After the advent of the Internet of Things and 5G networks, edge computing became the center of attraction. The tasks demanding high computation are generally offloaded to the cloud since the edge is resource-limited. The Edge Cloud is a promising platform where the devices can offload delay-sensitive workloads. In this regard, scheduling holds great importance in offloading decisions in the Edge Cloud collaboration. The ultimate objectives of scheduling are the quality of experience, minimizing latency, and increasing performance. An abundance of efforts on scheduling has been done in the past. In this paper, we have surveyed proposed scheduling strategies in the context of edge cloud computing in various aspects such as advantages and demerits, QoS parameters, and fault tolerance. We have also surveyed such scheduling approaches to evaluate which one is feasible under what circumstances. We first classify all the algorithms into heuristic algorithms and meta-heuristics, and we subcategorize algorithms in each class further based on extracted attributes of algorithms. We hope that this survey will be very thoughtful in the development of new scheduling techniques. Issues, challenges, and future directions have also been examined.
Cloud computing has been developed as one of the prominent paradigm for providing on demand resources to the end user based on signed service level agreement and pay as use model. Cloud computing provides resources using multitenant architecture where infrastructure is generated from multiple or single geographical distributed cloud datacenters. Scheduling of cloud application requests to cloud infrastructure is one of the main research area in cloud computing. Researchers have developed many scheduling applications for which they have used different simulators available in the market such as …
Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self service provisioning, elasticity and pay per use are the key features of Cloud Computing. It provides different types of resources over the Internet to perform user submitted tasks. In cloud environment, huge number of tasks are executed simultaneously, an effective Task Scheduling is required to gain better performance of the cloud system. Various Cloud Based Task Scheduling algorithms are available that schedul…
Edge computing is an emerging technology which places computing at the edge of the network to provide an ultra-low latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the power of edge computing by offloading computation to cloudlets in edge-clouds. However, the task scheduling of computation offloading in edge-clouds faces a two-fold challenge. First, as cloudlets are geographically distributed, it is difficult for each cloudlet to perform load balancing without centralized control. Second, as tasks of computation offloading h…
Companies are rushing to deliver their services and solutions through the cloud. The scheduling process is very critical in reducing delays. Scheduling also has a role in accessing resources without excessive waiting time. All this in context of modern advances in infrastructure and the emergence of Blockchain-as-a-service. What if integration is done between a hybrid scheduling algorithm and blockchain technology via the cloud. This integration aims to enhance and provide the service uninterruptedly. This method is distinguished, compared to other scheduling algorithms such as shortest-job-fi…
Cloud computing is an emerging technology in distributed computing which facilitates pay per model as per user demand and requirement.Cloud consist of a collection of virtual machine which includes both computational and storage facility. The primary aim of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud is developing day by day and faces many challenges, one of them is scheduling. Scheduling refers to a set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its scheduling strategy acco…
Cloud computing is a newly emerging distributed system which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non-cooperative game model for reliability-based task scheduling approach. This model takes the steady…
In multi-cloud environment, task scheduling has attracted a lot of attention due to NP-Complete nature of the problem. Moreover, it is very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. In this paper, we propose a genetic algorithm (GA) based approach for solving task scheduling problem. The algorithm is described with innovative idea of fitness function derivation and mutation. The proposed algorithm is exposed to rigorous testing using various benchmark datase…
Cloud Computing is emerging as a new computational paradigm shift. Hadoop-MapReduce has become a powerful Computation Model for processing large data on distributed commodity hardware clusters such as Clouds. In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority based schedulers also. In this paper we study various scheduler improvements possible with Hadoop and also provided some guidelines on how to improve the scheduling in Hadoop in Cloud Environments.
