Workload Consolidation Research Articles

Fuzzy based energy efficient workload management system for flash crowd

Many organizations are moving towards the cloud to meet sudden spikes due to a flash crowd. The exponential growth in requests and the offloading of the computations have increased data centers which in turn increases energy consumption and carbon footprint. This paper aims to propose an energy-efficient workload management scheme for the green cloud. The novelty of the work entails i) Developing a three-staged scheduling scheme that maps Virtual Machines with the active Physical Machines resulting in the consolidation of workload. ii) Deploying Fuzzy Decision Maker to analyze under-utilized and over-utilized servers based on the cores and the type of workload. The efficiency of the proposed system is tested through a trace-based simulation. The Power Usage Effectiveness benchmarked with standard algorithms claims that this work averagely conserves 1.9% energy by triggering 60% lesser migrations and reduces the execution time of cloud instances averagely by 6 clock cycles with a minimum Service Level Agreement Violations of 0.1%

A Hybrid Approach for Optimizing Carbon Footprint in InterCloud Environment

This paper focuses on the problem of workload placement in an InterCloud with the view of minimizing the carbon footprint of such a computing environment. In order to reduce the ecological impact of the data center Greenhouse Gas (GhG) emissions, this paper addresses the problem as a whole, by proposing a global mathematical formulation, based on the joint optimization of the Virtual Machine (VM) placement and their related traffics, along with a workload consolidation method and a cooling maximization technique that considers the dynamic behavior of the cooling fans. As the Virtual Machine Placement Problem (VMPP) is classified as an NP-hard problem, with the addition of the traffic embedding, the problem becomes more complex and stays NP-hard. Therefore, we propose a hybrid approach, for solving such problem and find good feasible solutions in a polynomial time. The results obtained from comparing with the exact method and other reference approaches help in assessing the efficiency of the proposed algorithm, as the carbon footprint costs are relatively close to the lower bound, with an average gap of about 3 percent, and found within a reasonable amount of time.

The Effect of Cloud Workload Consolidation on Cloud Energy Consumption and Performance in Multi-Tenant Cloud Infrastructure

The Effect of Cloud Workload Consolidation on Cloud Energy Consumption and Performance in Multi-Tenant Cloud Infrastructure

SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in Clouds

Cloud computing emerged as one of the leading computational paradigms due to elastic resource provisioning and pay-as-you-go model. Large data centers are used by the service providers to host the various services. These data centers consume enormous energy, which leads to increase in operating costs and carbon footprints. Therefore, green cloud computing is a necessity, which not only reduces energy consumption, but also affects the environment positively. In order to reduce the energy consumption, workload consolidation approach is used that consolidates the tasks in minimum possible servers. However, workload consolidation may lead to service level agreement (SLA) violations due to non-availability of resources on the server. Therefore, workload consolidation techniques should consider the aforementioned problem. In this paper, we present two consolidation based energy-efficient techniques that reduce energy consumption along with resultant SLA violations. In addition to that, we also enhanced the existing Enhanced-Conscious Task Consolidation (ECTC) and Maximum Utilization (MaxUtil) techniques that attempt to reduce energy consumption and SLA violations. Experimental results show that the proposed techniques perform better than the selected heuristic based techniques in terms of energy, SLA, and migrations.

A novel index system describing program runtime characteristics for workload consolidation

Workload consolidation is a common method to improve the resource utilization in clusters or data centers. In order to achieve efficient workload consolidation, the runtime characteristics of a program should be taken into consideration in scheduling. In this paper, we propose a novel index system for efficiently describing the program runtime characteristics. With the help of this index system, programs can be classified by the following runtime characteristics: 1) dependence to multi-dimensional resources including CPU, disk I/O, memory and network I/O; and 2) impact and vulnerability to resource sharing embodied by resource usage and resource sensitivity. In order to verify the effectiveness of this novel index system in workload consolidation, a scheduling strategy, Sche-index, using the new index system for workload consolidation is proposed. Experiment results show that compared with traditional least-loaded scheduling strategy, Sche-index can improve both program performance and system resource utilization significantly.

