Top 5 Ways to Optimize for Cost Efficiency with the CloudAmazon Web Services
This session covers the Top 5 ways you can reduce the cost of your workloads in the AWS Cloud including high-level architectures and when to use and our numerous pricing options for components of those architectures.
We walk through several examples to illustrate when to use each feature, configuration or pricing option. This session is aimed at technically savvy managers and engineers who need to reduce their cloud spending.
Reasons to attend:
Learn about Reserved Instances, On-Demand Instances and Spot Instances.
Discover ways of running more for less in Amazon EC2.
If you are already running a workload in AWS, attend this webinar to learn how to run the same workload at reduced costs.
This webinar discussed strategies to help save money in the AWS Cloud. From turning systems off at night, to implementing bidding strategies on the spot market, there are many ways in which you can manage and your reduce costs with AWS.
This webinar dived into the differences between instance types; explain how you can reduce costs with Reserved Instances, the spot market and by architecting to reduce costs. It also discussed how to combine on-demand pricing with spot pricing to perform cost effective big data analysis, and introduce customer examples to illustrate how AWS customers gain the most from AWS whilst at the same time managing their spend.
The Total Cost of Ownership of Cloud Storage (TCO) - AWS Cloud Storage for th...Amazon Web Services
AWS Technology Evangelist Jinesh Varia discusses how to think about TCO of Storage and typical mistakes prospects make when trying to apples to apples comparison between cloud infrastructure service and on-premises storage
In this talk from the Dublin Websummit 2014 AWS Technical Evangelist Ian Massingham discusses practices and techniques for optimising and lowering the cost of operations for applications and services that you are running on the AWS cloud.
Includes a discussion of the fundamental tenets of pricing for AWS services, plus tips and tricks for reducing the amount that you need to spend with AWS in order to run your workloads on the AWS cloud.
Weighing the financial considerations of owning and operating a data center facility versus employing a cloud infrastructure requires detailed and careful analysis. In practice, it is not as simple as just measuring potential hardware expense alongside utility pricing for compute and storage resources. The Total Cost of Ownership (TCO) is often the financial metric used to estimate and compare direct and indirect costs of a product or a service. Given the large differences between the two models, it is challenging to perform accurate apples-to-apples cost comparisons between on-premises data centers and cloud infrastructure that is offered as a service. In this session, we explain the economic benefits of deploying applications in AWS over deploying equivalent applications hosted in an on-premises environment.
AWS Summit London 2014 | Optimising TCO for the AWS Cloud (100)Amazon Web Services
This introductory level business focused session will help you to understand how to calculate, track and optimise the costs of using AWS to deliver your applications and run other IT workloads.
Top 5 Ways to Optimize for Cost Efficiency with the CloudAmazon Web Services
This session covers the Top 5 ways you can reduce the cost of your workloads in the AWS Cloud including high-level architectures and when to use and our numerous pricing options for components of those architectures.
We walk through several examples to illustrate when to use each feature, configuration or pricing option. This session is aimed at technically savvy managers and engineers who need to reduce their cloud spending.
Reasons to attend:
Learn about Reserved Instances, On-Demand Instances and Spot Instances.
Discover ways of running more for less in Amazon EC2.
If you are already running a workload in AWS, attend this webinar to learn how to run the same workload at reduced costs.
This webinar discussed strategies to help save money in the AWS Cloud. From turning systems off at night, to implementing bidding strategies on the spot market, there are many ways in which you can manage and your reduce costs with AWS.
This webinar dived into the differences between instance types; explain how you can reduce costs with Reserved Instances, the spot market and by architecting to reduce costs. It also discussed how to combine on-demand pricing with spot pricing to perform cost effective big data analysis, and introduce customer examples to illustrate how AWS customers gain the most from AWS whilst at the same time managing their spend.
The Total Cost of Ownership of Cloud Storage (TCO) - AWS Cloud Storage for th...Amazon Web Services
AWS Technology Evangelist Jinesh Varia discusses how to think about TCO of Storage and typical mistakes prospects make when trying to apples to apples comparison between cloud infrastructure service and on-premises storage
In this talk from the Dublin Websummit 2014 AWS Technical Evangelist Ian Massingham discusses practices and techniques for optimising and lowering the cost of operations for applications and services that you are running on the AWS cloud.
Includes a discussion of the fundamental tenets of pricing for AWS services, plus tips and tricks for reducing the amount that you need to spend with AWS in order to run your workloads on the AWS cloud.
Weighing the financial considerations of owning and operating a data center facility versus employing a cloud infrastructure requires detailed and careful analysis. In practice, it is not as simple as just measuring potential hardware expense alongside utility pricing for compute and storage resources. The Total Cost of Ownership (TCO) is often the financial metric used to estimate and compare direct and indirect costs of a product or a service. Given the large differences between the two models, it is challenging to perform accurate apples-to-apples cost comparisons between on-premises data centers and cloud infrastructure that is offered as a service. In this session, we explain the economic benefits of deploying applications in AWS over deploying equivalent applications hosted in an on-premises environment.
