Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
This document proposes a framework for intelligently placing datacenters to optimize response time, availability, costs and emissions. It defines relevant parameters like costs, response time, consistency delay and availability. It formulates the placement problem as an optimization problem aiming to minimize costs while meeting constraints. The problem is solved using simulated annealing and linear programming. A tool is developed to automatically select datacenter locations based on this approach. Experimental results demonstrate millions of dollars can be saved through optimized placement.
This document proposes a new Ranking Chaos Optimization (RCO) algorithm to solve the dual scheduling problem of cloud services and computing resources (DS-CSCR) in private clouds. It introduces the DS-CSCR concept and models the characteristics of cloud services and computing resources. The RCO algorithm uses ranking selection, individual chaos, and dynamic heuristic operators. Experimental results show RCO has better searching ability, time complexity, and stability compared to other algorithms for solving DS-CSCR. Future work is needed to study additional quality of service properties and improve RCO for other optimization problems.
Energy proportionality is the key in order to reduce the Total Cost of Ownership (TCO) of Warehouse Scale Computer (WSC) systems, yet is difficult to achieve in practice. Typical WSC hardware usually does not meet this principle. Furthermore, critical services (e.g. billing) require all servers to remain up regardless the current traffic intensity. These two issues make existing power management technique ineffective at reducing energy use in a WSC dimension. We present Hybrid Performance-aware Power-capping Orchestrator (HyPPO), a distributed Observe Decide Act (ODA) control loop for optimizing energy proportionality of a distribute containerized infrastructures. This first version of HyPPO uses Kubernetes resource metrics (e.g. milli-cpus consumption) in order to dynamically adjust node power consumption, while respecting the Service Level Agreement (SLA) agreement defined by the containerized application owners.
Task Scheduling using Tabu Search algorithm in Cloud Computing Environment us...AzarulIkhwan
1. The document proposes using Tabu Search algorithm for task scheduling in cloud computing environments using CloudSim simulator. It aims to maximize throughput and minimize turnaround time compared to traditional algorithms like FCFS.
2. The methodology section describes how CloudSim simulator works and the components involved in task scheduling. It also provides an overview of how the Tabu Search algorithm guides the search process to avoid getting stuck at local optima.
3. The expected result is that Tabu Search algorithm will provide higher throughput and lower turnaround times for cloud tasks compared to FCFS, as Tabu Search is designed to escape local optima and find better solutions.
This document provides an overview of task scheduling algorithms for load balancing in cloud computing. It begins with introductions to cloud computing and load balancing. It then surveys several existing task scheduling algorithms, including Min-Min, Max-Min, Resource Awareness Scheduling Algorithm, QoS Guided Min-Min, and others. It discusses the goals, workings, results and problems of each algorithm. It identifies the need for an optimized task scheduling algorithm. It also discusses tools like CloudSim that can be used to simulate scheduling algorithms and evaluate performance.
CoolDC'16: Seeing into a Public Cloud: Monitoring the Massachusetts Open CloudAta Turk
The document describes a monitoring system for the Massachusetts Open Cloud (MOC) public cloud. It has four phases: 1) collecting data from different cloud layers, 2) storing and consolidating the data, 3) hosting services to access the data, and 4) implementing applications that use the monitoring data. Two case studies are presented that use the monitoring data to reduce datacenter power costs: peak shaving and participating in regulation service reserve programs. The monitoring system allows for more optimization and innovation in public clouds by exposing detailed performance and resource usage data.
Genetic Algorithm for task scheduling in Cloud Computing EnvironmentSwapnil Shahade
This document proposes a modified genetic algorithm to schedule tasks in cloud computing environments. It begins with an introduction and background on cloud computing and task scheduling. It then describes the standard genetic algorithm approach and introduces the modified genetic algorithm which uses Longest Cloudlet to Fastest Processor and Smallest Cloudlet to Fastest Processor scheduling algorithms to generate the initial population. The implementation and results show that the modified genetic algorithm reduces makespan and cost compared to the standard genetic algorithm.
Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
This document proposes a framework for intelligently placing datacenters to optimize response time, availability, costs and emissions. It defines relevant parameters like costs, response time, consistency delay and availability. It formulates the placement problem as an optimization problem aiming to minimize costs while meeting constraints. The problem is solved using simulated annealing and linear programming. A tool is developed to automatically select datacenter locations based on this approach. Experimental results demonstrate millions of dollars can be saved through optimized placement.
This document proposes a new Ranking Chaos Optimization (RCO) algorithm to solve the dual scheduling problem of cloud services and computing resources (DS-CSCR) in private clouds. It introduces the DS-CSCR concept and models the characteristics of cloud services and computing resources. The RCO algorithm uses ranking selection, individual chaos, and dynamic heuristic operators. Experimental results show RCO has better searching ability, time complexity, and stability compared to other algorithms for solving DS-CSCR. Future work is needed to study additional quality of service properties and improve RCO for other optimization problems.
Energy proportionality is the key in order to reduce the Total Cost of Ownership (TCO) of Warehouse Scale Computer (WSC) systems, yet is difficult to achieve in practice. Typical WSC hardware usually does not meet this principle. Furthermore, critical services (e.g. billing) require all servers to remain up regardless the current traffic intensity. These two issues make existing power management technique ineffective at reducing energy use in a WSC dimension. We present Hybrid Performance-aware Power-capping Orchestrator (HyPPO), a distributed Observe Decide Act (ODA) control loop for optimizing energy proportionality of a distribute containerized infrastructures. This first version of HyPPO uses Kubernetes resource metrics (e.g. milli-cpus consumption) in order to dynamically adjust node power consumption, while respecting the Service Level Agreement (SLA) agreement defined by the containerized application owners.
Task Scheduling using Tabu Search algorithm in Cloud Computing Environment us...AzarulIkhwan
1. The document proposes using Tabu Search algorithm for task scheduling in cloud computing environments using CloudSim simulator. It aims to maximize throughput and minimize turnaround time compared to traditional algorithms like FCFS.
2. The methodology section describes how CloudSim simulator works and the components involved in task scheduling. It also provides an overview of how the Tabu Search algorithm guides the search process to avoid getting stuck at local optima.
3. The expected result is that Tabu Search algorithm will provide higher throughput and lower turnaround times for cloud tasks compared to FCFS, as Tabu Search is designed to escape local optima and find better solutions.
This document provides an overview of task scheduling algorithms for load balancing in cloud computing. It begins with introductions to cloud computing and load balancing. It then surveys several existing task scheduling algorithms, including Min-Min, Max-Min, Resource Awareness Scheduling Algorithm, QoS Guided Min-Min, and others. It discusses the goals, workings, results and problems of each algorithm. It identifies the need for an optimized task scheduling algorithm. It also discusses tools like CloudSim that can be used to simulate scheduling algorithms and evaluate performance.
CoolDC'16: Seeing into a Public Cloud: Monitoring the Massachusetts Open CloudAta Turk
The document describes a monitoring system for the Massachusetts Open Cloud (MOC) public cloud. It has four phases: 1) collecting data from different cloud layers, 2) storing and consolidating the data, 3) hosting services to access the data, and 4) implementing applications that use the monitoring data. Two case studies are presented that use the monitoring data to reduce datacenter power costs: peak shaving and participating in regulation service reserve programs. The monitoring system allows for more optimization and innovation in public clouds by exposing detailed performance and resource usage data.
Genetic Algorithm for task scheduling in Cloud Computing EnvironmentSwapnil Shahade
This document proposes a modified genetic algorithm to schedule tasks in cloud computing environments. It begins with an introduction and background on cloud computing and task scheduling. It then describes the standard genetic algorithm approach and introduces the modified genetic algorithm which uses Longest Cloudlet to Fastest Processor and Smallest Cloudlet to Fastest Processor scheduling algorithms to generate the initial population. The implementation and results show that the modified genetic algorithm reduces makespan and cost compared to the standard genetic algorithm.
