Scheduling broadcasts with deadlines

We investigate the problem of scheduling broadcasts in data delivering systems via broadcast, where a number of requests from several clients can be simultaneously satisfied by one broadcast of a server. Most of prior work has focused on minimizing the total flow time of requests. It assumes that once a request arrives, it will be held until satisfied. In this paper, we are concerned with the situation that clients may leave the system if their requests are still unsatisfied after waiting for some time, that is, each request has a deadline. The problem of maximizing the throughput, for example, the total number of satisfied requests, is developed, and there are given online algorithms achieving constant competitive ratios.     

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.     

Some algorithms for analysis and synthesis of real-time multiprocessor computing systems

The problem of preemptive scheduling in a real-time multiprocessor computing system with release time/deadline intervals is investigated. Approximate algorithms based on the generalization of a single-processor algorithm of relative priority are developed and compared to the exact maximum flow algorithm. An algorithm has been developed for the case where requests for the tasks occur periodically with given periods. An algorithm for determining the values of the processor performance for which there exists an admissible schedule for a given assembly of tasks with release time/deadline intervals has been developed.     

Online call control in cellular networks revisited

Wireless communication networks based on frequency division multiplexing (FDM in short) play an important role in the field of communications, in which each request can be satisfied by assigning a frequency. To avoid interference, each assigned frequency must be different from the neighboring assigned frequencies. Since frequencies are scarce resources, the main problem in wireless networks is how to fully utilize the given bandwidth of frequencies. In this paper, we consider the online call control problem. Given a fixed bandwidth of frequencies and a sequence of communication requests arriving over time, each request must be either satisfied immediately after its arrival by assigning an available frequency, or rejected. The objective of the call control problem is to maximize the number of accepted requests. We study the asymptotic performance of this problem, i.e., the number of requests in the sequence and the bandwidth of frequencies are very large. In this paper, we give a 7/3-competitive algorithm, say CACO, for the call control problem in cellular networks, improving the previous 2.5-competitive result, and show that CACO is best possible among a class of HYBRID algorithms.     

Temporal pre-fetching of dynamic web pages

Although caching has been shown as an efficient technique to reduce the delay in generating web pages to meet the page requests from web users, it becomes less effective if the pages are dynamic and contain dynamic contents. In this paper, instead of using caching, we study the effectiveness of using pre-fetching to resolve the problems in handling dynamic web pages. Pre-fetching is a proactive caching scheme since a page is cached before the receipt of any page request for the page. In addition to the problem of which pages to be pre-fetched, another equally important question is when to perform the pre-fetching. To resolve the prediction and timing problems, we explore the temporal properties of the dynamic web pages and the timing issues in accessing the pages to determine which pages to be pre-fetched and the best time to pre-fetch the pages to maximize the cache hit probability of the pre-fetched page. If the required pages can be found in the cache validly, the response times of the requests can be greatly reduced. The proposed scheme is called temporal pre-fetching (TPF) in which we prioritize pre-fetching requests based on the predicted usability of the to-be pre-fetched pages. To minimize the impact of incorrect prediction in pre-fetching on processing of on-demand page requests, a qualifying examination is performed to remove unnecessary and low usability pre-fetching requests while they are waiting to be processed and just before their processing. We have implemented the proposed TPF scheme in a web server system and experiments have been performed to study its performance characteristics compared with conventional cache-only scheme using a benchmark auction application under different system and application settings. As shown in the experiment results, the overall system performance, i.e., response time, is improved as more page requests can be served immediately using pre-fetched pages.     

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。     

Utility driven optimization of real time data broadcast schedules

Data dissemination in wireless environments is often accomplished by on-demand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in on-demand broadcast scheduling has focused on the timely servicing of requests so as to minimize the number of missed deadlines. However, there exists many environments where the utility of the received data is an equally important criterion as its timeliness. Missing the deadline may reduce the utility of the data but does not necessarily make it zero. In this work, we address the problem of scheduling real time data broadcasts with such soft deadlines. We investigate search based optimization techniques to develop broadcast schedulers that make explicit attempts to maximize the utility of data requests as well as service as many requests as possible within an acceptable time limit. Our analysis shows that heuristic driven methods for such problems can be improved by hybridizing them with local search algorithms. We further investigate the option of employing a dynamic optimization technique to facilitate utility gain, thereby eliminating the requirement of a heuristic in the process. An evolution strategy based stochastic hill-climber is investigated in this context.     

