Predictive control for energy aware consolidation in cloud datacentersieeepondy
Predictive control for energy aware consolidation in cloud datacenters
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IEEE PROJECTS 2016-2017
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From Data to insight: Emerging Opportunities in Africa for 2018mdn_dan
When the oil price was last in $100 per barrel range Africa was a major hotspot for exploration and appraisal.
Due to various geopolitical developments the region quickly cooled off and has been largely quiet until now. With such a diverse range of basins, countries and petroleum systems anyone considering entering the region needs a robust and accurate benchmark.
Using a dataset purchased from DrillingInfo and Alteryx we explored some of the trends within the contienent from Q1/Q2 this year.
Alexander Fölling, Christian Grimme,
Joachim Lepping, and Alexander Papaspyrou: The Gain of Resource Delegation in Distributed Computing Environments
15th Workshop on Job Scheduling for Parallel Processing ; April 23, 2010 - Atlanta, GA, USA
Predictive control for energy aware consolidation in cloud datacentersieeepondy
Predictive control for energy aware consolidation in cloud datacenters
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
From Data to insight: Emerging Opportunities in Africa for 2018mdn_dan
When the oil price was last in $100 per barrel range Africa was a major hotspot for exploration and appraisal.
Due to various geopolitical developments the region quickly cooled off and has been largely quiet until now. With such a diverse range of basins, countries and petroleum systems anyone considering entering the region needs a robust and accurate benchmark.
Using a dataset purchased from DrillingInfo and Alteryx we explored some of the trends within the contienent from Q1/Q2 this year.
Alexander Fölling, Christian Grimme,
Joachim Lepping, and Alexander Papaspyrou: The Gain of Resource Delegation in Distributed Computing Environments
15th Workshop on Job Scheduling for Parallel Processing ; April 23, 2010 - Atlanta, GA, USA
This talk will introduce a flexible accounting framework with data visualization capabilities called MICHAL, that we at CESNET developed for our infrastructure. Framework is able to gather data from multiple sources, OpenNebula being one of them, process it and present the result in a form of charts. MICHAL isn't bind to only one platform and can be easily extended to support accounting of multiple parts of the infrastructure. As part of the presentation, we will discuss our data gathering techniques, MICHAL's design and functionality, currently available data processing modules for IaaS cloud and plans for the future development.
Dear Colleagues,
Call for papers for another Machine Learning special issue of SEG/AAPG Journal of Interpretation focusing on the Seismic Data Analysis has been announced.
We look forward to your contribution.
Vikram Jayaram
Special Section Editor
Interpretation
Cardinality Estimation through Histogram in Apache Spark 2.3 with Ron Hu and ...Databricks
Apache Spark 2.2 shipped with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Skewed data distributions are often inherent in many real world applications. In order to deal with skewed distributions effectively, we added equal-height histograms to Apache Spark 2.3. Leveraging reliable statistics and histogram helps Spark make better decisions in picking the most optimal query plan for real world scenarios.
In this talk, we’ll take a deep dive into how Spark’s Cost-Based Optimizer estimates the cardinality and size of each database operator. Specifically, for skewed distribution workload such as TPC-DS, we will show histogram’s impact on query plan change, hence leading to performance gain.
Service description in the nfv revolution trends, challenges and a way forwardieeepondy
Service description in the nfv revolution trends, challenges and a way forward
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
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IEEE PROJECTS 2016-2017
-----------------------------------
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Scalable cloud–sensor architecture for the internet of thingsieeepondy
Scalable cloud–sensor architecture for the internet of things
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
This talk will introduce a flexible accounting framework with data visualization capabilities called MICHAL, that we at CESNET developed for our infrastructure. Framework is able to gather data from multiple sources, OpenNebula being one of them, process it and present the result in a form of charts. MICHAL isn't bind to only one platform and can be easily extended to support accounting of multiple parts of the infrastructure. As part of the presentation, we will discuss our data gathering techniques, MICHAL's design and functionality, currently available data processing modules for IaaS cloud and plans for the future development.
Dear Colleagues,
Call for papers for another Machine Learning special issue of SEG/AAPG Journal of Interpretation focusing on the Seismic Data Analysis has been announced.
We look forward to your contribution.
Vikram Jayaram
Special Section Editor
Interpretation
Cardinality Estimation through Histogram in Apache Spark 2.3 with Ron Hu and ...Databricks
Apache Spark 2.2 shipped with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Skewed data distributions are often inherent in many real world applications. In order to deal with skewed distributions effectively, we added equal-height histograms to Apache Spark 2.3. Leveraging reliable statistics and histogram helps Spark make better decisions in picking the most optimal query plan for real world scenarios.