Intelligent Placement of Datacenters for Internet ServicesMaria Stylianou
Course: Execution Environments for Distributed Computing 6th Presentation (10-15min):
Intelligent Placement of Datacenters for Internet Services
Source: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5961695
Task Scheduling methodology in cloud computing Qutub-ud- Din
This document outlines a proposed methodology for developing efficient task scheduling strategies in cloud computing. It begins with introductions to cloud computing and task scheduling. It then reviews several relevant existing task scheduling algorithms from literature that focus on objectives like reducing costs, minimizing completion time, and maximizing resource utilization. The problem statement indicates the goals are to reduce costs, minimize completion time, and maximize resource allocation. An overview of the proposed methodology's flow is then provided, followed by references.
This document discusses automatic energy-aware scheduling for distributed computing. It summarizes the Green500 list which ranks supercomputers by energy efficiency. Server virtualization can improve efficiency by consolidating workloads. Automatic scheduling that places applications dynamically based on power usage could address underutilization. Current solutions include VMturbo's intelligent workload management and using machine learning to model scheduling. The conclusion is that automatic energy-based scheduling should be more widely adopted to further improve supercomputer efficiency.
REVIEW PAPER on Scheduling in Cloud ComputingJaya Gautam
This document reviews scheduling algorithms for workflow applications in cloud computing. It discusses characteristics of cloud computing, deployment and service models, and the importance of scheduling in cloud computing. The document analyzes several scheduling algorithms proposed in literature that consider parameters like makespan, cost, load balancing, and priority. It finds that algorithms like Max-Min, Min-Min, and HEFT perform better than traditional algorithms in optimizing these parameters for workflow scheduling in cloud environments.
IRJET- Time and Resource Efficient Task Scheduling in Cloud Computing Environ...IRJET Journal
This document summarizes a research paper that proposes a Task Based Allocation (TBA) algorithm to efficiently schedule tasks in a cloud computing environment. The algorithm aims to minimize makespan (completion time of all tasks) and maximize resource utilization. It first generates an Expected Time to Complete (ETC) matrix that estimates the time each task will take on different virtual machines. It then sorts tasks by length and allocates each task to the VM that minimizes its completion time, updating the VM wait times. The algorithm is evaluated using CloudSim simulation and is shown to reduce makespan, execution time and costs compared to random and first-come, first-served scheduling approaches.
Reza Rahimi is a principal staff algorithm and software architect at Huawei who has done research on self-tuning and managing services. His PhD topic was on QoS-aware resource management in mobile cloud computing. He has since worked on topics like intelligent cloud management and optimization, mobile cloud computing, and low complexity secure code for big data in cloud storage.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
An optimized scientific workflow scheduling in cloud computingDIGVIJAY SHINDE
The document discusses optimizing scientific workflow scheduling in cloud computing. It begins with definitions of workflow and cloud computing. Workflow is a group of repeatable dependent tasks, while cloud computing provides applications and hardware resources over the Internet. There are three cloud service models: SaaS, PaaS, and IaaS. The document explores how to efficiently schedule workflows in the cloud to reduce makespan, cost, and energy consumption. It reviews different scheduling algorithms like FCFS, genetic algorithms, and discusses optimizing objectives like time and cost. The document provides a literature review comparing various workflow scheduling methods and algorithms. It concludes with discussing open issues and directions for future work in optimizing workflow scheduling for cloud computing.
Scheduling of Heterogeneous Tasks in Cloud Computing using Multi Queue (MQ) A...IRJET Journal
This document proposes a Multi Queue (MQ) task scheduling algorithm for heterogeneous tasks in cloud computing. It aims to improve upon the Round Robin and Weighted Round Robin algorithms by overcoming their drawbacks. The MQ algorithm splits tasks and resources into separate queues based on size/length and speed. Small tasks are scheduled on slower resources and large tasks on faster resources. The document compares the performance of MQ to Round Robin and Weighted Round Robin algorithms based on makespan, average resource utilization, and load balancing level using CloudSim simulations. The results show that MQ scheduling performs better than the other algorithms in most cases in terms of these metrics.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
This document discusses green computing and proposes a simulator for evaluating green scheduling algorithms. It begins with background on green computing and why it is important. It then outlines the key components of the simulator, including: a computation model using DAGs, an energy consumption model based on CPU throttling levels, and an abstraction for energy-aware schedulers. The document describes classes for modeling cores, throttling levels, and the overall simulation framework, which is designed to be extensible to different scheduling algorithms, core types, and energy models. The goal is to simulate and evaluate scheduling heuristics to minimize energy consumption while meeting performance targets.