In recent years with the advent of high bandwidth internet access availability, the cloud computing applications have boomed. With more and more applications being run over the cloud and an increase in the overall user base of the different cloud platforms, the need for highly efficient job scheduling techniques has also increased. The task of a conventional job scheduling algorithm is to determine a sequence of execution for the jobs, which uses the least resources like time, processing, memory, etc. Generally, the user requires more services and very high efficiency. An efficient scheduling …
This chapter presents software architectures of the big data processing platforms. It will provide an in-depth knowledge on resource management techniques involved while deploying big data processing systems on cloud environment. It starts from the very basics and gradually introduce the core components of resource management which we have divided in multiple layers. It covers the state-of-art practices and researches done in SLA-based resource management with a specific focus on the job scheduling mechanisms.
With the increasing and elastic demand for cloud resources, finding an optimal task scheduling mechanism become a challenge for cloud service providers. Due to the time-varying nature of resource demands in length and processing over time and dynamics and heterogeneity of cloud resources, existing myopic task scheduling solutions intended to maximize the performance of task scheduling are inefficient and sacrifice the long-time system performance in terms of resource utilization and response time. In this paper, we propose an optimal solution for performing foresighted task scheduling in a clo…
Infrastructure-as-a-Service (IaaS) clouds have become more popular enabling users to run applications under virtual machines. Energy efficiency for IaaS clouds is still challenge. This paper investigates the energy-efficient scheduling problems of virtual machines (VMs) onto physical machines (PMs) in IaaS clouds along characteristics: multiple resources, fixed intervals and non-preemption of virtual machines. The scheduling problems are NP-hard. Most of existing works on VM placement reduce the total energy consumption by using the minimum number of active physical machines. There, however, a…
Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem. A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is propotional to the execution cost billed to customers. In this paper, we proposed a League Championship Algorithm (LCA) based makespan time minimization scheduling technique in IaaS cloud. The LCA is a sports-inspired population based algorithmic framework for global optimization over a continuous search space. Three other existing algorithms that is, First Come Firs…
In cloud environments, load balancing task scheduling is an important issue that directly affects resource utilization. Unquestionably, load balancing scheduling is a serious aspect that must be considered in the cloud research field due to the significant impact on both the back end and front end. Whenever an effective load balance has been achieved in the cloud, then good resource utilization will also be achieved. An effective load balance means distributing the submitted workload over cloud VMs in a balanced way, leading to high resource utilization and high user satisfaction. In this pape…
Reliability, security and stability of cloud services without sacrificing too much resources have become a desired feature in the area of workload management in clouds. The paper proposes and evaluates a lightweight framework for scheduling a workload which part could be unreliable. This unreliability could be caused by various types of failures or attacks. Our framework for robust workload scheduling efficiently combines classic fault-tolerant and security tools, such as packet/job scanning, with workload scheduling, and it does not use any heavy resource-consuming tools, e.g., cryptography o…
Cloud computing revolutionised the industry with its elastic, on-demand approach to computational resources, but has lead to a tremendous impact on the environment. Data centers constitute 1.1-1.5% of total electricity usage in the world. Taking a more informed view of the electrical grid by analysing real-time electricity prices, we set the foundations of a grid-conscious cloud. We propose a scheduling algorithm that predicts electricity price peaks and throttles energy consumption by pausing virtual machines. We evaluate the approach on the OpenStack cloud manager through an empirical approa…
Workflow decision making is critical to performing many practical workflow applications. Scheduling in edge-cloud environments can address the high complexity problem of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, because of the heterogeneous resources in edge-cloud environments and the complicated data dependencies among the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. The existing studies have …
In this paper, thinking over characteristics of ant colony optimization Algorithm, taking into account the characteristics of cloud computing, combined with clonal selection algorithm (CSA) global optimum advantage of the convergence of the clonal selection algorithm (CSA) into every ACO iteration, speeding up the convergence rate, and the introduction of reverse mutation strategy, ant colony optimization algorithm avoids local optimum. Depth study of the cloud environment ant colony clonal selection algorithm resource scheduling policy, clonal selection algorithm converges to solve optimizati…
