The focus of the paper is to generate an advance algorithm of resource allocation and load balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine. In VM while processes are allocate they executes in queue , the first process get resources , other remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR) Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...SaikiranReddy Sama
In Dynamic Resource Allocation, WE PRESENT A SYSTEM THAT USES VIRTUALIZATION TECHNOLOGY TO ALLOCATE DATA CENTER RESOURCES DYNAMICALLY.
WE INTRODUCE THE CONCEPT OF “SKEWNESS”.
And BY MINIMIZING SKEWNESS, WE CAN COMBINE DIFFERENT TYPES OF WORKLOADS NICELY AND IMPROVE THE OVERALL UTILIZATION OF SERVER RESOURCES.
WE DEVELOP A SET OF HEURISTICS THAT PREVENT OVERLOAD IN THE SYSTEM EFFECTIVELY WHILE SAVING ENERGY USED.
Dynamic resource Allocation using Virtual Machines For Cloud Computing
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...SaikiranReddy Sama
In Dynamic Resource Allocation, WE PRESENT A SYSTEM THAT USES VIRTUALIZATION TECHNOLOGY TO ALLOCATE DATA CENTER RESOURCES DYNAMICALLY.
WE INTRODUCE THE CONCEPT OF “SKEWNESS”.
And BY MINIMIZING SKEWNESS, WE CAN COMBINE DIFFERENT TYPES OF WORKLOADS NICELY AND IMPROVE THE OVERALL UTILIZATION OF SERVER RESOURCES.
WE DEVELOP A SET OF HEURISTICS THAT PREVENT OVERLOAD IN THE SYSTEM EFFECTIVELY WHILE SAVING ENERGY USED.
Dynamic resource Allocation using Virtual Machines For Cloud Computing
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
33. dynamic resource allocation using virtual machinesmuhammed jassim k
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
Load Balancing in Cloud Computing Environment: A Comparative Study of Service...Eswar Publications
Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The
load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
These All Cloud Computing Architectures have been Discussed in this Lecture
Hypervisor Clustering Architecture
Load Balanced Virtual Server Instances Architecture
Non-Disruptive Service Relocation Architecture
Zero Downtime Architecture
Cloud Balancing Architecture
Resource Reservation Architecture
Dynamic Failure Detection and Recovery Architecture
Bare-Metal Provisioning Architecture
Rapid Provisioning Architecture
Storage Workload Management Architecture
Virtualization Technology using Virtual Machines for Cloud ComputingIJMER
Cloud computing is the delivery of computing and storage capacity as a service to a community of end users. The name “cloud computing” comes from the use of a cloud-shaped symbol as an abstraction for the complex infrastructure it contains in system diagrams. Cloud computing entrusts services with a user's software, data and computation over a network. End users access cloud-based applications through a web browser or mobile application or a light-weight desktop while the business software and user's data are stored on servers at a remote location. Proponents claim that cloud computing environment allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables IT industry to more rapidly adjust resources to meet fluctuating and unpredictable business demand. In this paper, we present a system that uses virtualization technology to allocate the data center resources dynamically based on the application demands and support green computing by optimizing the number of servers in use. This method multiplexes virtual to physical resources adaptively based on the changing demand. We use the concept of skewness metric to combine virtual machines with different resource characteristics appropriately so that the capacities of servers are well utilized.
Cloud Computing System models for Distributed and cloud computing & Performan...hrmalik20
Advantage of Clouds over Traditional
Distributed Systems,Clouds,Service-Oriented Architecture (SOA) Layered Architecture,Performance Metrics and Scalability Analysis,System Efficiency,Performance Challenges in Cloud Computing,What is cloud computing and why is it distinctive?,CLOUD SERVICE DELIVERY MODELS AND THEIR
PERFORMANCE CHALLENGES,Cloud computing security,What does Cloud Computing Security mean,Cloud Security Landscape,Distinctions between Security and Privacy,Energy Efficiency of Cloud Computing,How energy-efficient is cloud computing?
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Finance and insurance driving expansions and relocations in the market
As of third quarter, metro employment in the finance and insurance industries finally approached pre-recession levels.
The office market saw substantial leasing activity from firms like Ally Financial which recently relocated 150 employees to the Shoreview Corporate Center with plans to add another 250 jobs by 2017.
Other firms like One Beacon Insurance Group, Securian Financial Group, Travelers Companies, and General Casualty Company have either invested in new space or absorbed existing space in all corners of the Minneapolis-St. Paul market.
