Minimum Completion Time for Power-Aware Scheduling in Cloud Computing

Mehdi, Nawfal A.; Mamat, Ali; Amer, Ali A.; Abdul-Mehdi, Ziyad T.

doi:10.1109/dese.2011.30

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…Kim et al [5] investigate power-aware provisioning of virtual machines (VMs) for real-time services and show the performance results through simulation. Similar studies are conducted in [6,7]. Calheiros et al [8] propose a simulation-based framework, called It is also worth noting that the studies mentioned earlier lack confidence interval analysis and other probabilistic validation techniques to prove their models' correctness and accuracy.…”

Section: Related Studiesmentioning

confidence: 92%

On the performance and power consumption analysis of elastic clouds

Guo

Pang

et al. 2016

Concurrency and Computation

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Cloud computing is a novel paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Elasticity is one of the most attractive features of cloud computing. Elastic clouds are able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible. However, elasticity adds complexity, which makes quantitative analysis of cloud performance and power consumption difficult. Such analysis is required to evaluate and quantify the cost-benefit of a strategy portfolio and the quantitative runtime performance and power consumption experienced by cloud-users. In this study, we present a comprehensive analytical approach to performance and power consumption analysis of elastic clouds. Several metrics are defined and evaluated: expected task completion time, power consumption rate, and task rejection rate under different load conditions, elasticity intensities, and error intensities. To validate the proposed approach, we obtain experimental data through a real-world cloud and conduct a confidence interval analysis. The analysis results suggest the perfect coverage of theoretical results by corresponding experimental confidence intervals. 4368 K.-Y. GUO ET AL.up/out as long as the workload is high, and scaling back in/down when possible, which potentially brings energy saving and operational cost reduction. Elasticity is one of the essential characteristics of scalable cloud, which distinguish the cloud paradigm from traditional computing systems such as gird and cluster computing. However, elasticity is not always welcomed because it also leads to nonnegligible performance degradations caused by lack of available computing resources when facing bursty/high workload and extra operational cost needed by up-scaling/down-scaling activities.Another notable feature influencing performance and power consumption of elastic cloud is machine error. Such errors are caused by accidental storage unavailability, component malfunction, software bug, software aging, storage space fragmentation accumulation, memory leak, extra-longresponse message, connection failure, message loss, wrong input sequence, external intrusion, and so on. Errors are a major reason causing performance and availability degradation in cloud computing systems. Such degradation could be further amplified because of the fact that elastic cloud usually maintains a subset of active machines rather than put all machines online. Such reduced number of active machines are obviously more susceptible to machine errors than those of non-elastic clouds where all machines are kept active.Errors occur at different levels, and not all errors especially hardware and platform-related errors can be detected and repaired by operating system-level (OS-level) detectors. The complexity of error hierarchy and the fact that lower-level hardware/platform errors are more rare than higherlevel software/tas...

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Section: Related Studiesmentioning

confidence: 92%

On the performance and power consumption analysis of elastic clouds

Guo

Pang

et al. 2016

Concurrency and Computation

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“…Moreover, recent research on scheduling of services in cloud computing systems has been focused on energy aware and "green" scheduling approaches [13], [14]. Berl, et al [15], examined several methods and technologies used for energy-efficient operation of computer hardware and network infrastructure, and identified a number of challenges for energy-conscious approaches in cloud computing environments.…”

Section: Prior Work and Contributionmentioning

confidence: 99%

Can everybody be happy in the cloud? Delay, profit and energy-efficient scheduling for cloud services

Koutsandria

Skevakis

Sayegh

et al. 2016

Journal of Parallel and Distributed Computing

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The rapid development of Cloud Computing provides consumers and service providers with a wide range of opportunities and challenges. Considering the substantial infrastructure investments being made by cloud providers, the reduction of operating expenses (OPEX) while maximizing the profit of the provided services is of great importance. One way to achieve this is by maximizing the efficiency of resource utilization. However, profit maximization does not necessarily coincide with the improvement of a user's Quality of Service (QoS); users generating higher profit for the provider may be scheduled first, causing high delays to low-paying users. Further, the contradictory nature of users' and providers' needs also extends to the energy consumption problem, as the minimization of service delays could cause cloud resources to be constantly "on", leading to high energy consumption, high costs for providers and undue environmental impact. The objective of our work is to analyze this multi-dimensional trade-off. We first investigate the problem of efficient resource allocation strategies for time-varying traffic, and propose a new algorithm, MinDelay, which aims at achieving the minimum service delay while taking into account provider's profit. Then, we propose E-MinDelay, an energy-efficient approach for CPU-intensive tasks in cloud systems. Furthermore, we propose an improved version of the Energy Conscious Task Consolidation (ECTC) algorithm, which combines task consolidation and migration techniques with E-MinDelay. Our results demonstrate that energy consumption and service delays corresponding to profit loss can be simultaneously decreased using an efficient scheduling algorithm.

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“…A RED-BL (Relocate Energy Demand to Better Locations) exploit the geo diversity in electricity priced markets [20] to provide optimal workload mapping. Many optimization problems have been formulated and optimization algorithms developed in the aim to comprise efficient task scheduling techniques [28][29][30][31][32][33][34], mostly considering the server layer, between servers, and not much attention has been paid to scheduling techniques at the datacenter layer, between datacenters.…”

Section: Related Workmentioning

confidence: 99%

Green and FairWorkload Distribution in Geographically Distributed Datacenters

Alawnah

Ahmad

Alrashid

2014

JGE

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To accommodate user applications and requests, service providers host large computer systems over an infrastructure of distributed datacenters in different geographical locations. Consequently, energy and power consumption increases, increasing electrical and cooling costs. Hence more attention is paid to green computing and exploiting renewable energy sources, along with decreasing electrical costs so that these datacenters are operated efficiently and effectively, with less impact on the environment. In this paper, a socially responsible scheduling scheme that distributes load from several regions to geographically distributed datacenters will be extended. Optimal electricity, social and latency costs will be sought within service level agreements, ensuring fairness between different regions and highlighting its importance. Adopting standard gradient methods and Lagrange multipliers, an optimization algorithm called Green-Fair is proposed that provides optimal selection of datacenters. Experimental results show the effectiveness of the Green-Fair algorithm in utilizing green energy sources, reducing the number of SLA violations significantly, reducing latency costs and preserving fairness requirements by the SLAs while having little effect on the service providers' overall operational costs.

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Minimum Completion Time for Power-Aware Scheduling in Cloud Computing

Cited by 28 publications

References 17 publications

On the performance and power consumption analysis of elastic clouds

On the performance and power consumption analysis of elastic clouds

Can everybody be happy in the cloud? Delay, profit and energy-efficient scheduling for cloud services

Green and FairWorkload Distribution in Geographically Distributed Datacenters

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