REGION BASED DATA CENTRE RESOURCE ANALYSIS FOR BUSINESSESijsrd.com
To meet up the needs of large-scale multi-tenant data centres and clouds, data center and cloud architectures are continuously forming modifications. These needs are primarily focused around seven dimensions: scalability in computing, storage, and bandwidth, scalability in network services, efficiency in resource utilization, agility in service creation, cost efficiency, service reliability, and security. This article focuses on the first five dimensions as they are related to networking. . Large data centers are handling thousands of servers, exabytes of storage, terabits per second of traffic, and tens of thousands of tenants. Data centres are interconnected across the wide area network via routing and transport technologies to provide a pool of resources, known as the cloud. High-capacity transport intra- and inter-datacentre are being achieved by High-speed optical interfaces and dense wavelength-division multiplexing optical transport. This paper presents data centre resource analysis based on region
Earned Value Management (EVM) is an effective tool for project performance measurement that, if planned properly, can play a vital role for project success. EVM is based on scope, time and cost only while risks are not accounted for in planning process. Moreover, there are difficulties in acquiring real-time Actual Cost (AC) data for continuous monitoring and control, mainly due to the communication gap between engineers and accountants. Researchers have proposed different extensions of EVM for specific projects and in general. In order to apply the proposed EVM extensions, a real-time tourism facility project with sustainable energy & water resource at Kund Malir, Baluchistan is taken as a model. Costs, schedules, scope and risks are hypothetical in the model. Planned EVM is applied to the model with and without risks. Risk Costs and Scheduled Buffers are added in Planned Value (PV) calculations basing on probability-impact matrix factors. Furthermore, task level EVM models or Task-EVMs are integrated into Project-EVM or Master-EVM in order to minimize the problems being faced in acquiring real-time data. Industry specific application and research of EVM extensions proposed in this paper can be a good area for future research. Moreover, establishment of tourism facilities at unexplored or less explored areas of Sindh and Baluchistan can be another real-time research as well as a business project.
REGION BASED DATA CENTRE RESOURCE ANALYSIS FOR BUSINESSESijsrd.com
To meet up the needs of large-scale multi-tenant data centres and clouds, data center and cloud architectures are continuously forming modifications. These needs are primarily focused around seven dimensions: scalability in computing, storage, and bandwidth, scalability in network services, efficiency in resource utilization, agility in service creation, cost efficiency, service reliability, and security. This article focuses on the first five dimensions as they are related to networking. . Large data centers are handling thousands of servers, exabytes of storage, terabits per second of traffic, and tens of thousands of tenants. Data centres are interconnected across the wide area network via routing and transport technologies to provide a pool of resources, known as the cloud. High-capacity transport intra- and inter-datacentre are being achieved by High-speed optical interfaces and dense wavelength-division multiplexing optical transport. This paper presents data centre resource analysis based on region
Earned Value Management (EVM) is an effective tool for project performance measurement that, if planned properly, can play a vital role for project success. EVM is based on scope, time and cost only while risks are not accounted for in planning process. Moreover, there are difficulties in acquiring real-time Actual Cost (AC) data for continuous monitoring and control, mainly due to the communication gap between engineers and accountants. Researchers have proposed different extensions of EVM for specific projects and in general. In order to apply the proposed EVM extensions, a real-time tourism facility project with sustainable energy & water resource at Kund Malir, Baluchistan is taken as a model. Costs, schedules, scope and risks are hypothetical in the model. Planned EVM is applied to the model with and without risks. Risk Costs and Scheduled Buffers are added in Planned Value (PV) calculations basing on probability-impact matrix factors. Furthermore, task level EVM models or Task-EVMs are integrated into Project-EVM or Master-EVM in order to minimize the problems being faced in acquiring real-time data. Industry specific application and research of EVM extensions proposed in this paper can be a good area for future research. Moreover, establishment of tourism facilities at unexplored or less explored areas of Sindh and Baluchistan can be another real-time research as well as a business project.