33. dynamic resource allocation using virtual machinesmuhammed jassim k
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
Load Balancing in Cloud Computing Environment: A Comparative Study of Service...Eswar Publications
Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The
load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
These All Cloud Computing Architectures have been Discussed in this Lecture
Hypervisor Clustering Architecture
Load Balanced Virtual Server Instances Architecture
Non-Disruptive Service Relocation Architecture
Zero Downtime Architecture
Cloud Balancing Architecture
Resource Reservation Architecture
Dynamic Failure Detection and Recovery Architecture
Bare-Metal Provisioning Architecture
Rapid Provisioning Architecture
Storage Workload Management Architecture
Virtualization Technology using Virtual Machines for Cloud ComputingIJMER
Cloud computing is the delivery of computing and storage capacity as a service to a community of end users. The name “cloud computing” comes from the use of a cloud-shaped symbol as an abstraction for the complex infrastructure it contains in system diagrams. Cloud computing entrusts services with a user's software, data and computation over a network. End users access cloud-based applications through a web browser or mobile application or a light-weight desktop while the business software and user's data are stored on servers at a remote location. Proponents claim that cloud computing environment allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables IT industry to more rapidly adjust resources to meet fluctuating and unpredictable business demand. In this paper, we present a system that uses virtualization technology to allocate the data center resources dynamically based on the application demands and support green computing by optimizing the number of servers in use. This method multiplexes virtual to physical resources adaptively based on the changing demand. We use the concept of skewness metric to combine virtual machines with different resource characteristics appropriately so that the capacities of servers are well utilized.
Cloud Computing System models for Distributed and cloud computing & Performan...hrmalik20
Advantage of Clouds over Traditional
Distributed Systems,Clouds,Service-Oriented Architecture (SOA) Layered Architecture,Performance Metrics and Scalability Analysis,System Efficiency,Performance Challenges in Cloud Computing,What is cloud computing and why is it distinctive?,CLOUD SERVICE DELIVERY MODELS AND THEIR
PERFORMANCE CHALLENGES,Cloud computing security,What does Cloud Computing Security mean,Cloud Security Landscape,Distinctions between Security and Privacy,Energy Efficiency of Cloud Computing,How energy-efficient is cloud computing?
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Finance and insurance driving expansions and relocations in the market
As of third quarter, metro employment in the finance and insurance industries finally approached pre-recession levels.
The office market saw substantial leasing activity from firms like Ally Financial which recently relocated 150 employees to the Shoreview Corporate Center with plans to add another 250 jobs by 2017.
Other firms like One Beacon Insurance Group, Securian Financial Group, Travelers Companies, and General Casualty Company have either invested in new space or absorbed existing space in all corners of the Minneapolis-St. Paul market.
Hermosa Beach real estate statistics and analysis for September 2015. Includes homes sales, listings and historical performance on a month to month and year over year basis.
The Sun diameter = 400 folds the moon diameter, but the Sun/ Earth distance= 400 folds the Moon/ Earth distance, for that reason the people on the Earth see the sun size equals the moon size approximately, that enables the total solar eclipse to be occurred before our eyes!
Is it just "a pure coincidence"? or there's a physical reason causes the equity of The Sun & Moon Angular Diameters to occur the solar eclipse.
2011 APA Positioning Planning Deaprtments - MinneapolisJoseph Horwedel
One of three presentations made at the National Planning Conference in Boston in 2011 on Positioning Planning Departments in difficult times. This is the presentation made by Barbara Sporlein from Minneapolis.
Cloud computing is the set of distributed computing nodes. It is the use of computing resources that are delivered as a service over a network. Virtualization plays a crucial role in cloud computing. Typically VMs are offered in different types, each type have its own characteristics which includes number of CPU cores, amount of main memory, etc. and cost. Presently, static algorithms are being used for scheduling VM instances in cloud. Instead of these, an algorithm is proposed here which dynamically detects the load and then schedules the tasks. The main purpose of the proposed scheduling strategy is to find the minimally loaded computational node. Upon receiving task requests from the clients, server has to schedule these to a minimally loaded node among all available computing nodes.