In this talk, we’ll take a deep dive into how Spark’s Cost-Based Optimizer estimates the cardinality and size of each database operator. Specifically, for skewed distribution workload such as TPC-DS, we will show histogram’s impact on query plan change, hence leading to performance gain.
Service description in the nfv revolution trends, challenges and a way forwardieeepondy
Service description in the nfv revolution trends, challenges and a way forward
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
Scalable cloud–sensor architecture for the internet of thingsieeepondy
Scalable cloud–sensor architecture for the internet of things
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
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Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey IJECEIAES
In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers.
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
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
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Chennai-26.
Score based deadline constrained workflow scheduling algorithm for cloud systemsijccsa
Cloud Computing is the latest and emerging trend in information technology domain. It offers utility- based
IT services to user over the Internet. Workflow scheduling is one of the major problems in cloud systems. A
good scheduling algorithm must minimize the execution time and cost of workflow application along with
QoS requirements of the user. In this paper we consider deadline as the major constraint and propose a
score based deadline constrained workflow scheduling algorithm that executes workflow within
manageable cost while meeting user defined deadline constraint. The algorithm uses the concept of score
which represents the capabilities of hardware resources. This score value is used while allocating
resources to various tasks of workflow application. The algorithm allocates those resources to workflow
application which are reliable and reduce the execution cost and complete the workflow application within
user specified deadline. The experimental results show that score based algorithm exhibits less execution
time and also reduces the failure rate of workflow application within manageable cost. All the simulations
have been done using CloudSim toolkit.
Recently, lot of interest have been put forth by researchers to improve workload scheduling in cloud platform. However, execution of scientific workflow on cloud platform is time consuming and expensive. As users are charged based on hour of usage, lot of research work have been emphasized in minimizing processing time for reduction of cost. However, the processing cost can be reduced by minimizing energy consumption especially when resources are heterogeneous in nature; very limited work have been done considering optimizing cost with energy and processing time parameters together in meeting task quality of service (QoS) requirement. This paper presents cost and performance aware workload scheduling (CPA-WS) technique under heterogeneous cloud platform. This paper presents a cost optimization model through minimization of processing time and energy dissipation for execution of task. Experiments are conducted using two widely used workflow such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost in comparison with standard workload scheduling model.
The concept of Genetic algorithm is specifically useful in load balancing for best virtual
machines distribution across servers. In this paper, we focus on load balancing and also on
efficient use of resources to reduce the energy consumption without degrading cloud
performance. Cloud computing is an on demand service in which shared resources, information,
software and other devices are provided according to the clients requirement at specific time. It‟s
a term which is generally used in case of Internet. The whole Internet can be viewed as a cloud.
Capital and operational costs can be cut using cloud computing. Cloud computing is defined as a
large scale distributed computing paradigm that is driven by economics of scale in which a pool
of abstracted virtualized dynamically scalable , managed computing power ,storage , platforms
and services are delivered on demand to external customer over the internet. cloud computing is
a recent field in the computational intelligence techniques which aims at surmounting the
computational complexity and provides dynamically services using very large scalable and
virtualized resources over the Internet. It is defined as a distributed system containing a
collection of computing and communication resources located in distributed data enters which
are shared by several end users. It has widely been adopted by the industry, though there are
many existing issues like Load Balancing, Virtual Machine Migration, Server Consolidation,
Energy Management, etc.
Cost-Efficient Task Scheduling with Ant Colony Algorithm for Executing Large ...Editor IJCATR
The aim of cloud computing is to share a large number of resources and pieces of equipment to compute and store knowledge and information for great scientific sources. Therefore, the scheduling algorithm is regarded as one of the most important challenges and problems in the cloud. To solve the task scheduling problem in this study, the ant colony optimization (ACO) algorithm was adapted from social theories with a fair and accurate resource allocation approach based on machine performance and capacity. This study was intended to decrease the runtime and executive costs. It was also meant to optimize the use of machines and reduce their idle time. Finally, the proposed method was compared with Berger and greedy algorithms. The simulation results indicate that the proposed algorithm reduced the makespan and executive cost when tasks were added. It also increased fairness and load balancing. Moreover, it made the optimal use of machines possible and increased user satisfaction. According to evaluations, the proposed algorithm improved the makespan by 80%.