Scheduling Bag-of-Tasks (BoT) applications on the cloud can be more challenging than grid and cluster environ- ments. This is because a user may have a budgetary constraint or a deadline for executing the BoT application in order to keep the overall execution costs low. The research in this paper is motivated to investigate task scheduling on the cloud, given two hard constraints based on a user-defined budget and a deadline. A heuristic algorithm is proposed and implemented to satisfy the hard constraints for executing the BoT application in a cost effective manner. The proposed algorithm is …
Scheduling of service requests in Cloud computing has traditionally focused on the reduction of pre-service wait, generally termed as waiting time. Under certain conditions such as peak load, however, it is not always possible to give reasonable response times to all users. This work explores the fact that different users may have their own levels of tolerance or patience with response delays. We introduce scheduling strategies that produce better assignment plans by prioritising requests from users who expect to receive the results earlier and by postponing servicing jobs from those who are m…
Cloud computing has attracted both end-users and Cloud Service Providers (CSPs) in recent years. Improving resource utilization rate (RUtR), such as CPU and memory usages on servers, while maintaining Quality-of-Service (QoS) is one key challenge faced by CSPs with warehouse-scale data centers. Prior works proposed various algorithms to reduce energy cost or to improve RUtR, which either lack the fine-grained task scheduling capabilities, or fail to take a comprehensive system model into consideration. This article presents H2O-Cloud, a Hierarchical and Hybrid Online task scheduling framework …
In this paper, we study the market-oriented online bi-objective service scheduling problem for pleasingly parallel jobs with variable resources in cloud environments, from the perspective of SaaS (Software-as-as-Service) providers who provide job-execution services. The main process of scheduling SaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS providers to schedule end users' jobs and charges users accordingly. This problem has several particular features, such as the job-oriented end users, the pleasingly parallel jobs with soft deadline constraints, the online…
Fast, reliable, and efficient data transmission across wide-area networks is a predominant bottleneck for data-intensive cloud applications. This paper introduces OneDataShare, which is designed to eliminate the issues plaguing effective cloud-based data transfers of varying file sizes and across incompatible transfer end-points. The vision of OneDataShare is to achieve high-speed data communication, interoperability between multiple transfer protocols, and accurate estimation of delivery time for advance planning, thereby maximizing user-profit through improved and faster data analysis for bu…
The utilization of cloud environments to deploy scientific workflow applications is an emerging trend in scientific community. In this area, the main issue is the scheduling of workflows, which is known as an NP-complete problem. Apart from respecting user-defined deadline and budget, energy consumption is a major concern for cloud providers in implementing the scheduling strategy. The types and the number of virtual machines (VMs) used are determinant to handle those issues, and their determination is highly influenced by the structure of the workflow. In this paper, we propose two workflow s…
Cloud computing has grown to become a popular distributed computing service offered by commercial providers. More recently, Edge and Fog computing resources have emerged on the wide-area network as part of Internet of Things (IoT) deployments. These three resource abstraction layers are complementary, and provide distinctive benefits. Scheduling applications on clouds has been an active area of research, with workflow and dataflow models serving as a flexible abstraction to specify applications for execution. However, the application programming and scheduling models for edge and fog are still…
The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. They need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy c…
Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clouds rely on the tenants to provide the resource specification for their database workloads. However, they tend to over-estimate the resource requirement of their databases, resulting in the unnecessarily high cost and low Cloud utilization. A methodology that automatically suggests the "just-enough" resource specification that fulfills the performance requirement of every database workload is profitable. To this end,…
Scientific workflows have been predominantly used for complex and large scale data analysis and scientific computation/automation and the need for robust workflow scheduling techniques has grown considerably. But, most of the existing workflow scheduling algorithms do not provide the required reliability and robustness. In this paper, a new fault tolerant workflow scheduling algorithm that learns replication heuristics in an unsupervised manner has been proposed. Furthermore, the use of light weight synchronized checkpointing enables efficient resubmission of failed tasks and ensures workflow …