Dynamic Load Calculation in A Distributed System using centralized approachIJARIIT
The building of networks and the establishment of communication protocols have led to distributed systems, in which computers that are linked in a network cooperate on a task. The task is divided by the master node into small parts (sub problems) and is given to the nodes of the distributed system to solve, which gives better performance in time complexity to solve the problem compared to the time required to solve the problem in a single machine. Load balancing is the process of redistributing the work load among nodes of the distributed system to improve both resource utilization and job response time while also avoiding a situation where some nodes are heavily loaded while others are idle or doing little work. So before sending these parts of problem by the master to the nodes, master node should know the actual work load of all the nodes. We try a dynamic approach to find out the work load of each participating nodes in the distributed system by the master before sending the parts of the problem to the nodes.
This paper describes an algorithm which runs in the master machine and collects information from the nodes of the distributed system (client server application) and calculates the current work load of the nodes of the distributed system. The algorithm is developed in such a way that it can calculate the loads of the nodes dynamically. This means the loads can be evaluated if new nodes are added or deleted or during current performance of the nodes. The whole system is implemented on linux machine and local area network.
Virtual Machine Incorporated Sharing Model for Resource Utilizationidescitation
Cooperation and autonomy of virtual machines are
important features of virtualization where resources are
shared among virtual machines in a resource constrained
cloud environment. To facilitate resource sharing, this paper
proposes a resource sharing facility, called the VM Incorporated
RPC, that coordinates the remote procedure call (RPC) with
virtual machine based memory management. In this paper,
we present a process based resource sharing model in case of
collocated virtual machines. Evaluation of our algorithm
demonstrates that sharing of resources within collocated
virtual machines often results in utilizing almost 90% of the
resource potential when compared to inter machine sharing
which contributes a lesser amount of resource utilization.
Cloud Computing Load Balancing Algorithms Comparison Based SurveyINFOGAIN PUBLICATION
Cloud computing is an online primarily based computing. This computing paradigm has increased the employment of network wherever the potential of 1 node may be used by alternative node. Cloud provides services on demand to distributive resources like info, servers, software, infrastructure etc. in pay as you go basis. Load reconciliation is one amongst the vexing problems in distributed atmosphere. Resources of service supplier have to be compelled to balance the load of shopper request. Totally different load reconciliation algorithms are planned so as to manage the resources of service supplier with efficiency and effectively. This paper presents a comparison of assorted policies used for load reconciliation.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the
heterogeneous resources from different network are used simultaneously to solve a particular problem that
need huge amount of resources. Potential of Grid computing depends on my issues such as security of
resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling
is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing
resources and is an NP-complete problem. To achieve the promising potential of grid computing, an
effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to
improve the performance of resources i.e. makespan time & resource utilization. With this, we have
classified various tasks scheduling heuristic in grid on the basis of their characteristics.
Optimized Assignment of Independent Task for Improving Resources Performance ...Ricardo014
Grid computing has emerged from category of distributed and parallel computing where the heterogeneous resources from different network are used simultaneously to solve a particular problem that need huge amount of
resources. Potential of Grid computing depends on my issues such as security of resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling is one of the core steps to
efficiently exploit the capabilities of heterogeneous distributed computing resources and is an NP-complete problem. To achieve the promising potential of grid computing, an effective and efficient job scheduling algorithm is
proposed, which will optimized two important criteria to improve the performance of resources i.e. makespan time & resource utilization. With this, we have classified various tasks scheduling heuristic in grid on the basis of
their characteristics.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the heterogeneous resources from different network are used simultaneously to solve a particular problem that need huge amount of resources. Potential of Grid computing depends on my issues such as security of resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing resources and is an NP-complete problem. To achieve the promising potential of grid computing, an effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to improve the performance of resources i.e. makespan time & resource utilization. With this, we have classified various tasks scheduling heuristic in grid on the basis of their characteristics.
Analysis of a Pool Management Scheme for Cloud Computing Centres by Using Par...IJERA Editor
A monolithic model may suffer from and poor scalability due to large number of parameters. A cloud user may
submit a super task at once. The user request is sent to the global queue and then to the Resource Assigning
Module (RAM). A number of heterogeneous server pools placed in the RAM. First is Hot, in which the servers
will be handling the jobs currently, second is Warm, in which the servers are kept in ideal state, then Finally
Cold, in which the servers are Turned Off state. Initially the request is send to Hot, if those servers are busy the
request is forwarded to warm, then finally if required to Cold if both the hot and warm server pools are busy.
The user submitted supertask may split so that the individual task run on different physical machines, this is
called as partial acceptance policy. So the supertask rejection ratio will be reduced.
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
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.
