This document proposes a new efficient decentralized load balancing algorithm for cloud computing. It consists of two phases: 1) a request sequencing phase where incoming user requests are sequenced to minimize wait times, and 2) a load transferring phase where a load balancer calculates resource utilization of each VM and transfers tasks to less utilized VMs. This algorithm aims to improve load balancing performance and achieve more efficient resource utilization in cloud computing environments.
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
ieee standard base paper.-Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
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.
Load balancing In cloud - In a semi distributed systemAchal Gupta
Load Balancing in Cloud
What is load balancing in Cloud in semi distributed system and why it is better than a centralized system and distributed system
In the FACTS-based transmission line, if the fault does not include FACTS device, then the impedance calculation is like an ordinary transmission line, and when the fault includes FACTS, then the impedance calculation accounts for the impedances introduced by FACTS device.
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.
Virtual Machine Migration and Allocation in Cloud Computing: A Reviewijtsrd
Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessed through internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre copy and post copy approach. The process to move running applications or VMs from one physical machine to another is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the service is unavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. Khushbu Singh Chandel | Dr. Avinash Sharma "Virtual Machine Migration and Allocation in Cloud Computing: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29556.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29556/virtual-machine-migration-and-allocation-in-cloud-computing-a-review/khushbu-singh-chandel
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
Live virtual machine migration based on future prediction of resource require...Tapender Yadav
This document gives the brief description of the work done during the Summer Internship at Institute for Development and Research in Banking Technology (IDRBT), Hyderabad. The project was undertaken from May 2014 - July 2014 under the exemplary guidance of Dr. G. R. Gangadharan, Asst. Professor, IDRBT, Hyderabad.
Hybrid Scheduling Algorithm for Efficient Load Balancing In Cloud ComputingEswar Publications
In cloud computing environment, various users send requests for the transmission of data for different demands. The access to different number of users increase load on the cloud servers. Due to this, the cloud server does not provide best efficiency. To provide best efficiency, load has to be balanced. The highlight of this work is the division of different jobs into tasks. The job dependency checking is done on the basis of directed acyclic graph. The dependency checking the make span has to be created on the basis of first come first serve and priority based scheduling algorithms. In this paper, each scheduling algorithm has been implemented sequentially and the hybrid algorithm (round robin and priority based) has also been compared with other scheduling algorithms.
ieee standard base paper.-Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
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.
Load balancing In cloud - In a semi distributed systemAchal Gupta
Load Balancing in Cloud
What is load balancing in Cloud in semi distributed system and why it is better than a centralized system and distributed system
In the FACTS-based transmission line, if the fault does not include FACTS device, then the impedance calculation is like an ordinary transmission line, and when the fault includes FACTS, then the impedance calculation accounts for the impedances introduced by FACTS device.
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.
Virtual Machine Migration and Allocation in Cloud Computing: A Reviewijtsrd
Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessed through internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre copy and post copy approach. The process to move running applications or VMs from one physical machine to another is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the service is unavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. Khushbu Singh Chandel | Dr. Avinash Sharma "Virtual Machine Migration and Allocation in Cloud Computing: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29556.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29556/virtual-machine-migration-and-allocation-in-cloud-computing-a-review/khushbu-singh-chandel
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
Live virtual machine migration based on future prediction of resource require...Tapender Yadav
This document gives the brief description of the work done during the Summer Internship at Institute for Development and Research in Banking Technology (IDRBT), Hyderabad. The project was undertaken from May 2014 - July 2014 under the exemplary guidance of Dr. G. R. Gangadharan, Asst. Professor, IDRBT, Hyderabad.
Hybrid Scheduling Algorithm for Efficient Load Balancing In Cloud ComputingEswar Publications
In cloud computing environment, various users send requests for the transmission of data for different demands. The access to different number of users increase load on the cloud servers. Due to this, the cloud server does not provide best efficiency. To provide best efficiency, load has to be balanced. The highlight of this work is the division of different jobs into tasks. The job dependency checking is done on the basis of directed acyclic graph. The dependency checking the make span has to be created on the basis of first come first serve and priority based scheduling algorithms. In this paper, each scheduling algorithm has been implemented sequentially and the hybrid algorithm (round robin and priority based) has also been compared with other scheduling algorithms.
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...IJCNCJournal
Cloud computing is a new technology that brings new challenges to all organizations around the world.
