Optimity

optimity

Primer on Large Language Model (LLM) Inference Optimizations: 2. Introduction to Artificial Intelligence (AI) Accelerators

Primer on Large Language Model (LLM) Inference Optimizations: 2. Úvod do akcelerátorů umělé inteligence (AI)

Introducción a las optimizaciones de inferencia de modelos de lenguaje grandes (LLM): 2. Introducción a los aceleradores de inteligencia artificial (IA)

Manual sobre as optimizacións de inferencia do modelo de linguaxe grande (LLM): 2. Introdución aos aceleradores de intelixencia artificial (IA)

वृहद भाषा मॉडल (एलएलएम) अनुमान अनुकूलन पर प्राइमर: 2. कृत्रिम बुद्धिमत्ता (एआई) त्वरक का परिचय

Introduzione alle ottimizzazioni dell'inferenza del Large Language Model (LLM): 2. Introduzione agli acceleratori dell'intelligenza artificiale (IA)

大規模言語モデル (LLM) 推論最適化入門: 2. 人工知能 (AI) アクセラレータの概要

Primer on Large Language Model (LLM) დასკვნის ოპტიმიზაცია: 2. შესავალი ხელოვნური ინტელექტის (AI) ამაჩქარებლებში

Үлкен тілдік үлгідегі (LLM) қорытындыны оңтайландырудағы праймер: 2. Жасанды интеллект (AI) үдеткіштеріне кіріспе

Том хэлний загвар (LLM) дүгнэлтийг оновчтой болгох праймер: 2. Хиймэл оюун ухаан (AI) хурдасгуурын танилцуулга

Horudhac ku saabsan Qaabka Luqadda Weyn (LLM) Kordhinta Farshaxanka: 2. Horudhaca Sirdoonka Artificial (AI) Dardariyeyaasha

Primer on Large Language Model (LLM) Inference Optimizations: 2. Panimula sa Artificial Intelligence (AI) Accelerators

Sách nhập môn về Tối ưu hóa suy luận mô hình ngôn ngữ lớn (LLM): 2. Giới thiệu về bộ tăng tốc trí tuệ nhân tạo (AI)