Similar to Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an Advance Algorithm (20)
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an Advance Algorithm
1. ISSN (e): 2250 – 3005 || Volume, 05 || Issue, 10 ||October – 2015 ||
International Journal of Computational Engineering Research (IJCER)
www.ijceronline.com Open Access Journal Page 7
Efficient Resource Allocation to Virtual Machine in Cloud
Computing Using an Advance Algorithm
Rajeev Kumar 1
, Aditya Sharma2
1
Deptt. of C.S.E.,Arni University, Kangra,India
2
Deptt. of C.S.E, Arni University, Kangra, India
I. INTRODUCTION
Cloud computing has attracted attention as an important platform for software deployment, with perceived
benefits such as elasticity to fluctuating load, and reduced operational costs compared to running in enterprise
data centers. While some software is written from scratch especially for the cloud, many organizations also wish
to migrate existing applications to a cloud platform. A cloud environment is one of the most shareable
environments where multiple clients are connected to the common environment to access the services and the
products. A cloud environment can be public or the private cloud. In such environment, all the resources are
available on an integrated environment where multiple users can perform the request at same time. In such case ,
some approach is required to perform the effective scheduling and the resource allocation.
II. RESOURCE ALLOCATION
There are different algorithm that defines the load balancing to provide resources on the criteria as following
2.1 Token Routing: The main objective of the algorithm is to minimize the system cost by moving the tokens
around the system. But in a scalable cloud system agents cannot have the enough information of distributing the
work load due to communication bottleneck. So the workload distribution among the agents is not fixed. The
drawback of the token routing algorithm can be removed with the help of heuristic approach of token based load
balancing. This algorithm provides the fast and efficient routing decision. In this algorithm agent does not need
to have an idea of the complete knowledge of their global state and neighbor’s working load. To make their
decision where to pass the token they actually build their own knowledge base. This knowledge base is actually
derived from the previously received tokens. So in this approach no communication overhead is generated.
2.2 Round Robin: In this algorithm, the processes are divided between all processors. Each process is assigned
to the processor in a round robin order. The process allocation order is maintained locally independent of the
allocations from remote processors. Though the work load distributions between processors are equal but the job
processing times for different processes are not same. So at any point of time some nodes may be heavily loaded
and others remain idle. This algorithm is mostly used in web servers where Http requests are of similar nature
and distributed equally.
2.3 Randomized: Randomized algorithm is of type static in nature. In this algorithm process can be handled by
a particular node n with a probability p. The process allocation order is maintained for each processor
independent of allocation from remote processor.
This algorithm works well in case of processes are of equal loaded. [10]. However, problem arises when loads
are of different computational complexities. Randomized algorithm does not maintain deterministic approach. It
works well when Round Robin algorithms generate solver head for process queue.
ABSTRACT:
The focus of the paper is to generate an advance algorithm of resource allocation and load
balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine.
In VM while processes are allocate they executes in queue , the first process get resources , other
remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the
algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR)
Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
KEYWORDS: VM(Virtual machine)
2. Efficient Resource Allocation to Virtual Machine…
www.ijceronline.com Open Access Journal Page 8
2.4 Central queuing: This algorithm works on the principal of dynamic distribution. Each new activity arriving
at the queue manager is inserted into the queue. When request for an activity is received by the queue manager it
removes the first activity from the queue and sends it to the requester. If no ready activity is present in the queue
the request is buffered, until a new activity is available. But in case new activity comes to the queue while there
are unanswered requests in the queue the first such request is removed from the queue and new activity is
assigned to it. When a processor load falls under the threshold then the local load manager sends a request for
the new activity to the central load manager.
2.6 Connection mechanism: Load balancing algorithm can also be based on least connection mechanism which
is a part of dynamic scheduling algorithm. It needs to count the number of connections for each server
dynamically to estimate the load. The load balancer records the connection number of each server. The number
of connection increases when a new connection is dispatched to it, and decreases the number when connection
finishes or timeout happens.
III. ALGORITHM
The algorithm provide parallel processes to each virtual machine rather than serial processes one by one.
Figure 1. VM Function
*a. *Input the M number of Clouds with L1, L2, L3………….., Ln (n tends to no. of last virtual
machine))number of Virtual Machines associated with each cloud.
*b. *Define the available memory and load for each virtual machine.
*c. *Assign the priority to each cloud.
*d. *Input n number of user process request with some parameters specifications like arrival time, process time,
required memory etc.
