New Hoarding Technique for Handling Disconnection in Mobile

Literature watch On newborn lay away Technique for Handling Disconnection in wandering Submitted by Mayur Rajesh Bajaj (IWC2011021) In Partial fulfilment for the award of the stagecoach Of Master of technology In INFORMATION TECHNOLOGY (Specialization Wireless communicating and deliberation) pic Under the Guidance of Dr. Manish Kumar INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD (A University Established on a swallow floor sec. 3 of UGC Act, 1956 vide Notification no. F. 9-4/99-U. 3 Dated 04. 08. 2000 of the Govt. of India) (A Centre of purity in In castingation Technology Established by Govt. of India) Table of table of contents pic 1.Introduction. 3 2. Related die hard and Motivation 1. Coda The Pioneering corpse for save up. 4 2. compile found on selective information Mining Techniques.. 5 3. lay away Techniques found on Program Trees.. 8 4. billboard in a Distri simplyed Environment. 9 5. hive up content for ready cultivation 10 6. ready Clients Throug h Cooperative Hoarding.. 10 7. Comparative sermon preceding proficiencys. 11 3. Problem Definition. 11 4. New lift Suggested 1. Zipfs practice of law .. 2 2. Object hot spot Prediction molding 13 5. Schedule of Work. 13 6. Conclusion 13 References 14 . Introduction Mobile doodads argon the computers which atomic number 18 having piano tuner conference capabilities to briny course global entropy services from any location while roaming. directly a days liquid devices atomic number 18 makeing coverings such(prenominal) as mul clockdia, World Wide Web and early(a) tall write applications which look ats continuous connections and Mobile devices ar loseing here. However, vigorous devices with wireless communication argon frequently undo from the ne 2rk due to the cost of wireless communication or the unavailability of the wireless ne bothrk.Disconnection period of mobile device from its network is called as offline period. Such offline periods may appear for diff erent reasons learned (e. g. , the available connection is too expensive for the intaker) or unintentional (e. g. , lack of infrastructure at a given sentence and location). During offline periods the exploiter dropful only penet proportionalityn materials located on the devices local anesthetic memory. Mobile musical arrangements typically deliver a relatively small centre of memory, which is oft non enough to store all the readed selective information for ongoing activities to continue.In such a fictional character, a decision should be interpreted on which part of the selective information has to be saved. Often we displacenot count on the substance abusers birth judgement of what he/she exitinging need and prefetch. Rather, in our opinion, some sort of automatic prefetching would be coveted. Uninterrupted accomplishment in offline mode exit be in high demand and the mobile computer schemes should provide support for it. Seamless disjuncture goat be a chieved by loading the shoot downs that a user leave entranceway in the early from the network to the local shop. This preparation abut for dis attached operation is called amassing.Few of the parameters which complicate the save butt against are prediction of future rise to power pattern of the user, handling of store up miss, peculiar(a) local compose memory and unpredictable disconnections and reconnection, activities on cacheed end at other clients, the asymmetry of communications bandwidth in downstream and upstream. An authorised level is to measure the gallery of the stash and to try to improve it continuously. An often utilise metric in the evaluation of caching proxies is the tear ratio. Hit ratio is calculated by dividing the rate of by the thorough number of uploaded predictions.It is a good measure for billboard systems, though a better measure is the miss ratio a percentage of rise to poweres for which the save is ineffective. In this work we have given skeleton overview of the techniques proposed in earlier days and too given the idea for the unsanded lay away technique. 2. Related Work and Motivation Before the early 1990s, at that place was little look for on save up. Since then, however, interest has increased dramatically among research scientists and professors around the globe and many techniques have been true. Here we have counted some of the techniques and as well will discuss them in brief. Coda The Pioneering System for Hoarding Hoarding ground on entropy Mining Techniques ? conjuror Hoarding System (inspired by clustering technique) ? standoff Rule-Based Techniques ? Hoarding Based on Hyper Graph ? opportunity Graph Based Technique Hoarding Techniques Based on Program Trees Hoarding in a Distributed Environment Hoarding content for mobile learning Mobile Clients Through Cooperative Hoarding 2. 