Modified rate-monotonic algorithm for scheduling periodic jobs withdeferred deadlines

The deadline of a request is the time instant at which its execution must complete. The deadline of the request in any period of a job with deferred deadline is some time instant after the end of the period. The authors describe a semi-static priority-driven algorithm for scheduling periodic jobs with deferred deadlines: each job is assigned two priorities, the higher one for old requests and the lower one for the current request. This algorithm is called the modified rate-monotonic algorithm and is based on the well-known rate-monotonic algorithm. It is shown that the modified rate-monotonic algorithm is optimal when the deadline of every job is deferred by max (1, γ-1) periods or more, where γ is the ratio between the longest period and the shortest period. When the deadline of each job is deferred by one period of the job, any set of n independent jobs whose total utilization is equal to or less than [1+n(2¹n/-1)]/2 can be feasibly scheduled by this algorithm. This bound approaches 0.845 when n approaches infinity     

基于期限的多请求数据检索问题研究

给定一个包含多条信道的集合以及一个包含多个请求的集合,其中每一个请求包含多个请求数据项并且希望在一定期限内下载到,基于期限的多请求数据检索问题指当客户配有多条天线时寻找一个在期限内下载多个请求的数据检索序列,使得所有天线的最大访问延迟最小化。大多数现有数据检索方法关注于单个请求或者单条天线,很少研究当客户配有多条天线时多请求的数据检索问题,尤其是每一个请求的检索有时间约束。基于此,本文提出一种多请求的数据检索算法,以调度合适地天线检索这些请求并找到关于这些请求的检索序列,从而平衡在各天线上的访问延迟。针对单请求的数据检索,本文采用最大团思想寻找下载该请求中所有请求数据项的访问模式,使得检索该请求的访问延迟以及期限丢失率最小化。     

Scheduling with slack time

Summary We consider a scheduling problem concerning a set of jobs {J ₁, J ₂,..., J _n } in which the job J _i requests C _i units of computation time every T _i units of time periodically. These jobs are to be executed by a timeshared single-processor computing system. It is assumed that the requests for each job arrive at the system at the beginning of the request periods and that deadline for completion of the requested computation in each period coincides with the beginning of the next period. For a set of jobs that is schedulable by a certain algorithm, the time span between the completion of a request and its deadline is referred to as the slack time of the request. It is shown here that when the set of jobs is scheduled according to the rate monotonic algorithm, the slack time of the first request of any job is no larger than the slack time of any subsequent request of that job. This result enables us to determine lower bounds to slack times of all requests.This work was partially supported by the Office of Naval Research under the Contract Number N00014-79-C-0775 and by NSF-MCS-8107647     

Scheduling on-demand data broadcast in mixed-type request environments

In many on-demand broadcast environments, there can be a mix of requests, in which some of the requests have real-time constraints, while other requests have no time constraints associated with them. We refer to such environments as mixed-type environments. Existing strategies for on-demand data in broadcast systems typically only consider how to minimize the wait time of the requests, while scheduling strategies for real-time requests typically only consider how to minimize the number of deadlines missed. How to satisfy both of these constraints is a challenging problem whose solution can benefit many applications. In this paper, we present an on-demand broadcast cost model that is more general than existing broadcast cost models because it considers both response time and number of deadlines missed. An analysis of the system is presented as a Markov decision process to ascertain the feasibility of an optimal policy. We propose two scheduling strategies for mixed-type broadcast systems that are based on our cost model: maximum paid cost first and maximum value gained first. The simulation results show that both of our strategies always achieve the best result, when compared to existing broadcast strategies, for varying request arrival rates, real-time to non-real-time request ratios, missed deadline weight values, wait-time bounds on the non-real-time requests, and varying broadcast rates.     

Dynamic Scheduling of Multimedia Documents in a Single Server Multiple Clients Environment

In a typical single server and multiple client distributed multimedia system, clients may send sporadic requests to the server for certain multimedia documents. The requests must be served with a fast response time and with the required quality of service guarantee. This requires the server to determine the transmission schedule of each multimedia stream while ensuring necessary inter- and intrastream synchronizations. There are two major drawbacks in the existing scheduling algorithms. First, it is assumed that all channels are available at the beginning of the scheduling, but in reality, requests arrive when others are in service; second, the cost of the scheduling itself is usually ignored. In general a feasible scheduling algorithm should have the following features: (1) the schedule must be generated in real time, (2) it should have small scheduling cost, and (3) it must be capable of handling multiple requests from multiple clients. In this paper, we propose two dynamic scheduling algorithms whose worst time complexity isO(n log nm+nm), wherenis the total number of data units in a retrieved multimedia document andmdenotes the number of available channels. The salient feature of the proposed algorithms is their inherent dynamic nature which can adjust the scheduling times for each individual request according to the slack time between consecutive requests. If the slack time between two requests is large, the scheduler can run longer in an attempt to find a better solution. This reduces the response time while maintaining a good quality of presentation. Through both simulation and analysis, we evaluate our algorithms and demonstrate their applicability in a realistic environment.     