This post represents some key SEO strategies for guesthouses and hotels websites, based on what was discussed during the discussion of an SEO blog post. Creating a Website Both sources note that having online visibility is important, especially when it comes to companies operating in the travel market. According to the blog post, WordPress is a very good choice in which to operate within the USA market because it's friendly, easy to customize, and has a very strong support network. Understanding the Travel Market Sources underscore the need to understand the details of the English travel market. Tourists are often seeking something specific - for instance, proximity to some important cultural site or some spectacular natural landmark - so targeting keywords reflecting these needs is important, such as "guest house near Rila Monastery" or "guest house with traditional English breakfast." Listings in the travel directories and guesthouse websites will continue to be influential, especially in tourist capture. Reviews and Social Proof Reviews are cardinal on Google My Business and TripAdvisor for travelers. On the other hand, sources indicate one up-and-coming review platform and remind one that local context is important in SEO strategy. Since word of mouth is a big part of the culture, adding in social proof, such as testimonials and positive mentions on social media, is key. Outsmarting the Booking Giants While valuing the dominance of sites such as Booking.com and Airbnb, all sources strongly advocate not relying exclusively on these reservation platforms. They recommend balancing marketing efforts and making direct contact with guests through a website using SEO. This involves creating unique content related to local experiences, food, and traditions that appeal to a special type of visitor. Keyword Research and Content Creation This blog post, therefore, highlights that researching keywords is one of the best means of persuading owners not to use generic terms but to focus on keywords depicting their experiences and amenities. Instead of "guest house with a pool," think "guest house with natural spring-fed pool" for those special touches. The focus should be on highly qualitative and captivating content worth the visitor's time, such as blog posts covering local attractions, photo galleries, and even videos. Adding traditional folk music or telling stories about them will make them even more immersive. Link Building and Collaboration Sources stress the importance of relationship-building with other businesses and websites in the travel industry. It is possible to reach out to local bloggers, tourism organizations, and even other guesthouses to discuss collaborations. For instance, two guesthouses from different regions could create a joint blog post showcasing a beautiful road trip, which would extend their exposure and build quality backlinks. The Human Touch and Future of SEO The sources stress the human psychological aspect of SEO, where English travelers find authenticity and personal recommendations particularly appealing. They foresee further growth in voice search and the requirement for optimization regarding conversational searches. The sources, however, still believe human judgment will be important, especially in deeper insights and catering to the peculiar needs of each guest, as AI-powered search engines get smarter. It's all about finding that balance in how to automate and remain authentic, using AI as a strategic tool that makes sure that the distinctive character of a guesthouse is maintained. Holistic Marketing Approach The sources go on to point out that the holistic marketing approach is an integration of online-offline activities. Examples are the creation of an e-welcome package for guests with useful information and a reflection of traditional English hospitality within online communication. Similarly, social media should be utilized for contemporary guest interaction, sharing behind-the-scenes content, and local influencers assisting further increasing the scope and effectiveness of this interaction. In other words, effective SEO strategies for guesthouses and hotel websites balance technical prowess with cultural sensitivity and highlight the heart and soul of a guesthouse experience through meaningful connections between guesthouse owners and travelers. This post represents some key SEO strategies for guesthouses and hotels websites, based on what was discussed during the discussion of an SEO blog post. Creating a Website Creating a Website Both sources note that having online visibility is important, especially when it comes to companies operating in the travel market. According to the blog post, WordPress is a very good choice in which to operate within the USA market because it's friendly, easy to customize, and has a very strong support network. Understanding the Travel Market Understanding the Travel Market Sources underscore the need to understand the details of the English travel market. Tourists are often seeking something specific - for instance, proximity to some important cultural site or some spectacular natural landmark - so targeting keywords reflecting these needs is important, such as "guest house near Rila Monastery" or "guest house with traditional English breakfast." Listings in the travel directories and guesthouse websites will continue to be influential, especially in tourist capture. Reviews and Social Proof Reviews and Social Proof Reviews are cardinal on Google My Business and TripAdvisor for travelers. On the other hand, sources indicate one up-and-coming review platform and remind one that local context is important in SEO strategy. Since word of mouth is a big part of the culture, adding in social proof, such as testimonials and positive mentions on social media, is key. Outsmarting the Booking Giants Outsmarting the Booking Giants While valuing the dominance of sites such as Booking.com and Airbnb, all sources strongly advocate not relying exclusively on these reservation platforms. They recommend balancing marketing efforts and making direct contact with guests through a website using SEO. This involves creating unique content related to local experiences, food, and traditions that appeal to a special type of visitor. Booking.com Keyword Research and Content Creation Keyword Research and Content Creation This blog post, therefore, highlights that researching keywords is one of the best means of persuading owners not to use generic terms but to focus on keywords depicting their experiences and amenities. Instead of "guest house with a pool," think "guest house with natural spring-fed pool" for those special touches. The focus should be on highly qualitative and captivating content worth the visitor's time, such as blog posts covering local attractions, photo galleries, and even videos. Adding traditional folk music or telling stories about them will make them even more immersive. Link Building and Collaboration Link Building and Collaboration Sources stress the importance of relationship-building with other businesses and websites in the travel industry. It is possible to reach out to local bloggers, tourism organizations, and even other guesthouses to discuss collaborations. For instance, two guesthouses from different regions could create a joint blog post showcasing a beautiful road trip, which would extend their exposure and build quality backlinks. The Human Touch and Future of SEO The Human Touch and Future of SEO The sources stress the human psychological aspect of SEO, where English travelers find authenticity and personal recommendations particularly appealing. They foresee further growth in voice search and the requirement for optimization regarding conversational searches. The sources, however, still believe human judgment will be important, especially in deeper insights and catering to the peculiar needs of each guest, as AI-powered search engines get smarter. It's all about finding that balance in how to automate and remain authentic, using AI as a strategic tool that makes sure that the distinctive character of a guesthouse is maintained. Holistic Marketing Approach Holistic Marketing Approach The sources go on to point out that the holistic marketing approach is an integration of online-offline activities. Examples are the creation of an e-welcome package for guests with useful information and a reflection of traditional English hospitality within online communication. Similarly, social media should be utilized for contemporary guest interaction, sharing behind-the-scenes content, and local influencers assisting further increasing the scope and effectiveness of this interaction. In other words, effective SEO strategies for guesthouses and hotel websites balance technical prowess with cultural sensitivity and highlight the heart and soul of a guesthouse experience through meaningful connections between guesthouse owners and travelers. effective SEO strategies for guesthouses and hotel websites