In a hybrid cloud, individual cloud service providers (CSPs) often have incentive to use each other's resources to off-load peak loads or place load closer to the end user. However, CSPs have to keep track of contributions and gains in order to disincentivize long-term free-riding. We show CloudShare, a distributed version of a load balancing algorithm DirectCloud based on the Shapley value---a powerful fairness concept from game theory. CloudShare coordinates CSPs by a ZooKeeper-based coordination layer; each CSP runs a broker that interacts with local resources (such as Kubernetes-managed cl…
The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the qual…
The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource i…
Cloud-based computing systems could get oversubscribed due to budget constraints of cloud users which causes violation of Quality of Experience(QoE) metrics such as tasks' deadlines. We investigate an approach to achieve robustness against uncertain task arrival and oversubscription through smart reuse of computation while similar tasks are waiting for execution. Our motivation in this study is a cloud-based video streaming engine that processes video streaming tasks in an on-demand manner. We propose a mechanism to identify various types of "mergeable" tasks and determine when it is appropria…
We propose throughput and cost optimal job scheduling algorithms in cloud computing platforms offering Infrastructure as a Service. We first consider online migration and propose job scheduling algorithms to minimize job migration and server running costs. We consider algorithms that assume knowledge of job-size on arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. Specifically this algorithm yields a trade-off between delay and costs. We then relax the job-s…
Infrastructure as a Service model of cloud computing is a desirable platform for the execution of cost and deadline constrained workflow applications as the elasticity of cloud computing allows large-scale complex scientific workflow applications to scale dynamically according to their deadline requirements. However, scheduling of these multitask workflow jobs in a distributed computing environment is a computationally hard multi-objective combinatorial optimization problem. The critical challenge is to schedule the workflow tasks whilst meeting user quality of service (QoS) requirements and t…
Cloud computing is a new archetype that provides dynamic computing services to cloud users through the support of datacenters that employs the services of datacenter brokers which discover resources and assign them Virtually. The focus of this research is to efficiently optimize resource allocation in the cloud by exploiting the Max-Min scheduling algorithm and enhancing it to increase efficiency in terms of completion time (makespan). This is key to enhancing the performance of cloud scheduling and narrowing the performance gap between cloud service providers and cloud resources consumers/use…
At present there are a number of barriers to creating an energy efficient workload scheduler for a Private Cloud based data center. Firstly, the relationship between different workloads and power consumption must be investigated. Secondly, current hardware-based solutions to providing energy usage statistics are unsuitable in warehouse scale data centers where low cost and scalability are desirable properties. In this paper we discuss the effect of different workloads on server power consumption in a Private Cloud platform. We display a noticeable difference in energy consumption when servers …
Cloud computing environments often have to deal with random-arrival computational workloads that vary in resource requirements and demand high Quality of Service (QoS) obligations. It is typical that a Service-Level-Agreement (SLA) is employed to govern the QoS obligations of the cloud computing service provider to the client. A typical challenge service-providers face every day is maintaining a balance between the limited resources available for computing and the high QoS requirements of varying random demands. Any imbalance in managing these conflicting objectives may result in either dissat…
League Championship Algorithm (LCA) is a sports-inspired population based algorithmic framework for global optimization over a continuous search space first proposed by Ali Husseinzadeh Kashan in the year 2009. A common characteristic between all population based optimization algorithms similar to the LCA is that, they attemt to move a population of achievable solutions to potential areas of the search space during optimization. In this paper, we proposed a job scheduling algorithm based on the L CA optimization technique for the infrastructure as a service (IaaS) cloud. Three other establishe…
Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic nature of VCs (i.e., limited contact duration among vehicles), caused by vehicles' mobility, poses unique challenges to the execution of computation-intensive applications/tasks with directed acyclic graph (DAG) structure, where each task consists of multiple interdependent components (subtasks). In this paper, we study scheduling of DAG tasks over dynamic VCs, where multiple subtasks of a DAG task are dispersed across v…
iDynamics: A Configurable Emulation Framework for Evaluating Microservice Scheduling Policies under Controllable Cloud-Edge Dynamics
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia（墨尔本大学计算与信息系统学院量子云计算与分布式系统实验室）