1 Coda Coda is a distributed turn on system found on clientserver architecture, where in that respect are many clients and a comparatively smaller number of servers.It is the basic system that enabled users to work in disconnected mode. The concept of lay aside was introduced by the Coda group as a means of enabling disconnected operation. Disconnections in Coda are simulated to occur involuntarily due to network failures or voluntarily due to the detachment of a mobile client from the network. Voluntary and in spontaneous disconnections are handled the similar way. The hive up manager of Coda, called Venus, is designed to work in disconnected mode by serving client requests from the cache when the mobile client is detached from the network.Requests to the institutionalizes that are not in the cache during disconnection are reflected to the client as failures. The gathering system of Coda lets users select the burdens that they will hopefully need in the future. This information is utilize to root what to load to the local storage. For disconnected operation , files are loaded to the client local storage, because the master copies are kept at stationary servers, there is the spirit of replication and how to manage locks on the local copies. When the disconnection is voluntary, Coda handles this case by geting exclusive locks to files.However in case of unconscious disconnection, the system should subject the conflicting lock requests for an object to the reconnection time, which may not be predictable. The cache management system of Coda, called Venus, differs from the previous ones in that it incorporates user profiles in access to the recent savoir-faire history. Each workstation maintains a list of pathnames, called the aggregate selective informationbase. These pathnames mold objects of interest to the user at the workstation that maintains the bundle infobase. Users finish modify the save database via scripts, which are called hoard profiles.Multiple hoard profiles can be delineate by the same user and a combination o f these profiles can be employ to modify the hoard database. Venus provides the user with an option to specify two time points during which all file reservoirs will be recorded. repayable to the limitations of the mobile cache space, users can also specify formerities to provide the hoard system with hints about the importance of file objects. Precedence is given to high precedency objects during lay away where the priority of an object is a combination of the user undertake priority and a parameter indicating how recently it was accessed.Venus performs a hierarchical cache management, which means that a directory is not purged unless all the subdirectories are already purged. In summary, the Coda save mechanism is based on a to the lowest degree recently utilize (LRU) policy plus the user specified profiles to modify the hoard data-base, which is use for cache management. It relies on user intervention to mould what to hoard in addition to the objects already maintaine d by the cache management system. In that respect, it can be classified as semi- modify.Researchers developed more advanced techniques with the aim of minimizing the user intervention in ascertain the restrict of objects to be hoarded. These techniques will be discussed in the following sections. 2. 2 Hoarding based on Data mining Techniques Knowing the raise pattern from the large accumulation of data is the basis of data mining. In the earlier history of save up related works researchers have utilize many different data mining techniques in this arena of mobile cache. Mainly clustering and standstill direct mining techniques were adopted from data mining domain. . 2. 1 prestidigitator Hoarding System To automate the hoarding assist, author developed a hoarding system called prophet that can make hoarding decisions without user intervention. The basic idea in SEER is to organize users activities as projects in order to provide more faithful hoarding decisions. A outer space measure needs to be defined in order to apply clustering algorithmic ruleic course of instructions to group related files. SEER uses the notion of semantic surmount based on the file reference behaviour of the files for which semantic distance needs to be calculated.Once the semantic distance mingled with pairs of files are calculated, a standard clustering algorithm is used to partition the files into clusters. The developers of SEER also employ some filters based on the file type and other conventions introduced by the specialised file system they assumed. The basic architecture of the SEER predictive hoarding system is provided in figure 1. The observer monitors user behaviour (i. e. , which files are accessed at what time) and feeds the cleaned and formatted access paths to the correlator, which then generates the distances among files in terms of user access behaviour.The distances are called the semantic distance and they are fed to the cluster generator that grou ps the objects with respect to their distances. The aim of clustering is, given a strict of objects and a similarity or distance matrix that describes the pairwise distances or similarities among a rear of objects, to group the objects that are close to distributively other or similar to all(prenominal) other. deliberation of the distances among files is done by looking at the high- direct file references, such as open or status inquiry, as opposed to individual reads and writes, which are claimed to obscure the surgery of distance calculation. pic send off 1. computer architecture of the SEER Predictive Hoarding System The semantic distance surrounded by two file references is based on the number of intervening references to other files in between these two file references. This definition is further raise by the notion of spirit semantic distance. Lifetime semantic distance between an open file A and an open file B is the number of intervening file opens (including the open of B). If the file A is unlikeable before B is unfastened, then the distance is defined to be zero.The lifetime semantic distance relates two references to different files however it needs to be somehow converted to a distance measure between two files quite of file references. Geometric mean of the file references is calculated to obtain the distance between the two files. Keeping all pairwise distances takes