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.
In computer science, with the constant networking and middleware development, scheduling in distributed processing systems is one of the topics which has gained attention in the last two decades. Casavant and Kuhl @cite_115 present a taxonomy of scheduling in general purpose distributed systems. The classification presented by the authors include local and global, static and dynamic, distributed and non-distributed, cooperative and non-cooperative scheduling, as well as some approaches to solve the problem, such as optimal and sub-optimal, heuristic, and approximate. This presented classification is complete in some sense, and it is still valid nowadays. However the current state of distributed systems indeed demands the addition of new branches in this taxonomy.
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One measure of the usefulness of a general-purpose distributed computing system is the system's ability to provide a level of performance commensurate to the degree of multiplicity of resources present in the system. A taxonomy of approaches to the resource management problem is presented in an attempt to provide a common terminology and classification mechanism necessary in addressing this problem. The taxonomy, while presented and discussed in terms of distributed scheduling, is also applicable to most types of resource management. > A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Static scheduling of a program represented by a directed task graph on a multiprocessor system to minimize the program completion time is a well-known problem in parallel processing. Since finding an optimal schedule is an NP-complete problem in general, researchers have resorted to devising efficient heuristics. A plethora of heuristics have been proposed based on a wide spectrum of techniques, including branch-and-bound, integer-programming, searching, graph-theory, randomization, genetic algorithms, and evolutionary methods. The objective of this survey is to describe various scheduling algorithms and their functionalities in a contrasting fashion as well as examine their relative merits in terms of performance and time-complexity. Since these algorithms are based on diverse assumptions, they differ in their functionalities, and hence are difficult to describe in a unified context. We propose a taxonomy that classifies these algorithms into different categories. We consider 27 scheduling algorithms, with each algorithm explained through an easy-to-understand description followed by an illustrative example to demonstrate its operation. We also outline some of the novel and promising optimization approaches and current research trends in the area. Finally, we give an overview of the software tools that provide scheduling mapping functionalities. During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach has to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to a taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored. The capacity and robustness of cellular MIMO systems is very sensitive to other-cell interference which will in practice necessitate network level interference reduction strategies. As an alternative to traditional static frequency reuse patterns, this paper investigates intercell scheduling among neighboring base stations. We show analytically that cooperatively scheduled transmission, which is well within the capability of present systems, can achieve an expanded multiuser diversity gain in terms of ergodic capacity as well as almost the same amount of interference reduction as conventional frequency reuse. This capacity gain over conventional frequency reuse is O (M t square-root of log Ns) for dirty paper coding and O (min (Mr, Mt) square-root of log Ns) for time division, where Ns is the number of cooperating base stations employing opportunistic scheduling in an M t x M r MIMO system. From a theoretical standpoint, an interesting aspect of this analysis comes from an altered view of multiuser diversity in the context of a multi-cell system. Previously, multiuser diversity capacity gain has been known to grow as O(log log K), from selecting the maximum of K exponentially-distributed powers. Because multicell considerations such as the positions of the users, lognormal shadowing, and pathless affect the multiuser diversity gain, we find instead that the gain is O(square-root of 2logic K), from selecting the maximum of a compound Iognormal-exponential distribution. Finding the maximum of such a distribution is an additional contribution of the paper. Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the non-collaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the non-collaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands. 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 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. We consider distributed opportunistic scheduling (DOS) in wireless ad-hoc networks, where many links contend for the same channel using random access. In such networks, distributed opportunistic scheduling involves a process of joint channel probing and distributed scheduling. Due to channel fading, the link condition corresponding to a successful channel probing could be either good or poor. In the latter case, further channel probing, although at the cost of additional delay, may lead to better channel conditions and hence higher transmission rates. The desired tradeoff boils down to judiciously choosing the optimal stopping strategy for channel probing and the rate threshold. In this paper, we pursue a rigorous characterization of the optimal strategies from two perspectives, namely, a network-centric perspective and a user-centric perspective. We first consider DOS from a network-centric point of view, where links cooperate to maximize the overall network throughput. Using optimal stopping theory, we show that the optimal strategy turns out to be a pure threshold policy, where the rate threshold can be obtained by solving a fixed point equation. We further devise an