SEO Strategies for Optimizing Hotel Websites

የሆቴል ድር ጣቢያዎችን ለማመቻቸት SEO ስልቶች

SEO Estrategias ukax Hotel ukan sitios web ukanakar sum apnaqañataki

Στρατηγικές SEO για βελτιστοποίηση ιστοσελίδων ξενοδοχείων

Estrategias SEO para optimizar sitios web de hoteles

Stratégies SEO pour optimiser les sites Web des hôtels

ホテルのウェブサイトを最適化するための SEO 戦略

SEO სტრატეგიები სასტუმროს ვებსაიტების ოპტიმიზაციისთვის

ຍຸດທະສາດ SEO ສໍາລັບການເພີ່ມປະສິດທິພາບເວັບໄຊທ໌ໂຮງແຮມ

Зочид буудлын вэбсайтыг оновчтой болгох SEO стратеги

SEO-strategieën voor het optimaliseren van hotelwebsites

SEO Strategies Yekukwidziridza Hotels Webhusaiti

กลยุทธ์ SEO สำหรับการเพิ่มประสิทธิภาพเว็บไซต์โรงแรม

AWS DynamoDB is a fully managed, serverless document store that offers single-digit millisecond performance at any scale. It supports various data models and supports transactions, global tables for multi-region replication. It is crucial to design the schema well in DynamoDB, otherwise it can lead to significant cost implications and degraded performance. In this article we will go through the techniques that can be applied for cost optimizations and scalability for an orchestration application. The pricing structure of DynamoDB can be found here and here. Orchestration Application The application is an orchestration service that stores requests from clients, sends the event to multiple upstreams. Each upstream fetches the request on receipt of event, processes it and replies with it’s status. The caller then asynchronously can find out the status of the request. The status contains each upstream responses. This application experiences high volume surge for several minutes followed by inactivity. On-demand pricing is better choice for these type of applications. We will start with a simple schema solution for this application and then go into detail to optimize the schema for better cost and performance. The cost estimates can be calculated using AWS Pricing Calculator Scenario 1 We store the request as well as upstream responses in the same dynamo document
Everytime the upstream sends the response, we update this document
Inputs

Size of document storing request = 100 KB
Size of responses from upstreams = 100 KB
Total average document size = 200 KB
Requests per day = 1 Million
number of upstream services to handle each request = 10 Pricing Operation

Calculation

Total Cost



Write Operations







1 write request

1 WRU per 200 KB = 200 WRUs

-



10 upstream writes

10 * 200 WRUs = 2,000 WRUs

-



1 million requests

1 million * 2,200 WRUs = $1.25 * 2,200

$2,750











Read Operations (Strongly consistent)







1 read request

1 RRU per 200 KB = 50 RRUs

-



10 upstream reads

10 * 50 RRUs = 500 RRUs

-



1 million requests

1 million * 500 RRUs = $0.25 * 500

$125











Total Cost per Day



$2,875 Scenario 2 The major cost in previous scenario is due to writes with larger item size. In our application, we really don’t need to maintain the request data and responses data in a single document. If we split them into two, we do not need to write whole document each time the upstream sends reply. We update only the responses document which is 100 KB, reducing writes on 100 KB per upstream per request Pricing Operation

Calculation

Total Cost



Write Operations







1 write request

1 WRU per 100 KB = 100 WRUs

-



10 upstream writes

10 * 100 WRUs = 1,000 WRUs

-



1 million requests

1 million * 1,100 WRUs = $1.25 * 1,100

$1,375











Read Operations (Strongly Consistent)







1 read request

1 RRU per 100 KB = 25 RRUs

-



10 upstream reads

10 * 25 RRUs = 250 RRUs

-



1 million requests

1 million * 250 RRUs = $0.25 * 250

$62.5











Total Cost per Day



$1,437.5 This design resulted in 50% reduction in cost from Scenario 1 Scenario 3 Looking deep into the use case of the upstream responses, there is no dependency on replies between each upstream services. We do not need to write each reply in a single document. If we keep them in a single document, it makes easier to read them. Given our use case, the writes are higher compared to reads. So we can try to split the responses document into separate ones for each upstream service. In this scenario, we will have 10 documents with 10 KB size for upstream responses instead of single 100 KB document. Pricing Operation

Calculation

Total Cost



Write Operations







1 write request

1 WRU per 100 KB (request) = 100 WRUs

-



1 write request (upstream)

1 WRU per 10 KB = 10 WRUs

-



10 upstream writes

10 * 10 WRUs = 100 WRUs

-



1 million requests

1 million * 200 WRUs = $1.25 * 200

$250











Read Operations (Strongly consistent)







1 read request

1 RCU per 100 KB = 25 RRUs

-



10 upstream reads

10 * 25 RCUs = 250 RRUs

-



1 million requests

1 million * 250 RRUs = 0.25 * 250

$62.5











Total Cost per Day



$312.5 This design achieves 78.26% reduction in cost compared to Scenario 2 and 89.1% reduction compared to Scenario 1 Conclusion The schema design plays an important role in DynamoDb cost optimizations. The size of each document should be kept as minimal as possible for write heavy applications. To reduce costs, each document’s size should be minimized, as larger items incur higher write costs. AWS DynamoDB is a fully managed, serverless document store that offers single-digit millisecond performance at any scale. It supports various data models and supports transactions, global tables for multi-region replication. It is crucial to design the schema well in DynamoDB, otherwise it can lead to significant cost implications and degraded performance. In this article we will go through the techniques that can be applied for cost optimizations and scalability for an orchestration application. The pricing structure of DynamoDB can be found here and here . here here Orchestration Application The application is an orchestration service that stores requests from clients, sends the event to multiple upstreams. Each upstream fetches the request on receipt of event, processes it and replies with it’s status. The caller then asynchronously can find out the status of the request. The status contains each upstream responses. This application experiences high volume surge for several minutes followed by inactivity. On-demand pricing is better choice for these type of applications. We will start with a simple schema solution for this application and then go into detail to optimize the schema for better cost and performance. The cost estimates can be calculated using AWS Pricing Calculator AWS Pricing Calculator Scenario 1 We store the request as well as upstream responses in the same dynamo document Everytime the upstream sends the response, we update this document Inputs