AWS Summit London 2014 | Optimising TCO for the AWS Cloud (100)Amazon Web Services
This introductory level business focused session will help you to understand how to calculate, track and optimise the costs of using AWS to deliver your applications and run other IT workloads.
Cost is often the conversation starter when customers think about moving to the cloud. AWS helps lower costs for customers through its “pay only for what you use” pricing model, frequent price drops, and pricing model choice to support variable & stable workloads. In this session, you will learn about the financial considerations of owning and operating a traditional data center or managed hosting provider versus utilizing AWS. We will detail our TCO methodology and showcase cost comparisons for some common customer use-cases. We’ll also cover a few AWS cost optimization areas, including Spot and Reserved Instances, EC2 Auto Scaling, and consolidated billing.
Cost is often the conversation starter when customers think about moving to the cloud. AWS helps lower costs for customers through its “pay only for what you use” pricing model, frequent price drops, and pricing model choice to support variable & stable workloads. In this session, you will learn about the financial considerations of owning and operating a traditional data center or managed hosting provider versus utilizing AWS. We will detail our TCO methodology and showcase cost comparisons for some common customer use-cases. We’ll also cover a few AWS cost optimization areas, including Spot and Reserved Instances, EC2 Auto Scaling, and consolidated billing.
The webinar based on this presentation discussed strategies that you can adopt to help you save money in the AWS Cloud. From turning systems off at night, to implementing bidding strategies on the spot market, there are many ways in which you can manage and reduce your costs with AWS.
Dive into the differences between instance types; explain how you can reduce costs with Reserved Instances, the spot market and by architecting to reduce costs. We'll discuss how to combine on-demand pricing with spot pricing to perform cost effective big data analysis, and introduce customer examples to illustrate how AWS customers gain the most from AWS whilst at the same time managing their spend.
Topics include:
• Understand different cost optimisation strategies you can employ in the AWS Cloud
• Learn how to take advantage of different instance types
• Discover architectural principles behind cost optimisation in AWS
• Learn about tools to help you keep on top of your AWS spend
You can find a recording of this webinar on YouTube here: http://youtu.be/kId90Q7b6kY
Optimizing for Cost in the AWS Cloud - 5 Ways to Further Save - AWS Summit 20...Amazon Web Services
AWS Technology Evangelist Jinesh Varia discusses how you can optimize your costs in the cloud and further reduce you cost and save. He discusses a number of data points of how customers are saving money with AWS
Explore the financial considerations of owning and operating a traditional data center versus utilizing cloud infrastructure. The session will consider many cost factors which can be overlooked when comparing models, such as provisioning, procurement, training, support contracts and software licensing. Learn how to further reduce your current costs on AWS and improve your spend predictability. Join this webinar to learn more.
Intended for customers who have (or will have) thousands of instances on AWS, this session is about reducing the complexity of managing costs for these large fleets so they run efficiently. Attendees will learn about common roadblocks that prevent large customers from cost optimizing, tools they can use to efficiently remove those roadblocks, and techniques to monitor their rate of cost optimization. The session will include a case study that will talk in detail about the millions of dollars saved using these techniques. Customers will learn about a range of templates they can use to quickly implement these techniques, and also partners who can help them implement these templates.
Presented by: Guy Kfir, Senior Account Manager, Amazon Web Services
Customer Guest: David Costa, CTO, Fredhopper
Amazon Web Services provides a way to acquire and use infrastructure on-demand, so that you pay only for what you consume. This session is an introduction to best practices how you can take advantage of options like Reserved Instances, Scheduled Instances, Spot Instances and Spot Blocks to continuously optimize your cost of running your infrastructure services even further.
This session is a deep dive into techniques used by successful customers who optimized their use of AWS. Learn tricks and hear tips you can implement right away to reduce waste, choose the most efficient instance, and fine-tune your spending; often with improved performance and a better end-customer experience. We showcase innovative approaches and demonstrate easily applicable methods to save you time and money with Amazon EC2, Amazon S3, and a host of other services.
Reducing Cost & Maximizing Efficiency: Tightening the Belt on AWS (CPN211) | ...Amazon Web Services
This session dives deep into techniques used by successful customers who optimized their use of AWS. Learn tricks and hear tips you can implement right away to reduce waste, choose the most efficient instance, and fine-tune your spending, often with improved performance and a better end-customer experience. We showcase innovative approaches and demonstrate easily-applicable methods for cost optimizing Amazon EC2, Amazon S3, and a host of other services to save you time and money.
Optimizing Your AWS Applications and Usage to Reduce CostsAmazon Web Services
Many customers choose AWS because they need a highly reliable, scalable, and low-cost platform on which to run their applications. Low “pay only for what you use” pricing and frequent price decreases are just the beginning of how AWS can help you optimize your usage and achieve lower costs. In this session, you will learn about a few simple tools for monitoring and managing your AWS resource usage that you can start using right away, as well as some innovative features that can help you operate at lower costs programmatically. Cost allocation reporting, detailed usage reports, billing alerts, EC2 Auto Scaling, Spot and Reserved Instances, and idle resource detection are just a few of the tools and features we will cover.
