Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Multi-Robot Task Allocation: A Spatial Queuing Approach

Multi-Robot Task Allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to match a set of robots to a set of tasks so that the tasks can be completed by the robots while optimizing a certain metric such as the time required to complete all tasks, distance traveled by the robots and energy expended by the robots. We consider a scenario where the tasks can appear dynamically and the location of tasks are not known a priori by the robots. Additionally, for a task to be completed, it needs to be performed by multiple robots. This setting is called the MR-ST-TA (multi-robot, single-task, time- extended assginment) category of MRTA; solving the MRTA problem for this category is a known NP-hard problem. In this thesis, we address this problem by proposing a new algorithm that uses a spatial queue-based model to allocate tasks between robots while comparing its performance to several other known methods. We have implemented these algorithms on an accurately simulated model of Corobot robots within the Webots simulator for different numbers of robots and tasks. The results show that our method is adept in all proffered environments, especially scenarios that benefit from path planning, whereas other methods display inherent weakness at one end of the spectrum: a decentralized greedy approach exhibits inefficient behavior as the robot to task ratio dips below one, whereas the Hungarian method (an offline algorithm) fails to keep pace as the robot count increases

Scheduling of space to ground quantum key distribution

Satellite-based platforms are currently the only feasible way of achieving intercontinental range for quantum communication, enabling thus the future global quantum internet. Recent demonstrations by the Chinese spacecraft Micius have spurred an international space race and enormous interest in the development of both scientific and commercial systems. Research efforts so far have concentrated upon in-orbit demonstrations involving a single satellite and one or two ground stations. Ultimately satellite quantum key distribution should enable secure network communication between multiple nodes, which requires efficient scheduling of communication with the set of ground stations. Here we present a study of how satellite quantum key distribution can service many ground stations taking into account realistic constraints such as geography, operational hours, and most importantly, weather conditions. The objective is to maximise the number of keys a set of ground stations located in the United Kingdom could share while simultaneously reflecting the communication needs of each node and its relevance in the network. The problem is formulated as a mixed-integer linear optimisation program and solved to a desired optimality gap using a state of the art solver. The approach is presented using a simulation run throughout six years to investigate the total number of keys that can be sent to ground stations

Deadline-Aware Reservation-Based Scheduling

The ever-growing need to improve return-on-investment (ROI) for cluster infrastructure that processes data which is being continuously generated at a higher rate than ever before introduces new challenges for big-data processing frameworks. Highly complex mixed workload arriving at modern clusters along with a growing number of time-sensitive critical production jobs necessitates cluster management systems to evolve. Most big-data systems are not only required to guarantee that production jobs will complete before their deadline, but also minimize the latency for best-effort jobs to increase ROI. This research presents DARSS, a deadline-aware reservation-based scheduling system. DARSS addresses the above-stated problem by using a reservation-based approach to scheduling that supports temporal requirements of production jobs while keeping the latency for best-effort jobs low. Fined-grained resource allocation enables DARSS to schedule more tasks than a coarser-grained approach would. Furthermore, DARSS schedules production jobs as close to their deadlines as possible. This scheduling policy allows the system to maximize the number of low-priority tasks that can be scheduled opportunistically. DARSS is a scalable system that can be integrated with YARN. DARSS is evaluated on a simulated cluster of 300 nodes against a workload derived from Google Borg's trace. DARSS is compared with Microsoft's Rayon and YARN's built-in scheduler. DARSS achieves better production job acceptance rate than both YARN and Rayon. The experiments show that all of the production jobs accepted by DARSS complete before their deadlines. Furthermore, DARSS has a higher number of best-effort jobs serviced than Rayon. And finally, DARSS has lower latency for best-effort jobs than Rayon

Improving the end-to-end latency of datacenter applications using coordination across application components

To handle millions of user requests every second and process hundreds of terabytes of data each day, many organizations have turned to large datacenter-scale computing systems. The applications running in these datacenters consist of a multitude of dependent logical components or stages which perform specific functionality. These stages are connected to form a directed acyclic graph (DAG), with edges representing input-output dependencies. Each stage can run over tens to thousands of machines, and involves multiple cluster sub-systems such as storage, network and compute. The scale and complexity of these applications can lead to significant delays in their end-to-end latency. However, the organizations running these applications have strict requirements on this latency as it directly affects their revenue and operational costs. Addressing this problem, the goal of this dissertation is to develop scheduling and resource allocation techniques to optimize for the end-to-end latency of datacenter applications. The key idea behind these techniques is to utilize coordination between different application components, allowing us to efficiently allocate cluster resources. In particular, we develop planning algorithms that coordinate the storage and compute sub-systems in datacenters to determine how many resources should be allocated to each stage in an application along with where in the cluster should they be allocated, to meet application requirements (e.g., completion time goals, minimize average completion time etc.). To further speed up applications at runtime, we develop a few latency reduction techniques: reissuing laggards elsewhere in the cluster, returning partial results and speeding up laggards by giving them extra resources. We perform a global optimization to coordinate across all the stages in an application DAG and determine which of these techniques works best for each stage, while ensuring that the cost incurred by these techniques is within a given end-to-end budget. We use application characteristics to predict and determine how resources should be allocated to different application components to meet the end-to-end latency requirements. We evaluate our techniques on two different kinds of datacenter applications: (a) web services, and (b) data analytics. With large-scale simulations and an implementation in Apache Yarn (Hadoop 2.0), we use workloads derived from production traces to show that our techniques can achieve more than 50% reduction in the 99th percentile latency of web services and up to 56% reduction in the median latency of data analytics jobs