Size of document storing request = 100 KB
Size of responses from upstreams = 100 KB
Total average document size = 200 KB
Requests per day = 1 Million
number of upstream services to handle each request = 10 We store the request as well as upstream responses in the same dynamo document Everytime the upstream sends the response, we update this document Inputs Size of document storing request = 100 KB
Size of responses from upstreams = 100 KB
Total average document size = 200 KB
Requests per day = 1 Million
number of upstream services to handle each request = 10 Size of document storing request = 100 KB Size of responses from upstreams = 100 KB Total average document size = 200 KB Requests per day = 1 Million number of upstream services to handle each request = 10 Size of document storing request = 100 KB Size of responses from upstreams = 100 KB Total average document size = 200 KB Requests per day = 1 Million number of upstream services to handle each request = 10 Pricing Operation

Calculation

Total Cost



Write Operations







1 write request

1 WRU per 200 KB = 200 WRUs

-



10 upstream writes

10 * 200 WRUs = 2,000 WRUs

-



1 million requests

1 million * 2,200 WRUs = $1.25 * 2,200

$2,750











Read Operations (Strongly consistent)







1 read request

1 RRU per 200 KB = 50 RRUs

-



10 upstream reads

10 * 50 RRUs = 500 RRUs

-



1 million requests

1 million * 500 RRUs = $0.25 * 500

$125











Total Cost per Day



$2,875 Operation

Calculation

Total Cost Write Operations 1 write request

1 WRU per 200 KB = 200 WRUs

- 10 upstream writes

10 * 200 WRUs = 2,000 WRUs

- 1 million requests

1 million * 2,200 WRUs = $1.25 * 2,200

$2,750 Read Operations (Strongly consistent) 1 read request

1 RRU per 200 KB = 50 RRUs

- 10 upstream reads

10 * 50 RRUs = 500 RRUs

- 1 million requests

1 million * 500 RRUs = $0.25 * 500

$125 Total Cost per Day



$2,875 Operation Calculation Total Cost Operation Operation Calculation Calculation Total Cost Total Cost Write Operations Write Operations Write Operations Write Operations 1 write request 1 WRU per 200 KB = 200 WRUs - 1 write request 1 write request 1 WRU per 200 KB = 200 WRUs 1 WRU per 200 KB = 200 WRUs - - 10 upstream writes 10 * 200 WRUs = 2,000 WRUs - 10 upstream writes 10 upstream writes 10 * 200 WRUs = 2,000 WRUs 10 * 200 WRUs = 2,000 WRUs - - 1 million requests 1 million * 2,200 WRUs = $1.25 * 2,200 $2,750 1 million requests 1 million requests 1 million * 2,200 WRUs = $1.25 * 2,200 1 million * 2,200 WRUs = $1.25 * 2,200 $2,750 $2,750 Read Operations (Strongly consistent) Read Operations (Strongly consistent) Read Operations (Strongly consistent) Read Operations (Strongly consistent) 1 read request 1 RRU per 200 KB = 50 RRUs - 1 read request 1 read request 1 RRU per 200 KB = 50 RRUs 1 RRU per 200 KB = 50 RRUs - - 10 upstream reads 10 * 50 RRUs = 500 RRUs - 10 upstream reads 10 upstream reads 10 * 50 RRUs = 500 RRUs 10 * 50 RRUs = 500 RRUs - - 1 million requests 1 million * 500 RRUs = $0.25 * 500 $125 1 million requests 1 million requests 1 million * 500 RRUs = $0.25 * 500 1 million * 500 RRUs = $0.25 * 500 $125 $125 Total Cost per Day $2,875 Total Cost per Day Total Cost per Day Total Cost per Day $2,875 $2,875 $2,875 Scenario 2 The major cost in previous scenario is due to writes with larger item size. In our application, we really don’t need to maintain the request data and responses data in a single document. If we split them into two, we do not need to write whole document each time the upstream sends reply. We update only the responses document which is 100 KB, reducing writes on 100 KB per upstream per request Pricing Operation

Calculation

Total Cost



Write Operations







1 write request

1 WRU per 100 KB = 100 WRUs

-



10 upstream writes

10 * 100 WRUs = 1,000 WRUs

-



1 million requests

1 million * 1,100 WRUs = $1.25 * 1,100

$1,375











Read Operations (Strongly Consistent)