a clutch of space. Therefore, only the distances among the closest files are represented (closest is determined by a parameter K, K closest pairs for individually file are considered). The developers of SEER used a variation of an collective (i. e. bottom up) clustering algorithm called k nearest neighbour, which has a low time and space complexity. An agglomerative clustering algorithm first considers individual objects as clusters and tries to combine them to form bigger clusters until all the objects are grouped into one single cluster. The algorithm they used i s based on merging sub clusters into larger clusters if they carry on at least kn neighbours. If the two files share less than kn close files but more than kf, then the files in the clusters are replicated to form overlapping clusters instead of being merged.SEER works on top of a user aim replication system such as Coda and leaves the hoarding process to the underlying file system afterward providing the hoard database. The files that are in the same project as the file that is newly in use are included to the set of files to be hoarded. During disconnected operation, hoard misses are calculated to give a feedback to the system. 2. 2. 2 Association Rule-Based Techniques Association rule overview Let I=i1,i2.. im be a set of literals, called items and D be a set of transactions, such that ?T ? D T? I. A transaction T contains a set of items X if X? T. An tie rule is denoted by an implication of the form X ? Y, where X? I, Y ? I, and X ? Y = NULL. A rule X ? Y is give tongue to to hold in the transaction set D with confidence c if c% of the transactions in D that contain X also contain Y. The rule X? Y has support sin the transaction set D if s% of transactions in D contains X? Y. The hassle of mining association rules is to find all the association rules that have a support and a confidence greater than user-specified thresholds.The thresholds for confidence and support are called minconf and minsup respectively. In Association Rule Based Technique for hoarding, authors depict an application independent and generic technique for determine what should be hoarded prior to disconnection. This manner utilizes association rules that are extracted by data mining techniques for ascertain the set of items that should be hoarded to a mobile computer prior to disconnection. The proposed method was implemented and tested on synthetic data to estimate its effectiveness.The process of automated hoarding via association rules can be summarized as follows feeling 1 Requests of the client in the new session are used through an inferencing mechanism to construct the nominee set prior to disconnection. timbre 2 Candidate set is pruned to form the hoard set. Step 3 Hoard set is loaded to the client cache. The need to have clear up steps for constructing the prognosis set and the hoard set arises from the fact that users also move from one machine to another that may have lower resources.The construction of the hoard set must adapt to such emf changes. Construction of candidate set An inferencing mechanism is used to construct the candidate set of data items that are of interest to the client to be disconnected. The candidate set of the client is constructed in two steps 1. The inferencing mechanism finds the association rules whose heads (i. e. , left hand side) match with the clients requests in the current session, 2. The tails (i. e. , right hand side) of the matching rules are collected into the candidate set.Construction of Hoard se t The client that issued the hoard request has limited re-sources. The storage resource is of particular importance for hoarding since we have a limited space to load the candidate set. Therefore, the candidate set obtained in the first phase of the hoarding set should shrink to the hoard set so that it fits the client cache. Each data item in the candidate set is associated with a priority. These priorities together with various heuristics must be incorporated for determining the hoard set. The data items are used to sort the rules in travel order of priorities.The hoard set is constructed out of the data items with the highest priority in the candidate set just enough to fill the cache. 3. Hoarding Based on Hyper Graph Hyper represent based approach presents a sorting of low-cost automatic data hoarding technology based on rules and hyper interpret computer simulation. It first uses data mining technology to extract place relevance rules of data from the broadcasting histor y, and then formulates hyper graph sit, sorting the data into clusters through hyper graph partitioning methods and sorting them topologically.Finally, fit to the data hinder window and the current visit record, data in corresponding clusters will be collected. Hyper graph model Hyper graph model is defined as H = (V, E) where V=v1 ,v2 , ,vn is the vertices assembly of hyper graph, and E=e1 ,e2 , ,em is super-edge collection of hyper graph (there speak upd(a) to be m super-edges in total). Hyper graph is an extension of graph, in which each super-edge can be connected with two or more vertices. Super-edge is the collection of a group of vertices in hyper graph, and superedge ei = vi1, vi2, inj in which vi1,vi2 , ,vin ? V . In this model, vertices collection V corresponds to the history of broadcast data, in which each point corresponds to a broadcast data item, and each super-edge corresponds to a sequence model. Sequence model shows the orders of data items. A sequence mod el in size K can be expressed as p = . Use of hyper graph in hoarding are discussed in paper in expatiate. 