A cross cloud scheduling algorithm at SaaS level on Rack Space, Go Daddy and Azure platforms

Energy intensity is one of the most important energy feature that has a dramatic value in energy system of Iran. Indeed, Iran is one of the most energy intensive countries in the world and its main reason is related to high energy consumption in household section. In this article, we present a non-linear model that considers three scenarios in management of household energy demand reducing. Therefore, a rational percentage of energy consumption reduction in the household sector, which firstly eliminates the imbalance between energy production and consumption, and secondly, derives a rational amount of profit from various reduction scenarios, will be presented. The mentioned rational profits in this article are obtained from three scenarios. In the first scenario, it is assumed that the percentage reduction in household energy consumption will be allocated to reducing energy demand in the industrial sector, resulting in profits from value-added creation in this sector. In the second scenario, all benefits from reducing energy consumption in the household sector will be devoted to energy exports, yielding profits from this source. Finally, in the third scenario, the reduction in energy consumption will lead to a decrease in energy supply and consequently a reduction in energy supply costs. To conduct a comprehensive study, a combination of the mentioned scenarios has also been modeled and investigated. The model results indicate that with a 25% reduction in household energy consumption in the 2024-2034 timeframe, the energy imbalance will be eliminated, and allocating 5% of this reduction entirely to the industrial sector will result in profits equivalent to $164.18 billion. However, it should be noted that in the considered combined scenarios, the greater the share of the first scenario, the higher the resulting profit, and the optimal point is achieved in the first scenario.

An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment

With ever increasing usage of IT infrastructure around the globe, the increase in energy consumption and carbon emission is a worrisome situation for everyone because of its direct effect on environment. The environment is being dangerously impacted by Information Technology infrastructure extensive use and its waste. Cloud computing has emerged as one step further solution to deploy all virtualized IT resources as per need on self-service basis as a rental method for the users. Different cloud deployment methods have emerged according to the need of users, institutions and communities. In last decade virtualization has been a milestone in enabling optimal usage of information technology resources. Cloud computing has provided a platform for deploying everything as a Service in IT, which has changed the economics of IT based business and organizations from Capital Expenditure to Operational Expenditure. For enabling Cloud services we first need to understand our existing infrastructure. In second step we should be able to virtualize it for optimal usage. Then with the help of existing cloud applications we can deliver and deploy information technology as a resource at various levels such as infrastructure, platform, and software. Keywords; cloud computing, carbon emission, energy consumption, IT waste, virtualization. The Advancement in the field of information technology has enabled us to optimize the use of physical infrastructure with the help of virtualization, most of which is only used in specific applications like vitalizing a local area network on switch or vitalizing a compute resource to run more than one operating system on a desktop. The benefits of same has not been completely implemented and utilized in industrial environments where we require a large amount of IT resources. The advantage of using cloud is to enable complete utilization of Information technology resources, where most of the resources are underutilized. The cost involved in maintenance and management of those resources is huge which can be saved due to centralized management of private cloud infrastructure. We create private cloud keeping in mind. • Availability and performance requirements for the cloud • Infrastructure sizing, performance and continual availability requirements • The need for data security or isolation • The appropriate user interface With advent of IT as a service in cloud computing technology energy consumption and related carbon emissions and waste are considerably lesser than in-house implementation. Technology in Cloud Computing creates an able structure for sharing resources with the help of virtualization among many; it greatly decreases or abolishes the need for most products, equipment, utilities and facilities. The results are more sustainable IT infrastructures and processes which help in keeping environment safe. • Highest resource consumption and effectiveness • Reduced energy consumption by up to 95 % with the help of virtualization which optimizes the use of physical infrastructure and thereby helps IT to be delivered as a service. • Displaced utility costs Workstation utility costs are cut by 90% due to change of capital expenditure into operational expenditure in industries or organizations across the world. • Reduced electronic waste as No more batteries and minimal software/hardware waste. • Reduced waste overall due to consumption of computational power as service, no more as infrastructure. • Reduced ecological impact of workstations as compared to thin clients as anything related to computation can be provided as a service on a client connected with internet to the cloud provider.

The resource monitoring and management services together play a vital role in improving the overall performance of fog computing services. The monitoring system continuously keeps track of all resources by collecting and analyzing the status information and alert the user when the performance decreases. Resource management involves load balancing, resource scheduling and allocation and it requires accurate resource status which is provided by resource monitoring system to take scheduling and allocation decisions. The resource management activities are NP-hard problems and require optimal techniques to improve resource utilization and reduce energy consumption and latency. This paper proposes resource management model using improved bio-inspired algorithms and fog monitoring model to improve resource utilization and reduce energy consumption. The simulation results show that the proposed model is effective in terms of execution time, response time and energy consumption compared to the state of art techniques.