Resource Scheduling and Evaluation of Heuristics with Resource Reservation in...Eswar Publications
The "cloud" is a combination of various hardware and software that work jointly to bring many aspects of computing to the users as an online service. Some uniqueness of Cloud Computing is pay-per-use, elastic capacity, misapprehension of unlimited resources, self-service interface, virtualized resources etc. Various applications running on cloud environment would expect better Quality of Service (QoS) from Cloud environment. Improvement in Quality of Service (QoS) is possible through better job scheduling and reservation of resources in advance for execution of jobs. In this paper effects of Reservation Rate and Time Factor on the performance parameters like Resource Utilization, Waiting Time, Minimum Execution Time and Success Rate of Reserved
jobs have been studied for various job scheduling algorithms and their performance have been calculated in resource reservation environment in Cloud.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
An Integrated Framework for Parameter-based Optimization of Scientific Workflowsvijayskumar
Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.
Energy efficient resource management for high-performance clustersXiao Qin
In the past decade, high-performance cluster computing platforms have been widely used to solve challenging and rigorous engineering tasks in industry and scientific applications. Due to extremely high energy cost,reducing energy consumption has become a major
concern in designing economical and environmentally friendly cluster computing
infrastructures for many high-performance applications. The primary focus of this talk is to illustrate how to improve energy efficiency of clusters and storage systems without significantly degrading performance. In this talk, we will first describe a general architecture
for building energy-efficient cluster computing platforms. Then, we will outline several energyefficient scheduling algorithms designed for high-performance clusters and large-scale storage systems. The experimental results using both synthetic and real world applications
show that energy dissipation in clusters can be reduced with a marginal degradation of system performance.
A performance study on the vm startup time in the cloudmingtemp
http://www.cs.virginia.edu/~mm5bw/papers/Cloud%20VM%20Startup%20Performance%20Study.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6253534
www.mingmao.org
Amazon Web Service, EC2, Windows Azure, Rackspace, Spot Instances, VM Image, OS,
Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflowsmingtemp
http://www.cs.virginia.edu/~mm5bw/papers/WorkflowAutoScaling.pdf
The presentation for SC 2011
http://dl.acm.org/citation.cfm?id=2063449
www.mingmao.org
Task Scheduling and Asynchronous Processing Evolved. Zend Server Job QueueSam Hennessy
Find out how Zend Server's Job Queues can give you the tools to effetely manage and debug the running of offline PHP tasks. Whether you need code that will execute at set intervals or you're offloading work from your web servers. Zend Server gives you a system that lets you develop the tasks easily as well as monitor and debug simply.
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
An Introduction To Applied Evolutionary Meta Heuristicsbiofractal
This presentation introduces some of the main themes in modern evolutionary algorithm research while emphasising their application to problems that exhibit real-world complexity.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal1
Currently cloud computing has turned into a promising technology and has become a great key for
satisfying a flexible service oriented , online provision and storage of computing resources and user’s
information in lesser expense with dynamism framework on pay per use basis.In this technology Job
Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources,
administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic
Scheduling Approach to schedule resources, by taking account of computation time and makespan with two
detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We
believe that proposed hyper-heuristic achieve better results than other individual heuristics
Resource Scheduling and Evaluation of Heuristics with Resource Reservation in...Eswar Publications
The "cloud" is a combination of various hardware and software that work jointly to bring many aspects of computing to the users as an online service. Some uniqueness of Cloud Computing is pay-per-use, elastic capacity, misapprehension of unlimited resources, self-service interface, virtualized resources etc. Various applications running on cloud environment would expect better Quality of Service (QoS) from Cloud environment. Improvement in Quality of Service (QoS) is possible through better job scheduling and reservation of resources in advance for execution of jobs. In this paper effects of Reservation Rate and Time Factor on the performance parameters like Resource Utilization, Waiting Time, Minimum Execution Time and Success Rate of Reserved
jobs have been studied for various job scheduling algorithms and their performance have been calculated in resource reservation environment in Cloud.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
An Integrated Framework for Parameter-based Optimization of Scientific Workflowsvijayskumar
Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.
Energy efficient resource management for high-performance clustersXiao Qin
In the past decade, high-performance cluster computing platforms have been widely used to solve challenging and rigorous engineering tasks in industry and scientific applications. Due to extremely high energy cost,reducing energy consumption has become a major
concern in designing economical and environmentally friendly cluster computing
infrastructures for many high-performance applications. The primary focus of this talk is to illustrate how to improve energy efficiency of clusters and storage systems without significantly degrading performance. In this talk, we will first describe a general architecture
for building energy-efficient cluster computing platforms. Then, we will outline several energyefficient scheduling algorithms designed for high-performance clusters and large-scale storage systems. The experimental results using both synthetic and real world applications
show that energy dissipation in clusters can be reduced with a marginal degradation of system performance.
