Abstract Scheduling is essentially a decision-making process that enables resource sharing among a number of activities by determining their execution order on the set of available resources. The emergence of distributed systems brought new challenges on scheduling in computer systems, including clusters, grids, and more recently clouds. On the other hand, the plethora of research makes it hard for both newcomers researchers to understand the relationship among different scheduling problems and strategies proposed in the literature, which hampers the identification of new and relevant research avenues. In this paper we introduce a classification of the scheduling problem in distributed systems by presenting a taxonomy that incorporates recent developments, especially those in cloud computing. We review the scheduling literature to corroborate the taxonomy and analyze the interest in different branches of the proposed taxonomy. Finally, we identify relevant future directions in scheduling for distributed systems.
We highlight two conclusions achieved by the authors in @cite_75 which are touched by contributions given by our survey: (i) grid workflow scheduling problem is still not fully addressed by existing work''; and (ii) there are almost no workflow scheduling approaches which are based on an adaptive cost model for criteria''. As a contribution to (i), in this survey we expand the general distributed system scheduling to comprise it. As a contribution to (ii), we include scheduling taxonomies for utility grids and cloud computing environments.
@cite_75
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The workflow scheduling problem which is considered difficult on the Grid becomes even more challenging when multiple scheduling criteria are used for optimization. The existing approaches can address only certain variants of the multi-criteria workflow scheduling problem, usually considering up to two contradicting criteria being scheduled in some specific Grid environments. A comprehensive description of the problem can be an important step towards more general scheduling approaches. Based on the related work and on our own experience, we propose several novel taxonomies of the multi-criteria workflow scheduling problem, considering five facets which may have a major impact on the selection of an appropriate scheduling strategy: scheduling process, scheduling criteria, resource model, task model, and workflow model. We analyze different existing workflow scheduling approaches for the Grid, and classify them according to the proposed taxonomies, identifying the most common use cases and the areas which have not been sufficiently explored yet. Scheduling workflows in cloud service environment has attracted great enthusiasm, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the scheduling environment. Ignoring these uncertain factors often leads to the violation of workflow deadlines and increases service renting costs of executing workflows. This study devotes to improving the performance for cloud service platforms by minimizing uncertainty propagation in scheduling workflow applications that have both uncertain task execution time and data transfer time. To be specific, a novel scheduling architecture is designed to control the count of workflow tasks directly waiting on each service instance (e.g., virtual machine). Based on this architecture, we develop an unceRtainty-aware Online Scheduling Algorithm (ROSA) to schedule dynamic and multiple workflows with deadlines. The proposed ROSA skillfully integrates both the proactive and reactive strategies. Then, on the basis of real-world workflow traces, five groups of simulation experiments are carried out to compare ROSA with five typical algorithms. The comparison results reveal that ROSA performs better than the five compared algorithms with respect to costs (up to 56 ), deviation (up to 70 ), resource utilization (up to 37 ) and fairness (up to 37). We consider a market-based resource allocation model for batch jobs in cloud computing clusters. In our model, we incorporate the importance of the due date of a job rather than the number of servers allocated to it at any given time. Each batch job is characterized by the work volume of total computing units (e.g., CPU hours) along with a bound on maximum degree of parallelism. Users specify, along with these job characteristics, their desired due date and a value for finishing the job by its deadline. Given this specification, the primary goal is to determine the scheduling of cloud computing instances under capacity constraints in order to maximize the social welfare (i.e., sum of values gained by allocated users). Our main result is a new ( C (C-k) ⋅ s (s-1))-approximation algorithm for this objective, where C denotes cloud capacity, k is the maximal bound on parallelized execution (in practical settings, k l C) and s is the slackness on the job completion time i.e., the minimal ratio between a specified deadline and the earliest finish time of a job. Our algorithm is based on utilizing dual fitting arguments over a strengthened linear program to the problem. Based on the new approximation algorithm, we construct truthful allocation and pricing mechanisms, in which reporting the job true value and properties (deadline, work volume and the parallelism bound) is a dominant strategy for all users. To that end, we provide a general framework for transforming allocation algorithms into truthful mechanisms in domains of single-value and multi-properties. We then show that the basic mechanism can be extended under proper Bayesian assumptions to the objective of maximizing revenues, which is important