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.     

Budget-Deadline Constrained Workflow Planning for Admission Control

In this paper, we assume an environment with multiple, heterogeneous resources, which provide services of different capabilities and of a different cost. Users want to make use of these services to execute a workflow application, within a certain deadline and budget. The problem considered in this paper is to find a feasible plan for the execution of the workflow which would allow providers to decide whether they can agree with the specific constraints set by the user. If they agree to admit the workflow, providers can allocate services for its execution in a way that both deadline and budget constraints are met while account is also taken of the existing load in the provider’s environment (confirmed reservations from other users whose requests have been accepted). A novel heuristic is proposed and evaluated using simulation with four different real-world workflow applications.     

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 processes with release times, deadlines, precedence andexclusion relations

An algorithm that finds an optimal schedule on a single processor for a given set of processes is presented. Each process starts executing after its release time and completes its computation before its deadline and a given set of precedence relations and exclusion relations defined on ordered pairs of process segments are satisfied. This algorithm can be applied to the important and previously unsolved problem of automated pre-run-time scheduling of processes with arbitrary precedence and exclusion in hard-real-time systems     

Differentiated Real-Time Data Services for E-Commerce Applications

The demand for real-time e-commerce data services has been increasing recently. In many e-commerce applications, it is essential to process user requests within their deadlines, i.e., before the market status changes, using fresh data reflecting the current market status. However, current data services are poor at processing user requests in a timely manner using fresh data. To address this problem, we present a differentiated real-time data service framework for e-commerce applications. User requests are classified into several service classes according to their importance, and they receive differentiated real-time performance guarantees in terms of deadline miss ratio. At the same time, a certain data freshness is guaranteed for all transactions that commit within their deadlines. A feedback-based approach is applied to differentiate the deadline miss ratio among service classes. Admission control and adaptable update schemes are applied to manage potential overload. A simulation study, which reflects the e-commerce data semantics, shows that our approach can achieve a significant performance improvement compared to baseline approaches. Our approach can support the specified per-class deadline miss ratios maintaining the required data freshness even in the presence of unpredictable workloads and data access patterns, whereas baseline approaches fail.     

Online traveling salesman problem with deadline and advanced information

We consider the online version of the traveling salesman problem, where instances are not known in advance. Requests are released over time regardless whether the server is en route or not. This problem has been described as online TSP. Current literature about online TSP assumes that each request becomes known at its release time and will always remain active. We model the customers’ waiting psychology and service preparation time into the online TSP with the objective to serve as many requests as possible. More specifically, each request has a disclosure time before accepting service at its release time, and a deadline, which is no bigger than its release time plus the travel time from origin to its position. We give lower bounds for the competitive ratios, online algorithms, and quantify the influence of advanced information on competitive ratios.     

An object replication algorithm for real-time distributed databases

A real-time distributed database system (RTDDBS) must maintain the consistency constraints of objects and must also guarantee the time constraints imposed by each request arriving at the system. Such a time constraint of a request is usually defined as a deadline period, which means that the request must be serviced on or before its time constraint. Servicing these requests may incur I/O costs, control-message transferring costs or data-message transferring costs. As a result, in our work, we first present a mathematical model that considers all these costs. Using this cost model, our objective is to service all the requests on or before their respective deadline periods and minimize the total servicing cost. To this end, from theoretical standpoint, we design a dynamic object replication algorithm, referred to as Real-time distributed dynamic Window Mechanism (RDDWM), that adapts to the random patterns of read-write requests. Using competitive analysis, from practical perspective, we study the performance of RDDWM algorithm under two different extreme conditions, i.e., when the deadline period of each request is sufficiently long and when the deadline period of each request is very short. Several illustrative examples are provided for the ease of understanding. Recommended by: Ashfaq Khokhar     

A real-time scheduling framework for embedded systems with environmental energy harvesting

Real-time scheduling refers to the problem in which there is a deadline associated with the execution of a task. In this paper, we address the scheduling problem for a uniprocessor platform that is powered by a renewable energy storage unit and uses a recharging system such as photovoltaic cells. First, we describe our model where two constraints need to be studied: energy and deadlines. Since executing tasks require a certain amount of energy, classical task scheduling like earliest deadline is no longer convenient. We present an on-line scheduling scheme, called earliest deadline with energy guarantee (EDeg), that jointly accounts for characteristics of the energy source, capacity of the energy storage as well as energy consumption of the tasks, and time. In order to demonstrate the benefits of our algorithm, we evaluate it by means of simulation. We show that EDeg outperforms energy non-clairvoyant algorithms in terms of both deadline miss rate and size of the energy storage unit.     