1 read request

1 RRU per 100 KB = 25 RRUs

-



10 upstream reads

10 * 25 RRUs = 250 RRUs

-



1 million requests

1 million * 250 RRUs = $0.25 * 250

$62.5











Total Cost per Day



$1,437.5 Operation

Calculation

Total Cost Write Operations 1 write request

1 WRU per 100 KB = 100 WRUs

- 10 upstream writes

10 * 100 WRUs = 1,000 WRUs

- 1 million requests

1 million * 1,100 WRUs = $1.25 * 1,100

$1,375 Read Operations (Strongly Consistent) 1 read request

1 RRU per 100 KB = 25 RRUs

- 10 upstream reads

10 * 25 RRUs = 250 RRUs

- 1 million requests

1 million * 250 RRUs = $0.25 * 250

$62.5 Total Cost per Day



$1,437.5 Operation Calculation Total Cost Operation Operation Calculation Calculation Total Cost Total Cost Write Operations Write Operations Write Operations Write Operations 1 write request 1 WRU per 100 KB = 100 WRUs - 1 write request 1 write request 1 WRU per 100 KB = 100 WRUs 1 WRU per 100 KB = 100 WRUs - - 10 upstream writes 10 * 100 WRUs = 1,000 WRUs - 10 upstream writes 10 upstream writes 10 * 100 WRUs = 1,000 WRUs 10 * 100 WRUs = 1,000 WRUs - - 1 million requests 1 million * 1,100 WRUs = $1.25 * 1,100 $1,375 1 million requests 1 million requests 1 million * 1,100 WRUs = $1.25 * 1,100 1 million * 1,100 WRUs = $1.25 * 1,100 $1,375 $1,375 Read Operations (Strongly Consistent) Read Operations (Strongly Consistent) Read Operations (Strongly Consistent) Read Operations (Strongly Consistent) 1 read request 1 RRU per 100 KB = 25 RRUs - 1 read request 1 read request 1 RRU per 100 KB = 25 RRUs 1 RRU per 100 KB = 25 RRUs - - 10 upstream reads 10 * 25 RRUs = 250 RRUs - 10 upstream reads 10 upstream reads 10 * 25 RRUs = 250 RRUs 10 * 25 RRUs = 250 RRUs - - 1 million requests 1 million * 250 RRUs = $0.25 * 250 $62.5 1 million requests 1 million requests 1 million * 250 RRUs = $0.25 * 250 1 million * 250 RRUs = $0.25 * 250 $62.5 $62.5 Total Cost per Day $1,437.5 Total Cost per Day Total Cost per Day Total Cost per Day $1,437.5 $1,437.5 $1,437.5 This design resulted in 50% reduction in cost from Scenario 1 50% reduction Scenario 3 Looking deep into the use case of the upstream responses, there is no dependency on replies between each upstream services. We do not need to write each reply in a single document. If we keep them in a single document, it makes easier to read them. Given our use case, the writes are higher compared to reads. So we can try to split the responses document into separate ones for each upstream service. In this scenario, we will have 10 documents with 10 KB size for upstream responses instead of single 100 KB document. Pricing Operation

Calculation

Total Cost



Write Operations







1 write request

1 WRU per 100 KB (request) = 100 WRUs

-



1 write request (upstream)

1 WRU per 10 KB = 10 WRUs

-



10 upstream writes

10 * 10 WRUs = 100 WRUs

-



1 million requests

1 million * 200 WRUs = $1.25 * 200

$250











Read Operations (Strongly consistent)







1 read request

1 RCU per 100 KB = 25 RRUs

-



10 upstream reads

10 * 25 RCUs = 250 RRUs

-



1 million requests

1 million * 250 RRUs = 0.25 * 250

$62.5











Total Cost per Day



$312.5 Operation

Calculation

Total Cost Write Operations 1 write request

1 WRU per 100 KB (request) = 100 WRUs

- 1 write request (upstream)

1 WRU per 10 KB = 10 WRUs

- 10 upstream writes

10 * 10 WRUs = 100 WRUs

- 1 million requests

1 million * 200 WRUs = $1.25 * 200

$250 Read Operations (Strongly consistent) 1 read request

1 RCU per 100 KB = 25 RRUs

- 10 upstream reads

10 * 25 RCUs = 250 RRUs

- 1 million requests

1 million * 250 RRUs = 0.25 * 250

$62.5 Total Cost per Day



$312.5 Operation Calculation Total Cost Operation Operation Calculation Calculation Total Cost Total Cost Write Operations Write Operations Write Operations Write Operations 1 write request 1 WRU per 100 KB (request) = 100 WRUs - 1 write request 1 write request 1 WRU per 100 KB (request) = 100 WRUs 1 WRU per 100 KB (request) = 100 WRUs - - 1 write request (upstream) 1 WRU per 10 KB = 10 WRUs - 1 write request (upstream) 1 write request (upstream) 1 WRU per 10 KB = 10 WRUs 1 WRU per 10 KB = 10 WRUs - - 10 upstream writes 10 * 10 WRUs = 100 WRUs - 10 upstream writes 10 upstream writes 10 * 10 WRUs = 100 WRUs 10 * 10 WRUs = 100 WRUs - - 1 million requests 1 million * 200 WRUs = $1.25 * 200 $250 1 million requests 1 million requests 1 million * 200 WRUs = $1.25 * 200 1 million * 200 WRUs = $1.25 * 200 $250 $250 Read Operations (Strongly consistent) Read Operations (Strongly consistent) Read Operations (Strongly consistent) Read Operations (Strongly consistent) 1 read request 1 RCU per 100 KB = 25 RRUs - 1 read request 1 read request 1 RCU per 100 KB = 25 RRUs 1 RCU per 100 KB = 25 RRUs - - 10 upstream reads 10 * 25 RCUs = 250 RRUs - 10 upstream reads 10 upstream reads 10 * 25 RCUs = 250 RRUs 10 * 25 RCUs = 250 RRUs - - 1 million requests 1 million * 250 RRUs = 0.25 * 250 $62.5 1 million requests 1 million requests 1 million * 250 RRUs = 0.25 * 250 1 million * 250 RRUs = 0.25 * 250 $62.5 $62.5 Total Cost per Day $312.5 Total Cost per Day Total Cost per Day Total Cost per Day $312.5 $312.5 $312.5 This design achieves 78.26% reduction in cost compared to Scenario 2 and 89.1% reduction compared to Scenario 1 78.26% reduction 89.1% reduction Conclusion The schema design plays an important role in DynamoDb cost optimizations. The size of each document should be kept as minimal as possible for write heavy applications. To reduce costs, each document’s size should be minimized, as larger items incur higher write costs.