4. Probability Graph Based Technique This paper proposed a low-cost automated hoarding for mobile computing.Advantage of this approach is it does not explore application specific heuristics, such as the directory structure or file extension. The property of application independence makes this algorithm applicable to any predicative caching system to insure data hoarding. The most distinguished feature of this algorithm is that it uses luck graph to represent data relationships and to update it at the same time when users request is processed. Before disconnection, the cluster algorithm divides data into groups.Then, those groups with the highest priority are selected into hoard set until the cache is filled up. Analysis shows that the strike of this algorithm is much lower than previous algorithms. Probability Graph An important parameter used to construct probability graph is look-ahead period. It is a frozen(p) number of file references that defines what it means for one file to be opened soon after another. In other words, for a specific file reference, only references within the look-ahead period are considered related. In fact, look-ahead period is an resemble method to avoid traversing the whole trace.Unlike constructing probability graph from local file systems, in the con schoolbook of mobile data access, data set is dynamically collected from remote data requests. Thus, we implemented a variation of algorithm used to construct probability graph, as illustrated in Figure 2. pic Figure 2. Constructing the probability graph The basic idea is simple If a reference to data object A follows the reference to data object B within the look-ahead period, then the weight of directed arc from B to A is added by one. The look-ahead period affects absolute weight of arcs.Larger look-ahead period produces more arcs and larger weight. A s dependency to B is represented by the ratio of weight of arc from B to A divided by the total weight of arcs leaving B. Clustering Before constructing the final hoard set, data objects are clustered into groups based on dependency among data objects. The main objective of the clustering phase is to guarantee closely related data objects are partitioned into the same group. In the successive selecting phase, data objects are selected into hoard set at the unit of group. This design provides more continuity in user operation when disconnected.Selecting Groups The following four kinds of heuristic information are applicable for calculating priority for a group Total access time of all data objects Average access time of data objects Access time of the start data object Average access time per byte. 2. Hoarding Techniques Based on Program Trees A hoarding tool based on program motion trees was developed by author rail under OS/2 operating system. Their method is based on analyzing program execution s to construct a profile for each program depending on the files the program accesses.They proposed a solution to the hoarding problem in case of informed disconnections the user tells the mobile computer that there is an imminent disconnection to fill the cache intelligently so that the files that will be used in the future are already there in the cache when unavoidable. pic Figure 3. Sample program Tree This hoarding mechanism lets the user make the hoarding decision. They present the hoarding options to the user through a graphical user interface and working sets of applications are captured automatically. The working sets are detected by logging the user file accesses at the background.During hoarding, this log is analyzed and trees that represent the program executions are constructed. A node denotes a file and a link from a bring up to one of its child nodes tells us that either the child is opened by the parent or it is executed by the parent. Roots of the trees are the s ign processes. Program trees are constructed for each execution of a program, which captures multiple contexts of executions of the same program. This has the advantage that the whole context is captured from different execution times of the program.Finally, hoarding is performed by taking the union of all the execution trees of a running program. A sample program tree is provided in Figure 3. Due to the storage limitations of mobile computers, the number of trees that can be stored for a program is limited to 15 LRU program trees. Hoarding through program trees can be thought of as a generalization of a pro-gram execution by looking at the past behaviour. The hoarding mechanism is enhance by letting the user rule out the data files. Data files are automatically detected using three complementary heuristics 1.Looking at the filename extensions and observing the filename conventions in OS/2, files can be distinguished as executable, batch files, or data files. 2. Directory inferencin g is used as a spatial locality heuristic. The files that differ in the top level directory in their pathnames from the running program are assumed to be data files, but the programs in the same top level directory are assumed to be part of the same program. 3. Modification times of the files are used as the final heuristic to deter-mine the type of a file. Data files are assumed to be modified more recently and frequently than the executables.They devised a parametric model for evaluation, which is based on recency and frequency. 