Tremendous evolution towards cloud computing provides changing demands to work in virtualized environment, i.e. applications such as Internet of Things (IoT), healthcare system, video streaming that are transferred to cloud, that needs responses in certain time period, that is, deadline. Moreover, cloud computing acquires substantial amount of energy whilst offering services to meet applications need, that leads to higher operational cost. In specific, bulky task to offer services to deadline based task while reducing energy consumption. However, effectual task scheduling is an attractive way to reduce energy utilization whilst guaranteeing fulfilled services for cloud users. Task scheduling in cloud is measured as multi-objective minimization crisis which comprises of make-span and reduction of energy consumption. Initially, novel learning based scheduling approach for deadline tasks has been proposed. This model is an adaptive decision making approach assists schedulers to recognize finest responses Then, decision based scheduling (DSA) algorithm is provided which investigates tasks’ heterogeneity by guaranteeing time needed for scheduling tasks. Extensive simulation was carried out in MATLAB simulation environment to show applicability and effectiveness of DSA for deadline scheduling in cloud environment.

China’s economy adopts an extensive growth method. This economic growth method relies on resource input, which consumes huge energy and causes excessive pressure on resources and the environment. The economic growth rate of Fujian Province ranks among the highest in the country in recent years, and its energy consumption is still on the high side. Industrial enterprises, especially the manufacturing industry, account for the majority of energy consumption. In order to ensure the sustainable development of enterprises and society, the government needs to make plans for energy conservation and consumption reduction, policy support and enterprise energy conservation and consumption reduction measures to promote energy conservation and consumption reduction in the whole society, and strive to achieve coordinated development of economic growth and environmental protection.

Nowadays, chip multi-processors (CMP) are being widely used in embedded systems due to their superior performance for parallel computing. However, they also incur significantly large power consumption. To solve this issue, designers of embedded operating system must provide an efficient thread scheduling algorithm, which not only maximizes the system performance, but also minimizes the energy consumption. Further, if the scheduler makes decisions without considering the precedence relationships among threads, the decisions could conflict with the thread behavior which could result in poor performance and large energy consumption. In this paper, we propose a synchronization-aware dynamic thread scheduling algorithm (SA), which reduces the busy-waiting time caused by spinlock, and with performance improvement and energy saving. SA has two major objectives, including (1) overall high performance, in terms of less completion time and less turnaround time, and (2) low energy consumption for all threads. The experimental results show that SA indeed improves performance and reduces energy consumption compared to the original scheduling algorithm of Linux kernel version 2.6. In a real-world case, the Digital Video Recording (DVR) system, SA achieves performance speedup by at most 1.21 and saves the energy consumption by at most 28.6%.

Renewable energy consumption in economic sectors in the EU-27. The impact on economics, environment and conventional energy sources. A 20-year perspective

The UK has been one of the few countries that has successfully decoupled final energy consumption from economic growth over the past 15 years. This study investigates the drivers of final energy consumption in the UK productive sectors between 1997 and 2013 using a decomposition analysis that incorporates two novel features. Firstly, it investigates to what extent changes in thermodynamic efficiency have contributed to overall changes in sectoral energy intensities. Secondly, it analyses how much of the structural change in the UK economy is driven by the offshoring of energy-intensive production overseas. The results show that energy intensity reductions are the strongest factor reducing energy consumption. However, only a third of the energy savings from energy intensity reductions can be attributed to reductions in thermodynamic efficiency with reductions in the exergy intensity of production making up the reminder. In addition the majority of energy savings from structural change are a result of offshoring, which constitutes the second biggest factor reducing energy consumption. In recent years the contributions of all decomposition factors have been declining with very little change in energy consumption after 2009. This suggests that a return to the strong reductions in energy consumption observed between 2001 and 2009 in the UK productive sectors should not be taken for granted. Given that further reductions in UK final energy consumption are needed to achieve global targets for climate change mitigation, additional policy interventions are needed. Such policies should adopt a holistic approach, taking into account all sectors in the UK economy as well as the relationship between the structural change in the UK and in the global supply chains delivering the goods and service for consumption and investment in the UK.

Cloud computing is one of the emerging computer technology. It is an internet based technology and provides shared resources like databases, storage, servers, softwares to the users as per their demand. Users then pay according to the usage. There are various aspects which influence the system performance and scheduling is one of them. So there is a need of an coherent scheduling algorithm that can enhance the overall performance. Scheduling algorithms mainly emphasize on completion time, cost and makespan. Furthermore there are many heuristic based algorithms for scheduling but it is observed that genetic algorithm converges faster and gives optimal results. In this research an efficient scheduling approach is introduced which focus on reduction of energy consumption. The overall energy which is consumed is determined by resource utilization. The scheduling policy is based on genetic algorithm. The proposed approach assigns the task to virtual machine according to the utilization so that overall energy consumption is minimized. Comparison is made between FCFS and the proposed approach. The experiment results show that our approach has reduced the energy consumption with respect to FCFS.