Amazon EC2 allows you to bid for and run spare EC2 capacity, known as Spot instances, in a dynamically priced market. On average, customers save 80% to 90% compared to On Demand prices by using Spot instances. Achieving these savings has historically required time and effort to find the best deals while managing compute capacity as supply and demand fluctuate.
In the event of a disaster, you need to be able to recover lost data quickly to ensure business continuity. For critical applications, keeping your time to recover and data loss to a minimum as well as optimizing your overall capital expense can be challenging. This session presents AWS-enabled solutions, along with Disaster Recovery architectures, that you can leverage when building highly available and disaster resilient applications. We will provide recommendations on how to improve your disaster recovery plan and discuss example scenarios showing how to recover from an event. Presented by CloudEndure.
Amazon Aurora is a MySQL-compatible database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. This session introduces you to Amazon Aurora, explains common use cases for the service, and helps you get started with building your first Amazon Aurora–powered application.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all of your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing, scale-out architecture, and columnar direct-attached storage to minimize I/O time and maximize performance. Learn how you can gain deeper business insights and save money and time by migrating to Amazon Redshift. Take away strategies for migrating from on-premises data warehousing solutions, tuning schema and queries, and utilizing third party solutions.
APN Partner Webinar - Having Effective and Critical TCO ConversationsAmazon Web Services
Customers always want to understand how AWS cost models compare to other alternatives. Using the new AWS TCO Calculator, we will outline how AWS breaks down cost drivers when it educates customers who are evaluating cloud vs. looking at other models of computing: on-prem, virtualized, and co-lo. Discussion will also center on best practices to capture the true costs of these alternative computing approaches, and how to have meaningful customer conversations with respect to TCO.
• Learn: What is TCO and why it matters
• Understand: TCO evaluation Methodology used by AWS
• Hear: Best practices around TCO, demonstration of online TCO calculator
You can find the recording of this webinar here: http://youtu.be/BaPEf_f0N5U
Cost is often the conversation starter when customers think about moving to the cloud. AWS helps lower costs for customers through its “pay only for what you use” pricing model, frequent price drops, and pricing model choice to support variable & stable workloads. In this session, you will learn about the financial considerations of owning and operating a traditional data center or managed hosting provider versus utilizing AWS. We will detail our TCO methodology and showcase cost comparisons for some common customer use-cases. We’ll also cover a few AWS cost optimization areas, including Spot and Reserved Instances, EC2 Auto Scaling, and consolidated billing.
Cost is often the conversation starter when customers think about moving to the cloud. AWS helps lower costs for customers through its “pay only for what you use” pricing model, frequent price drops, and pricing model choice to support variable & stable workloads. In this session, you will learn about the financial considerations of owning and operating a traditional data center or managed hosting provider versus utilizing AWS. We will detail our TCO methodology and showcase cost comparisons for some common customer use-cases. We’ll also cover a few AWS cost optimization areas, including Spot and Reserved Instances, EC2 Auto Scaling, and consolidated billing.
The webinar based on this presentation discussed strategies that you can adopt to help you save money in the AWS Cloud. From turning systems off at night, to implementing bidding strategies on the spot market, there are many ways in which you can manage and reduce your costs with AWS.
Dive into the differences between instance types; explain how you can reduce costs with Reserved Instances, the spot market and by architecting to reduce costs. We'll discuss how to combine on-demand pricing with spot pricing to perform cost effective big data analysis, and introduce customer examples to illustrate how AWS customers gain the most from AWS whilst at the same time managing their spend.
Topics include:
• Understand different cost optimisation strategies you can employ in the AWS Cloud
• Learn how to take advantage of different instance types
• Discover architectural principles behind cost optimisation in AWS
• Learn about tools to help you keep on top of your AWS spend
You can find a recording of this webinar on YouTube here: http://youtu.be/kId90Q7b6kY
Optimizing for Cost in the AWS Cloud - 5 Ways to Further Save - AWS Summit 20...Amazon Web Services
AWS Technology Evangelist Jinesh Varia discusses how you can optimize your costs in the cloud and further reduce you cost and save. He discusses a number of data points of how customers are saving money with AWS
Explore the financial considerations of owning and operating a traditional data center versus utilizing cloud infrastructure. The session will consider many cost factors which can be overlooked when comparing models, such as provisioning, procurement, training, support contracts and software licensing. Learn how to further reduce your current costs on AWS and improve your spend predictability. Join this webinar to learn more.
Intended for customers who have (or will have) thousands of instances on AWS, this session is about reducing the complexity of managing costs for these large fleets so they run efficiently. Attendees will learn about common roadblocks that prevent large customers from cost optimizing, tools they can use to efficiently remove those roadblocks, and techniques to monitor their rate of cost optimization. The session will include a case study that will talk in detail about the millions of dollars saved using these techniques. Customers will learn about a range of templates they can use to quickly implement these techniques, and also partners who can help them implement these templates.