for public clouds. We empirically evaluate the benefits of our approach through simulations on data-center job traces, and show that the revenues obtained under our mechanism are comparable with an ideal fixed-price mechanism, which sets an on-demand price using oracle knowledge of users' valuations. Finally, we discuss how our model can be extended to accommodate uncertainties in job work volumes, which is a practical challenge in cloud settings. One of the main challenges in Grid systems is designing an adaptive, scalable, and model-independent method for job scheduling to achieve a desirable degree of load balancing and system efficiency. Centralized job scheduling methods have some drawbacks, such as single point of failure and lack of scalability. Moreover, decentralized methods require a coordination mechanism with limited communications. In this paper, we propose a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS is a model free approach that uses the information of jobs and their completion time to estimate the efficiency of resources. In this method, there are a learner agent and several scheduler agents that perform the task of learning and job scheduling with the use of a coordination strategy that maintains the communication cost at a limited level. We evaluated the efficiency of the CLDS method by designing and performing a set of experiments on a simulated Grid system under different system scales and loads. The results show that the CLDS can effectively balance the load of system even in large scale and heavy loaded Grids, while maintains its adaptive performance and scalability. Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. In grid environments applications require dynamic scheduling for optimized assignment of tasks on available resources, so the optimization represents a key solution for scheduling. This paper presents an evaluation of multi-objective decentralized scheduling models for the problem of task allocation. It also presents a survey of existing optimization solutions for grid scheduling. The surveyed scheduling solutions are: random and best of n random, exhaustive search, simulated annealing, game theory, ad-hoc greedy scheduler, and genetic algorithm for decentralized scheduling. We carry out our experiments with various scheduling scenarios and with heterogeneous input tasks and computation resources. We also present the methods to evaluate and validate the described scheduling methods. We present several experimental results that offer a support for near-optimal algorithm selection. For the problem of online real-time scheduling of jobs on a single processor, previous work presents matching upper and lower bounds on the competitive ratio that can be achieved by a deterministic algorithm. However, these results only apply to the non-strategic setting in which the jobs are released directly to the algorithm. Motivated by emerging areas such as grid computing, we instead consider this problem in an economic setting, in which each job is released to a separate, self-interested agent. The agent can then delay releasing the job to the algorithm, inflate its length, and declare an arbitrary value and deadline for the job, while the center determines not only the schedule, but the payment of each agent. For the resulting mechanism design problem (in which we also slightly strengthen an assumption from the non-strategic setting), we present a mechanism that addresses each incentive issue, while only increasing the competitive ratio by one. We then show a matching lower bound for deterministic mechanisms that never pay the agents. To program in distributed computing environments such as grids and clouds, workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. With the development of cloud technology and extensive deployment of cloud platform, the problem of workflow scheduling in cloud becomes an important research topic. The challenges of the problem lie in: NP-hard nature of task-resource mapping; diverse QoS requirements; on-demand resource provisioning; performance fluctuation and failure handling; hybrid resource scheduling; data storage and transmission optimization. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this paper, we firstly conduct taxonomy and comparative review on workflow scheduling algorithms. Then, we make a comprehensive survey of workflow scheduling in cloud environment in a problem---solution manner. Based on the analysis, we also highlight some research directions for future investigation. In heterogeneous distributed environment, it is a great challenge to schedule multiple workflows submitted at different times. Particularly, scheduling of concurrent workflows with deadline and budget constraints makes the problem become more complex. Recent studies have proposed dynamic scheduling strategies for concurrent workflows which have limitations in inconsistent environments. Therefore, this paper presents a new dynamic scheduling algorithm for concurrent workflows. This algorithm proposes a uniform ranking that considers the time and costs for both workflows and workgroups to assign priorities for tasks. In the resource selection phase, it controls the resource selection range for each task based on an optimistic budget for the current task and selects resources for the current task according to a defined bi-factor. The experimental results show that our algorithm outperforms the existing algorithms in both consistent and inconsistent environments. Execution of workflow applications in Cloud environments involves many uncertainties because of elastic resource provisioning and unstable performance of multitenant virtual machines (VM) instances over time. These uncertainties are usually either neglected by existing researches, or modeled with some probability distribution function. To address this gap, we extend a multi-objective workflow scheduling algorithm (MOHEFT) in two directions: (1) to deal with the dynamic nature of Cloud environments offering a potentially infinite amount of on-demand resources, and (2) to consider robustness as an objective that mitigates the variability in VM performance over time. Our new robust model, called R-MOHEFT, considers uncertainty in processing times of workflow activities without a precise estimation or known distribution function within an uncertainty interval. We approach this scheduling problem as a three-objective optimisation that considers makespan, monetary cost, and robustness as simultaneous objectives of a commercial Cloud environment. Our new algorithm is able to estimate the Pareto optimal set of scheduling solutions that resist against fluctuations in processing times three times better than its MOHEFT predecessor, with a tradeoff of only 15 worse Pareto frontier. R-MOHEFT's hypervolume suffers by only 5 to 16 , compared to the MOHEFT's drawback of 38 to surprisingly 87 , when the processing time fluctuates up to its double value. There is an increasing need for cloud service performance that can be tailored to customer requirements. In the context of jobs submitted to cloud computing clusters, a crucial requirement is the specification of job completion-times. A natural way to model this specification, is through client job utility functions that are dependent on job completion-times. We present a method to allocate and schedule heterogeneous resources to jointly optimize the utilities of jobs in a cloud. Specifically: (i) we formulate a completion-time optimal resource allocation (CORA) problem to apportion cluster resources across the jobs that enforces max-min fairness among job utilities, and (ii) starting with an integer programming problem, we perform a series of steps to transform it into an equivalent linear programming problem, and (iii) we implement the proposed framework as a utility-aware resource scheduler in the widely used Hadoop data processing framework, and finally (iv) through extensive experiments with real-world datasets, we show that our prototype achieves significant performance improvement over existing resource-allocation policies. The problem of optimally scheduling tasks onto heterogeneous resources in grids, minimizing the makespan of these tasks, has proved to be NP-complete. There is no best scheduling algorithm for all grid computing systems. An alternative is to select an appropriate scheduling algorithm to use in a given grid environment because of the characteristics of the tasks, machines and network connectivity. In this paper a survey is presented on the problem and the different aspects of job scheduling in grids such as (a) fault-tolerance; (b) security; and (c) simulation of grid job scheduling strategies are discussed. This paper also presents a discussion on the future research topics and the challenges of job scheduling in grids. In this paper, we introduce an approach for application-aware resource block scheduling of elastic and inelastic adaptive real-time traffic in fourth generation Long Term Evolution (LTE) systems. The users are assigned to resource blocks. A transmission may use multiple resource blocks scheduled over frequency and time. In our model, we use logarithmic and sigmoidal-like utility functions to represent the users applications running on different user equipments (UE)s. We present an optimal problem with utility proportional fairness policy, where the fairness among users is in utility percentage (i.e user satisfaction with the service) of the corresponding applications. Our objective is to allocate the resources to the users with priority given to the adaptive real-time application users. In addition, a minimum resource allocation for users with elastic and inelastic traffic should be guaranteed. Every user subscribing for the mobile service should have a minimum quality-of-service (QoS) with a priority criterion. We prove that our scheduling policy exists and achieves the maximum. Therefore the optimal solution is tractable. We present a centralized scheduling algorithm to allocate evolved NodeB (eNodeB) resources optimally with a priority criterion. Finally, we present simulation results for the performance of our scheduling algorithm and compare our results with conventional proportional fairness approaches. The results show that the user satisfaction is higher with our proposed method. Parallel computing on volatile distributed resources requires schedulers that consider job and resource characteristics. We study unconventional computing environments containing devices spread throughout a single large organization. The devices are not necessarily typical general purpose machines; instead, they could be processors dedicated to special purpose tasks (for example printing and document processing), but capable of being leveraged for distributed computations. Harvesting their idle cycles can simultaneously help resources cooperate to perform their primary task and enable additional functionality and services. A new burstiness metric characterizes the volatility of the high-priority native tasks. A burstiness-aware scheduling heuristic opportunistically introduces grid jobs (a lower priority workload class) to avoid the higher-priority native applications, and effectively harvests idle cycles. Simulations based on real workload traces indicate that this approach improves makespan by an average of 18.3 over