A performance study on the vm startup time in the cloudmingtemp
http://www.cs.virginia.edu/~mm5bw/papers/Cloud%20VM%20Startup%20Performance%20Study.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6253534
www.mingmao.org
Amazon Web Service, EC2, Windows Azure, Rackspace, Spot Instances, VM Image, OS,
Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflowsmingtemp
http://www.cs.virginia.edu/~mm5bw/papers/WorkflowAutoScaling.pdf
The presentation for SC 2011
http://dl.acm.org/citation.cfm?id=2063449
www.mingmao.org
Task Scheduling and Asynchronous Processing Evolved. Zend Server Job QueueSam Hennessy
Find out how Zend Server's Job Queues can give you the tools to effetely manage and debug the running of offline PHP tasks. Whether you need code that will execute at set intervals or you're offloading work from your web servers. Zend Server gives you a system that lets you develop the tasks easily as well as monitor and debug simply.
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
An Introduction To Applied Evolutionary Meta Heuristicsbiofractal
This presentation introduces some of the main themes in modern evolutionary algorithm research while emphasising their application to problems that exhibit real-world complexity.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal1
Currently cloud computing has turned into a promising technology and has become a great key for
satisfying a flexible service oriented , online provision and storage of computing resources and user’s
information in lesser expense with dynamism framework on pay per use basis.In this technology Job
Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources,
administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic
Scheduling Approach to schedule resources, by taking account of computation time and makespan with two
detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We
believe that proposed hyper-heuristic achieve better results than other individual heuristics
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal
Currently cloud computing has turned into a promising technology and has become a great key for satisfying a flexible service oriented , online provision and storage of computing resources and user’s information in lesser expense with dynamism framework on pay per use basis.In this technology Job Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources, administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic Scheduling Approach to schedule resources, by taking account of computation time and makespan with two detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We believe that proposed hyper-heuristic achieve better results than other individual heuristics.
The cloud environment offers an appropriate location for the implementation of huge range of scientific applications. However, in the existing workflows the major dispute is to assign the assets to the tasks in a well-organized way so, that it acquires less finishing time and load on every virtual machines will be impartial. To overcome this problem, GA_ MINMIN has been proposed that combines the features of GA and MINMIN scheduling algorithms. This algorithm is fundamentally a three-layer structure where GA is connected on the main level and hereditary calculation was performed for distributing belonging in an advanced way. At second level, the execution request of the assignments was resolved based on their size. This would be finished with the assistance of MIN-MIN. At third level, all the virtual machines have been running in parallel so that task response time will get decreased with more advanced outcomes. The proposed algorithm has been executed on the simulation environment.
Load Balancing Algorithm to Improve Response Time on Cloud Computingneirew J
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
A Survey on Service Request Scheduling in Cloud Based ArchitectureIJSRD
Cloud computing has become quite popular now-a-days. It facilitates the users to store and process their data which is stored in 3rd party data centers. Today in IT sector everything is run and managed on the cloud environment. As the number of users is increasing day by day, faster and efficient processing of large volume of data and resources is desired at all levels. So the management of resources attains prime importance. While using cloud computing various issues are encountered like load balancing, traffic while computation etc. Job scheduling is one of the solution of these problems which reduces the waiting time and maximizes the quality of services. In job scheduling “priority†is an important factor. In this paper, we will be discussing various scheduling algorithms and a review on dynamic priority scheduling algorithm.
A Survey on Service Request Scheduling in Cloud Based ArchitectureIJSRD
Cloud computing has become quite popular now-a-days. It facilitates the users to store and process their data which is stored in 3rd party data centers. Today in IT sector everything is run and managed on the cloud environment. As the number of users is increasing day by day, faster and efficient processing of large volume of data and resources is desired at all levels. So the management of resources attains prime importance. While using cloud computing various issues are encountered like load balancing, traffic while computation etc. Job scheduling is one of the solution of these problems which reduces the waiting time and maximizes the quality of services. In job scheduling “priority†is an important factor. In this paper, we will be discussing various scheduling algorithms and a review on dynamic priority scheduling algorithm.