Presented by: Guy Kfir, Senior Account Manager, Amazon Web Services
Customer Guest: David Costa, CTO, Fredhopper
Amazon Web Services provides a way to acquire and use infrastructure on-demand, so that you pay only for what you consume. This session is an introduction to best practices how you can take advantage of options like Reserved Instances, Scheduled Instances, Spot Instances and Spot Blocks to continuously optimize your cost of running your infrastructure services even further.
This session is a deep dive into techniques used by successful customers who optimized their use of AWS. Learn tricks and hear tips you can implement right away to reduce waste, choose the most efficient instance, and fine-tune your spending; often with improved performance and a better end-customer experience. We showcase innovative approaches and demonstrate easily applicable methods to save you time and money with Amazon EC2, Amazon S3, and a host of other services.
Reducing Cost & Maximizing Efficiency: Tightening the Belt on AWS (CPN211) | ...Amazon Web Services
This session dives deep into techniques used by successful customers who optimized their use of AWS. Learn tricks and hear tips you can implement right away to reduce waste, choose the most efficient instance, and fine-tune your spending, often with improved performance and a better end-customer experience. We showcase innovative approaches and demonstrate easily-applicable methods for cost optimizing Amazon EC2, Amazon S3, and a host of other services to save you time and money.
Optimizing Your AWS Applications and Usage to Reduce CostsAmazon Web Services
Many customers choose AWS because they need a highly reliable, scalable, and low-cost platform on which to run their applications. Low “pay only for what you use” pricing and frequent price decreases are just the beginning of how AWS can help you optimize your usage and achieve lower costs. In this session, you will learn about a few simple tools for monitoring and managing your AWS resource usage that you can start using right away, as well as some innovative features that can help you operate at lower costs programmatically. Cost allocation reporting, detailed usage reports, billing alerts, EC2 Auto Scaling, Spot and Reserved Instances, and idle resource detection are just a few of the tools and features we will cover.
Amazon EC2 allows you to bid for and run spare EC2 capacity, known as Spot instances, in a dynamically priced market. On average, customers save 80% to 90% compared to On Demand prices by using Spot instances. Achieving these savings has historically required time and effort to find the best deals while managing compute capacity as supply and demand fluctuate.
In the event of a disaster, you need to be able to recover lost data quickly to ensure business continuity. For critical applications, keeping your time to recover and data loss to a minimum as well as optimizing your overall capital expense can be challenging. This session presents AWS-enabled solutions, along with Disaster Recovery architectures, that you can leverage when building highly available and disaster resilient applications. We will provide recommendations on how to improve your disaster recovery plan and discuss example scenarios showing how to recover from an event. Presented by CloudEndure.
Amazon Aurora is a MySQL-compatible database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. This session introduces you to Amazon Aurora, explains common use cases for the service, and helps you get started with building your first Amazon Aurora–powered application.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all of your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing, scale-out architecture, and columnar direct-attached storage to minimize I/O time and maximize performance. Learn how you can gain deeper business insights and save money and time by migrating to Amazon Redshift. Take away strategies for migrating from on-premises data warehousing solutions, tuning schema and queries, and utilizing third party solutions.
APN Partner Webinar - Having Effective and Critical TCO ConversationsAmazon Web Services
Customers always want to understand how AWS cost models compare to other alternatives. Using the new AWS TCO Calculator, we will outline how AWS breaks down cost drivers when it educates customers who are evaluating cloud vs. looking at other models of computing: on-prem, virtualized, and co-lo. Discussion will also center on best practices to capture the true costs of these alternative computing approaches, and how to have meaningful customer conversations with respect to TCO.
• Learn: What is TCO and why it matters
• Understand: TCO evaluation Methodology used by AWS
• Hear: Best practices around TCO, demonstration of online TCO calculator
You can find the recording of this webinar here: http://youtu.be/BaPEf_f0N5U
Energy Efficient Heuristic Base Job Scheduling Algorithms in Cloud ComputingIOSRjournaljce
Cloud computing environment provides the cost efficient solution to customers by the resource provisioning and flexible customized configuration. The interest of cloud computing is growing around the globe at very fast pace because it provides scalable virtualized infrastructure by mean of which extensive computing capabilities can be used by the cloud clients to execute their submitted jobs. It becomes challenge for the cloud infrastructure to manage and schedule these jobs originated by different cloud users to available resources in such a manner to strengthen the overall performance of the system. As the number of user increases the job scheduling become an intensive task. Energy efficient job scheduling is one constructive solution to streamline the resource utilization as well as to reduce the energy consumption. Though there are several scheduling algorithms available, this paper intends to present job scheduling based on two Heuristic approaches i.e. Efficient MQS (Multi-queue job scheduling) and ACO (Ant colony optimization) and further evaluating the effectiveness of both approaches by considering the parameter of energy consumption and time in cloud computing.