Improving response time for user requests on cloud computing is a critical issue to combat bottlenecks. As
for cloud computing, bandwidth to from cloud service providers is a bottleneck. With the rapid development
of the scale and number of applications, this access is often threatened by overload. Therefore, this paper
our proposed Throttled Modified Algorithm(TMA) for improving the response time of VMs on cloud
computing to improve performance for end-user. We have simulated the proposed algorithm with the
CloudAnalyts simulation tool and this algorithm has improved response times and processing time of the
cloud data center.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A Novel Switch Mechanism for Load Balancing in Public CloudIJMER
In cloud computing environment, one of the core design principles is dynamic scalability,
which guarantees cloud storage service to handle the growing amounts of application data in a flexible
manner or to be readily enlarged. By integrating several private and public cloud services, the hybrid
clouds can effectively provide dynamic scalability of service and data migration. A load balancing is a
method of dividing computing loads among numerous hardware resources. Due to unpredictable job
arrival pattern and the capacities of the nodes in cloud differ for the load balancing problem. In this load
control is very crucial to improve system performance and maintenance. This paper presents a switch
mechanism for load balancing in cloud computing. The load balancing model given in this work is aimed
at the public cloud which has numerous nodes with distributed computing resources in many different
geographical areas. Thus, this model divides the public cloud environment into several cloud partitions.
When the cloud environment is very large and complex, these divisions simplify the load balancing. The
cloud environment has a main controller that chooses the suitable partitions for arriving jobs while the
balancer for each cloud partition chooses the best load balancing strategy
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.
The Concept of Load Balancing Server in Secured and Intelligent NetworkIJAEMSJORNAL
Hundreds and thousands of data packets are routed every second by computer networks which are complex systems. The data should be routed efficiently to handle large amounts of data in network. A core networking solution which is responsible for distribution of incoming traffic among servers hosting the same content is load balancing. For example, if there are ten servers within a network and two of them are doing 95% of the work, the network is not running very efficiently. If each server was handling about 10% of the traffic, the network would run much faster.Networks get more efficient with the help of Load balancing. The traffic is evenly distributed amongst the network making sure no single device is overwhelmed.When a request is balanced across multiple servers, it prevents any server from becoming a single point of failure. It improves overall availability and responsiveness. To evenly split the traffic load among several different servers web servers; often use load balancing.Load balancing requires hardware or software that divides incoming traffic amongst the available serverseither it is done on a local network or a large web server. High amount of traffic is received by a network that have one server dedicated to balance the load among other servers and devices in the network. This server is often known as load balancer. Load balancing is used by clusters or multiple computers that work together, to spread out processing jobs among the available systems.
Modified Active Monitoring Load Balancing with Cloud Computingijsrd.com
Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.
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.
Cloud computing is a new computing paradigm that, just as electricity was firstly generated at home and
evolved to be supplied from a few utility providers, aims to transform computing into a utility. It is a mapping
strategy that efficiently equilibrates the task load into multiple computational resources in the network based on the
system status to improve performance. The objective of this research paper is to show the results of Hybrid DEGA,
in which GA is implemented after DE
A Comparative Study of Load Balancing Algorithms for Cloud ComputingIJERA Editor
Cloud Computing is fast growing technology in both industry research and academy. User can access the cloud
service and pay based on the usage of resource. Balancing the load is major task of cloud service provider with
minimum response time, maximum throughput and better resource utilization. There are many load balancing
algorithms proposed to assign a user request to cloud resource in efficient manner. In this paper three load balancing
algorithms are simulated in Cloud Analyst and results are compared.
Cloud computing is that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
Cloud computing is that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
Similar to An Efficient Decentralized Load Balancing Algorithm in Cloud Computing (20)
Name Entity Recognition problems in biomedical literatureAisha Kalsoom
Named Entity Recognition is one of the vast techniques in Natural Language Processing. NER techniques can be applied on biomedical data but there are some problems which are mentioned in the presentation.
Insilico comparative analysis of critical residues of CSN gene in 41 mammals:...Aisha Kalsoom
1.Introduction
2.Aims and Objective
3.Significance
4.Materials and Methods
5.Protein Sequence of Livestock Species
6.Protein Sequence of Orthologous Species
7.Natural Variants
8.Post Translational Modifications
9.Protein Structure Prediction
10.Protein Mutation Comparison
11.Conservation Scenario of cow β-casein in 41 species
12.Identification of closely related species to cow
13.Gene Structure Analysis
14.Identification Of Novel Mutational Sites, Protein Quality And Composition
15.Conclusion
16.Recommendation
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/
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.
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.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
3. Introduction
Aims and Objective
RelatedWork
Problem statement
Proposed work
Proposed algorithm
Conclusion
Recommendations
References
3
4. The cloud computing is a distributed internet based paradigm,
designed for remote sharing and usage of different resources and
services with high reliability over the large networks
Load balancing in cloud is to balancing load among resource
to obtain resource utilization, maximum throughput;
minimum response time and overhead should be avoided
Dynamic load balancing algorithms distribute the work
among processors during the execution of the algorithm
4
5. Literature review of different mechanisms and algorithms
proposed for load balancing in cloud computing.