Detecting Lateral Movement with a Compute-Intense Graph KernelData Works MD
Cybersecurity Analytics on a D-Wave Quantum Computer
Effective cybersecurity analysis requires frequent exploration of graphs of many types and sizes, the computational cost of which can be overwhelming if not carefully chosen. After briefly introducing the D-Wave quantum computing system, we describe an analytic for finding “lateral movement” in an enterprise network, i.e., an intruder or insider threat hopping from system to system to gain access to more information. This analytic depends on maximum independent set, an NP-hard graph kernel whose computational cost grows exponentially with the size of the graph and so has not been widely used in cyber analysis. The growing strength of D-Wave’s quantum computers on such NP-hard problems will enable new analytics. We discuss practicalities of the current implementation and implications of this approach.
Steve Reinhardt has built hardware/software systems that deliver new levels of performance usable via conceptually simple interfaces, including Cray Research’s T3E distributed-memory systems, ISC’s Star-P parallel-MATLAB software, and YarcData/Cray’s Urika graph-analytic systems. He now leads D-Wave’s efforts working with customers to map early applications to D-Wave systems.
Hybrid Task Scheduling Approach using Gravitational and ACO Search AlgorithmIRJET Journal
The document proposes a hybrid task scheduling approach for cloud computing called ACGSA that combines ant colony optimization and gravitational search algorithms. It describes using the Cloudsim simulator to test the performance of ACGSA and comparing it to ant colony optimization. The results show that ACGSA achieves better performance than the basic ant colony approach on relevant parameters like task scheduling time and resource utilization.
Learning Software Performance Models for Dynamic and Uncertain EnvironmentsPooyan Jamshidi
This document provides background on Pooyan Jamshidi's research related to learning software performance models for dynamic and uncertain environments. It summarizes his past work developing techniques for modeling and optimizing performance across different systems and environments, including using transfer learning to reuse performance data from related sources to build more accurate models with fewer measurements. It also outlines opportunities for using transfer learning to adapt performance models to new environments and systems.
Empirical studies have revealed that a significant amount of energy is lost unnecessarily in the
network architectures, protocols, routers and various other network devices. Thus there is a need for techniques
to obtain green networking in the computer architecture which can lead to energy saving. Green networking is
an emerging phenomenon in the computer industry because of its economic and environmental benefits. Saving
energy leads to cost-cutting and lower emission of greenhouse gases which are apparently one of the major
threats to the environment. ’Greening’ as the name suggests is the process of constructing network architecture
in such a way so as to avoid unnecessary loss of power and energy due its various components and can be
implemented using various techniques out of which four are mentioned in this review paper, namely Adaptive
link rate (ALR), Dynamic Voltage and Frequency scaling(DVFS), Interface proxying and energy aware
applications and software.
This document summarizes and compares various scheduling algorithms used in cloud computing environments. It begins with an introduction to cloud computing and the need for scheduling algorithms in cloud environments. It then describes several existing scheduling algorithms, including compromised-time-cost scheduling, particle swarm optimization-based heuristic, improved cost-based algorithm, resource-aware scheduling, innovative transaction intensive cost-constraint scheduling, scalable heterogeneous earliest-finish-time algorithm, and multiple QoS constrained scheduling strategy of multi-workflows. These algorithms aim to optimize metrics such as execution time, cost, deadline, load balancing, and quality of service. The document concludes by comparing the different scheduling strategies.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
This document discusses using neural networks for adaptive digital filter design to cancel linear noise. It begins by introducing adaptive filters and their use in noise cancellation applications. An adaptive noise cancellation system structure is shown using an adaptive filter to estimate noise from a reference input and subtract it from the noisy primary input. Neural networks can be used for adaptive filtering, with the exact random basis function (RBF) network presented as a suitable architecture. Simulation results show that the RBF network achieves much lower error than a linear layer function by producing an output signal close to the desired target. The paper concludes the RBF network is well-suited for this application as it minimizes the error between the output and target signals, effectively canceling linear noise