LOAD BALANCING IN AUTO SCALING-ENABLED CLOUD ENVIRONMENTSijccsa
Cloud computing is growing in popularity and it has been continuously updated with more improvements.
Auto scaling is one of such improvements that help to maintain the availability of customer’s subscribed
cloud system. The appearance of an auto scaling mechanism in the cloud system with many existing system
mechanisms is an issue that needs to be considered. Because, normally, there is no free drawbacks
whenever a new part is added to a certain stable system. In this paper, we consider how existing load
balancing and auto scaling impact on each other. For the purpose, we have modeled a cloud system with
an auto scaler and a load balancer and implementing simulations based on the constructed model. Also
based on the results from the computer simulations we proposed about choosing load balancers for
subscribed cloud system with auto scaling service.
Load Balancing in Auto Scaling Enabled Cloud Environmentsneirew J
Cloud computing is growing in popularity and it has been continuously updated with more improvements.
Auto scaling is one of such improvements that help to maintain the availability of customer’s subscribed
cloud system. The appearance of an auto scaling mechanism in the cloud system with many existing system
mechanisms is an issue that needs to be considered. Because, normally, there is no free drawbacks
whenever a new part is added to a certain stable system. In this paper, we consider how existing load
balancing and auto scaling impact on each other. For the purpose, we have modeled a cloud system with
an auto scaler and a load balancer and implementing simulations based on the constructed model. Also
based on the results from the computer simulations we proposed about choosing load balancers for
subscribed cloud system with auto scaling service.
Performance Improvement of Cloud Computing Data Centers Using Energy Efficien...IJAEMSJORNAL
Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better usage of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved.
A Strategic Evaluation of Energy-Consumption and Total Execution Time for Clo...idescitation
Cloud computing is a very budding area in the
research field and as well as in the IT enterprises. Cloud
Computing is basically on-demand network access to a
collection of physical resources which can be provisioned
according to the need of cloud user under the supervision of
Cloud Service provider interaction. In this era of rapid usage
of Internet all over the world, Cloud computing has become
the center of Internet-oriented business place. For enterprises,
cloud computing is the worthy of consideration and they try to
build business systems with minimal costs, higher profits and
more choice; for large-scale industry, energy consumption
and total execution tome are the two important aspects of
cloud computing. In the current scenario, IT Enterprises are
trying to minimize the energy-consumption which, in turn,
maximizes the profit of the industry. And they are also trying
to reduce total execution time which, in turn, is concerned
with providing better Quality of Service (QoS). Therefore, in
this paper we have made an attempt to evaluate energy-
consumption and total execution time using CloudSim
simulator which helps to make evaluation performance of
energy consumption and total execution time of user
application.
Review and Comparison of Tasks Scheduling in Cloud Computing ijfcstjournal
Recently, there has been a dramatic increase in the popularity of cloud computing systems that rent
computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same
physical infrastructure. It is a virtual pool of resources which are provided to users via Internet. It gives
users virtually unlimited pay-per-use computing resources without the burden of managing the underlying
infrastructure. One of the goals is to use the resources efficiently and gain maximum profit. Scheduling is a
critical problem in Cloud computing, because a cloud provider has to serve many users in Cloud
computing system. So scheduling is the major issue in establishing Cloud computing systems. The
scheduling algorithms should order the jobs in a way where balance between improving the performance
and quality of service and at the same time maintaining the efficiency and fairness among the jobs. This
paper introduces and explores some of the methods provided for in cloud computing has been scheduled.
Finally the waiting time and time to implement some of the proposed algorithm is evaluated.
Virtual Machine Migration and Allocation in Cloud Computing: A Reviewijtsrd
Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessed through internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre copy and post copy approach. The process to move running applications or VMs from one physical machine to another is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the service is unavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. Khushbu Singh Chandel | Dr. Avinash Sharma "Virtual Machine Migration and Allocation in Cloud Computing: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29556.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29556/virtual-machine-migration-and-allocation-in-cloud-computing-a-review/khushbu-singh-chandel
Task scheduling is an important aspect to improve the utilization of resources in the Cloud Computing. This paper proposes a Divide and Conquer based approach for heterogeneous earliest finish time algorithm. The proposed system works in two phases. In the first phase it assigns the ranks to the incoming tasks with respect to size of it. In the second phase, we properly assign and manage the task to the virtual machine with the consideration of ideal time of respective virtual machine. This helps to get more effective resource utilization in Cloud Computing. The experimental results using Cybershake Scientific Workflow shows that the proposed Divide and Conquer HEFT performs better than HEFT in terms of task's finish time and response time. The result obtained by experimentally demonstrate that the proposed DCHEFT performance superiorly.
