Online Scheduling Algorithm Research Articles

A Study on the Performance of a Three-Stage Load-Balancing Switch

There has been a great deal of interest recently in load-balancing switches due to their simple architecture and high forwarding bandwidth. Nevertheless, the mis-sequencing problem of the original load-balancing switch hinders the performance of underlying TCP applications. Several load-balancing switch designs have been proposed to address this mis-sequencing issue. They solve this mis-sequencing problem at the cost of either algorithmic complexity or special hardware requirements. In this paper, we address the mis-sequencing problem by introducing a three-stage load-balancing switch architecture enhanced with an output load-balancing mechanism. This three-stage load-balancing switch achieves a high forwarding capacity while preserving the order of packets without the need of costly online scheduling algorithms. Theoretical analyses and simulation results show that this three-stage load-balancing switch provides a transmission delay that is upper-bounded by that of an output-queued switch plus a constant that depends only on the number of input/output ports, indicating the same forwarding capacity as an output-queued switch.

The Power of Reordering for Online Minimum Makespan Scheduling

In the classic minimum makespan scheduling problem, we are given an input sequence of jobs with processing times. A scheduling algorithm has to assign the jobs to $m$ parallel machines. The objective is to minimize the makespan, which is the time it takes until all jobs are processed. In this paper, we consider online scheduling algorithms without preemption. However, we do not require that each arriving job has to be assigned immediately to one of the machines. A reordering buffer with limited storage capacity can be used to reorder the input sequence in a restricted fashion so as to schedule the jobs with a smaller makespan. This is a natural extension of lookahead. We present an extensive study of the power and limits of online reordering for minimum makespan scheduling. As a main result, we give, for $m$ identical machines, tight and, in comparison to the problem without reordering, much improved bounds on the competitive ratio for minimum makespan scheduling with reordering buffers. Depending on $m$, the achieved competitive ratio lies between 4/3 and 1.4659. This optimal ratio is achieved with a buffer of size $\Theta(m)$. We show that larger buffer sizes do not result in an additional advantage and that a buffer of size $\Omega(m)$ is necessary to achieve this competitive ratio. Further, we present several algorithms for different buffer sizes. For $m$ uniformly related machines, we give a scheduling algorithm that achieves a competitive ratio of 2 with a reordering buffer of size $m$. Considering that the best known competitive ratio for uniformly related machines without reordering is 5.828, this result further emphasizes the power of online reordering.

Energy-Efficient Transmission Scheduling in Mobile Phones using Machine Learning and Participatory Sensing

Energy efficiency is important for smartphones because they are powered by batteries with limited capacity. Existing work has shown that the tail energy of the third-generation (3G)/fourth-generation (4G) network interface on a mobile device would lead to low energy efficiency. To solve the tail energy minimization problem, some online scheduling algorithms have been proposed, but with a big gap from the offline algorithms that work depending on the knowledge of future transmissions. In this paper, we study the tail energy minimization problem by exploiting the techniques of machine learning and participatory sensing. We design a client-server architecture, in which the training process is conducted in a server, and mobile devices download the constructed predictor from the server to make transmission decisions. A system is developed and deployed on real hardware to evaluate the performance of our proposal. The experimental results show that it can significantly improve the energy efficiency of mobile devices while incurring minimum overhead.

Thermal-Aware Scheduling of Batch Jobs in Geographically Distributed Data Centers

Decreasing the soaring energy cost is imperative in large data centers. Meanwhile, limited computational resources need to be fairly allocated among different organizations. Latency is another major concern for resource management. Nevertheless, energy cost, resource allocation fairness, and latency are important but often contradicting metrics on scheduling data center workloads. Moreover, with the ever-increasing power density, data center operation must be judiciously optimized to prevent server overheating. In this paper, we explore the benefit of electricity price variations across time and locations. We study the problem of scheduling batch jobs to multiple geographically-distributed data centers. We propose a provably-efficient online scheduling algorithm - GreFar - which optimizes the energy cost and fairness among different organizations subject to queueing delay constraints, while satisfying the maximum server inlet temperature constraints. GreFar does not require any statistical information of workload arrivals or electricity prices. We prove that it can minimize the cost arbitrarily close to that of the optimal offline algorithm with future information. Moreover, we compare the performance of GreFar with ones of a similar algorithm, referred to as T-unaware, that is not able to consider the server inlet temperature in the scheduling process. We prove that GreFar is able to save up to 16 percent of energy-fairness cost with respect to T-unaware.