Quality of Service based Task Scheduling Algorithms in Cloud Computing IJECEIAES
In cloud computing resources are considered as services hence utilization of the resources in an efficient way is done by using task scheduling and load balancing. Quality of service is an important factor to measure the trustiness of the cloud. Using quality of service in task scheduling will address the problems of security in cloud computing. This paper studied quality of service based task scheduling algorithms and the parameters used for scheduling. By comparing the results the efficiency of the algorithm is measured and limitations are given. We can improve the efficiency of the quality of service based task scheduling algorithms by considering these factors arriving time of the task, time taken by the task to execute on the resource and the cost in use for the communication.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
Intelligent Placement of Datacenters for Internet ServicesMaria Stylianou
Course: Execution Environments for Distributed Computing 6th Presentation (10-15min):
Intelligent Placement of Datacenters for Internet Services
Source: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5961695
Task Scheduling methodology in cloud computing Qutub-ud- Din
This document outlines a proposed methodology for developing efficient task scheduling strategies in cloud computing. It begins with introductions to cloud computing and task scheduling. It then reviews several relevant existing task scheduling algorithms from literature that focus on objectives like reducing costs, minimizing completion time, and maximizing resource utilization. The problem statement indicates the goals are to reduce costs, minimize completion time, and maximize resource allocation. An overview of the proposed methodology's flow is then provided, followed by references.
This document discusses automatic energy-aware scheduling for distributed computing. It summarizes the Green500 list which ranks supercomputers by energy efficiency. Server virtualization can improve efficiency by consolidating workloads. Automatic scheduling that places applications dynamically based on power usage could address underutilization. Current solutions include VMturbo's intelligent workload management and using machine learning to model scheduling. The conclusion is that automatic energy-based scheduling should be more widely adopted to further improve supercomputer efficiency.
REVIEW PAPER on Scheduling in Cloud ComputingJaya Gautam
This document reviews scheduling algorithms for workflow applications in cloud computing. It discusses characteristics of cloud computing, deployment and service models, and the importance of scheduling in cloud computing. The document analyzes several scheduling algorithms proposed in literature that consider parameters like makespan, cost, load balancing, and priority. It finds that algorithms like Max-Min, Min-Min, and HEFT perform better than traditional algorithms in optimizing these parameters for workflow scheduling in cloud environments.
IRJET- Time and Resource Efficient Task Scheduling in Cloud Computing Environ...IRJET Journal
This document summarizes a research paper that proposes a Task Based Allocation (TBA) algorithm to efficiently schedule tasks in a cloud computing environment. The algorithm aims to minimize makespan (completion time of all tasks) and maximize resource utilization. It first generates an Expected Time to Complete (ETC) matrix that estimates the time each task will take on different virtual machines. It then sorts tasks by length and allocates each task to the VM that minimizes its completion time, updating the VM wait times. The algorithm is evaluated using CloudSim simulation and is shown to reduce makespan, execution time and costs compared to random and first-come, first-served scheduling approaches.
Reza Rahimi is a principal staff algorithm and software architect at Huawei who has done research on self-tuning and managing services. His PhD topic was on QoS-aware resource management in mobile cloud computing. He has since worked on topics like intelligent cloud management and optimization, mobile cloud computing, and low complexity secure code for big data in cloud storage.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
An optimized scientific workflow scheduling in cloud computingDIGVIJAY SHINDE
The document discusses optimizing scientific workflow scheduling in cloud computing. It begins with definitions of workflow and cloud computing. Workflow is a group of repeatable dependent tasks, while cloud computing provides applications and hardware resources over the Internet. There are three cloud service models: SaaS, PaaS, and IaaS. The document explores how to efficiently schedule workflows in the cloud to reduce makespan, cost, and energy consumption. It reviews different scheduling algorithms like FCFS, genetic algorithms, and discusses optimizing objectives like time and cost. The document provides a literature review comparing various workflow scheduling methods and algorithms. It concludes with discussing open issues and directions for future work in optimizing workflow scheduling for cloud computing.