DYNAMIC ALLOCATION METHOD FOR EFFICIENT LOAD BALANCING IN VIRTUAL MACHINES FO...acijjournal
This paper proposes a Dynamic resource allocation method for Cloud computing. Cloud computing is a model for delivering information technology services in which resources are retrieved from the internet through web-based tools and applications, rather than a direct connection to a server. Users can set up
and boot the required resources and they have to pay only for the required resources. Thus, in the future providing a mechanism for efficient resource management and assignment will be an important objective of Cloud computing. In this project we propose a method, dynamic scheduling and consolidation mechanism that allocate resources based on the load of Virtual Machines (VMs) on Infrastructure as a service (IaaS). This method enables users to dynamically add and/or delete one or more instances on the basis of the load and the conditions specified by the user. Our objective is to develop an effective load balancing algorithm using Virtual Machine Monitoring to
maximize or minimize different performance parameters(throughput for example) for the Clouds of
different sizes (virtual topology de-pending on the application requirement).
Shceduling iot application on cloud computingEman Ahmed
Resource scheduling considers the execution time of every distinct workload, but most importantly, the overall performance is also based on type of workload i.e. with different QoS requirements (heterogeneous workloads) and with similar QoS requirements (homogenous workloads).
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
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Designing Resource-Aware Applications for the Cloud with ABS
1. Designing Resource-Aware Applications
for the Cloud with ABS
Einar Broch Johnsen
University of Oslo, Norway
einarj@ifi.uio.no
1st Intl. Workshop on Formal Methods for and on the Cloud (iFMCloud)
Reykjavik, Iceland, 04 June 2016
http://www.envisage-project.eu
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 0 / 22
2. We want to make e↵ective use of cloud computing
to meet service requirements
Cloud API
Application
Service
I Virtualization makes elastic
amounts of resources available to
application-level services
I Metered resources: Resources on
the Cloud are pay-on-demand
I Services need to share and scale
resources
I Digitalization: new services need
to share resources with old services
Discovering bad resource management after deployment
on the Cloud can be a very costly (wasting both time and money!)
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 1 / 22
3. Services deployed on the cloud:
Predicting behavior from models
ApplicationServer
DC4
DC3
CalcServerDC2
CalcServer
DC1
CalcServer
AppWorkflow
CloudProvider
Invoke task
# of VM
Total Cost
Application Server
=
Application Workflow
+
Application Resource Management
(load balancing, scalability)
CalcServer
=
Independent tasks
(can be parallelized)
AppRM
I Resource-aware design:
Build software that can
dynamically modify
its own deployment
to improve perfor-
mance and/or
reduce cost
I Model-based deployment decisions at design time using service models
I Formal semantics: Architects and developers can simulate and analyze at
design time how an application runs on the cloud
H¨ahnle, Johnsen. Designing Resource-Aware Cloud Applications. IEEE Computer 48(6), 2015
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 2 / 22
4. What kind of questions can we answer using models?
Berndnaut Smilde: Nimbus II, 2012
Model-based analysis of
performance vs. cost
1 How will the response time and cost of running my system change
if I double the number of servers?
2 Can I meet my performance requirements with my current
deployment strategy? What about fluctuations in client tra c?
3 Can I control the performance of my system better by using a
custom resource manager?
Use the model to
predict behavior
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 3 / 22
5. Conceptual Parts of a Deployed Cloud Service
Provisioning Layer
Legal Contract Layer
Formal Service Contract
Executable Model of Client Layer
Cloud API
Simulation
“early modeling”
Formal Methods
“early analysis”
Provisioning
“runtime monitoring”
Combine techniques based on abstract executable models
I Formal modeling using Abstract Behavioral Specifications (ABS)
I Formal methods: Verification, Performance Analysis, Cost Analysis,
Advanced Type Systems, Code Generation, Test-Case Generation
I Monitoring: Framework to generate monitors for SLA-compliance
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 4 / 22
6. Example: Phone Services - Abstract Behavioral Model
Telephone Service
interface TelephoneService {
Unit call(Int calltime);
}
class TelephoneServer implements TelephoneService {
Int callcount = 0;
Unit call(Int calltime){
while (calltime > 0) { [Cost: 1] calltime = calltime 1;
await duration(1, 1); }
callcount = callcount + 1;
}
}
SMS Service
interface SMSService {
Unit sendSMS();
}
class SMSServer implements SMSService {
Int smscount = 0;
Unit sendSMS() {[Cost: 1] smscount = smscount + 1;}
}
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 5 / 22
7. Example: The New Year’s Eve Client Behavior
50 70
Alternate
sms and call
Huge number of
sms per time interval
time
Alternate
sms and call
Midnight Window
class NYEclient(Int frequency,TelephoneService ts,SMSService smss){
Time created=now(); Bool call=false;
Unit normalBehavior(){ ... }
Unit midnightWindow(){ ... } // Switch at appropriate time...
}
{// Main block:
DC smscomp = new DeploymentComponent(”smscomp”, Speed(50));
DC telcomp = new DeploymentComponent(”telcomp”, Speed(50));
[DC: smscomp] SMSService sms = new SMSServer();
[DC: telcomp] TelephoneService tel = new TelephoneServer();
Client c = new NYEbehavior(1,tel,sms); ... // Clients
}
How to deploy
the services?