*e. *Arrange the process requests in order of memory requirement
*f. *For i=1 to n
*g. *{
*h. *Identify the priority Cloud and Associated VM having Available
Memory(L1,L2,L3…………Ln)>Required Memory(i)
*i. *Perform the initial allocation of process to that particular VM and
the Cloud
*j. *}
*k. *For i=1 to n
*l. *{
*m. *Identify the Free Time slot on priority cloud to perform the allocation. As the free slot identify, record the
start time, process time, turnaround time and the deadline of the process.
*n. *}
*o. *fori=1 to n
*p. *(
*q.* start queue Q1.
*r. *{
*s. Process i1allocate to VM L1.
*t. *Print "Migration Done”
*u. Process i2, i3………in allocate to VM L2, L3………………,Ln respectively.
*w.* Q1, i1, p1 ends till i(n+1) allots to L1 again.
*X. * start new Queue Q2 [i (n+1)],
Q3 {I (2n+1)},……………. Respectively.
*y. *}
*z. *}
3. Efficient Resource Allocation to Virtual Machine…
www.ijceronline.com Open Access Journal Page 9
IV. EXPERIMENTAL REVIEW
Larger waiting time and Response time
In round robin architecture the time the process spends in the ready queue waiting for the processor to get
executed is known as waiting time and the time [13] the process completes its
Figure: Process scheduling in shortest round robin
Intelligent time slice generation
A new way of intelligent time slice calculation has been proposed which allocates the frame exclusively for each
task based on priority, shortest CPU burst time and context switch avoidance time.
Let the original time slice (OTS) is the time slice to be given to any process if it deserves no special
consideration
The intelligent time slice of process P1 is same as the original time slice of four milliseconds and time slice of
four milliseconds is assigned to process P1. After the execution of four milliseconds time slice the CPU is
allocated to process P2. Since the CPU burst of process P2 is lesser than the assumed CPU burst (ATS), one
milliseconds of SC has been included. The process P3 has the highest priority, so priority component is added
and the total of five milliseconds is allocated to process P3. The Balanced CPU burst for process P4 is leaser
than OTS, context switch component is added and a total of eight millisecond time slice is given to process P4.
Process P5 is given a total of five milliseconds with one millisecond of priority component is added to original
time slice. After executing a cycle the processor will again be allocated to process P1 for the next cycle and
continuously schedules in the same manner.
Steps for scheduling are as follows
Step 1:
Master system (VMM) receives information regarding virtual machine from slave (VM-1….n). If the master
node capability doesn’t catch the data, it will determine the virtual machine to be dead. This study proposed by
parameter W.
If W=0 is set up, it will define the virtual machine to be working and still alive now.
If W=1 then node is dead.
If W=2 then node is in previous state.
Step 2: If Master node receives the data from slave, then it gets the information’s regarding data (memory used,
CPU time etc...)
Step 3: Then Master node builds the weighted table containing the details which is collected from step 2.
4. Efficient Resource Allocation to Virtual Machine…
www.ijceronline.com Open Access Journal Page 10
Step 4: Then the master node sorts (Round-robin method) all the virtual machines according to their
performance. Which is 1≦i≦N.Where N is the number of the virtual machines.
Step 5: The scheduling capability generates the weighted table.
Step 6: The virtual machine control capability receives the weighted table from the Step 5, and distributes the
task to the virtual machines according to the weighted value.
V. RESULT
The proposed algorithm and existing round robin algorithm implemented like graphical simulation. Java
language is used for implementing VM load balancing algorithm. Assuming the application is deployed in one
data centre having virtual machines (with 2048Mb of memory in each VM running on physical processors
capable of speeds of 100 MIPS) and Parameter Values are as under Table discuss the Parameter value’s which
are used for Experiment.
Parameter Value
Data Center OS Linux
VM Memory 2048mb
Data Center Architecture X86
Service Broker Policy Optimize Response
Time
VM Bandwidth 1000
Table 1. Parameter values used for Experiment
These experimental results shows that weighted round robin method improves the performance by consuming
less time for scheduling virtual machines.
Figure 2. The results based on Round robin algorithm
Figure 3. The Data Centre for Round robin algorithm
5. Efficient Resource Allocation to Virtual Machine…
www.ijceronline.com Open Access Journal Page 11
Figure 4. The results based on Weighted Round robin table
Figure 5. The Data Centre for Weighted Round robin table
Figure 6. Comparison of results between Round Robin and weighted round robin For Overall response
time
Figure 7. Comparison of results between Round Robin and Weighted Round Robin for Data Center
processing time.
