What type of storage is used on mobile devices group of answer choices?

3.2.2 Mobile Cloud Storage

Storage capacity is another constraint of mobile devices. There are many existing storage services for mobile devices, e.g., Dropbox, Box, iCloud, Google Drive, and Skydrive [60][87]. Besides manually uploading the files or data onto the cloud, one desired feature of mobile cloud storage services is the automatic synchronization between mobile devices and the cloud. Multimedia data generated by mobile devices demands a stable and high available storage solution. This is the reason that many smart phone operating systems natively implant the multimedia data synchronization feature, e.g., iCloud for iOS, Skydrive for Windows Phone, Google Drive for Android, etc. Moreover, mobile users' behavior data such as location traces, browsing history, personal contacts, and preference settings need to be kept in a reliable and protected storage space. Most existing commercial cloud storage solutions are built on a centralized data center, which is appropriate for Internet clouds.

Storage mobility has been gradually becoming a recent research focus. WhereStore [257] is a location-based data storage solution for smart phones. It uses filtered replication (a filter expressing the set of data items that are likely to be accessed in the near future) along with each device's location history to distribute data items between smart phones and the cloud. STACEE [213] proposes a peer-to-peer cloud storage where mobile phones, tablets, set-top boxes, modems and networked storage devices can all contribute as storage within these storage clouds. It provides a peer-to-peer (P2P) cloud storage solution and addresses the storage issue for mobile users as a QoS-aware scheduling problem.

12.5.6.3 Excess information, multiple rule match

The storage capacity of the human conscious memory is extremely limited. If a number of conditions are relevant to the problem being analyzed, it's possible that some of them will simply fall out of consideration due to capacity problems.

One of the possible results of information loss is that several rules will match. This happens because not all of the context information is available and being used. If the choice between the matching rules is made without the missing context information, an error can result. Reason refers to these types of errors as informational overload.

3.2.3 Additional Storage

Adding storage capacity for the final products (binary decisionV Sst,gt) does not change the polyhedral representation of a mode, it only affects the upper bound of inventory:

(10)INVgt, h≤INVgU+∑st∈ST,t'∈ Tinvest,t'≤tINVst,gUVSst,gt'∀t∈T,h∈H,g∈G

11.2.9 Identify data retention capacity requirements

The data storage capacity requirements for long-term data retention records must be specified. The operational or business model should be evaluated to determine the anticipated most-excessive amount of data records that would need to be supported for a given time period. Operational projections should be used to determine the periodic demand for data storage capacity. Factors that must be considered when preforming capacity planning are location of data storage facilities, data record retention duration, recovery of deleted data record storage space, and periodic demand for new data record creation. The data retention capacity requirements will affect the software interaction with a database management system, as well as directly impact the configuration of the computing environment.

3.2 Capacity constraints

Production and storage capacity expansions are bounded within upper and lower limits:

(2) NPpgtPCappmin≤PCapEpgt≤NPpgtPCappmax∀p,g,t

(3)NSsgtSCapsmin≤SCapEsgt≤NSsgtSCapsmax∀s,g,t

The consumption of sugar cane (PU1,gt) in time t and sub-region g is limited by the available capacities of sugar cane plantations (CapCropg):

(4) PUigt≤CapCropg∀i=1,g,t,

12.1.3.1.1 Meta LUN concatenation

When the existing LUN storage capacity is not enough for a particular business application, how can we increase the storage capacity? One of the solutions is meta LUN. The way to expand LUN storage capacity is to join other LUNs. This is also called “concatenation.” Any mirror and parity types of RAID can be concatenated. For example, a LUN on RAID-1 can be joined with a LUN on RAID-5. However, a LUN on RAID-0 can be only joined with another LUN on RAID-0.

Concatenation LUN expansion is a quick solution to increase LUN storage capacity, but it will not provide any benefits for LUN performance. In order to improve not only the capacity but also performance, we have to adopt meta LUN striping.

Design problem

Given a maximum storage capacity of 10 maf. What minimum storage capacity must be held in reserve to prevent outflow from exceeding flood stage threshold at 10 maf? The following procedure requires role inversion between X 1 and X 4 and automatic reinversion:

X1={Endowhile< 10,otherwiseExoat10}, that is, X 1 becomes Exo in response to flood control.

X4={Endowhile<lowerbound,otherwiseExoatlowerbound−Δ}, that is, X 4 becomes Exo in response to minimum storage capacity, or

X4={Endowhile>10,otherwiseExoat10 }, that is, X 4 becomes Exo in response to reservoir at full capacity.

Start with solution vector (1, 6, 7, 8, 10, 11, 12), initial flood reservation of 2 maf and X 3 = X 4 = 8 maf, and X 9 = 30.623191.

Iteratively, solve for X 1. If X 1 > 10, invert roles and repeat with X 1 = 10 and solution vector (4, 6, 7, 8, 10, 11, 12).

Iteratively, solve for X 4. If X 4 < minimum storage capacity, reinvert to solution vector (1, 6, 7, 8, 10, 11, 12), or reinvert if X 4 > 10, reduce X 3 by Δ = 0.5 maf, automatically reinvert and repeat from beginning.

Distributed storage

One way to scale storage capacity along with the amount of server resources is to provide storage on every server board. There are actually three types of storage. Boot loader storage holds a small program used to bring up the processor into a state where the bare metal hypervisor or operating system can be loaded and run. This can be a relatively small flash memory device on the board. The hypervisor or operating system can be loaded from a drive on the board or from a shared storage location within the rack. This allows multiple servers to be booted from a single drive. Other storage can exist on each server board and be shared with other servers in the rack using protocols such as iSCSI.

Distributed storage is a good choice for applications such as web hosting, allowing each client to have dedicated server and storage resources on the same board. For applications that require large amounts of storage, dedicated storage arrays may need to be added to the rack. It is also possible to share storage resources between servers within the rack for applications that require a larger amount of processing and storage resources. Storage access by a CPU on a different server card will steal CPU cycles from the local CPU in order to service the request (for example, emulating an iSCSI target). This type of resource clustering also adds additional traffic to the network within the rack. Sharing storage resources between racks can be implemented as long as the additional storage request processing in the server and additional network traffic is accounted for.

The Virtual Data Mart

As computing power and disk storage capacity increased, it became obvious in the early 1990s that a business could appeal to customers directly by using characteristics and historical account information, and customer relationship management (CRM) was born. One-to-one marketing appeals could be supported, and businesses became “smarter” in their ability to convince customers to buy more goods and services. The success of CRM operations changed the way some companies looked at their data. No longer must companies view their databases in terms of just accounts and products, but rather, they could view their customers directly, in terms of all their associated accounts, products, and demographic data. These “logical” data marts could even be implemented as “views” in an RDBMS.

Archiving

Despite the massive increases in storage capacity in recent years, medical imaging has also advanced and thus produces even larger data sets. It has been estimated that 80% of PACS (Picture Archiving And Communications System) images are never viewed again. However, as a reliable method for identifying those 80% has not yet been achieved, all images must be kept, but keeping them online (on expensive storage) is not a sensible option. Thus old images are generally archived onto removable media (again, tape is usual) or onto a slower, less expensive system and the original data deleted to free up space.25 There are several algorithms for identifying data suitable for archiving, but the most common is based on age: not the age of the data, but the time since it was last accessed. In order to implement such a system it is therefore imperative that each access updates the record, either in the database (for single items) or by the operating system (in the case of files).