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 6 / 22
9. Load Balancing in Deployment Scenarios
smscomp
telcomp
Client
Client
sms()
call(n)
sms()
call(n)
tel
sms
telb
smsb
request()
Resource awareness: resource reallocation, object mobility, job distribution
I dc.load(e): average load on dc during the last e time intervals
I dc.total(): currently allocated resources on dc
I dc.transfer(dc2, r): transfer r resources to dc2
Load Balancing Strategy for the Phone Services
Example: Reallocate 1/2⇥total resources upon request from partner
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 8 / 22
10. Example: Simulation Results
Johnsen, Owe, Schlatte, Tapia Tarifa:
Dynamic Resource Reallocation between Deployment Components. Proc. ICFEM 2010
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 9 / 22
11. ABS: Abstract Behavioral Specification
ABS: Between design-oriented and implementation-oriented specification
I State-of-the-art modeling language: actors + OO
I Models follow the execution flow of OO programs,
but abstract from implementation details using ADTs
I ABS allows time modeling and deployment modeling
I Java-like syntax: intuitive to the programmer
ABS is a formal, tool-supported modelling language
I Operational semantics allows advanced analysis techniques
I Simulation tool for rapid prototyping
I Automated worst-case resource and deadlock analysis
I Automated optimization of static deployment
I Semi-automated scalable verification of functional correctness (KeY)
I Code generation into Java and Haskell (preserves cost bounds)
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 10 / 22
12. Deployment Components
Deployment components are abstract execution locations
I Each deployment
component has a given
resource capacity
I Objects execute in the
context of a deployment
component
Server ...
...
objectEnv
[cost] Task1
[cost] Task2
object 1
[cost] Task1
[cost] Task2
object n
[cost] Task1
[cost] Task2
I The resources are shared between the component’s objects
I Object execution uses resources in a deployment component
(via Cost annotations)
I How resources are assigned and consumed,
depends on the kind of resource
Johnsen, Schlatte, Tapia Tarifa. Integrating deployment architectures and resource consumption
in timed object-oriented models. J. Log. Algebr. Meth. Program. 84(1), 2015
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 11 / 22
13. A Resource-Aware Application in ABS
interface CalcServer {
Unit process(Int cost);
DC getDC();
}
class Server implements CalcServer {
Unit process(Int cost) { [Cost: cost] skip; }
DC getDC() { return thisDC(); }
}
interface ApplicationServer {
Bool request(Int cost);
}
ApplicationServer
DC4
DC3
CalcServerDC2
CalcServer
DC1
CalcServer
AppWorkflow
CloudProvider
Invoke task
# of VM
Total Cost
Application Server
=
Application Workflow
+
Application Resource Management
(load balancing, scalability)
CalcServer
=
Independent tasks
(can be parallelized)
AppRM
class ConstantBalancer(CloudProvider provider, Int serverSize) implements ApplicationServer {
Server server; DC dc; Bool initialized = False;
Unit run() {
Fut<DC> f = provider!createMachine(serverSize); await f?; dc = f.get;
[DC: dc] server = new Server(); initialized = True;
}
Bool request (Int cost) {
await initialized;
Fut<Unit> r = server!process(cost); await r?; return (durationValue(deadline()) > 0);
}
}
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 12 / 22
14. A Resource-Aware Application in ABS
interface CalcServer {
Unit process(Int cost);
DC getDC();
}
class Server implements CalcServer {
Unit process(Int cost) { [Cost: cost] skip; }
DC getDC() { return thisDC(); }
}
interface ApplicationServer {
Bool request(Int cost);
}
ApplicationServer
DC4
DC3
CalcServerDC2
CalcServer
DC1
CalcServer
AppWorkflow
CloudProvider
Invoke task
# of VM
Total Cost
Application Server
=
Application Workflow
+
Application Resource Management
(load balancing, scalability)
CalcServer
=
Independent tasks
(can be parallelized)
AppRM
class DynamicBalancer(CloudProvider provider) implements ApplicationServer {
Map<Int, Set<Server>> sleepingMachines = EmptyMap;
Int machineStartTime = ... // a constant representing the time it takes to start a machine;
Bool request (Int cost) {
Int requiredResources = (cost / durationValue(deadline())) + 1 + machineStartTime;
Server server = this.getMachine(requiredResources);
Fut<Unit> r = server!process(cost); await r?;
this.dropMachine(server); return durationValue(deadline()) > 0;
}
Server getMachine(Int size) { ... } // take machine of size if it exists, otherwise create one
Unit dropMachine(Server server) { ...}
}
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 12 / 22
15. Rapid Prototyping: Simulation Results
I Define client behavior
to model a load spike
time
Increase the # of requests
I Simulate the di↵erent scenarios with ABS simulator
User scenario
Load spike
Strategy QoS Total Cost
Constant balancer 53% 200
As-needed balancer 100% 128
I QoS: measure the successful requests (i.e., requests completed within
the deadline) divided by the total number of requests
I Total Cost: measures the accumulated sum of CPU resources made
available by the cloud provider.