The Non-Optimality of the Monotonic Priority Assignments for Hard Real-Time Offset Free Systems

In this paper, we study the problem of scheduling hard real-time periodic tasks with static priority pre-emptive algorithms. We consider tasks which are characterized by a period, a hard deadline, a computation time and an offset (the time of the first request), where the offsets may be chosen by the scheduling algorithm, hence the denomination offset free systems.We study the rate monotonic and the deadline monotonic priority assignments for this kind of system and we compare the offset free systems and the asynchronous systems in terms of priority assignment. Hence, we show that the rate and the deadline monotonic priority assignments are not optimal for offset free systems.     

On-line multiversion database concurrency control

This paper presents a new model for studying the concurrency vs. computation time tradeoffs involved in on-line multiversion database concurrency control. The basic problem that is studied in our model is the following: Given:a current database system state which includes information such as which transaction previously read a version from which other transaction; which transaction has written which versions into the database; and the ordering of versions previously written; anda set of read and write requests of requesting transactions. Question: Does there exist a new database system state in which the requesting transactions can be immediately put into execution (their read and write requests satisfied, or in the case of predeclared writeset transactions, write requests are guaranteed to be satisfied) while preserving consistency under a given set of additional constraints? (The amount of concurrency achieved is defined by the set of additional constraints). In this paper we derive “limits” of performance achievable by polynomial time concurrency control algorithms. Each limit is characterized by a minimal set of constraints that allow the on-line scheduling problem to be solved in polynomial time. If any one constraint in that minimal set is omitted, although it could increase the amount of concurrency, it would also have the dramatic negative effect of making the scheduling problem NP-complete; whereas if we do not omit any constraint in the minimal set, then the scheduling problem can be solved in polynomial time. With each of these limits, one can construct an efficient scheduling algorithm that achieves an optimal level of concurrency in polynomial computation time according to the constraints defined in the minimal set.     

Real time stochastic scheduling in broadcast systems with decentralized data storage

Data broadcasting is an efficient method to disseminate information to a large group of requesters with common interests. Performing such broadcasts typically involve the determination of a broadcast schedule intended to maximize the quality of service provided by the broadcast system. Earlier studies have proposed solutions to this problem in the form of heuristics and local search techniques designed to achieve minimal deadline misses or maximal utility. An often ignored factor in these studies is the possibility of the data items being not available locally, but rather have to be fetched from data servers distributed over a network, thereby inducing a certain level of stochasticity in the actual time required to serve a data item. This stochasticity is introduced on behalf of the data servers which themselves undergo a dynamic management of serving data requests. In this paper we revisit the problem of real time data broadcasting under such a scenario. We investigate the efficiency of heuristics that embed the stochastic nature of the problem in their design and compare their performance with those proposed for non-stochastic broadcast scheduling. Further, we extend our analysis to understand the various factors in the problem structure that influence these heuristics, and are often exploited by a better performing one.     

Algorithms for Minimizing Response Time in Broadcast Scheduling

In this paper we study the following problem. There are n pages which clients can request at any time. The arrival times of requests for pages are known in advance. Several requests for the same page may arrive at different times. There is a server that needs to compute a good broadcast schedule. Outputting a page satisfies all outstanding requests for the page. The goal is to minimize the average waiting time of a client. This problem has recently been shown to be NP-hard. For any fixed , 0 < \le &frac;, we give a 1/-speed, polynomial time algorithm with an approximation ratio of 1/(1 – ). For example, setting = &frac; gives a 2-speed, 2-approximation algorithm. In addition, we give a 4-speed, 1-approximation algorithm improving the previous bound of 6-speed, 1-approximation algorithm.     

An analysis of service schedules for the mobile k-server problem

In the mobile k-server problem, servers with fixed home locations travel to locations of requests to provide service and then return to their home locations. Given the home locations of the k-servers, the service times, release times and locations of the n requests, we seek to determine a service schedule so that the total waiting time of all requests is minimized. In this paper, we exploit the strong connection between the mobile k-server problem and job scheduling. We present optimal algorithms for a few special cases of the problem and provide worst-case performance analysis of some heuristics for the general case. For the special cases it is possible to rank order competing sets of home locations with respect to total (or average) waiting time.