Cost Optimization Techniques for AWS DynamoDB in Orchestration Applications

Primer on Large Language Model (LLM) Inference Optimizations: 1. Background and Problem Formulation

Einführung in die Inferenzoptimierung von Large Language Models (LLM): 1. Hintergrund und Problemformulierung

Introducción a las optimizaciones de inferencia de modelos de lenguaje grandes (LLM): 1. Antecedentes y formulación del problema

Primer on Large Language Model (LLM) következtetések optimalizálása: 1. Háttér és probléma megfogalmazása

大規模言語モデル (LLM) 推論最適化入門: 1. 背景と問題の定式化

Үлкен тілдік үлгідегі (LLM) қорытындыны оңтайландырудағы праймер: 1. Фондық және мәселені тұжырымдау

Primer na ba optimisations ya inférence ya modèle ya monoko ya munene (LLM): 1. Formulation ya ba fond mpe ba problèmes

Том хэлний загвар (LLM) дүгнэлтийн оновчлолын талаархи праймер: 1. Үндэслэл ба асуудлын томъёолол

ठूला भाषा मोडेल (LLM) इन्फरेन्स अप्टिमाइजेसनमा प्राइमर: 1. पृष्ठभूमि र समस्या सूत्रीकरण

Primer on Large Language Model (LLM) Inference Optimizations: 1. Tlhamo ya Lemorago le Mathata

Учебник по оптимизации вывода большой языковой модели (LLM): 1. Предыстория и формулировка проблемы

Grundläggande om Large Language Model (LLM) slutledningsoptimeringar: 1. Bakgrund och problemformulering

Katta til modeli (LLM) xulosasini optimallashtirish bo'yicha primer: 1. Fon va muammoni shakllantirish

Optimizing Tech Design Meetings with Structured Ticket Templates

Optimaliseer tegniese ontwerpvergaderings met gestruktureerde kaartjiesjablone

Аптымізацыя сустрэч па тэхнічным дызайне з дапамогай структураваных шаблонаў білетаў

Optimalizace schůzek technického designu pomocí strukturovaných šablon vstupenek

Optimización de reuniones de diseño tecnológico con plantillas de tickets estructuradas

構造化されたチケット テンプレートを使用して技術設計会議を最適化する

ტექნიკური დიზაინის შეხვედრების ოპტიმიზაცია ბილეთების სტრუქტურირებული შაბლონებით

Оптимизирање на состаноци за технолошки дизајн со структурни шаблони за билети

संरचित टिकट टेम्प्लेटहरूको साथ टेक डिजाइन बैठकहरू अनुकूलन गर्दै

Optimaliseren van technische ontwerpvergaderingen met gestructureerde ticketsjablonen

Go kaonafatša Dikopano tša Moralo wa Theknolotši ka Dithempleite tša Thekethe tše di Rulagantšwego

د جوړښت شوي ټکټ ټیمپلیټونو سره د ټیک ډیزاین غونډې اصلاح کول

Ukuthuthukisa Imihlangano Yedizayini Yezobuchwepheshe Ngezifanekiso Zamathikithi Ahleliwe

Facing 2025 Cybersecurity Head-On: Why Ignoring Tech Optimization Could Cost You More Than Data