A comprehensive review of energy efficiency in cloud computing environment

High energy consumption in the cloud has become a huge problem in the data center. Energy represents direct significant cost in the operation of the data center. In Information Technology, infrastructure, Internet applications are in more demand. Cloud computing provides IT resources in the form of infrastructure, platform and application by providing services through the Internet Technology. This leads to more energy being consumed as cloud is used to provide IT services from the IT resources to the IT industry and to the Organizations. To analyze power consumed in the data center, applications are deployed in cloud and tested using different workload conditions. Virtualization depicts more energy utilization in the cloud data center. In this paper discussed about the comparison of cloud and cloud computing, cloud type providers, component performance through secured shell. Identified the various levels of energy consumptions in the cloud. the different techniques which is used to reduce the power consumption in the server and workload consolidation using various parameters are considered.

Energy and communication aware task mapping for MPSoCs

Minimizing energy consumption and network load is a major challenge for network-on-chip (NoC) based multi-processor systems-on-chip (MPSoCs). Efficient task and core mapping can greatly reduce the overall energy consumption and communication overhead among the interdependent tasks. In this paper, we propose a novel Knapsack based bin packing algorithm for workload consolidation that places tasks in such a manner that utilization of available processing elements is maximized, while network overhead, regarding the communication among the tasks, is minimized. We also propose a task swapping algorithm that attempts to further optimize the task placement produced by the bin packing algorithms. Moreover, several core mapping techniques are implemented and the performance of each technique is evaluated under varying configurations. In addition, we also apply a Pareto-efficient algorithm, on top of the bin packing algorithms, attempting to explore the solution in two dimensions, i.e., energy consumption and network load. The experimental results show that the proposed Knapsack based bin packing algorithm coupled with the Pareto-efficient algorithm achieves significant energy savings and reduction in network load as compared to state-of-the-art algorithms, as well as the greedy algorithm. Particularly, the Pareto-efficient algorithm when applied on top of the Knapsack algorithm shows on average 50% and 55% reduction in energy consumption and network load as compared to the greedy algorithm, respectively. While the proposed Pareto-efficient algorithm applied with Knapsack algorithm also demonstrate superior performance compared to three other state-of-the-art heuristics.

On the Carbon Footprint Optimization in an InterCloud Environment

In this paper, we address the problem of Virtual Machine (VM) placement in an InterCloud with regard to the reduction of carbon footprint in such computing environment. In order to minimize data centers’ Greenhouse Gas (GhG) emissions, this paper proposes a mathematical formulation, where the placement approach is stated as a Mixed-Integer Nonlinear Programming (MINLP) problem which aims at minimizing the carbon footprint of the InterCloud. The proposed formulation presents an accurate carbon footprint evaluation based on joint optimization techniques, such as smart and performance-aware workload consolidation, along with cooling efficiency maximization, while considering the greenness factor of the data centers and the dynamic behavior of the equipment cooling fans. Simulations with an exact method showed that, compared with other baseline approaches, the proposed mathematical model leads to optimal configurations with minimal GhG emissions in a single cloud, as well as in an InterCloud environment, and can yield savings of up to 65 percent. The proposed model has also been compared with an approach that performs blind VM consolidation, and the obtained results showed that such model can simultaneously lead to VM configuration that yields the minimum carbon footprint while performing smart VM consolidation in order to prevent Service Level Agreement (SLA) violations.