Deadline division-based heuristic for cost optimization in workflow scheduling

Cost optimization for workflow applications described by Directed Acyclic Graph (DAG) with deadline constraints is a fundamental and intractable problem on Grids. In this paper, an effective and efficient heuristic called DET (Deadline Early Tree) is proposed. An early feasible schedule for a workflow application is defined as an Early Tree. According to the Early Tree, all tasks are grouped and the Critical Path is given. For critical activities, the optimal cost solution under the deadline constraint can be obtained by a dynamic programming strategy, and the whole deadline is segmented into time windows according to the slack time float. For non-critical activities, an iterative procedure is proposed to maximize time windows while maintaining the precedence constraints among activities. In terms of the time window allocations, a local optimization method is developed to minimize execution costs. The two local cost optimization methods can lead to a global near-optimal solution. Experimental results show that DET outperforms two other recent leveling algorithms. Moreover, the deadline division strategy adopted by DET can be applied to all feasible deadlines.     

Network caching strategies for a shared data distribution for a predefined service demand sequence

In this paper, we address the problem of minimizing the cost of transferring a document or a file requested by a set of users geographically separated on a network of nodes. We concentrate on theoretical aspects of data migration and caching on high-speed networks. Following the information caching paradigm introduced in the literature, we present polynomial time optimal caching strategies that minimize the total monetary cost of all the service requests by the users on a high-speed network. We consider a scenario in which a large pool of customers from one or more remote sites on a network demand a document, situated at some site, for their use. We also assume that the users can request the document at different time instants. This process of distributing the requested document incurs communication costs due to the use of communication resources and caching costs of the document at some server sites before it is delivered to the users at their desired time instances. We configure the network as a fully connected topology in which the service providers manage and control the distribution of the requested document among the users. For a high-speed network, we show that a single copy of the requested document is sufficient to serve all the user requests in an optimal manner. We extend the study to a homogeneous case in which the communication costs are identical and caching costs at all the sites are identical. In this case, we demonstrate the adaptability of the algorithm in generating more than one copy when needed by the minimization process. Using these strategies, the network service providers can decide when, where, and for how long the requested documents must be cached at vantage sites to obtain an optimal solution. Illustrative examples are provided to ease the understanding.     

Caching and optimized request routing in cloud-based content delivery systems

Geographically distributed cloud platforms enable an attractive approach to large-scale content delivery. Storage at various sites can be dynamically acquired from (and released back to) the cloud provider so as to support content caching, according to the current demands for the content from the different geographic regions. When storage is sufficiently expensive that not all content should be cached at all sites, two issues must be addressed: how should requests for content be routed to the cloud provider sites, and what policy should be used for caching content using the elastic storage resources obtained from the cloud provider. Existing approaches are typically designed for non-elastic storage and little is known about the optimal policies when minimizing the delivery costs for distributed elastic storage.In this paper, we propose an approach in which elastic storage resources are exploited using a simple dynamic caching policy, while request routing is updated periodically according to the solution of an optimization model. Use of pull-based dynamic caching, rather than push-based placement, provides robustness to unpredicted changes in request rates. We show that this robustness is provided at low cost. Even with fixed request rates, use of the dynamic caching policy typically yields content delivery cost within 10% of that with the optimal static placement. We compare request routing according to our optimization model to simpler baseline routing policies, and find that the baseline policies can yield greatly increased delivery cost relative to optimized routing. Finally, we present a lower-cost approximate solution algorithm for our routing optimization problem that yields content delivery cost within 2.5% of the optimal solution.     

Cost-aware reliability task scheduling of automotive cyber-physical systems

New generation of automotives has become a typical Cyber-Physical System, including various sensors, complex communication networks and distributed computing devices. Manufacturers pay great attention to functional safety and cost control of automotive. High-end computing devices such as electronic control units (ECU) that can perform multiple tasks bring higher reliability and also lead to soaring costs. In this paper, we propose an efficient scheduling algorithm that satisfies cost and functional reliability constraints under multifunctional mixed-criticality task replication execution scenarios. Using the proposed fault-tolerant hardware cost-aware multifunctional safety assurance (FT_HCMSA) algorithm can reduce the deadline miss ratio (DMR) of high-criticality function significantly while meeting the autumotive functional safety and hardware cost constraints. FT_HCMSA uses a fine-grained ECU replacement strategy to optimize task allocation. Our experiments validate that FT_HCMSA can reduce the DMR of high-criticality level functions while meet the requirements of safety and cost.     

