Consensus and agreement algorithms, Distributed Systems : Notes

•

0 likes•80 views

Consensus and agreement algorithms Some notes on Distributed Systems Written while studying the course Distributed Systems at IIIT Hyderabad, by professor Kishore Kothapalli. The book used is Distributed Computing: Principles, Algorithms, and Systems by Ajay D. Kshemkalyani and Mukesh Singhal.

Engineering

cONSENGUs AND AGRELMINT ALGHORITHMS
A)8MCuRoNes chMO NI ATION
a veo m r in the
ECEman)
euna.
Cdnaute data wele pr riet und.)
pagate aNe nts) dugnguisihet bren reciep ob *
re nidtngc
NETNOAK ceNNLCTivITY SENDER IDENTIFICATIoN
PYM u t crntcttd Cegca) Gnder o a ms atuag noa
CHANNLL REURBL UWAu TMENTI CmeD
MESsn GtS
channels a ltabie, ent pretcses
ad. woress
may tomper a
m
Calao cnled bvat mág)
AGREEMENT VARIABLE
c be u dme vEat4).
LSNgENDUS FROBLEMAGREEMENT PRobLEM
acprecs Fas al value,at all
allnonbu p t agTce abeut ininal
wutt a T on ona valve
valee
u r a Procaa4.
nafon
rttment: Valu termination
vaVd m tnitial valuE a ade
d vsivd art aame
al.d
ama
n Prec a deoide

renan e t u n t
Ah rece b
* m a l vefue
o vtr proceLs nd arriv d on
*** n ma, najont
h c
decik on tueutd
* t
n rnt jer
P e d a s inee tiwe are no t u a
FAIL STOP CoNSEN&US
a gonithm hai
6*
round. a* S** ech
,
d p r e t dndi
vorinble +o
Fo acm=
a other prscee drt o aund
* aunds, ar ent raune h a no
ade
a e ena
d6 *a reund cern@nge
rEachad. 6urë rond do rot ac e ongengus. m mpler OC Cgen)n*)
Y2ANTINE R oNEENES
engtnus on b achitved (5°').
deure4 Pread **reuna
" )

*
j*ecc4, and *
ch ecei a
r
e N d 4-tncn precete
chtvtd e r h e n a t eeit Corm-nutt) Ca achieue
n
uneng themEelve
tender
eattw
:
P
P

aher ynch, paterson
6alorcmodel hchronous tevn s8cronous stom
agreementnone
Vagreement
V conmon knauoledg V encovrent common knbwle
axtstop
-stop V agreement X 0g reemet
bn proLesses
aC6t rounds
byzantine ogreeme x agreemnt
36 D proacsses
ng) raonds
intrdaoducing a leader Coill Solve
al problemS
but what cb the teader beco mes 6aulty?
PAXOS githona egslative consensus stem used on paxes istand on qrecece
althbugh no deterministic outt folerant consensus proto ol an gvarentee Proqress
in an aure netuerk. Cproved 6cher, gneh, and paterton), paros guarentes
Saedy C0onitenw
nd the Conditions at ould prevent t rom maki
Progrags dre dekfieutt to proveke,

Similar to Consensus and agreement algorithms, Distributed Systems : Notes

cpi.pdf

HyperXGaming4

CBSE Physics Journal.pdf

sayanmondal324

Termination detection - Distributed systems : Notes

Subhajit Sahu

Stamp duty circular

mianagpur

Embedded system assignment 4 .pdf

SankalpShrivastav

unit-5.pdf

elaelango

Logical time - Distributed systems : Notes

Subhajit Sahu

Attachment Documents.PDF

Romulo Labayne Bedia

Adobe Scan 06-Mar-2024.pdfhhuiiiiijjhgftr5t

s73678sri

non creamy layer g.o 2009,2015,2016 all new

shanavas chithara

7th Semester (December; January-2014 and 2015) Electronics and Communication ...

BGS Institute of Technology, Adichunchanagiri University (ACU)

Global state and Snapshot recording algorithms - Distributed systems : Notes

Subhajit Sahu

Similar to Consensus and agreement algorithms, Distributed Systems : Notes (12)

cpi.pdf

CBSE Physics Journal.pdf

Termination detection - Distributed systems : Notes

Stamp duty circular

Embedded system assignment 4 .pdf

unit-5.pdf

Logical time - Distributed systems : Notes

Attachment Documents.PDF

Adobe Scan 06-Mar-2024.pdfhhuiiiiijjhgftr5t

non creamy layer g.o 2009,2015,2016 all new

7th Semester (December; January-2014 and 2015) Electronics and Communication ...

Global state and Snapshot recording algorithms - Distributed systems : Notes

More from Subhajit Sahu

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Subhajit Sahu

Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.