Improving Efficiency of Edge Computing Infrastructures through Orchestration Models †

Edge computing is an effective paradigm for proximity in computation, but must inexorably face mobility issues and traffic fluctuations. While software orchestration may provide effective service handover between different edge infrastructures, seamless operation with negligible service disruption necessarily requires pre-provisioning and the need to leave some network functions idle for most of the time, which eventually results in large energy waste and poor efficiency. Existing consolidation algorithms are largely ineffective in these conditions because they lack context, i.e., the knowledge of which resources are effectively used and which ones are just provisioned for other purposes (i.e., redundancy, resilience, scaling, migration). Though the concept is rather straightforward, its feasibility in real environments must be demonstrated. Motivated by the lack of energy-efficiency mechanisms in cloud management software, we have developed a set of extensions to OpenStack for power management and Quality of Service, explicitly targeting the introduction of more context for applications. In this paper, we briefly describe the overall architecture and evaluate its efficiency and effectiveness. We analyze performance metrics and their relationship with power consumption, hence extending the analysis to specific aspects that cannot be investigated by software simulations. We also show how the usage of context information can greatly improve the effectiveness of workload consolidation in terms of energy saving.

Balancing QoS and power consumption in programmable 5G infrastructures

AbstractThe advent of fifth generation is promising to push far more intelligence than today to the network boundary, hence boosting novel computing models based on fog/edge paradigms. The need for proximity in computation, coupled with various forms of mobility, will be responsible for dynamic shifting of workload within the system, with large fluctuations in resource usage. This eventually turns into poor energy efficiency of the whole infrastructure. However, improving efficiency usually deteriorates quality of service, hence the dilemma about how to balance these two contrasting aspects.In this paper, we propose a framework that leverages the increasing programmability of ICT infrastructures to pursue a linear relationship between power consumption and workload, while safeguarding quality of service. Our approach is based on workload consolidation and extensions to existing cloud management software. We collected both real measurements from an experimental testbed and performance analysis from simulations to evaluate the consolidation strategy in more complex environments.

An Approach for Detection of Overloaded Host to Consolidate Workload in Cloud Datacenter

This article describes the process of workload consolidation through detection of overloaded hosts in Cloud datacenter which leads to saving in energy consumption. Cloud computing is a novice paradigm where virtual resources are provisioned on pay-as-you-go basis. Upon receiving users' job requirement, it is mapped onto virtual resources running on hosts in datacenter. To achieve workload consolidation, it is required to detect the overloaded hosts. Overloaded host detection is carried out for balancing workload, creating a list of overloaded hosts which will be useful while placing VMs (by not putting a VM on already overloaded host) to reduce Service Level Agreement (SLA) violation and while checking the underloaded host, the overloaded hosts are omitted to reduce computational cost. Most common mechanism to detect overloaded hosts is to calculate upper threshold values based on hosts' utilization statically or dynamically. Most researchers recommend dynamic calculation of threshold values. In this research, the authors propose to use moving range (MR) method of variables control charts to calculate upper threshold. The experimentation results show that MR performs better in terms of reduction in SLA violation, minimization in VM migration.

Semi-online task assignment policies for workload consolidation in cloud computing systems

Satisfying on-demand access to cloud computing infrastructures under quality-of-service constraints while minimising the wastage of resources is an important challenge in data centre resource management. In this paper we tackle this challenge in a semi-online workload management system allocating tasks with uncertain duration to physical servers. Our semi-online framework, based on a bin packing approach, allows us to gather information on incoming tasks during a short time window before deciding on their assignments. Our contributions are as follows: (i) we propose a formal framework capturing the semi-online consolidation problem; (ii) we propose a new dynamic and real-time allocation algorithm based on the incremental merging of bins; and (iii) an adaptation of standard bin packing heuristics with a local search algorithm for the semi-online context considered here. We provide a systematic study of the impact of varying time-period size and varying the degrees of uncertainty on the duration of incoming tasks. The policies are compared in terms of solution quality and solving time on a data-set extracted from a real-world cluster trace.Our results show that, around periods of high demand, our best policy saves up to 40% of the resources compared to the other polices, and is robust to uncertainty in the task durations. Finally, we show that small increases in the allowable time window allows a significant improvement, but that larger time windows do not necessarily improve resource usage for real world datasets.

