Adaptive Energy-Aware Algorithms to Minimize Power Consumption and SLA Violation in Cloud Computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Monika Singh ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Power Consumption ◽  
Large Scale ◽  
Minimum Energy ◽  
Service Level Agreement ◽  
Migration Policy ◽  
Service Level ◽  
Vm Placement ◽  
Energy Aware ◽  
Suggested Approach ◽  
Placement Decision

: With the establishment of virtualized datacenters on a large scale, cutting-edge technology requires more energy to deliver the services 24*7 hours. With this expansion and accumulation of information on a massive scale on datacenters, the consumption of excessive amount of power results in high operational costs and power consumption. Therefore, there is an urgent need to make the environment more adaptive and dynamic, where the overutilization and underutilization of hosts is well known to the system and active measures can be taken accordingly. To serve this purpose, an energy efficient method for the detection of overloaded and under-loaded hosts has been proposed in this paper. For implementing VM migration, VM placement decision has also been taken to save energy and reduce SLA (Service Level Agreement) rate over the cloud. In the paper, a novel adaptive heuristics approach has been presented that concerns with the utilization of resources for a dynamic consolidation of VMs based on the mustered data from the usage of resources by VMs, while ensuring the high level of relevancy to the SLA. After identification of under-load and overload hosts, VM placement decision has been taken in the way that takes minimum energy consumption. Minimum migration policy has been adopted in the proposed methodology to minimize execution time. The validation of effectiveness and efficiency of the suggested approach has been performed by using real-world workload traces in CloudSim simulator.

Multiple MapReduce Jobs in Distributed Scheduler for Big Data Applications

2018 ◽  
Vol 7 (12) ◽  
pp. 34
Author(s):  
Kasi Perumal Sundaraj ◽  
Madhusudhan Rao T ◽  
Praveen Chander P G
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Heuristic Algorithms ◽  
Service Level Agreement ◽  
Service Level ◽  
Integer Program ◽  
Energy Aware ◽  
Data Intensive ◽  
Big Data Applications ◽  
Energy Aware Scheduling

The majority of large-scale data intensive applications executed by data centers are based on MapReduce or its open-source implementation, Hadoop. Such applications are executed on large clusters requiring large amounts of energy, making the energy costs a considerable fraction of the data center’s overall costs. Therefore minimizing the energy consumption when executing each MapReduce job is a critical concern for data centers. In this paper, we propose a framework for improving the energy efficiency of MapReduce applications, while satisfying the service level agreement (SLA).We first model the problemof energy-aware scheduling of a single MapReduce job as an Integer Program. We then propose two heuristic algorithms, called energy-aware MapReduce scheduling algorithms (EMRSA-I and EMRSA-II), that find the assignments of map and reduce tasks to the machine slots in order to minimize the energy consumed when executing the application. Our algorithm able to find near optimal job schedules consuming approximately 40 percent less energy on average than the schedules obtained by a common practice scheduler that minimizes the makespan.

Error Recovery for SLA-Based Workflows within the Business Grid

2012 ◽  
pp. 1349-1375
Author(s):  
Dang Minh Quan ◽  
Jörn Altmann ◽  
Laurence T. Yang
Keyword(s):  
Large Scale ◽  
Service Level Agreement ◽  
Error Recovery ◽  
Service Level ◽  
Small Scale ◽  
Grid System ◽  
Negative Effects ◽  
Recovery Mechanism ◽  
Recovery Mechanisms ◽  
Grid Based

This chapter describes the error recovery mechanisms in the system handling the Grid-based workflow within the Service Level Agreement (SLA) context. It classifies the errors into two main categories. The first is the large-scale errors when one or several Grid sites are detached from the Grid system at a time. The second is the small-scale errors which may happen inside an RMS. For each type of error, the chapter introduces a recovery mechanism with the SLA context imposing the goal to the mechanisms. The authors believe that it is very useful to have an error recovery framework to avoid or eliminate the negative effects of the errors.

scholarly journals Distributed Meta-Brokering P2P Overlay for Scheduling in Cloud Federation