On-Line Algorithms for the Dynamic Traveling Repair Problem

We consider the dynamic traveling repair problem in which requests with deadlines arrive through time on points in a metric space. Servers move from point to point at constant speed. The goal is to plan the motion of servers so that the maximum number of requests are met by their deadline. We consider a restricted version of the problem in which there is a single server and the length of time between the arrival of a request and its deadline is constant. We give upper bounds for the competitive ratio of two very natural algorithms as well as several lower bounds for any deterministic algorithm. Most of the results in this paper are expressed as a function of β, the diameter of the metric space. In particular, we prove that the upper bound given for one of the two algorithms is within a constant factor of the best possible competitive ratio.     

一种新颖的带模糊截止时限的磁盘调度算法

设计了一种新的基于截止时限的磁盘调度算法，该算法支持带多优先级的请求。对于某些实时要求，其截止时限是不确定的或者不精确的，该算法采用模糊集来描述这类不确定性，模糊截止时限的隶属度函数表示对请求完成时间的满意程度。调度的目的是最优的指定优先级，使得截止时限的满意程度最大化。根据请求截止时限的不同，把满意程度划分为若干连续的区间。在每个不同的区间内，每个请求都对应有修正的截止时限，把请求按照其修正的截止时限非减的顺序分配优先级，才能实现请求优先级的最优配置。仿真结果表明该算法能有效的分配请求的优先级，降低请求的丢失率，保证了更多的请求得到满足。     

A note on on-line broadcast scheduling with deadlines

In this paper, we study an on-line broadcast scheduling problem with deadlines, in which the requests asking for the same page can be satisfied simultaneously by broadcasting this page, and every request is associated with a release time, deadline and a required page with a unit size. The objective is to maximize the number of requests satisfied by the schedule. In this paper, we focus on an important special case where all the requests have their spans (the difference between release time and deadline) less than 2. We give an optimal online algorithm, i.e., its competitive ratio matches the lower bound of the problem.     

A cost driven disk scheduling algorithm for multimedia object retrieval

This paper describes a novel cost-driven disk scheduling algorithm for environments consisting of multipriority requests. An example application is a video-on-demand (VOD) system that provides high and low quality services, termed priority 2 and 1, respectively. Customers ordering a high quality (priority 2) service pay a higher fee and are assigned a higher priority by the underlying system. Our proposed algorithm minimizes costs by maintaining one-queue and managing requests intelligently in order to meet the deadline of as many priority 1 requests as possible while maximizing the number of priority 2 requests that meet their deadline. Our algorithm is general enough to accommodate an arbitrary number of priority levels. Prior schemes, collectively termed "multiqueue" schemes maintain a separate queue for each priority level in order to optimize the performance of the high priority requests only. When compared with our proposed scheme, in certain cases, our technique provides more than one order of magnitude improvement in total cost.     

基于效益函数的网格任务调度算法

在动态、异构、分布广泛的网格环境中，对资源的调度是一个非常复杂而重要且具有挑战性的问题。本文针对网格环境中的动态性特点，特别是用户QoS要求的动态变化性，提出了一种基于效益函数的网格任务调度算法，并采用GridSim模拟器分别对该调度算法和模拟器自带的代价最优和时间最优的网格任务调度算法进行模拟。实验的结果表明：该调度算法更能体现用户对QoS要求的动态变化；在系统完成相同数量的网格任务时，消耗相同时间的情况下，该调度算法在代价上优于基于时间优化的调度算法；而花费相同预算的情况下，在时间上优于基于代价优化的调度算法。     

一种云计算环境下的工作流双向调度方法

为降低云计算中工作流调度的时间和成本,提出了一种双向调度算法,以实现后向Backward和前向Forward的双向调度。首先,Backward算法按照每个任务的最迟开始时间进行后向调度;此基础上,为降低虚拟机调度费用,Forward算法尽可能地提前调度每个任务,且在前向调度过程中充分考虑到工作流deadline、最大cost及传输时间的限制,从而实现对虚拟机的动态调度。由实验可知,本算法比BDA算法以及ICPCP算法更节约虚拟机调度成本,提高了调度的灵活性。     

