign In. Avigail Sugarman. Are there any reasons to opt for Rackspace vs. Like 0.
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