Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required quality of service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an evolutionary multi-objective optimization (EMO)-based algorithm to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform. Novel schemes for problem-specific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.

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Evolutionary Multi-Objective Workflow Scheduling in Cloud

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Task scheduling techniques in cloud computing: A literature survey

The growth of energy consumption has been explosive in current data centers, super computers, and public cloud systems. This explosion has led to greater advocacy of green computing, and many efforts and works focus on the task scheduling in order to reduce energy dissipation. In order to obtain more energy reduction as well as maintain the quality of service by meeting the deadlines, this paper proposes a DVFS-enabled Energy-efficient Workflow Task Scheduling algorithm: DEWTS. Through merging the relatively inefficient processors by reclaiming the slack time, DEWTS can leverage the useful slack time recurrently after severs are merged. DEWTS firstly calculates the initial scheduling order of all tasks, and obtains the whole makespan and deadline based on Heterogeneous-Earliest-Finish-Time (HEFT) algorithm. Through resorting the processors with their running task number and energy utilization, the underutilized processors can be merged by closing the last node and redistributing the assigned tasks on it. Finally, in the task slacking phase, the tasks can be distributed in the idle slots under a lower voltage and frequency using DVFS technique, without violating the dependency constraints and increasing the slacked makespan. Based on the amount of randomly generated DAGs workflows, the experimental results show that DEWTS can reduce the total power consumption by up to 46.5 % for various parallel applications as well as balance the scheduling performance.

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An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment

Executing a large program using clouds is a promising approach, as this class of programs may be decomposed into multiple sequences of tasks that can be executed on multiple virtual machines (VMs) in a cloud. Such sequences of tasks can be represented as a directed acyclic graph (DAG), where nodes are tasks and edges are precedence constraints between tasks. Cloud users pay for what their programs actually use according to the pricing models of the cloud providers. Early task scheduling algorithms are focused on minimizing makespan, without mechanisms to reduce the monetary cost incurred in the setting of clouds. We present a cost-efficient task-scheduling algorithm using two heuristic strategies.The first strategy dynamically maps tasks to the most cost-efficient VMs based on the concept of Pareto dominance. The second strategy, a complement to the first strategy, reduces the monetary costs of non-critical tasks. We carry out extensive numerical experiments on large DAGs generated at random as well as on real applications. The simulation results show that our algorithm can substantially reduce monetary costs while producing makespan as good as the best known task-scheduling algorithm can provide.

Cost-efficient task scheduling for executing large programs in the cloud

Energy consumption has become a major concern to the widespread deployment of cloud data centers. The growing importance for parallel applications in the cloud introduces significant challenges in reducing the power consumption drawn by the hosted servers. In this paper, we propose an enhanced energy-efficient scheduling (EES) algorithm to reduce energy consumption while meeting the performance-based service level agreement (SLA). Since slacking non-critical jobs can achieve significant power saving, we exploit the slack room and allocate them in a global manner in our schedule. Using random generated and real-life application workflows, our results demonstrate that EES is able to reduce considerable energy consumption while still meeting SLA.

Enhanced Energy-Efficient Scheduling for Parallel Applications in Cloud

In recent years large graph processing has emerged to be a popular application for companies because of the increasing large Web graph and social networks. The ever growing scale of graphs and recent emergence of cloud computing poses challenges to their efficient and cost-conscious scheduling approach for processing tasks. In this paper, we focus on the use of cloud resources for dispatching large graph processing tasks. We design a novel framework EComer that can be easily integrated into existing cloud infrastructure. The key component of this framework is a cost-conscious scheduling heuristic, called CCSH, which is an extension of Heterogeneous Earliest Finish Time (HEFT). Our algorithm CCSH first constructs a priority list of tasks and then assigns the task with the highest priority value to the cost-efficient virtual machine in a cloud setting. The comparison study, based on randomly generated large graphs and a real-life astronomy application model, demonstrates that our algorithm outperforms HEFT by exhibiting significant monetary cost savings at a reasonable increase in overall execution time.

Cost-Conscious Scheduling for Large Graph Processing in the Cloud

Virtualization and cloud computing technologies now make it possible to consolidate multiple online services, which are packed in virtual machines (VMs), into a smaller number of physical servers. However, it is still a challenging scheduling problem for cloud provider to dynamically manage the resource for VMs in order to handle variable workloads without service level agreement (SLA) violation. In this paper, we introduce a Prediction-based Dynamic Resource Scheduling (PDRS) solution to automate elastic resource scaling for virtualized cloud systems. Unlike traditional static consolidation or threshold-driven reactive scheduling, we both consider the dynamic workload fluctuations of each VM and the resource conflict handling problem. PDRS first employs an online resource prediction, which is a VM resource demand state predictor based on the Autoregressive Integrated Moving Average (ARIMA) model, to achieve adaptive resource allocation for cloud applications on each VM. Then we propose our prediction-based dynamic resource scheduling algorithms to dynamically consolidate the VMs with adaptive resource allocation to reduce the number of physical machines. Extensive experimental results show that our scheduling is able to realize automatic elastic resource allocation with acceptable effect on SLAs.

Prediction-based Dynamic Resource Scheduling for Virtualized Cloud Systems

How to achieve energy efficiency to run a cloud data center is a major challenge in the era of rising electricity cost and environmental protection. Various techniques have been devised to help reduce energy consumption for cloud data centers that consist of a large number of identical servers, including dynamic allocation of active servers, consolidating diverse applications to run on them, and adjusting the CPU speed of an active server. Leveraging these techniques, we use an Online Coloring Bin Packing problem to model the consolidation problem and devise an effective application-aware approximation algorithm to find a near-optimal solution. We show a 1.7 asymptotic approximation ratio. We then apply a Predictive Bayesian Network model to identify daily workload patterns and adjust resource provisioning accordingly. We evaluate our approaches using traces collected from a real data center and demonstrate that (1) our prediction algorithm is effective in estimating future demands, (2) our coordinated approaches can provide significant savings of energy and operational costs close to the near-optimal offline solution, and (3) our approaches incur little reliability costs in term of wear-and-tear of server components.

Qingjia Huang

Papers

Cost-efficient task scheduling for executing large programs in the cloud

Enhanced Energy-Efficient Scheduling for Parallel Applications in Cloud

Cost-Conscious Scheduling for Large Graph Processing in the Cloud

Prediction-based Dynamic Resource Scheduling for Virtualized Cloud Systems

Reducing Operational Costs through Consolidation with Resource Prediction in the Cloud