CARY, NC, October 28th, 2024/CyberNewsWire/--As the year-end approaches, it’s common for enterprises to discover they still have funds that must be utilized. Often, these L&D dollars are "use or lose," meaning they will be returned to the general fund if not invested. Recognizing this, INE Security is launching an initiative to guide organizations in investing in technical training before the year end. This approach not only ensures wise expenditure of remaining budgets but also significantly strengthens organizational security postures. Addressing Training Budgets: Year-End Budget Scenario: It's common for organizations to approach year-end with an unused budget designated for training. If not spent, these funds often return to general accounts or are lost altogether, missing an opportunity for strategic investment. Strategic Spending: INE Security encourages using these funds to invest in team cybersecurity training, turning what could be wasted resources into a pivotal investment in security and professional development. Advantages of Utilizing Surplus Funds: Proactively using surplus training budgets can help organizations make strategic decisions that align with long-term goals, improving security protocols and fostering a knowledgeable workforce. How Organizations Can Utilize Available Training Budgets: Skill Enhancement: Upgrade the team's skills to defend against the latest networking, cloud, and cybersecurity threats. Employee Retention: Invest in employees' growth to boost morale and retention - especially in a tight job market where your best employees may be lured to other companies. Future-Proofing: Prepare teams for future challenges with forward-looking training programs. Compliance and Standards: Ensure compliance with industry standards and regulations through accredited courses and certifications - bolstering your team for contract awards and impending compliance requirements (CMMC). “In a time when digital threats are increasing in both complexity and frequency, proactive Networking, Cloud, and Cybersecurity training is more crucial than ever,” said Dara Warn, CEO of INE Security. “We recognize that many organizations end the year with a surplus in their training budgets. This is an excellent opportunity to invest in vital training, ensuring teams are not only prepared but are ahead of the curve.” Organizations can benefit by engaging in INE Security’s expert-led courses and hands-on labs, which are designed to provide real-world experience and in-depth knowledge across Networking, Cloud, and Cybersecurity. In recognition of the often-underused training budgets available at year's end, INE Security is offering significant discounts for team training on two-year deals. This initiative not only helps organizations optimize their unspent training funds but also boosts their long-term security strategies. To learn more about INE’s comprehensive Networking, Cloud, and Cybersecurity training programs, users can visit www.ine.com or contact INE Security’s corporate training advisors directly. About INE INE Security is the premier provider of online technical training for the IT/IS industry. Harnessing a powerful hands-on lab platform, cutting-edge technology, a global video distribution network, and world-class instructors, INE Security is the top training choice for Fortune 500 companies worldwide and for IT/IS professionals looking to advance their careers. INE’s suite of learning paths offers an incomparable depth of expertise across cybersecurity, cloud, networking, and data science. INE Security is committed to delivering advanced technical training while also lowering the barriers worldwide for those looking to enter and excel in a cybersecurity career. Contact Director of Global Strategic Communications and Events Kathryn Brown INE Security kbrown@ine.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here CARY, NC, October 28th, 2024/CyberNewsWire/--As the year-end approaches, it’s common for enterprises to discover they still have funds that must be utilized. Often, these L&D dollars are "use or lose," meaning they will be returned to the general fund if not invested. Recognizing this, INE Security is launching an initiative to guide organizations in investing in technical training before the year end. This approach not only ensures wise expenditure of remaining budgets but also significantly strengthens organizational security postures. Addressing Training Budgets: Year-End Budget Scenario: It's common for organizations to approach year-end with an unused budget designated for training. If not spent, these funds often return to general accounts or are lost altogether, missing an opportunity for strategic investment. Strategic Spending: INE Security encourages using these funds to invest in team cybersecurity training, turning what could be wasted resources into a pivotal investment in security and professional development. Advantages of Utilizing Surplus Funds: Proactively using surplus training budgets can help organizations make strategic decisions that align with long-term goals, improving security protocols and fostering a knowledgeable workforce. How Organizations Can Utilize Available Training Budgets: Skill Enhancement: Upgrade the team's skills to defend against the latest networking, cloud, and cybersecurity threats. Employee Retention: Invest in employees' growth to boost morale and retention - especially in a tight job market where your best employees may be lured to other companies. Future-Proofing: Prepare teams for future challenges with forward-looking training programs. Compliance and Standards: Ensure compliance with industry standards and regulations through accredited courses and certifications - bolstering your team for contract awards and impending compliance requirements (CMMC). “In a time when digital threats are increasing in both complexity and frequency, proactive Networking, Cloud, and Cybersecurity training is more crucial than ever,” said Dara Warn, CEO of INE Security. “In a time when digital threats are increasing in both complexity and frequency, proactive Networking, Cloud, and Cybersecurity training is more crucial than ever,” said Dara Warn, CEO of INE Security. “We recognize that many organizations end the year with a surplus in their training budgets. This is an excellent opportunity to invest in vital training, ensuring teams are not only prepared but are ahead of the curve.” “We recognize that many organizations end the year with a surplus in their training budgets. This is an excellent opportunity to invest in vital training, ensuring teams are not only prepared but are ahead of the curve.” Organizations can benefit by engaging in INE Security’s expert-led courses and hands-on labs, which are designed to provide real-world experience and in-depth knowledge across Networking, Cloud, and Cybersecurity. In recognition of the often-underused training budgets available at year's end, INE Security is offering significant discounts for team training on two-year deals. This initiative not only helps organizations optimize their unspent training funds but also boosts their long-term security strategies. To learn more about INE’s comprehensive Networking, Cloud, and Cybersecurity training programs, users can visit www.ine.com or contact INE Security’s corporate training advisors directly. About INE INE Security is the premier provider of online technical training for the IT/IS industry. Harnessing a powerful hands-on lab platform, cutting-edge technology, a global video distribution network, and world-class instructors, INE Security is the top training choice for Fortune 500 companies worldwide and for IT/IS professionals looking to advance their careers. INE’s suite of learning paths offers an incomparable depth of expertise across cybersecurity, cloud, networking, and data science. INE Security is committed to delivering advanced technical training while also lowering the barriers worldwide for those looking to enter and excel in a cybersecurity career. Contact Director of Global Strategic Communications and Events Kathryn Brown INE Security kbrown@ine.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here