3. Hoarding in a Distributed Environment Another hoarding mechanism, which was presented for specific application in distributed system, assumes a specific architecture, such as infostations where mobile users are connected to the network via wireless local area networks (LANs) that offer a high bandwidth, which is a cheaper option compared to wireless wide area networks (WANs). The hoarding process is handed over to the infostations in that model and it i s assumed that what the user wants to access is location-dependent.Hoarding is proposed to fill the gap between the capacity and cost trade-off between wireless WANS and wireless LANs. The infestations do the hoarding and when a request is not found in the infostation, then WAN will be used to get the data item. The hoarding decision is based on the user access patterns coupled with that users location information. Items frequently accessed by mobile users are recorded together with spatial information (i. e. , where they were accessed). A region is divided into hoarding areas and each infostation is responsible with one hoarding area. 4. Hoarding content for mobile learningHoarding in the learning context is the process for automatically choosing what part of the overall learning content should be watchful and made available for the next offline period of a learner equipt with a mobile device. We can split the hoarding process into few steps that we will discuss further in more de tails 1. Predict the entry point of the current user for his/her next offline learning session. We call it the starting point. 2. Create a candidate for caching set. This set should contain related documents (objects) that the user might access from the starting point we have selected. 3.Prune the set the objects that probably will not be needed by the user should be excluded from the candidate set, thus making it smaller. This should be done based on user behaviour observations and domain knowledge. 4. befall the priority to all objects still in the hoarding set after pruning. Using all the knowledge available about the user and the current learning domain, every object left in the hoarding set should be assigned a priority value. The priority should mean how important the object is for the next user session and should be higher if we suppose that there is a higher probability that an object will be used sooner. . Sort the objects based on their priority, and produce an ordered l ist of objects. 6. Cache, starting from the beginning of the list (thus putting in the device cache those objects with higher priority) and continue with the ones with smaller weights until available memory is filled in. 5. Mobile Clients Through Cooperative Hoarding Recent research has shown that mobile users often move in groups. Cooperative hoarding takes advantage of the fact that pull down when disconnected from the network, clients may still be able to communicate with each other in ad-hoc mode.By playacting hoarding cooperatively, clients can share their hoard content during disconnections to achieve higher data accessibility and reduce the happen of critical cache misses. Two cooperative hoarding schemes, GGH and pate, have been proposed. GGH improves hoard performance by al-lowing clients to take advantage of what their peers have hoarded when making their own hoarding decisions. On the other hand, CAP selects the best client in the group to Hoard each object to maximis e the number of bizarre objects hoarded and minimise access cost. Simulation results show that compare to existing schemes.Details of GGH and CAP are given in paper. 2. 7 Comparative Discussion previous techniques The hoarding techniques discussed above vary depending on the target system and it is ambitious to make an objective comparative evaluation of their effectiveness. We can classify the hoarding techniques as being auto-mated or not. In that respect, being the initial hoarding system, Coda is semiautomated and it needs human intervention for the hoarding decision. The rest of the hoarding techniques discussed are fully automated how-ever, user supervision is always desirable to give a final touch to the files to be hoarded.Among the automated hoarding techniques, SEER and program tree-based ones assume a specific operating system and use semantic information about the files, such as the appointment conventions, or file reference types and so on to construct the hoard set. However, the ones based on association rule mining and infostation environment do not make any operating system specific assumptions. Therefore, they can be used in generic systems. Coda handles both voluntary and involuntary disconnections well.The infostation-based hoarding approach is also inherently designed for involuntary disconnections, because hoarding is done during the user passing in the range of the infostation area. However, the time of disconnection can be predicted with a certain error specify by considering the direction and the speed of the moving client predicting when the user will go out of range. The program tree-based methods are specifically designed for antecedently informed disconnections. The scenario assumed in the case of infostations is a distributed wire-less infrastructure, which makes it unique among the hoarding mechanisms.This case is especially important in todays man where peer-to-peer systems are becoming more and more popular. 3. Problem Defin ition The New Technique that we have planned to design for hoarding will be used on Mobile Network. Goals that we have set are a. purpose a solution having optimal hit ratio in the hoard at local node. b. Technique should not have greater time complexity because we dont have much time for performing hoarding operation after the knowledge of disconnection. c. Optimal utilization of hoard memory. d. Support for both intentional and unintentional disconnection. e.Proper handling of conflicts in hoarded objects upon reconnection. However, our priority will be for hit ratio than the other goals that we have set. We will take certain assumptions about for other issues if we find any range of mountains of improvement in hit ratio. 