Adjusting Bitset for graph : SHORT REPORT / NOTES

Subhajit Sahu

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Subhajit Sahu

Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is Multiply with different modes (map) 1. Performance of sequential execution based vs OpenMP based vector multiply. 2. Comparing various launch configs for CUDA based vector multiply. Sum with different storage types (reduce) 1. Performance of vector element sum using float vs bfloat16 as the storage type. Sum with different modes (reduce) 1. Performance of sequential execution based vs OpenMP based vector element sum. 2. Performance of memcpy vs in-place based CUDA based vector element sum. 3. Comparing various launch configs for CUDA based vector element sum (memcpy). 4. Comparing various launch configs for CUDA based vector element sum (in-place). Sum with in-place strategies of CUDA mode (reduce) 1. Comparing various launch configs for CUDA based vector element sum (in-place).

Adjusting primitives for graph : SHORT REPORT / NOTES

Subhajit Sahu

This includes: - Multiply with different modes (map) 1. Performance of sequential execution based vs OpenMP based vector multiply. 2. Comparing various launch configs for CUDA based vector multiply. - Sum with different storage types (reduce) 1. Performance of vector element sum using float vs bfloat16 as the storage type. - Sum with different modes (reduce) 1. Performance of sequential execution based vs OpenMP based vector element sum. 2. Performance of memcpy vs in-place based CUDA based vector element sum. 3. Comparing various launch configs for CUDA based vector element sum (memcpy). 4. Comparing various launch configs for CUDA based vector element sum (in-place). - Sum with in-place strategies of CUDA mode (reduce) 1. Comparing various launch configs for CUDA based vector element sum (in-place).

Experiments with Primitive operations : SHORT REPORT / NOTES

Subhajit Sahu

This includes: - Adjusting data types for rank vector - Adjusting Pagerank parameters - Adjusting Sequential approach - Adjusting OpenMP approach - Comparing sequential approach - Adjusting Monolithic (Sequential) optimizations (from STICD) - Adjusting Levelwise (STICD) approach - Comparing Levelwise (STICD) approach - Adjusting ranks for dynamic graphs - Adjusting Levelwise (STICD) dynamic approach - Comparing dynamic approach with static - Adjusting Monolithic CUDA approach - Adjusting Monolithic CUDA optimizations (from STICD) - Adjusting Levelwise (STICD) CUDA approach - Comparing Levelwise (STICD) CUDA approach - Comparing dynamic CUDA approach with static - Comparing dynamic optimized CUDA approach with static

PageRank Experiments : SHORT REPORT / NOTES

Subhajit Sahu

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Subhajit Sahu

For massive graphs that fit in RAM, but not in GPU memory, it is possible to take advantage of a shared memory system with multiple CPUs, each with multiple cores, to accelerate pagerank computation. If the NUMA architecture of the system is properly taken into account with good vertex partitioning, the speedup can be significant. To take steps in this direction, experiments are conducted to implement pagerank in OpenMP using two different approaches, uniform and hybrid. The uniform approach runs all primitives required for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid approach runs certain primitives in sequential mode (i.e., sumAt, multiply).

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

Subhajit Sahu

Below are the important points I note from the 2020 paper by Martin Grohe: - 1-WL distinguishes almost all graphs, in a probabilistic sense - Classical WL is two dimensional Weisfeiler-Leman - DeepWL is an unlimited version of WL graph that runs in polynomial time. - Knowledge graphs are essentially graphs with vertex/edge attributes ABSTRACT: Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

Subhajit Sahu

https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1 There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing). Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution. To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution. In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution. The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion. Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

Subhajit Sahu

My notes on shared memory parallelism. Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].

Shared memory Parallelism (NOTES)

Subhajit Sahu

**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include: - Random walk methods - Spectral partitioning - Label propagation - Greedy agglomerative and divisive algorithms - Clique percolation https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6 There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*. **Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community. Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity. Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**. The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

Subhajit Sahu

A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_. https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are: - Vertex following and Minimum label - Data caching - Graph coloring - Threshold scaling With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well. From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Subhajit Sahu

Application Areas of Community Detection: A Review : NOTES

Subhajit Sahu

This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram. Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex. https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329

Community Detection on the GPU : NOTES

Subhajit Sahu

Survey for extra-child-process package : NOTES

Subhajit Sahu

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Subhajit Sahu

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Subhajit Sahu

In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later. https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Subhajit Sahu

Can you ﬁx farming by going back 8000 years : NOTES

Subhajit Sahu

More from Subhajit Sahu (20)

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Adjusting Bitset for graph : SHORT REPORT / NOTES

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Adjusting primitives for graph : SHORT REPORT / NOTES

Experiments with Primitive operations : SHORT REPORT / NOTES

PageRank Experiments : SHORT REPORT / NOTES

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

Shared memory Parallelism (NOTES)