Scheduling of Heterogeneous Tasks in Cloud Computing using Multi Queue (MQ) A...IRJET Journal
This document proposes a Multi Queue (MQ) task scheduling algorithm for heterogeneous tasks in cloud computing. It aims to improve upon the Round Robin and Weighted Round Robin algorithms by overcoming their drawbacks. The MQ algorithm splits tasks and resources into separate queues based on size/length and speed. Small tasks are scheduled on slower resources and large tasks on faster resources. The document compares the performance of MQ to Round Robin and Weighted Round Robin algorithms based on makespan, average resource utilization, and load balancing level using CloudSim simulations. The results show that MQ scheduling performs better than the other algorithms in most cases in terms of these metrics.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
This document discusses green computing and proposes a simulator for evaluating green scheduling algorithms. It begins with background on green computing and why it is important. It then outlines the key components of the simulator, including: a computation model using DAGs, an energy consumption model based on CPU throttling levels, and an abstraction for energy-aware schedulers. The document describes classes for modeling cores, throttling levels, and the overall simulation framework, which is designed to be extensible to different scheduling algorithms, core types, and energy models. The goal is to simulate and evaluate scheduling heuristics to minimize energy consumption while meeting performance targets.
Detecting Lateral Movement with a Compute-Intense Graph KernelData Works MD
Cybersecurity Analytics on a D-Wave Quantum Computer
Effective cybersecurity analysis requires frequent exploration of graphs of many types and sizes, the computational cost of which can be overwhelming if not carefully chosen. After briefly introducing the D-Wave quantum computing system, we describe an analytic for finding “lateral movement” in an enterprise network, i.e., an intruder or insider threat hopping from system to system to gain access to more information. This analytic depends on maximum independent set, an NP-hard graph kernel whose computational cost grows exponentially with the size of the graph and so has not been widely used in cyber analysis. The growing strength of D-Wave’s quantum computers on such NP-hard problems will enable new analytics. We discuss practicalities of the current implementation and implications of this approach.
Steve Reinhardt has built hardware/software systems that deliver new levels of performance usable via conceptually simple interfaces, including Cray Research’s T3E distributed-memory systems, ISC’s Star-P parallel-MATLAB software, and YarcData/Cray’s Urika graph-analytic systems. He now leads D-Wave’s efforts working with customers to map early applications to D-Wave systems.
Hybrid Task Scheduling Approach using Gravitational and ACO Search AlgorithmIRJET Journal
The document proposes a hybrid task scheduling approach for cloud computing called ACGSA that combines ant colony optimization and gravitational search algorithms. It describes using the Cloudsim simulator to test the performance of ACGSA and comparing it to ant colony optimization. The results show that ACGSA achieves better performance than the basic ant colony approach on relevant parameters like task scheduling time and resource utilization.
Learning Software Performance Models for Dynamic and Uncertain EnvironmentsPooyan Jamshidi
This document provides background on Pooyan Jamshidi's research related to learning software performance models for dynamic and uncertain environments. It summarizes his past work developing techniques for modeling and optimizing performance across different systems and environments, including using transfer learning to reuse performance data from related sources to build more accurate models with fewer measurements. It also outlines opportunities for using transfer learning to adapt performance models to new environments and systems.
Empirical studies have revealed that a significant amount of energy is lost unnecessarily in the
network architectures, protocols, routers and various other network devices. Thus there is a need for techniques
to obtain green networking in the computer architecture which can lead to energy saving. Green networking is
an emerging phenomenon in the computer industry because of its economic and environmental benefits. Saving
energy leads to cost-cutting and lower emission of greenhouse gases which are apparently one of the major
threats to the environment. ’Greening’ as the name suggests is the process of constructing network architecture
in such a way so as to avoid unnecessary loss of power and energy due its various components and can be
implemented using various techniques out of which four are mentioned in this review paper, namely Adaptive
link rate (ALR), Dynamic Voltage and Frequency scaling(DVFS), Interface proxying and energy aware
applications and software.