The massive growth of cloud computing leads to huge amounts of energy consumption and release of carbon footprints as data centers are housed by a large number of servers. Consequently, the cloud service providers are looking for eco-friendly solutions to reduce energy consumption and carbon emissions. As a result, task scheduling has drawn attention, in which efficient resource utilization and minimum energy consumption take into great consideration. This is an exigent issue, especially for the heterogeneous environment. In this work, we put forward an energy-efficient task scheduling algorithm (ETSA) to address the demerits associated with task consolidation and scheduling. The proposed algorithm ETSA takes into account the completion time and total utilization of a task on the resources, and follows a normalization procedure to make a scheduling decision. We evaluate the proposed algorithm ETSA to measure energy efficiency and makespan in the heterogeneous environment. The experimental results are compared with recent algorithms, namely random, round robin, dynamic cloud list scheduling, energy-aware task consolidation, energy-conscious task consolidation and MaxUtil. The proposed algorithm ETSA provides an elegant trade-off between energy efficiency and makespan than the existing algorithms.

ObjectivesThe unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or image quality. This study investigates MR energy consumption and identifies strategies for improving energy efficiency, focusing on musculoskeletal MRI. We assess the potential savings achievable through (1) optimizing protocols, (2) incorporating deep learning (DL) accelerated acquisitions, and (3) optimizing the cooling system.Materials and methodsEnergy consumption measurements were performed on two MRI scanners (1.5-T Aera, 1.5-T Sola) in practices in Munich, Germany, between December 2022 and March 2023. Three levels of energy reduction measures were implemented and compared to the baseline. Wilcoxon signed-rank test with Bonferroni correction was conducted to evaluate the impact of sequence scan times and energy consumption.ResultsOur findings showed significant energy savings by optimizing protocol settings and implementing DL technologies. Across all body regions, the average reduction in energy consumption was 72% with DL and 31% with economic protocols, accompanied by time reductions of 71% (DL) and 18% (economic protocols) compared to baseline. Optimizing the cooling system during the non-scanning time showed a 30% lower energy consumption.ConclusionImplementing energy-saving strategies, including economic protocols, DL accelerated sequences, and optimized magnet cooling, can significantly reduce energy consumption in MRI scanners. Radiology departments and practices should consider adopting these strategies to improve energy efficiency and reduce costs.Clinical relevance statementMRI scanner energy consumption can be substantially reduced by incorporating protocol optimization, DL accelerated acquisition, and optimized magnetic cooling into daily practice, thereby cutting costs and environmental impact.Key PointsOptimization of protocol settings reduced energy consumption by 31% and imaging time by 18%.DL technologies led to a 72% reduction in energy consumption of and a 71% reduction in time, compared to the standard MRI protocol.During non-scanning times, activating Eco power mode (EPM) resulted in a 30% reduction in energy consumption, saving 4881 € ($5287) per scanner annually.

These days, the problems of scheduling and security are increasingly pressing as a result of the rapidly growing number of implementations that are operated in operating systems. Process scheduling and scheduling security is the most effective methodology of operating a system which can improve and degrade the performance of the computer system. The performance of scheduling algorithm in operating system can be measured by scheduling principles like average waiting time, average turnaround time, response time, throughput etc. Much research carried out over operating system security and round robin scheduling used in time sharing operating system in previous years to resolve the issues regarding to improvement in the performance of round robin (RR) scheduling and improving the security in the scheduling approach. This study discussed the security issues in operating systems, scheduling security and discrete structure of a new approach for RR scheduling. The researcher has suggested a list of policy for scheduling method security and proposed a new approach for RR scheduling and investigated the scheduling principles. The proposed methodology covers distinctive strategy for selection manner of process from ready queue than RR and used diverges time quantum. The proposed methodology has been validated experimentally by comparing with a numerous scheduling algorithm namely as Round Robin (RR), Adaptive Round Robin (ARR), Round Robin Remaining time (RRRT), improved Round Robin (IRR), an additional improvement on the improved Round Robin (AAAIRR), and an Enhanced Round Robin (ERR). Numerous simulations have been conducted to validate the effectiveness of the proposed algorithm to compelling the performance of system. At the conclusion the performance of computing system is dependent on security of operating system as well as secluding methods.

An efficient and optimized Markov chain-based prediction for server consolidation in cloud environment

Reducing energy consumption has become an important issue in designing hardware and software systems in recent years. Although low power hardware components are critical for reducing energy consumption, the switching activity, which is the main source of dynamic power dissipation in electronic systems, is largely determined by the software running on these systems. In this paper we present and evaluate several instruction scheduling algorithms that reorder a given sequence of instructions taking into account the energy considerations. We first compare a performance-oriented scheduling technique with three energy-oriented instruction scheduling algorithms from both performance (execution cycles of the resulting schedules) and energy consumption points of view. Then, we propose three scheduling algorithms that consider energy and performance at the same time. The results obtained show that these techniques are quite successful in reducing energy consumption and their performance is comparable to that of a pure performance-oriented scheduling.