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
On the Optimal Allocation of VirtualResources in Cloud Compu.docxhopeaustin33688
On the Optimal Allocation of Virtual
Resources in Cloud Computing Networks
Chrysa Papagianni, Aris Leivadeas, Symeon Papavassiliou,
Vasilis Maglaris, Cristina Cervelló-Pastor, and �Alvaro Monje
Abstract—Cloud computing builds upon advances on virtualization and distributed computing to support cost-efficient usage of
computing resources, emphasizing on resource scalability and on demand services. Moving away from traditional data-center oriented
models, distributed clouds extend over a loosely coupled federated substrate, offering enhanced communication and computational
services to target end-users with quality of service (QoS) requirements, as dictated by the future Internet vision. Toward facilitating the
efficient realization of such networked computing environments, computing and networking resources need to be jointly treated and
optimized. This requires delivery of user-driven sets of virtual resources, dynamically allocated to actual substrate resources within
networked clouds, creating the need to revisit resource mapping algorithms and tailor them to a composite virtual resource mapping
problem. In this paper, toward providing a unified resource allocation framework for networked clouds, we first formulate the optimal
networked cloud mapping problem as a mixed integer programming (MIP) problem, indicating objectives related to cost efficiency of
the resource mapping procedure, while abiding by user requests for QoS-aware virtual resources. We subsequently propose a method
for the efficient mapping of resource requests onto a shared substrate interconnecting various islands of computing resources, and
adopt a heuristic methodology to address the problem. The efficiency of the proposed approach is illustrated in a simulation/emulation
environment, that allows for a flexible, structured, and comparative performance evaluation. We conclude by outlining a proof-of-
concept realization of our proposed schema, mounted over the European future Internet test-bed FEDERICA, a resource virtualization
platform augmented with network and computing facilities.
Index Terms—Federated infrastructures, resource allocation, resource mapping, virtualization, cloud computing, quality of service
Ç
1 INTRODUCTION
CLOUD computing promises reliable services deliveredthrough next generation data centers that are built on
compute and storage virtualization technologies. According
to Buyya et al., [1] “a cloud is a type of parallel and distributed
system consisting of a collection of interconnected and virtualized
computers that are dynamically provisioned and presented as one
or more unified computing resources based on service-level
agreements established through negotiation between the service
provider and the consumers” and accessible as a composable
service via web 2.0 technologies.
Therefore, with respect to cloud computing there exist
the “as a service” definitions, which include software as a
service (SaaS), infrastructure as a se.
Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
works as a vital role in the cloud computing. Thus my protocol is designed to minimize the switching time,
improve the resource utilization and also improve the server performance and throughput. This method or
protocol is based on scheduling the jobs in the cloud and to solve the drawbacks in the existing protocols.
Here we assign the priority to the job which gives better performance to the computer and try my best to
minimize the waiting time and switching time. Best effort has been made to manage the scheduling of jobs
for solving drawbacks of existing protocols and also improvise the efficiency and throughput of the server.
Management of context aware software resources deployed in a cloud environmen...ijdpsjournal
In cloud computing environments, context information is continuously created by context providers and
consumed by the applications on mobile devices. An important characteristic of cloud-based context aware
services is meeting the service level agreements (SLAs) to deliver a certain quality of service (Qos), such as
guarantees on response time or price. The response time to a request of context-aware software is affected
by loading extensive context data from multiple resources on the chosen server. Therefore, the speed of
such software would be decreased during execution time. Hence, proper scheduling of such services is
indispensable because the customers are faced with time constraints. In this research, a new scheduling
algorithm for context aware services is proposed which is based on classifying similar context consumers
and dynamically scoring the requests to improve the performance of the server hosting highly-requested
context-aware software while reducing costs of cloud provider. The approach is evaluated via simulation
and comparison with gi-FIFO scheduling algorithm. Experimental results demonstrate the efficiency of the
proposed approach.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
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Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
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Heuristics for provisioning services to workflows in xaa s clouds
1. Heuristics for Provisioning Services to Workflows in XaaS Clouds
Abstract:
In XaaS clouds, resources as services (e.g., infrastructure, platform and software
as a service) are sold to applications such as scientific and big data analysis
workflows. Candidate services with various configurations (CPU type, memory
size, number of machines and so on) for the same task may have different
execution time and cost. Further, some services are priced rented by intervals
that be shared among tasks of the same workflow to save service rental cost.
Establishing a task-mode (service) mapping (to get a balance between time and
cost) and tabling tasks on rented service instances are crucial for minimizing the
client-oriented cost to rent services for the whole workflow. In this paper, a
multiple complete critical-path based heuristic (CPIS) is developed for the task-
mode mapping problem. A list based heuristic (LHCM) concerning the task
processing cost and task-slot matching is developed for tabling tasks on service
instances based on the result of task-mode mapping. Then, the effectiveness of
the proposed CPIS is compared with that of the previously proposed CPIL, the
existing state-of-the-art heuristics including PCP, SC-PCP ( an extension to PCP),
DET, and CPLEX. The effectiveness of the proposed LHCM is evaluated with its use
with different task-mode mapping algorithms. Experimental results show that the
proposed heuristics can reduce 24 percent of the service renting cost than the
compared algorithms on the test benchmarks at most for non-shareable services.
In addition, half of the service renting cost could be saved when LHCM is applied
to consolidate tasks on rented service instances.