This document summarizes and compares various scheduling algorithms used in cloud computing environments. It begins with an introduction to cloud computing and the need for scheduling algorithms in cloud environments. It then describes several existing scheduling algorithms, including compromised-time-cost scheduling, particle swarm optimization-based heuristic, improved cost-based algorithm, resource-aware scheduling, innovative transaction intensive cost-constraint scheduling, scalable heterogeneous earliest-finish-time algorithm, and multiple QoS constrained scheduling strategy of multi-workflows. These algorithms aim to optimize metrics such as execution time, cost, deadline, load balancing, and quality of service. The document concludes by comparing the different scheduling strategies.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
This document discusses using neural networks for adaptive digital filter design to cancel linear noise. It begins by introducing adaptive filters and their use in noise cancellation applications. An adaptive noise cancellation system structure is shown using an adaptive filter to estimate noise from a reference input and subtract it from the noisy primary input. Neural networks can be used for adaptive filtering, with the exact random basis function (RBF) network presented as a suitable architecture. Simulation results show that the RBF network achieves much lower error than a linear layer function by producing an output signal close to the desired target. The paper concludes the RBF network is well-suited for this application as it minimizes the error between the output and target signals, effectively canceling linear noise
Quality of Service based Task Scheduling Algorithms in Cloud Computing IJECEIAES
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Watch the video: https://wp.me/p3RLHQ-k94
Learn more: https://dirac.ac.uk/
and
http://hpcadvisorycouncil.com/events/2019/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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Intelligent Placement of Datacenter for Internet Services
1. EEDC
34330
Execution Intelligent Placement of
Environments for Datacenter for Internet
Distributed Services
Computing
Master in Computer Architecture,
Networks and Systems - CANS
Homework number: 6
by
Arinto Murdopo – arinto@gmail.com
2. Problem Statement
where? dónde? di mana? oú? waar?
Data Center
dove? どこですか? πού? 在哪里?어디?
Response time, availability,
cost, environmental concerns
2
3. Proposed Solution
Framework
Produce tool to compare
Solve optimization efficiency and accuracy
Problem
Characterization
3
4. Framework
Efficiently select data center locations
Response Time
Minimize Cost
Consistency
Availability
4
5. Solve Optimization Problem
Problem formulation
Approaches:
• Simple Linear Programming (LP0)
• Pre-set Linear Programming (LP1)
• Brute force (Brute)
• Heuristic-based on LP (Heuristic)
• Simulated Annealing plus LP1 (SA+LP1)
• Optimzed SA + LP1 (OSA + LP1)
5
6. Placement Tool
Available Inputs:
MaxS
1/ratioServerUser
MAXLAT
MAXDELAY
MINAVAIL
area of interest
Granularity
existing data center
6
7. Placement Tool
Location-dependent data:
Network backbones: latency data from backbone ISP
Power plants, transmission lines, and CO2 emissions:
obtained from DOE
Electricity, land, water and temperature: obtained from DOE
as well
Missing data are obtained from neighboring location
7
8. Placement Tool
Datacenter characteristics:
Cooling : CRACs and Water Chillers for cooling
Connection: It costs $500k/mile of transmission
line, and $480k/mile of fiber. Amortization of 12
years
Building: Its costs depends of the maximum
power
Land: 6 K square feet per Megawatt
8
9. Placement Tool
Datacenter characteristics:
Water: 24K gallons of water per MW per day
Server: Each server costs $2000 (4 years
amortization), each interconnect switch costs
$20K (4 years amortization)
Staff: $0.05 per Watt per month. $100K per year
salary for 1K servers
9
14. Sample Output
Specifications: Results
1. 60 K servers Three locations :
2. Latency <= 60 ms 1. Seattle(A, 1789 servers)
3. Consistency Delay <= 85 ms 2. St. Louis (B, 22712 servers)
4. Minimum Availability = 5 nines 3. Oklahoma city(C, 5501 servers)
14
15. Evaluation of Chosen Approach
Based on this specification:
1. 60 K servers
2. Latency <= 60 ms
3. Consistency Delay <= 85 ms
4. Minimum Availability = 5 nines
15
21. Exploring Placement Tradeoff
Availability
It is usually cheaper to build networks out of less redundant datacenters
Tier II data centers are the best option
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23. Exploring Placement Tradeoff
Green datacenters
Green network is less than $100k more expensive per
month than the cost-optimal network when the maximum
latency can be relatively high (> 70ms)
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25. Conclusions
• Proposed and implemented optimization
framework for automatic data center
placement for Internet Services
• Characterized US regions
• Evaluated solutions based on the framework
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