Latency-Sensitive Data Allocation and Workload Consolidation for Cloud Storage

Customers often suffer from the variability of data access time in (edge) cloud storage service, caused by network congestion, load dynamics, and so on. One efficient solution to guarantee a reliable latency-sensitive service (e.g., for industrial Internet of Things application) is to issue requests with multiple download/upload sessions which access the required data (replicas) stored in one or more servers, and use the earliest response from those sessions. In order to minimize the total storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to deal with. In this paper, we study the latency-sensitive data allocation problem, the latency-sensitive data reallocation problem and the latency-sensitive workload consolidation problem for cloud storage. We model the data access time as a given distribution whose cumulative density function is known, and prove that these three problems are NP-hard. To solve them, we propose an exact integer nonlinear program (INLP) and a Tabu Search-based heuristic. The simulation results reveal that the INLP can always achieve the best performance in terms of lower number of used nodes and higher storage and throughput utilization, but this comes at the expense of much higher running time. The Tabu Search-based heuristic, on the other hand, can obtain close-to-optimal performance, but in a much lower running time.

DTCF: deadline task consolidation first for energy minimisation in cloud data centres

The consumption of energy is a vital issue in the cloud, when more precisely administering a large-scale data centre. The cloud data centre accommodating more hosts consume an extent of energy for computation which increases the consumption of energy. To handle this issue, we propose a heuristics energy-efficient workload consolidation with deadline constraint to optimise energy in cloud data centre. The deadline task consolidation first algorithm ranks hosts for creating virtual machines (VMs) and validates the processing time of VMs and places the VM considering deadline of tasks, VMs can be placed in an increasing order of their respective processing time. Then, the decision for migrating VMs to other hosts is evaluated using Markov decision model. Through this process, for specific virtual machine necessities of workloads, we can reduce the number of hosts. The proposed algorithm is simulated in Cloudsim shows that it improves the workload consolidation quality, and is appropriate for deadline task consolidation in cloud data centre.

DTCF: deadline task consolidation first for energy minimisation in cloud data centres

The consumption of energy is a vital issue in the cloud, when more precisely administering a large-scale data centre. The cloud data centre accommodating more hosts consume an extent of energy for computation which increases the consumption of energy. To handle this issue, we propose a heuristics energy-efficient workload consolidation with deadline constraint to optimise energy in cloud data centre. The deadline task consolidation first algorithm ranks hosts for creating virtual machines (VMs) and validates the processing time of VMs and places the VM considering deadline of tasks, VMs can be placed in an increasing order of their respective processing time. Then, the decision for migrating VMs to other hosts is evaluated using Markov decision model. Through this process, for specific virtual machine necessities of workloads, we can reduce the number of hosts. The proposed algorithm is simulated in Cloudsim shows that it improves the workload consolidation quality, and is appropriate for deadline task consolidation in cloud data centre.

SLA-Aware Energy Efficient Resource Management for Cloud Environments

Cloud computing provides online services to customers using pay as you go model. The Cloud computing enables customers to outsource the large and complex tasks to the cloud data centers for the execution and result generations. Cloud data centers host the incoming tasks by providing resources, such as CPU, RAM, storage, and bandwidth. As the large data centers provide the basic resources to hosted tasks, they also consume a huge amount of energy, which leads to higher operating cost and CO2 traces. Therefore, research community felt the need to provide energy-efficient solutions that reduce the impact of the aforementioned issues. Consequently, researchers proposed many solutions, and majority of them are based upon the concept of consolidation. Consolidation techniques place the incoming tasks on minimum possible servers, thus increasing the resource utilization and decreasing energy consumption. In this paper, we use the same workload consolidation concept and present two techniques that reduce energy consumption while ensuring the negotiated quality-of-service. Moreover, we enhanced two existing techniques by improving the energy efficiency and introducing service level agreement (SLA) awareness to minimize the overall SLA violations. Performance evaluation of the proposed techniques is done based on fluctuating workloads, and results show that our techniques outperform existing techniques in terms of energy efficiency, SLA compliance, and performance assurance at the network level. Moreover, correctness of the proposed techniques is demonstrated by modeling and verifying them with the help of high-level Petri Nets, SMT-Lib, and Z3 solver.