Optimization of energy consumption in cloud computing datacenters IJECEIAES
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
Intelligent Workload Management in Virtualized Cloud EnvironmentIJTET Journal
Abstract— Cloud computing is a rising high performance computing environment with a huge scale, heterogeneous collection of self-sufficient systems and elastic computational design. To develop the overall performance of cloud computing, through the deadline constraint, a task scheduling replica is traditional for falling the system power utilization of cloud computing and recovering the yield of service providers. To improve the overall act of cloud environment, with the deadline constraint, a task scheduling model is conventional for reducing the system performance time of cloud computing and improving the profit of service providers. In favor of scheduling replica, a solving technique based on multi-objective genetic algorithm (MO-GA) is considered and the study is determined on programming rules, intersect operators, mixture operators and the scheme of arrangement of Pareto solutions. The model is designed based on open source cloud computing simulation platform CloudSim, to obtainable scheduling algorithms, the result shows that the proposed algorithm can obtain an enhanced solution, thus balancing the load for the concert of multiple objects.
Similar to Scaling and scheduling to maximize application performance within budget constraints (20)
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Scaling and scheduling to maximize application performance within budget constraints
1. Ming Mao, Marty Humphrey
CS Department, UVa
Scaling and Scheduling to Maximize
Application Performance within Budget
Constraints in Cloud Workflows
IPDPS 2013
(May 21st 2013)
1
2. 2
Dynamic scalability and cost saving are two of the most important factors when
considering cloud adoption
Two major benefits
- dynamic scalability and cost
A survey from 39 major technology companies [1]
Cloud benefits
On-demand self-services
Broad network access
Resource pooling
Rapid elasticity
Measured services
Cheaper maintenance
……
Why do you move into the cloud?
3. 3
Dynamic scalability – the ability to acquire/release resources in response to
demand dynamically
Dynamic scalability challenge → It relies on the users to tell the size of resource
pool
Over-provisioning → cost more than necessary, offset cloud advantages
Under-provisioning → hurt application performance, cannot meet service level agreements and
lose application customers
Cloud dynamic scalability
over-provisioning under-provisioning
4. 4
Problem - What resources should be acquired/released in the cloud,
and how should the computing activities be mapped to the cloud
resources, so that the application performance can be maximized
within the budget constrains?
In this paper, we discuss limited budget case
The unlimited budget case was discussed in our SC 11 paper
Solution - This paper argues that an automatic resource
provisioning and allocation mechanism, i.e., an auto-scaling
solution – is the key to successful cloud adoption. Essentially, an
auto-scaling solution needs to answer the following two questions:
Capacity determination (or resource provisioning)
what types of resources, how much and for how long
Job scheduling (or resource allocation)
map computing activities onto the cloud resources
Problem statement
5. 5
An application consists of service components. A workflow goes through different
service components and therefore consists of multiple connected tasks
Workload is a stream of workflow jobs not known in advance
Task precedence constraints need to be preserved
Jobs have individual priorities
Service oriented architecture (SOA) & workflow jobs
6. 6
Minimize job turnaround time within budget constraints
Problem formulation
Problem terminology
Cloud application
app = {Si}
Job class
J = {DAG(Si), priorityJ| Si ∈ app}
Cloud VM
VMv = {[𝐽 𝑆 𝑖]v , cv , lagv}
Workload
Wt = 𝑗𝑜𝑏𝐽
𝑆 𝑖
𝑗𝑜𝑏𝐽𝑆 𝑖
Scaling plan
Scalingt = {VMv → Nv}
Scheduling plan
Schedulet = { 𝑗𝐽
𝑆 𝑖
→VMv}
Goal
Min( 𝑗𝑜𝑏𝑡𝑢𝑟𝑛𝑎𝑟𝑜𝑢𝑛𝑑 × 𝑝𝑟𝑖𝑜𝑟𝑖𝑡𝑦/𝑗𝑜𝑏 𝑝𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑗𝑜𝑏 )
&&
Cost(app) <= B (budget, dollars/hour)
Target - The service provider has a limited budget and
aims to maximize the application performance.