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 13 / 22
16. Case Study: Montage (1)
Montage is a toolkit for assembling astronomical
images into customized mosaics
mProject
mProjExec
mImgtbl mOverlaps
mDiffExec
mDiff
mFitExec
mFitplane
mBgModel
mBackground
mBgExec
mAdd
Re-project
Image
Background
modeling
Background
matching
Final
mosaic
Partly ordered workflow and highly parallelizable tasks.
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 14 / 22
17. Case Study: Montage (2)
DC4
DC3
CalcServerDC2
CalcServer
DC1
CalcServer
CloudProvider
Invoke task
ApplicationServer
=
AppWorkflow + AppRM
CalcServer
=
Independent tasks
# of VM
Total Cost
ApplicationServer
AppWorkflow
AppRM
Model the Montage toolkit using the Cloud Provider API, run simulations
varying the di↵erent deployment scenario and compare the results.
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 15 / 22
18. Case Study: Montage (3)
Cost vs. Time Tradeo↵: can we reproduce the results
of other informal cloud simulation tools (GridSim)?
!"
!#"
!##"
!###"
!####"
!" $" %" &" !'" ($" '%" !$&"
!"
#$"
$!"
%$"
&!!"
&" #" '" (" &)" *#" )'" &#("
Logarithmicscale
60 cents for
1 processor
Logarithmicscale
approx 4 $ for
128 processors
approx 5.5 hrs
for 1 processor
approx 18 min for
128 processors
432
270
1152
CPUCost
89
2
8
Time
The cost of doing science on the cloud: The Montage example.
E. Deelman, G. Singh, M. Livny, G. B. Berriman, and J. Good.
(SC’08), pages 1–12. IEEE/ACM, 2008.
Johnsen, Schlatte, Tapia Tarifa. Modeling Resource-Aware Virtualized Applications for the
Cloud in Real-Time ABS. Proc. ICFEM 2012
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 16 / 22
19. Case Study: Fredhopper Replication Server (1)
The Fredhopper Access Server (FAS) is a distributed, concurrent OO
system providing search and merchandising services to e-Commerce
companies. The Replication Server is one part of FAS.
Acceptor
Cloud
Provider
ClientJob
ClientJob
ClientJob
SyncClient
job(schedule)
SyncClient
SyncClient...
...
LIVE STAGING
SyncServer create()
CLOUD
DC4
Connection
Thread
getConnection(schedule)
getConnection(schedule)
getConnection(schedule)
job(schedule)
job(schedule)
DC3
Connection
Thread
replication
DC2
Connection
Thread
replication
DC1
Connection
Thread
replication
I Very detailed model: consists of 5000 lines of ABS
Albert, de Boer, H¨ahnle, Johnsen, Schlatte, Tapia Tarifa, Wong.
Formal modeling and analysis of resource management for cloud architectures: an industrial
case study using Real-Time ABS. Service Oriented Computing and Applications 8(4), 2014
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 17 / 22
20. Case Study: Fredhopper Replication Server (2)
How does the accumulated cost in our model
compare to the actual Java implementation?
0
17.5
35
52.5
70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0
7500
15000
22500
30000
Runningtime[s]
Environments
Simulationcost
Model simulation cost Implementation running time
Measured execution time of the implementation (left scale)
Accumulated cost of the simulation (right scale)
The deviation roughly seems to correspond to the start-up time of JVM
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 18 / 22
21. Case Study: Hadoop YARN Clusters (1)
Open-source software framework that
implements a cluster management
technology for distributed processing.
Popular cloud framework
for big data processing:
I Resource allocation
I Code distribution
I Distributed data processing
Lin, Yu, Johnsen, Lee. ABS-YARN: A Formal Framework
for Modeling Hadoop YARN Clusters. Proc. FASE 2016
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 19 / 22
22. Case Study: Hadoop YARN Clusters (2)
How does the ABS YARN compare to the actual YARN implementation?
(a) The normalized starting time (b) The normalized finish time
(c) Cumulative completed jobs (d) Total number of completed jobs
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 20 / 22
23. The ABS Collaboratory
I ABS as a web service, with documentation and examples
under development at http://www.abs-models.org
I Tools are open source: https://github.com/abstools
I ABS API available for orchestration of Java code
I Eclipse plug-in for ABS
Get involved!
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 21 / 22
24. Summary
Virtualization requires novel modeling abstractions
Executable models, deployment components, reflection
ABS: Abstract Behavioral Specification
I Model deployed services with dynamic resource management
I High-level abstractions of low-level platform-specific concerns
I Analysis methods: performance, cost analysis, deadlock analysis, . . .
I More info and open source tools:
www.abs-models.org
Make your deployment decisions at design time!
ABS permits concise modeling and accurate prediction
Engineering Virtualized Services
[www.envisage-project.eu]
Einar Broch Johnsen (UiO) Designing Resource-Aware Applications iFMCloud, 04.06.2016 22 / 22