Energy efficient strategy for placement of virtual machines selected from underloaded servers in compute Cloud

Workload consolidation is a phase in Cloud datacenter where tasks are allocated among the available hosts in such a way that a minimal number of hosts is used and users’ need in terms of service level agreement (SLA) is fulfilled. To achieve workload consolidation, hosts are divided among three groups based on their utilization namely overloaded hosts, underloaded host and normal hosts. Detection of over or underloaded host is a challenging issue. Most of the existing researchers propose to use threshold values for such detection. We believe that there is a scope of improvement in existing methods of deciding underloaded hosts and subsequently taking off virtual machines (VMs) from them and placing them on other hosts. In this research, we propose Host Utilization Aware (HUA) Algorithm for underloaded host detection and placing its VMs on other hosts in a dynamic Cloud environment. We compare our proposed mechanism with existing one and with empirical analysis; it is shown that our proposal results into shutting off more number of hosts without compromising user’s workload requirement which leads to an energy-efficient workload consolidation with minimal migration costs and efficient utilization of active hosts.

Power Consumption Models for Multi-Tenant Server Infrastructures

Multi-tenant virtualized infrastructures allow cloud providers to minimize costs through workload consolidation. One of the largest costs is power consumption, which is challenging to understand in heterogeneous environments. We propose a power modeling methodology that tackles this complexity using a divide-and-conquer approach. Our results outperform previous research work, achieving a relative error of 2% on average and under 4% in almost all cases. Models are portable across similar architectures, enabling predictions of power consumption before migrating a tenant to a different hardware platform. Moreover, we show the models allow us to evaluate colocations of tenants to reduce overall consumption.

A LITERATURE REVIEW: EFFICIENT VM MIGRATION TECHNIQUES FOR ENERGY REDUCTION IN CLOUD COMPUTING

Broadly speaking cloud computing is nothing but a highly ‘utilitarian’ orientation of IT services where users benefited on a pay-as-you go basis. In a way it enables the hosting of pervasive applications from consumer, scientific and business domain. We expect all electronic gadgetry to be ‘energy efficient’ to possibly achievable limits. So our data centers hosting cloud application must be cost effective and the same time should avoid undue burden of carbon footprint. While excising economy on power consumption by (data center) outmost care needs to be taken so that it never at the cost services provided to end user i.e. SLA violation must be kept as low as possible. Virtualization technology is one of the key features in cloud data centers that can improve the efficiency of hardware utilization through resource sharing, migration, and consolidation of workloads. In this paper we shall cover VM Migration Algorithms for energy reduction in cloud computing along with other novel techniques.

CloudNetSim++: A GUI Based Framework for Modeling and Simulation of Data Centers in OMNeT++

State-of-the-art cloud simulators in use today are limited in the number of features they provide, lack real network communication models, and do not provide extensive Graphical User Interface (GUI) to support developers and researchers to extend the behavior of the cloud environment. We propose CloudNetSim++, a comprehensive packet level simulator that enables simulation of cloud environments. CloudNetSim++ can be used to evaluate a wide spectrum of cloud components, such as processing elements, storage, networking, Service Level Agreement (SLA), scheduling algorithms, fine grained energy consumption, and VM consolidation algorithms. CloudNetSim++ offers extendibility, which means that the developers and researchers can easily incorporate own algorithms for scheduling, workload consolidation, VM migration, and SLA agreement. The simulation environment of CloudNetSim++ offers a rich GUI that provides a high level view of distributed data centers connected with various network topologies. The package also includes an energy computation module that provides a fine grained analysis of energy consumed by each component. This paper shows the flexibility and effectiveness of CloudNetSim++ through experimental results demonstrated using real-world data center workloads. Moreover, to demonstrate the correctness of CloudNetSim++, we performed formal modeling, analysis, and verification using High-level Petri Nets, Satisfiability Modulo Theories (SMT), and Z3 solver.