random scheduling, and comes within 7.6 of the theoretical upper bound. Abstract As large-scale distributed systems gain momentum, the scheduling of workflow applications with multiple requirements in such computing platforms has become a crucial area of research. In this paper, we investigate the workflow scheduling problem in large-scale distributed systems, from the Quality of Service (QoS) and data locality perspectives. We present a scheduling approach, considering two models of synchronization for the tasks in a workflow application: (a) communication through the network and (b) communication through temporary files. Specifically, we investigate via simulation the performance of a heterogeneous distributed system, where multiple soft real-time workflow applications arrive dynamically. The applications are scheduled under various tardiness bounds, taking into account the communication cost in the first case study and the I O cost and data locality in the second. The simulation results provide useful insights into the impact of tardiness bound and data locality on the system performance. We study the problem of non-preemptive scheduling to minimize energy consumption for devices that allow dynamic voltage scaling. Specifically, consider a device that can process jobs in a non-preemptive manner. The input consists of (i) the set R of available speeds of the device, (ii) a set J of jobs, and (iii) a precedence constraint Π among J. Each job j in J, defined by its arrival time aj, deadline dj, and amount of computation cj, is supposed to be processed by the device at a speed in R. Under the assumption that a higher speed means higher energy consumption, the power-saving scheduling problem is to compute a feasible schedule with speed assignment for the jobs in J such that the required energy consumption is minimized. This paper focuses on the setting of weakly dynamic voltage scaling, i.e., speed change is not allowed in the middle of processing a job. To demonstrate that this restriction on many portable power-aware devices introduces hardness to the power-saving scheduling problem, we prove that the problem is NP-hard even if aj = aj ′ and dj = dj ′ hold for all j,j ′∈ Jand |R|=2. If |R|<∞, we also give fully polynomial-time approximation schemes for two cases of the general NP-hard problem: (a) all jobs share a common arrival time, and (b) Π = ∅ and for any j,j ′ ∈ J, aj ≤ aj ′ implies dj ≤ dj ′. To the best of our knowledge, there is no previously known approximation algorithm for any special case of the NP-hard problem. Motivated by distributed schedulers that combine the power-of-d-choices with late binding and systems that use replication with cancellation-on-start, we study the performance of the LL(d) policy which assigns a job to a server that currently has the least workload among d randomly selected servers in large-scale homogeneous clusters. We consider general job size distributions and propose a partial integro-differential equation to describe the evolution of the system. This equation relies on the earlier proven ansatz for LL(d) which asserts that the workload distribution of any finite set of queues becomes independent of one another as the number of servers tends to infinity. Based on this equation we propose a fixed point iteration for the limiting workload distribution and study its convergence. Datacenter-scale computing for analytics workloads is increasingly common. High operational costs force heterogeneous applications to share cluster resources for achieving economy of scale. Scheduling such large and diverse workloads is inherently hard, and existing approaches tackle this in two alternative ways: 1) centralized solutions offer strict, secure enforcement of scheduling invariants (e.g., fairness, capacity) for heterogeneous applications, 2) distributed solutions offer scalable, efficient scheduling for homogeneous applications. We argue that these solutions are complementary, and advocate a blended approach. Concretely, we propose Mercury, a hybrid resource management framework that supports the full spectrum of scheduling, from centralized to distributed. Mercury exposes a programmatic interface that allows applications to trade-off between scheduling overhead and execution guarantees. Our framework harnesses this flexibility by opportunistically utilizing resources to improve task throughput. Experimental results on production-derived workloads show gains of over 35 in task throughput. These benefits can be translated by appropriate application and framework policies into job throughput or job latency improvements. We have implemented and contributed Mercury as an extension of Apache Hadoop YARN. This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles. To better understand the challenges in developing effective cloud-based resource schedulers, we analyze the first publicly available trace data from a sizable multi-purpose cluster. The most notable workload characteristic is heterogeneity: in resource types (e.g., cores:RAM per machine) and their usage (e.g., duration and resources needed). Such heterogeneity reduces the effectiveness of traditional slot- and core-based scheduling. Furthermore, some tasks are constrained as to the kind of machine types they can use, increasing the complexity of resource assignment and complicating task migration. The workload is also highly dynamic, varying over time and most workload features, and is driven by many short jobs that demand quick scheduling decisions. While few simplifying assumptions apply, we find that many longer-running jobs have relatively stable resource utilizations, which can help adaptive resource schedulers.