Using hierarchical scheduling to support soft real-time applications in general-purpose operating systems

Journal ArticleThe CPU schedulers in general-purpose operating systems are designed to provide fast response time for interactive applications and high throughput for batch applications. The heuristics used to achieve these goals do not lend themselves to scheduling real-time applications, nor do they meet other scheduling requirements such as coordinating scheduling across several processors or machines, or enforcing isolation between applications, users, and administrative domains. Extending the scheduling subsystems of general-purpose operating systems in an ad hoc manner is time consuming and requires considerable expertise as well as source code to the operating system. Furthermore, once extended, the new scheduler may be as inflexible as the original. The thesis of this dissertation is that extending a general-purpose operating system with a general, heterogeneous scheduling hierarchy is feasible and useful. A hierarchy of schedulers generalizes the role of CPU schedulers by allowing them to schedule other schedulers in addition to scheduling threads. A general, heterogeneous scheduling hierarchy is one that allows arbitrary (or nearly arbitrary) scheduling algorithms throughout the hierarchy. In contrast, most of the previous work on hierarchical scheduling has imposed restrictions on the schedulers used in part or all of the hierarchy. This dissertation describes the Hierarchical Loadable Scheduler (HLS) architecture, which permits schedulers to be dynamically composed in the kernel of a general-purpose operating system. The most important characteristics of HLS, and the ones that distinguish it from previous work, are that it has demonstrated that a hierarchy of nearly arbitrary schedulers can be efficiently implemented in a general-purpose operating system, and that the behavior of a hierarchy of soft real-time schedulers can be reasoned about in order to provide guaranteed scheduling behavior to application threads. The flexibility afforded by HLS permits scheduling behavior to be tailored to meet complex requirements without encumbering users who have modest requirements with the performance and administrative costs of a complex scheduler. Contributions of this dissertation include the following. (1) The design, prototype implementation, and performance evaluation of HLS in Windows 2000. (2) A system of guarantees for scheduler composition that permits reasoning about the scheduling behavior of a hierarchy of soft real-time schedulers. Guarantees assure users that application requirements can be met throughout the lifetime of the application, and also provide application developers with a model of CPU allocation to which they can program. (3) The design, implementation, and evaluation of two augmented CPU reservation schedulers, which provide increase scheduling predictability when low-level operating system activity steals time from applications

On the Integration of Unmanned Aerial Vehicles into Public Airspace

Unmanned Aerial Vehicles will soon be integrated in the airspace and start serving us in various capacities such as package delivery, surveillance, search and rescue missions, inspection of infrastructure, precision agriculture, and cinematography. In this thesis, motivated by the challenges this new era brings about, we design a layered architecture called Internet of Drones (IoD). In this architecture, we propose a structure for the traffic in the airspace as well as the interaction between the components of our system such as unmanned aerial vehicles and service providers. We envision the minimal features that need to be implemented in various layers of the architecture, both on the Unmanned Aerial Vehicle (UAV)'s side and on the service providers' side. We compare and contrast various approaches in three existing networks, namely the Internet, the cellular network, and the air traffic control network and discuss how they relate to IoD. As a tool to aid in enabling integration of drones in the airspace, we create a traffic flow model. This model will assign velocities to drones according to the traffic conditions in a stable way as well as help to study the formation of congestion in the airspace. We take the novel problem posed by the 3D nature of UAV flights as opposed to the 2D nature of road vehicles movements and create a fitting traffic flow model. In this model, instead of structuring our model in terms of roads and lanes as is customary for ground vehicles, we structure it in terms of channels, density and capacities. The congestion is formulated as the perceived density given the capacity and the velocity of vehicles will be set accordingly. This view removes the need for a lane changing model and its complexity which we believe should be abstracted away even for the ground vehicles as it is not fundamentally related to the longitudinal movements of vehicles. Our model uses a scalar capacity parameter and can exhibit both passing and blocking behaviors. Furthermore, our model can be solved analytically in the blocking regime and piece-wise analytically solved when in the passing regime. Finally, it is not possible to integrate UAVs into the airspace without some mechanism for coordination or in other words scheduling. We define a new scheduling problem in this regard that we call Vehicle Scheduling Problem (VSP). We prove NP-hardness for all the commonly used objective functions in the context of Job Shop Scheduling Problem (JSP). Then for the number of missed deadlines as our objective function, we give a Mixed Integer Programming (MIP) formulation of VSP. We design a heuristic algorithm and compare the quality of the schedules created for small instances with the exact solution to the MIP instance. For larger instances, these comparisons are made with a baseline algorithm