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Application Areas of Community Detection: A Review : NOTES

Community Detection on the GPU : NOTES

Survey for extra-child-process package : NOTES

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

Recently uploaded

fluid mechanics gate notes . gate all pyqs answer

apareshmondalnita

Laundry firms currently use a manual system for the management and maintenance of critical information. The current system requires numerous paper forms, with data stores spread throughout the laundry management infrastructure. Often information is incomplete or does not follow management standards. Records are often lost in transit during computation requiring a comprehensive auditing process to ensure that no vital information is lost. Multiple copies of the same information exist in the laundry firm data and may lead to inconsistencies in data in various data stores. A significant part of the operation of any laundry firm involves the acquisition, management and timely retrieval of great volumes of information. This information typically involves; customer personal information and clothing records history, user information, price of delivery and received date, users scheduling as regards customers details and dealings in service rendered, also our products package waiting list. All of this information must be managed in an efficient and cost wise fashion so that the organization resources may be effectively utilized. We present the design and implementation of a laundry database management system (LBMS) used in a laundry establishment. Laundry firms are usually faced with difficulties in keeping detailed records of customers clothing; this little problem as seen to most laundry firms is highly discouraging as customers are filled with disappointments, arising from issues such as customer clothes mix-ups and untimely retrieval of clothes. The aim of this application is to determine the number of clothes collected, in relation to their owners, as this also helps the users fix a date for the collection of their clothes. Also customer’s information is secured, as a specific id is allocated per registration to avoid contrasting information.

Laundry management system project report.pdf

Kamal Acharya

A computer reservation system or central reservation system is a computerized system used to store and retrieve information and conduct transactions related to air travel, hotels, car rental, or activities. These systems typically allow users to book hotel rooms, rental cars, airline tickets as well as activities and tours. They also provide access to railway reservations and bus reservations in some markets, although these are not always integrated with the main system. For these systems to be accessible on mobile phones and computers outside the premises of the airport, cinema, train station or stadiums, they need to be on the internet or a network. This project focuses on the design and implementation of a web based cinema management system for the allocation of seat tickets online. The system would feature the registration of users, use of serial numbers and pins gotten from scratch cards sold and a printed slip. The system would have a store of all the seats and automate the generation of fresh serial numbers and pins.

A case study of cinema management system project report..pdf

Kamal Acharya

This project aims at the Introduction to app Service Management. This software is designed keeping in mind the user’s efficiency & ease of handling and maintenance , as and secured system over centralized data handling and providing with the features to get the complete study and control over the business. The report depicts the basics logic used for software development long with the Activity diagrams so that logics may be apprehended without difficulty. For detailed information, screen layouts, provided along with this report can be viewed. Although this report is prepared with considering the results required these may be across since the project is subjected to future enhancements as per the need of organizations.

Online resume builder management system project report.pdf

Kamal Acharya

It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.

Courier management system project report.pdf

Kamal Acharya

IT-601 Lecture Notes-UNIT-2.pdf Data Analysis

Dr. Radhey Shyam

The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message. When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely. When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item. Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.

Automobile Management System Project Report.pdf

Kamal Acharya

Natalia Rutkowska - BIM School Course in Kraków

bim.edu.pl

Toll Tax Management System is a web based application that can provide all the information related to toll plazas and the passenger checks in and pays the amount, then he/she will be provided by a receipt. With this receipt he/she can leave the toll booth without waiting for any verification call. The information would also cover registration of staff, toll plaza collection, toll plaza collection entry for vehicles, date wise report entry, Vehicle passes and passes reports b/w dates.

Toll tax management system project report..pdf

Kamal Acharya

In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on. My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways. In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.

Event Management System Vb Net Project Report.pdf

Kamal Acharya

Architectural Portfolio Sean Lockwood

seandesed

2024 DevOps Pro Europe - Growing at the edge

Paco Orozco

Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.

Quality defects in TMT Bars, Possible causes and Potential Solutions.

PrashantGoswami42

Blood Donation Management System is a web database application that enables the public to make online session reservation, to view nationwide blood donation events online and at the same time provides centralized donor and blood stock database. This application is developed by using ASP.NET technology from Visual Studio with the MySQL 5.0 as the database management system. The methodology used to develop this system as a whole is Object Oriented Analysis and Design; whilst, the database for BDMS is developed by following the steps in Database Life Cycle. The targeted users for this application are the public who is eligible to donate blood ,'system moderator, administrator from National Blood Center and the staffs who are working in the blood banks of the participating hospitals. The main objective of the development of this application is to overcome the problems that exist in the current system, which are the lack of facilities for online session reservation and online advertising on the nationwide blood donation events, and also decentralized donor and blood stock database. Besides, extra features in the system such as security protection by using password, generating reports, reminders of blood stock shortage and workflow tracking can even enhance the efficiency of the management in the blood banks. The final result of this project is the development of web database application, which is the BDMS.