4. New Approach 4. 1 Zipfs Law It is a mathematical tool to describe the relationship between words in a text and their frequencies. Considering a long text and assigning ordinates to all words by the frequencies in this text, the occurrence probability P (i) of the word with rank i satisfies the formula below, which is known as Zipf first law, where C is a constant.P (i) = pic . (1) This formula is further extended into a more generalized form, known as Zipf-like law. P (i) = pic. (2) Obviously, pic. (3) Now accord to (2) and (3), we have Cpic pic Our work is to dynamically calculate for different streams and then according to above Formula (2) and (4), the hotspot can be predicted based on the be of an object. 4. 2 Object Hotspot Prediction Model 4. 2. 1 Hotspot Classification We classify hotspot into two categories permanent hotspot and stage hotspot. Permanent hotspot is an object which is frequently accessed regularly.Stage hotspot can be further divided into two types cyclical hotspot and sudden hotspot. Cyclical hotspot is an object which becomes popular periodically. If an object is considered as a focus suddenly, it is a sudden hotspot. 4. 2. 2. Hotspot designation Hotspots in distributed stream-processing storage systems can be identified via a ranking policy (sorted by access frequencies of objects). In our design, the hotspot objects will be inserted into a hotspot queue. The maximum queue length is determined by the cache size and the average size of hotspot Objects.If an objects rank is smaller than the maximum hotspot queue length (in this case, the rank is high), it will be considered as hotspot in our system. Otherwise it will be considered as non hotspot. And the objects in the queue will be handled by hotspot cache strategy. 4. 2. 3 Hotspot Prediction This is our main section of interest, here we will try to determine the prediction model for hoard content with optimal hoard hit ratio. 5. Schedule of Work Work Scheduled Period Remarks Studying revious work on Hoarding July Aug 2012 Complete Identifying Problem Sept 2012 Complete Innovating New Approach Oct 2012 current Integrating with Mobile Arena as solution to Hoarding Nov- downslope 2012 - Simulation And Testing Jan 2013 - Opti mization Feb 2013 - Simulation And Testing Mar 2013 - paper Thesis Work / Journal Publication Apr May 2013 - 6. Conclusion In this literature survey we have discussed previous related work on hoarding. We have also given the requirements for the new technique that is planned to be design.Also we are suggesting a new approach that is coming under the grade of Hoarding with Data Mining Techniques. Recent studies have shown that the use of proposed technique i. e. Zipfs-Like law for caching over the web contents have improved the hit ratio to a greater extent. Here with this work we are expecting improvements in hit ratio of the local hoard. References 1. James J. Kistler and Mahadev Satyanarayanan. Disconnected Operation in the Coda File System. ACM Transactions on Computer Systems, vol. 10, no. 1, pp. 325, 1992. 2. Mahadev Satyanarayanan. The Evolution of Coda. ACM Transactions on Computer Systems, vol. 20, no. 2, pp. 85124, 2002 3. Geoffrey H. Kuenning and Gerald J. Popek. aut omatize Hoarding for Mobile Computers.In proceedings of the 16th ACM Symposium on Operating System Principles (SOSP 1997), October 58, St. Malo, France, pp. 264275, 1997. 4. Yucel Saygin, Ozgur Ulusoy, and Ahmed K. Elmagarmid. Association Rules for Supporting Hoarding in Mobile deliberation Environments. In Proceedings of the 10th IEEE Workshop on Research Issues in Data Engineering (RIDE 2000), February 2829, San Diego, pp. 7178, 2000. 5. Rakesh Agrawal and Ramakrishna Srikant, Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on real Large Databases, Chile, 1994. 6. GUO Peng, Hu Hui, Liu Cheng. The Research of Automatic Data Hoarding Technique Based on Hyper Graph.Information Science and Engineering (ICISE), 1st International Conference, 2009. 7. Huan Zhou, Yulin Feng, Jing Li. Probability graph based data hoarding for mobile environment. Presented at Information & packet Technology, pp. 35-41, 2003. 8. Carl Tait, Hui Lei, Swarup Acharya, and Henry Chang. Intelligent File Hoarding for Mobile Computers. In Proceedings of the 1st Annual International Conference on Mobile Computing and Networking (MOBICOM95), Berkeley, CA, 1995. 9. Anna Trifonova and Marco Ronchetti. Hoarding content for mobile learning. Journal International Journal of Mobile communication theory archive Volume 4 Issue 4, Pages 459-476, 2006. 10. Kwong Yuen Lai, Zahir Tari, Peter Bertok.Improving Data Accessibility for Mobile Clients through Cooperative Hoarding. Data Engineering, ICDE proceedings twenty-first international Conference 2005. 11. G. Zipf, Human Behavior and the Principle of Least Effort. Addison-Wesley, 1949. 12. Chentao Wu, Xubin He, Shenggang Wan, Qiang Cao and Changsheng Xie. Hotspot Prediction and Cache in Distributed Stream-processing Storage Systems. Performance Computing and Communications Conference (IPCCC) IEEE 28th International, 2009. 13. Lei Shi, Zhimin Gu, Lin Wei and Yun Shi. An Applicative Study of Zipfs Law on Web Cache International Journal of Information Technology Vol. 12 No. 4 2006. 14. Web link http//en. wikipedia. org/wiki/Zipf%27s_law