iterative algorithm for computing the threshold. Next, we explore DOS from a user-centric perspective, where each links seeks to maximize its own throughput. We treat the problem of rate threshold selections for different links as a non-cooperative game. We explore the existence and uniqueness of the Nash equilibrium, and show that the Nash equilibrium can be approached by the best response strategy. We then develop an online stochastic iterative algorithm using local observations only, and establish its convergence. Finally, we observe that there is an efficiency loss in terms of the throughput at the Nash equilibrium, and introduce apricing-based mechanism to mitigate the loss. Abstract We consider the NP-hard problem of scheduling jobs on a single machine against common due dates with respect to earliness and tardiness penalties. The paper covers two aspects: Firstly, we develop a problem generator and solve 280 instances with two new heuristics to obtain upper bounds on the optimal objective function value. Secondly, we demonstrate computationally that our heuristics are efficient in obtaining near-optimal solutions for small problem instances. The generated problem instances in combination with the upper bounds can be used as benchmarks for future approaches in the field of common due-date scheduling. Scope and purpose In connection with just-in-time production and delivery, earliness as well as tardiness penalties are of interest. Thus scheduling against common due dates has received growing attention during the last decade. Many algorithms have been developed to solve the different variants of this problem. But whenever a new algorithm for scheduling against common due dates is proposed, its quality is assessed only on a few self-generated examples. Hence it is difficult to evaluate the various approaches, particularly in comparison with each other. Therefore the goal of this paper is to present numerous benchmark problems together with some upper bounds on the optimal objective function value. The question of what can be computed, and how efficiently, is at the core of computer science. Not surprisingly, in distributed systems and networking research, an equally fundamental question is what can be computed in a distributed fashion. More precisely, if nodes of a network must base their decision on information in their local neighborhood only, how well can they compute or approximate a global (optimization) problem? In this paper we give the first polylogarithmic lower bound on such local computation for (optimization) problems including minimum vertex cover, minimum (connected) dominating set, maximum matching, maximal independent set, and maximal matching. In addition, we present a new distributed algorithm for solving general covering and packing linear programs. For some problems this algorithm is tight with the lower bounds, whereas for others it is a distributed approximation scheme. Together, our lower and upper bounds establish the local computability and approximability of a large class of problems, characterizing how much local information is required to solve these tasks. Reducing latency in distributed computing and data storage systems is gaining increasing importance. Several empirical works have reported on the efficacy of scheduling redundant requests in such systems. That is, one may reduce job latency by: 1) scheduling the same job at more than one server and 2) waiting only until the fastest of them responds. Several theoretical models have been proposed to explain the power of using redundant requests, and all of the existing results rely heavily on a common assumption: all redundant requests of a job can be immediately cancelled as soon as one of them is completed. We study how one should schedule redundant requests when such assumption does not hold. This is of great importance in practice, since cancellation of running jobs typically incurs non-negligible delays. In order to bridge the gap between the existing models and practice, we propose a new queueing model that captures such cancellation delays. We then find how one can schedule redundant requests to achieve the optimal average job latency under the new model. Our results show that even with a small cancellation overhead, the actual optimal scheduling policy differs significantly from the optimal scheduling policy when the overhead is zero. Furthermore, we study optimal dynamic scheduling policies, which appropriately schedule redundant requests based on the number of jobs in the system. Our analysis reveals that for the two-server case, the optimal dynamic scheduler can achieve 7 –16 lower average job latency, compared with the optimal static scheduler. We study the demand side management (DSM) problem when customers are equipped with energy storage devices. Two games are discussed: the first is a non-cooperative one played between the residential energy consumers, while the second is a Stackelberg game played between the utility provider and the energy consumers. We introduce a new cost function applicable to the case of users selling back stored energy. The non-cooperative energy consumption game is played between users who schedule their energy use to minimize energy cost. The game is shown to have a unique Nash equilibrium, that is also the global system optimal point. In the Stackelberg game, the utility provider sets the prices to maximize its profit knowing that users will respond by minimizing their cost. We provide existence and uniqueness results for the Stackelberg equilibrium. The Stackelberg game is shown to be the general case of the minimum Peak-to-Average power ratio (PAR) problem. Two algorithms, centralized and distributed, are presented to solve the Stackelberg game. We present results that elucidate the interplay between storage capacity, energy requirements, number of users and system performance measured in total cost and peak-to-average power ratio (PAR). It is known that CSMA CA channel access schemes are not well suited to meet the high traffic demand of wireless mesh networks. One