To study the advantages and flaws of various load
balancing algorithms to identify the problem in load
balancing in cloud computing.
To propose more efficient algorithm for load
balancing to maximize performance, reliability,
scalability and stability in cloud computing.
5
6. 6
• monitor resource utility over resource pool
• distribute available resources among severalVMs
• chance of performance degradation due to a large number of
resources employed in frequent dynamic migration
VMware Distributed
Resource Scheduler
• based on cloud portioning.
• categories idle, normal and overloaded on the basis of load degree
• method of selecting range for load degree has been left
unaddressed.
Game theory based
model
• the least loaded virtual machine for load transfer are selected
• the high migration cost is optimized.
• chance of inefficient service scheduling due to large no. ofVMs and
frequent service requests in the data centre
A genetic algorithm
based scheduling
mechanism
7. 7
• Using principle of Ant Colony Optimization.
• disperse a group of tasks evenly on idle nodes using artificial ants.
• convergence speed can be further improved in this system.
An inverse artificial
ants system
• finds theCPU utilization, required and available memory for eachVM.
• compares the available resources with required resources, if required resources
are available then proceed further otherwise discard the request
• this mechanism lacks in scalability.
Two phase based load
balancing mechanism
• more efficient as compared to other algorithms.
• Load agent, channel agent and migration agent.
• can be improved by reducing communication overhead between migration
agent and channel agent.
An autonomousAgent
Based Load Balancing
Algorithm (A2LB)
8. 8
• It may cause delays, compromised efficiency and less portability.
• There must be some comparison method to allocate resources on
priority basis.
no specific mechanism
to deal with many job
requests at a time
• Self destroy messages might cause extra communication increasing
overhead.
• Simplicity, reliability and efficiency of the algorithm are affected if
communication overhead is not resolved.
migration agent
communicates with
channel agent for self-
destroy message
• Maintenance of tables causes memory space overhead and affects
the performance of the algorithm by reducing the available
memory.
Channel Agent has to
maintain ResponseTables
for load balancing
11. Efficient Decentralized Load Balancing
Algorithm in cloud computing
based on the
dynamic
cloud
computing
environment
Request
sequencing
phase
Load
transferring
phase
11
12. Request
sequencing phase
User is the task request from the
clients to the cloud
Sequencer will sequence the task
requests from client so that task
waits in the queue for minimum
time period
Load
transferring phase
Load agent is responsible to
transfer the user request to theVM
in the cloud pool
Load Balancer will calculate the
used memory, CPU utilization and
response time of eachVM and
compare it with threshold value.
12
17. This work contributes in
two ways; first by providing
a sequencer ,incoming user
requests can be entertained
in more appropriate way.
second load balancer
calculate load status of all
VMs to transfer requested
task to normalVM more
efficiently.
Desired results can be
obtained
by implementing this
mechanism.
17
18. There is need to implement this work to get desired results
and to resolve more problems regarding load balancing.
Virtualization is the key concept of cloud computing, ifVMs are
located far from one another, there must be some mechanism
to minimize their service time.
More improved algorithms can be designed to provide more
reliability and scalability in load balancing in cloud computing.
18
19. A. Singh, D. Juneja and M. Malhotra (2015) ‘Autonomous Agent Based Load Balancing Algorithm in Cloud
Computing’, in proc. International Conference on Advanced ComputingTechnologies and Applications (ICACTA)
Procedia Computer Science, 45,pp. 832-841.
Liu, X. Jin andY.Wang (2005) ‘Agent-Based Load Balancing on homogeneous Minigrids: Macroscopic Modeling
and Characterization’, IEEETransactions on Parallel and Distributed Systems,Volume 1 6, NO.6.
M. Randles, D. Lamb, and A.Taleb-Bendia (2010) ‘A comparative study into distributed load balancing algorithms
for cloud computing’, in Proc. IEEE 24th International Conference onAdvanced Information Networking and
Applications, Perth, Australia. pp. 551-556.
S.C.Wang, K.Q.Yan, W.P.Liao and S.S.Wang (2010) ‘Towards a Load Balancing in a three-Level Cloud Computing
Network’, In Proc. ICCSIT, pp.108-113.
S. Osman, D. Subhraveti, G. Su and J. Nieh (2002) ‘The design and implementation of ZAP: a system for
migrating computing environments’, ACM SIGOPS Oper. Syst. Rev. 36(SI), 361–376.
Y.Xu, L.Wu, L. Guo, Z.Chen, L.Yang and Z.Shi (2011) ‘An Intelligent Load Balancing AlgorithmsTowards Efficient
Cloud Computing’, In Proc. AAAIWorkshop, pp. 27-32.
19