6. Efficient Resource Allocation to Virtual Machine…
www.ijceronline.com Open Access Journal Page 12
VI. CONCLUSION
The above algorithm distributes the process allocation in such a way that process does not concede
each other and the waiting state time for process is very much less .as well as all recourses (VMs, memory) are
using efficiently. That means the dead lock accruing chances are very much lees. .if the processes may allocate
to virtual machine at time. Processes may execute fast and chances of deadlock accruing is less. So we need an
algorithm that can describe how process execution can be done on virtual machine fast. A comparative study of
round robin architecture shortest round robin and intelligent time slice for round robin architecture is made. It is
concluded that the proposed architectures are superior as it has less waiting, response times, usually less
preemption and context switching thereby reducing the overhead and saving of memory space. Future work can
be based on these architectures modified and implemented for hard real time system where hard deadline
systems require partial outputs to prevent catastrophic events.
REFFERENCES:
[1] Anupama Prasanth, “Cloud Computing Services: A Survey”, International Journal of Computer Applications, Vol. 46, No.3,
May 2012, PP.0975 – 8887.
[2] Flavio Lombardi a, RobertoDiPietro, “Secure Virtualization For Cloud Computing”, Secure virtualization for cloud computing,
Journal of Network.
[3] Amazon cloud computing, virtualization and virtual machine, 2002.
[4] Thomas Weigold, Thorsten Kramp and Peter Buhler, “ePVM- An Embeddable Process Virtual Machine”Annual International
Computer Software and Applications Conference(COMPSAC 2007), IEEE.
[5] Rajeev Kumar, Rajiv Ranjan “ virtual Machine Scheduling To Avoid Deadlocks”, International Journal of Computer Science and
Information Technology Research ,Vol.2, Issue 2, pp:(369-372),June,2014.
[6] Robert Blazer, “Process Virtual Machine”, IEEE, 1993, PP.37-40.
[7] Loris Degioanni, Mario Baldi, Diego Buffa, FulvioRisso, Federico Stirano, GianlucaVarenni, “Network Virtual Machine
(NETVM): A New Architecture for Efficient and Portable Packet Processing Applications”, 8th International Conference on
Telecommunications, Zagreb, Croatia, June 15 - 17, 2005,PP.163-168.
[8] DongyaoWu,JunWei,ChushuGao,WenshenDou, “A Highly Concurrent Process Virtual Machine Based on Event-driven Process
Execution Model”, 2012 Ninth IEEE International Conference on e-Business Engineering,PP.61 – 69.
[9] Vue Hu, YueDongWang, “Process-Level Virtual Machine Embedded Chain”, 2011 International Conference on Computer
Science and Network Technology, 1EEE, December 24-26, 2011,PP.302 – 305.
[10] Yosuke Kuno, Kenichi Nii, SaneyasuYamaguchi, “A Study on Performance of Processes in Migrating Virtual Machines”, 2011
Tenth International Symposium on Autonomous Decentralized Systems,IEEE,2011, PP.568-572.
[11] GeetaJangra, PardeepVashist, ArtiDhounchak, “ Effective Scheduling in Cloud Computing is a Risk? ”,IJARCSSE,Volume 3,
Issue 8, August 2013,PP.148 – 152.
[12] Ghao G, Liu J, Tang Y, Sun W, Zhang F, Ye X, Tang N (2009) Cloud Computing: A Statistics Aspect of Users. In:First
International Conference on Cloud Computing (CloudCom), Beijing, China. Heidelberg: Springer Berlin. PP.347-358.
[13] Zhang S, Zhang S, Chen X, Huo X (2010) Cloud Computing Research and Development Trend. In: Second International
Conference on Future Networks (ICFN’10), Sanya, and Hainan, China. Washington, DC, USA: IEEE Computer Society. PP.93-
97
[14] Cloud Security Alliance (2011) Security guidance for critical areas of focus in Cloud
ComputingV3.0.Available:https://cloudsecurityalliance.org/guidance/csaguide.v3.0.pdfweb cite
[15] MarinosA, Briscoe G (2009) Community Cloud Computing. In: 1st International Conference on Cloud Computing (CloudCom),
Beijing, China. Heidelberg: Springer-Verlag Berlin.
[16] Khalid A (2010) Cloud Computing: applying issues in Small Business. International Conference on Signal Acquisition and
Processing (ICSAP’10)PP. 278 – 281.