基于至少连续满足弱硬实时限制的调度算法

对至少连续满足弱硬实时限制的性质进行了扩充，提出并证明了任务不满足子序列长度与任务连续满足的截止期限数之间的关系.在此基础上提出了改进的弱硬实时限制调度算法：MRA．MRA用于在弱硬实时系统中保证任务满足至少连续满足限制，是一种高效、易于实现的调度算法,仿真实验的结果表明，MRA调度算法在提高任务对限制的满足率和保证任务实时性方面优于同类算法．     

Customer satisfaction‐aware scheduling for utility maximization on geo‐distributed data centers

With the increasingly growing amount of service requests from the world‐wide customers, the cloud systems are capable of providing services while meeting the customers' satisfaction. Recently, to achieve the better reliability and performance, the cloud systems have been largely depending on the geographically distributed data centers. Nevertheless, the dollar cost of service placement by service providers (SP) differ from the multiple regions. Accordingly, it is crucial to design a request dispatching and resource allocation algorithm to maximize net profit. The existing algorithms are either built upon energy‐efficient schemes alone, or multi‐type requests and customer satisfaction oblivious. They cannot be applied to multi‐type requests and customer satisfaction‐aware algorithm design with the objective of maximizing net profit. This paper proposes an ant‐colony optimization‐based algorithm for maximizing SP's net profit (AMP) on geographically distributed data centers with the consideration of customer satisfaction. First, using model of customer satisfaction, we formulate the utility (or net profit) maximization issue as an optimization problem under the constraints of customer satisfaction and data centers. Second, we analyze the complexity of the optimal requests dispatchment problem and rigidly prove that it is an NP‐complete problem. Third, to evaluate the proposed algorithm, we have conducted the comprehensive simulation and compared with the other state‐of‐the‐art algorithms. Also, we extend our work to consider the data center's power usage effectiveness. It has been shown that AMP maximizes SP net profit by dispatching service requests to the proper data centers and generating the appropriate amount of virtual machines to meet customer satisfaction. Moreover, we also demonstrate the effectiveness of our approach when it accommodates the impacts of dynamically arrived heavy workload, various evaporation rate and consideration of power usage effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.     

Scheduling multiple task graphs in heterogeneous distributed real-time systems by exploiting schedule holes with bin packing techniques

The most crucial aspect of distributed real-time systems is the scheduling algorithm, which must guarantee that every job in the system will meet its deadline. In this paper, we evaluate by simulation the performance of strategies for the dynamic scheduling of composite jobs in a heterogeneous distributed real-time system. Each job that arrives in the system is a directed acyclic graph of component tasks and has an end-to-end deadline. For each scheduling policy, we provide alternative versions which allow the insertion of tasks into idle time slots, using various bin packing techniques. The comparison study, based on different workloads and system heterogeneity levels, shows that the alternative versions of the algorithms outperform their respective counterparts.     

云中截止时间动态分配的工作流调度成本优化算法

现如今,如何在满足截止时间约束的前提下降低工作流的执行成本,是云中工作流调度的主要问题之一。三步列表调度算法可以有效解决这一问题。但该算法在截止时间分配阶段只能形成静态的子截止时间。为方便用户部署工作流任务,云服务商为用户提供了的三种实例类型,其中竞价实例具有非常大的价格优势。为解决上述问题,提出了截止时间动态分配的工作流调度成本优化算法(S-DTDA)。该算法利用粒子群算法对截止时间进行动态分配,弥补了三步列表调度算法的缺陷。在虚拟机选择阶段,该算法在候选资源中增加了竞价实例,大大降低了执行成本。实验结果表明,相较于其他经典算法,该算法在实验成功率和执行成本上具有明显优势。综上所述,S-DTDA算法可以有效解决工作流调度中截止时间约束的成本优化问题。     

Scheduling for electricity cost in a smart grid

We study an offline scheduling problem arising in demand response management in a smart grid. Consumers send in power requests with a flexible set of timeslots during which their requests can be served. For example, a consumer may request the dishwasher to operate for 1 h during the periods 8am to 11am or 2pm to 4pm. The grid controller, upon receiving power requests, schedules each request within the specified duration. The electricity cost is measured by a convex function of the load in each timeslot. The objective of the problem is to schedule all requests with the minimum total electricity cost. As a first attempt, we consider a special case in which the power requirement and the duration a for which a request needs service are both unit-size. For this problem, we present a polynomial time offline algorithm that gives an optimal solution and shows that the time complexity can be further improved if the given set of timeslots forms a contiguous interval.