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 852 ◽  
Author(s):  
Sajid Latif ◽  
Syed Mushhad Gilani ◽  
Rana Liaqat Ali ◽  
Misbah Liaqat ◽  
Kwang-Man Ko
Keyword(s):  
Resource Management ◽  
Service Provision ◽  
Large Scale ◽  
Service Providers ◽  
Service Level Agreement ◽  
Service Level ◽  
Distributed Resources ◽  
Proposed Model ◽  
Geographically Distributed ◽  
Application Programming

The interconnected cloud (Intercloud) federation is an emerging paradigm that revolutionizes the scalable service provision of geographically distributed resources. Large-scale distributed resources require well-coordinated and automated frameworks to facilitate service provision in a seamless and systematic manner. Unquestionably, standalone service providers must communicate and federate their cloud sites with other vendors to enable the infinite pooling of resources. The pooling of these resources provides uninterpretable services to increasingly growing cloud users more efficiently, and ensures an improved Service Level Agreement (SLA). However, the research of Intercloud resource management is in its infancy. Therefore, standard interfaces, protocols, and uniform architectural components need to be developed for seamless interaction among federated clouds. In this study, we propose a distributed meta-brokering-enabled scheduling framework for provision of user application services in the federated cloud environment. Modularized architecture of the proposed system with uniform configuration in participating resource sites orchestrate the critical operations of resource management effectively, and form the federation schema. Overlaid meta-brokering instances are implemented on the top of local resource brokers to keep the global functionality isolated. These instances in overlay topology communicate in a P2P manner to maintain decentralization, high scalability, and load manageability. The proposed framework has been implemented and evaluated by extending the Java-based CloudSim 3.0.3 simulation application programming interfaces (APIs). The presented results validate the proposed model and its efficiency to facilitate user application execution with the desired QoS parameters.

Service Level Agreement Conformance in Virtual Machine Migration with an Improved Energy and Performance Management Method

2020 ◽  
Vol 17 (9) ◽  
pp. 3904-3906
Author(s):  
Susmita J. A. Nair ◽  
T. R. Gopalakrishnan Nair
Keyword(s):  
Energy Consumption ◽  
Performance Management ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Performance Degradation ◽  
Virtual Machine Migration ◽  
And Performance

Increasing demand of computing resources and the popularity of cloud computing have led the organizations to establish of large-scale data centers. To handle varying workloads, allocating resources to Virtual Machines, placing the VMs in the most suitable physical machine at data centers without violating the Service Level Agreement remains a big challenge for the cloud providers. The energy consumption and performance degradation are the prime focus for the data centers in providing services by strictly following the SLA. In this paper we are suggesting a model for minimizing the energy consumption and performance degradation without violating SLA. The experiments conducted have shown a reduction in SLA violation by nearly 10%.

SMA-LinR Energy and SLA aware autonomous management of virtual machines

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Virtual Machines ◽  
Moving Average ◽  
Service Level Agreement ◽  
Service Level ◽  
Quality Improvements ◽  
Vm Placement ◽  
Suitable Host ◽  
Management Of Resources ◽  
Autonomous Management ◽  
Reduce Energy Consumption

Cloud datacenters consume enormous energy and generate heat, which affects the environment. Hence, there must be proper management of resources in the datacenter for optimum usage of energy. Virtualization enabled computing improves the performance of the datacenters in terms of these parameters. Therefore, Virtual Machines (VMs) management is a required activity in the datacenter, which selects the VMs from the overloaded host for migration, VM migration from the underutilized host, and VM placement in the suitable host. In this paper, a method (SMA-LinR) has been developed using the Simple Moving Average (SMA) integrated with Linear Regression (LinR), which predicts the CPU utilization and determines the overloading of the host. Further, this predicted value is used to place the VMs in the appropriate PM. The main aim of this research is to reduce energy consumption (EC) and service level agreement violations (SLAV). Extensive simulations have been performed on real workload data, and simulation results indicate that SMA-LinR provides better EC and service quality improvements.

Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters

2021 ◽  
pp. 1-22
Author(s):  
Golnaz Berenjian ◽  
Homayun Motameni ◽  
Mehdi Golsorkhtabaramiri ◽  
Ali Ebrahimnejad
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Service Providers ◽  
Service Level Agreement ◽  
Main Idea ◽  
Service Level ◽  
Second Phase ◽  
Energy Aware ◽  
Slack Time ◽  
Dynamic Voltage

Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.

scholarly journals Efficient virtual machine placement algorithms for consolidation in cloud data centers

2020 ◽  
Vol 17 (1) ◽  
pp. 29-50
Author(s):  
Loiy Alsbatin ◽  
Gürcü Öz ◽  
Ali Ulusoy
Keyword(s):  
Virtual Machine ◽  
Service Level Agreement ◽  
Energy Performance ◽  
Service Level ◽  
Vm Placement ◽  
Cloud Data ◽  
Successful Approach ◽  
Cloud Data Centers ◽  
Placement Algorithms ◽  
Best Fit

Dynamic Virtual Machine (VM) consolidation is a successful approach to improve the energy efficiency and the resource utilization in cloud environments. Consequently, optimizing the online energy-performance tradeoff directly influences quality of service. In this study, algorithms named as CPU Priority based Best-Fit Decreasing (CPBFD) and Dynamic CPU Priority based Best-Fit Decreasing (DCPBFD) are proposed for VM placement. A number of VM placement algorithms are implemented and compared with the proposed algorithms. The algorithms are evaluated through simulations with real-world workload traces and it is shown that the proposed algorithms outperform the known algorithms. The simulation results clearly show that CPBFD and DCPBFD provide the least service level agreement violations, least VM migrations, and efficient energy consumption.

scholarly journals Review of the quality of service scheduling mechanisms in cloud

2018 ◽  
Vol 7 (3) ◽  
pp. 1677 ◽  
Author(s):  
K R RemeshBabu ◽  
Philip Samuel
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Service Level Agreement ◽  
Scheduling Algorithms ◽  
Service Level ◽  
Future Research ◽  
Efficient Management ◽  
Future Research Directions ◽  
Scheduling Mechanisms

Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.  

Resource Management Mechanisms to Support SLAs in IaaS Clouds

2012 ◽  
pp. 288-307 ◽  
Author(s):  
David Breitgand ◽  
Amir Epstein ◽  
Benny Rochwerger
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Capacity Management ◽  
Resource Provisioning ◽  
Cloud Provider ◽  
Vm Placement ◽  
Placement Problem ◽  
Efficient Manner ◽  
Placement Optimization ◽  
And Performance

The authors consider elastic multi-VM workloads corresponding to multi-tier application and study the fundamental problems of VM placement optimization, subject to policy constraints, elasticity requirements, and performance SLAs. Numerous algorithmic and architecture proposals appeared recently in the area of resource provisioning in IaaS. The chapter provides a comprehensive review of related work in this field and presents the authors’ recent scientific findings in this area obtained in the framework of an EU funded project, RESERVOIR. The chapter discusses horizontal elasticity support in IaaS, its relationship to SLA protection, VM placement optimization and efficient capacity management to improve cost-efficiency of cloud providers. Elastic services comprise multiple virtualized resources that can be added and deleted on demand to match variability in the workload. A Service owner profiles the service to determine its most appropriate sizing under different workload conditions. This variable sizing is formalized through a service level agreement (SLA) between the service owner and the cloud provider. The Cloud provider obtains maximum benefit when it succeeds to fully allocate the resource set demanded by the elastic service subject to its SLA. Failure to do so may result in SLA breach and financial losses to the provider. The chapter defines a novel combinatorial optimization problem called elastic services placement problem to maximize the provider’s benefit from SLA compliant placement. It demonstrates the feasibility of our approach through a simulation study, showing that we are capable of consistently obtaining good solutions in a time efficient manner. In addition, we discuss how resource utilization level can be improved through an advanced capacity management leveraging elastic workload resource consumption variability.

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