possible way to increase traffic carrying capacity is to use a spatial TDMA (STDMA) approach in conjunction with the physical interference model, which allows more aggressive scheduling than the protocol interference model on which CSMA CA is based. While an efficient centralized solution for STDMA with physical interference has been recently proposed, no satisfactory distributed approaches have been introduced so far. In this paper, we first prove that no localized distributed algorithm can solve the problem of building a feasible schedule under the physical interference model. Motivated by this, we design a global primitive, called SCREAM, which is used to verify the feasibility of a schedule during an iterative distributed scheduling procedure. Based on this primitive, we present two distributed protocols for efficient, distributed scheduling under the physical interference model, and we prove an approximation bound for one of the protocols. We also present extensive packet-level simulation results, which show that our protocols achieve schedule lengths very close to those of the centralized algorithm and have running times that are practical for mesh networks. We consider the problem of two wireless networks operating on the same (presumably unlicensed) frequency band. Pairs within a given network cooperate to schedule transmissions, but between networks there is competition for spectrum. To make the problem tractable, we assume transmissions are scheduled according to a random access protocol where each network chooses an access probability for its users. A game between the two networks is defined. We characterize the Nash Equilibrium behavior of the system. Three regimes are identified: one in which both networks simultaneously schedule all transmissions, one in which the denser network schedules all transmissions and the sparser only schedules a fraction, and one in which both networks schedule only a fraction of their transmissions. The regime of operation depends on the path loss exponent α, the latter regime being desirable but attainable only for α > 4. This suggests that in certain environments, rival wireless networks may end up naturally cooperating. To substantiate our analytical results, we simulate a system where networks iteratively optimize their access probabilities in a greedy manner. We also discuss a distributed scheduling protocol that employs carrier sensing and demonstrate via simulations that again a near cooperative equilibrium exists for sufficiently large α. Carrier sense multiple access (CSMA), which resolves contentions over wireless networks in a fully distributed fashion, has recently gained a lot of attentions since it has been proved that appropriate control of CSMA parameters guarantees optimality in terms of stability (i.e., scheduling) and system-wide utility (i.e., scheduling and congestion control). Most CSMA-based algorithms rely on the popular Markov chain Monte Carlo technique, which enables one to find optimal CSMA parameters through iterative loops of simulation-and-update. However, such a simulation-based approach often becomes a major cause of exponentially slow convergence, being poorly adaptive to flow topology changes. In this paper, we develop distributed iterative algorithms which produce approximate solutions with convergence in polynomial time for both stability and utility maximization problems. In particular, for the stability problem, the proposed distributed algorithm requires, somewhat surprisingly, only one iteration among links. Our approach is motivated by the Bethe approximation (introduced by Yedidia, Freeman, and Weiss) allowing us to express approximate solutions via a certain nonlinear system with polynomial size. Our polynomial convergence guarantee comes from directly solving the nonlinear system in a distributed manner, rather than multiple simulation-and-update loops in existing algorithms. We provide numerical results to show that the algorithm produces highly accurate solutions and converges much faster than the prior ones. Manufacturing scheduling is a complex combinatorial problem, particularly in distributed and dynamic environments. This paper presents a holonic approach to manufacturing scheduling, where the scheduling functions are distributed by several entities, combining their calculation power and local optimization capability. In this scheduling and control approach, the objective is to achieve fast and dynamic re-scheduling using a scheduling mechanism that evolves dynamically to combine centralized and distributed strategies, improving its responsiveness to emergence, instead of the complex and optimized scheduling algorithms found in traditional approaches. 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. Aerial robotics can be very useful to perform complex tasks in a distributed and cooperative fashion, such as localization of targets and search of point of interests (PoIs). In this work, we propose a distributed system of autonomous Unmanned Aerial Vehicles (UAVs), able to self-coordinate and cooperate in order to ensure both spatial and temporal coverage of specific time and spatial varying PoIs. In particular, we consider an UAVs system able to solve distributed dynamic scheduling problems, since each device is required to move towards a certain position in a certain time. We give a mathematical formulation of the problem as a multi-criteria optimization model, in which the total distances traveled by the UAVs (to be minimized), the customer satisfaction (to be maximized) and the number of used UAVs (to be minimized) are considered simultaneously. A dynamic variant of the basic optimization model, defined by considering the rolling horizon concept, is shown. We introduce a case study as an application scenario, where sport actions of a football match are filmed through a distributed UAVs system. The customer satisfaction and the traveled distance are used as performance parameters to evaluate the proposed approaches on the considered scenario. 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.