INE's Initiative To Optimize Year-End Training Budgets With Enhanced Cybersecurity And Networking 

مبادرة INE لتحسين ميزانيات التدريب في نهاية العام من خلال تحسين الأمن السيبراني ومهارات الشبكات

INE-nin Təkmil Kibertəhlükəsizlik və Şəbəkə ilə İl Sonu Təlim Büdcələrini Optimallaşdırmaq Təşəbbüsü

La iniciativa del INE para optimizar los presupuestos de formación de fin de año con mejoras en ciberseguridad y redes

ابتکار INE برای بهینه‌سازی بودجه‌های آموزشی پایان سال با امنیت سایبری پیشرفته و روابط عمومی شبکه

Iniciativa do INE para optimizar os orzamentos de formación de fin de ano cunha mellora da ciberseguridade e a rede Pr

INE のサイバーセキュリティとネットワーク強化による年末トレーニング予算の最適化の取り組み

ຂໍ້ລິເລີ່ມຂອງ INE ເພື່ອເພີ່ມປະສິດທິພາບງົບປະມານການຝຶກອົບຮົມທ້າຍປີດ້ວຍການເພີ່ມປະສິດທິພາບ Cybersecurity ແລະເຄືອຂ່າຍ Pr

Иницијативата на INE за оптимизирање на буџетите за обука до крајот на годината со зајакната сајбер безбедност и вмрежување Pr

INE को पहल परिष्कृत साइबरसुरक्षा र नेटवर्किङ Pr संग वर्ष-अन्त प्रशिक्षण बजेटहरू अनुकूलन गर्न

Iniciativa do INE para otimizar os orçamentos de treinamento de fim de ano com segurança cibernética aprimorada e rede Pr

Ташаббуси INE барои оптимизатсия кардани буҷетҳои омӯзишӣ дар охири сол бо мукаммали киберамният ва шабакавӣ

Ініціатива INE щодо оптимізації бюджетів на навчання на кінець року за допомогою покращеної кібербезпеки та мережевих технологій

Authors:
(1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk;
(2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Table of Links Abstract and 1 Introduction 2 Related Work 3 Framework 4 Main Results 5 A Case Study on Shortest-Path Distance 6 Conclusion and Discussion, and References 7 Proof of Theorem 1 8 Proof of Theorem 2 9 Procedure for Solving Eq. (6) 10 Additional Experiments Details and Results 11 Other Potential Applications 9 Procedure for Solving Eq. (6) First recall that the optimization problem we want to solve is as follows. where k is the given initial set size and Ds(.) is given in Def. 2 with shortest path distance as the metric. Eq. 6 amounts to the well-known k-center problem [14] in graph theory and is NP-hard. For our experiment, we use Eq. 6 as guidance and modify the simple greedy algorithm, farthest-first traversal, to be a sampling method for obtaining a solution. We now describe how we modified the standard greedy algorithm into a sampling algorithm. We start with describing the standard greedy algorithm. The procedure described above is a standard 2-approximation greedy algorithm for k-center problem. Next, we describe its sampling variant which use the distance as the chance of being sampled. 9.1 Running Time Complexity It is obvious that the procedure above runs in O(k) if the distance between any pair of vertices is given and runs in O(kn) if the distance needed to be computed for each step, where n is the number of vertice. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk; (2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Authors: Authors: (1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk; (2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Framework 3 Framework 4 Main Results 4 Main Results 5 A Case Study on Shortest-Path Distance 5 A Case Study on Shortest-Path Distance 6 Conclusion and Discussion, and References 6 Conclusion and Discussion, and References 7 Proof of Theorem 1 7 Proof of Theorem 1 8 Proof of Theorem 2 8 Proof of Theorem 2 9 Procedure for Solving Eq. (6) 9 Procedure for Solving Eq. (6) 10 Additional Experiments Details and Results 10 Additional Experiments Details and Results 11 Other Potential Applications 11 Other Potential Applications 9 Procedure for Solving Eq. (6) First recall that the optimization problem we want to solve is as follows. where k is the given initial set size and Ds(.) is given in Def. 2 with shortest path distance as the metric. Eq. 6 amounts to the well-known k-center problem [14] in graph theory and is NP-hard. For our experiment, we use Eq. 6 as guidance and modify the simple greedy algorithm, farthest-first traversal, to be a sampling method for obtaining a solution. We now describe how we modified the standard greedy algorithm into a sampling algorithm. We start with describing the standard greedy algorithm. The procedure described above is a standard 2-approximation greedy algorithm for k-center problem. Next, we describe its sampling variant which use the distance as the chance of being sampled. 9.1 Running Time Complexity It is obvious that the procedure above runs in O(k) if the distance between any pair of vertices is given and runs in O(kn) if the distance needed to be computed for each step, where n is the number of vertice. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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