Solution idea – a monitor-control loop that
makes scaling and scheduling decisions based
on updated workload and VM information
7. 7
Scheduling-first
Idea – allocate application budget to individual jobs based on priorities
and schedule tasks within job budget
Step 1 – Distribute budget: 𝐵𝑗 = 𝐵 × 𝑝𝑗/ 𝑝𝑗𝑗
Step 2 – Schedule tasks
for each job, schedule as many tasks as possible on their fast machines
Step 3 – Consolidate budget
return job budget to the application
the application uses the remaining budget collected from individual jobs to schedule
high priority tasks
Step 4 – Acquire instance
acquire instances and execute tasks based on the determined schedule plans
Minimize job turnaround time within budget constraints
Solution: scheduling-first
9. 9
Minimize job turnaround time within budget constraints
Solution: scaling-first
Scaling-first
Idea – determine the computing capacity by looking at the overall
workload and schedule tasks based on priority
Step 1 – determine the VMs
assume tasks run on their fastest machines and calculate the cost Cfast for the next
hour
acquire VMs proportionally based on Budget/Cfast
Step 2 – consolidate budget
use the remaining the budget to purchase new machines.
Step 3 – schedule tasks
schedule tasks based on task priority
10. 10
Minimize job turnaround time within budget constraints
Solution: scaling-first
Scaling-first
Step 1 – determine the VMs
Step 2 – consolidate budget
Step 3 – schedule tasks
Step 1: assume tasks run on fastest
machines and calculate Cfast and
acquire VMs proportionally based on
B/Cfast,
Step 2: the remaining $0.5 can be used to
purchase 1 L machine
Step 3: tasks are scheduled
based on their priorities
11. 11
Instance consolidation
Schedule tasks on different VM types to save partial instance hour cost
Budget allocation schemes
Evenly distributed – e.g. daily x/365, hourly x/8760
Based on workload – e.g. high on busy times, low on non-busy times
Workload prediction – $/hour → $/job
Minimize job turnaround time within budget constraints
Other considerations
12. Workload patterns
Application models
12
Time
72 hours
Task execution
Randomly generated
VM lag
5 min
Minimize job turnaround time within budget constraints
Evaluation – experiment setup
Baseline
Standard
VM Type Price
Micro $0.02/hour
Standard $0.080/hour
High-CPU $0.66/hour
High-Memory $0.45/hour
Extra-Large $1.3/hour
13. 13
Minimize job turnaround time within budget constraints
Evaluation – job turnaround time
above – weighted average job turnaround time for the hybrid application and cycle
workload pattern
Scheduling-first and scaling-first can save 9.8%- 45.2% cost compared to the standard
machine choice.
Scaling-first works better under small budget ranges while scheduling-first works better
under large budget ranges.
14. 14
Minimize job turnaround time within budget constraints
Evaluation – sensitivity to inaccurate parameters
left – scheduling-first’s sensitivity to inaccurate parameters (Hybrid application + Cycle
workload pattern)
right – scaling-first’s sensitivity to inaccurate parameters (Hybrid application + Cycle workload
pattern)
When the estimation error is within ±20%, the job turnaround time shows -10.2% – 16.7%
difference.
When the task estimation error reaches ±60%, the performance of both algorithms shows
significant degradation (more than ±25% difference)
15. 15
Minimize job turnaround time within budget constraints
Evaluation – instance consolidation
left – job turnaround time / resource utilization of scheduling-first’s instance consolidation
(Hybrid application + Cycle workload pattern)
right – job turnaround time / resource utilization of scaling-first’s instance consolidation
(Hybrid application + Cycle workload pattern)
When budget is low or high, the improvement is small. When the budget is in between, the
improvement is more significant (e.g. utilization rate improves 2.2% to 19.9% when the budget
is between $15/hour and $25/hour).
Scaling-first benefits more from instance consolidation process than scheduling-first
16. 16
Conclusions
choose appropriate VM types based on the workload.
Scheduling-first and scaling-first are trade-offs between the task execution time and
waiting time.
As long as the VM performance can be correctly ranked, the proposed mechanisms have
good tolerance to inaccurate parameters.
Instance consolidation is an efficient strategy to save partial instance hours and improve
resource utilization.
Future work
Other billing models – reserved instances, spot instances, $/min
Maximize application performance within budget constraints for data-intensive
applications
Hybrid and federate cloud environments
Develop evaluation benchmarks and simulation platforms
Conclusion and future work