Online blood donation management system project.pdf

Kamal Acharya

Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx

Md. Shahidul Islam Prodhan

Pharmacy management system project report..pdf

Kamal Acharya

The Ultimate Guide to External Floating Roofs for Oil Storage Tanks.docx

CenterEnamel

NO1 Pandit Amil Baba In Bahawalpur, Sargodha, Sialkot, Sheikhupura, Rahim Yar...

Amil baba

Presented at NUS: Fuzzing and Software Security Summer School 2024 This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.

Democratizing Fuzzing at Scale by Abhishek Arya

abh.arya

Peek implant persentation - Copy (1).pdf

Ayahmorsy

Recently uploaded (20)

fluid mechanics gate notes . gate all pyqs answer

Laundry management system project report.pdf

A case study of cinema management system project report..pdf

Online resume builder management system project report.pdf

Courier management system project report.pdf

IT-601 Lecture Notes-UNIT-2.pdf Data Analysis

Automobile Management System Project Report.pdf

Natalia Rutkowska - BIM School Course in Kraków

Toll tax management system project report..pdf

Event Management System Vb Net Project Report.pdf

Architectural Portfolio Sean Lockwood

2024 DevOps Pro Europe - Growing at the edge

Quality defects in TMT Bars, Possible causes and Potential Solutions.

Online blood donation management system project.pdf

Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx

Pharmacy management system project report..pdf

The Ultimate Guide to External Floating Roofs for Oil Storage Tanks.docx

NO1 Pandit Amil Baba In Bahawalpur, Sargodha, Sialkot, Sheikhupura, Rahim Yar...

Democratizing Fuzzing at Scale by Abhishek Arya

Peek implant persentation - Copy (1).pdf

Consensus and agreement algorithms, Distributed Systems : Notes

1. cONSENGUs AND AGRELMINT ALGHORITHMS A)8MCuRoNes chMO NI ATION a veo m r in the ECEman) euna. Cdnaute data wele pr riet und.) pagate aNe nts) dugnguisihet bren reciep ob * re nidtngc NETNOAK ceNNLCTivITY SENDER IDENTIFICATIoN PYM u t crntcttd Cegca) Gnder o a ms atuag noa CHANNLL REURBL UWAu TMENTI CmeD MESsn GtS channels a ltabie, ent pretcses ad. woress may tomper a m Calao cnled bvat mág) AGREEMENT VARIABLE c be u dme vEat4). LSNgENDUS FROBLEMAGREEMENT PRobLEM acprecs Fas al value,at all allnonbu p t agTce abeut ininal wutt a T on ona valve valee u r a Procaa4. nafon rttment: Valu termination vaVd m tnitial valuE a ade d vsivd art aame al.d ama n Prec a deoide

2. renan e t u n t Ah rece b * m a l vefue o vtr proceLs nd arriv d on *** n ma, najont h c decik on tueutd * t n rnt jer P e d a s inee tiwe are no t u a FAIL STOP CoNSEN&US a gonithm hai 6* round. a* S** ech , d p r e t dndi vorinble +o Fo acm= a other prscee drt o aund * aunds, ar ent raune h a no ade a e ena d6 *a reund cern@nge rEachad. 6urë rond do rot ac e ongengus. m mpler OC Cgen)n*) Y2ANTINE R oNEENES engtnus on b achitved (5°'). deure4 Pread **reuna " )

3. * j*ecc4, and * ch ecei a r e N d 4-tncn precete chtvtd e r h e n a t eeit Corm-nutt) Ca achieue n uneng themEelve tender eattw : P P

4. aher ynch, paterson 6alorcmodel hchronous tevn s8cronous stom agreementnone Vagreement V conmon knauoledg V encovrent common knbwle axtstop -stop V agreement X 0g reemet bn proLesses aC6t rounds byzantine ogreeme x agreemnt 36 D proacsses ng) raonds intrdaoducing a leader Coill Solve al problemS but what cb the teader beco mes 6aulty? PAXOS githona egslative consensus stem used on paxes istand on qrecece althbugh no deterministic outt folerant consensus proto ol an gvarentee Proqress in an aure netuerk. Cproved 6cher, gneh, and paterton), paros guarentes Saedy C0onitenw nd the Conditions at ould prevent t rom maki Progrags dre dekfieutt to proveke,

Consensus and agreement algorithms, Distributed Systems : Notes

Recommended

Recommended

More Related Content

Similar to Consensus and agreement algorithms, Distributed Systems : Notes

Similar to Consensus and agreement algorithms, Distributed Systems : Notes (12)

More from Subhajit Sahu

More from Subhajit Sahu (20)

Recently uploaded

Recently uploaded (20)

Consensus and agreement algorithms, Distributed Systems : Notes