Delay-Aware Online Service Scheduling in High-Speed Railway Communication Systems

We investigate the downlink service scheduling problem in relay-assisted high-speed railway (HSR) communication systems, taking into account stochastic packet arrivals and quality-of-service (QoS) requirements. The scheduling problem is formulated as an infinite-horizon average cost constrained Markov decision process (MDP), where the scheduling actions depend on the channel state information (CSI) and the queue state information (QSI). Our objective is to find a policy that minimizes the average end-to-end delay through scheduling actions under the service delivery ratio constraints. To address the challenge of centralized control and high complexity of traditional MDP approaches, we propose a distributed online scheduling algorithm based on approximate MDP and stochastic learning, where the scheduling policy is a function of the local CSI and QSI only. Numerical experiments are carried out to show the performance of the proposed algorithm.

On-line energy-aware scheduling algorithm in multiprocessor system

With the enhancement of computing performance in multiprocessor systems,the management of energy consumption becomes more important,and how to meet real-time constraints and effectively reduce energy consumption in the real-time scheduling is also a key issue.Based on multiprocessor computing systems,concerning randomly arrived task,OnLine Energy-Aware Scheduling Algorithm(OLEAS) was proposed.The algorithm meeting the task deadlines under the premise possibly puts the task scheduler on the least energy consumption producing processor.When a task on all the processors could not meet the deadline requirements,the part of the task between the processors shall be adjusted possibly to meet the deadline requirements.Meanwhile,OLEAS was in a bid to execute the task on a single processor according to the average voltage/frequency,thus reducing the energy consumption.When the new task did not meet the deadline requirements,the former voltage/frequency of unexecuted tasks should be one by one adjusted higher.Compared with the performance of EFF(Earliest Finish First),HVEA(Highest Voltage Energy-Aware),LVEA(Lowest Voltage Energy-Aware),MEG(Minimum Energy Greedy) and ME-MC(Minimum Energy Minimum Completion time) in simulated experiments,the final result shows OLEAS owns obviously comprehensive advantage in the aspect of meeting task deadlines and energy consumption saving.

Requirement-Aware Scheduling of Bag-of-Tasks Applications on Grids with Dynamic Resilience

Grids have been extensively deployed to handle various scientific and engineering applications that can be structured as bag-of-tasks (BoT). The scheduling of BoT applications on Grids is an important issue for achieving high performance. Grid scheduling involves a number of challenging issues, mainly due to the dynamic nature of the Grid. To deal with this dynamic nature, in this paper, we propose an online scheduling algorithm called prudent algorithm with replication (PAR) for scheduling Grid applications. PAR is shown to prudently make scheduling decisions in such a way that it can tolerate inaccurate performance predictions. Another point to note is that PAR adopts task duplication as an attempt to reduce serious schedule increases. Moreover, since the applications to be performed may widely vary in terms of their required hardware and software, we also capture the loads' various processing requirements in our algorithms, a unique feature that is applicable for running proprietary applications only on certain eligible processing nodes. Thus, in our problem formulation each application can only be processed by certain processors as both the applications and processing nodes are heterogeneous. We then present a task selection policy, referred to as requirement-aware load selection (RALS) policy to handle the contention of multiple applications that have various processing requirements but share the same computing resources. Based on RALS and PAR, we develop two scheduling algorithms: requirement-aware prudent algorithm with replication (RAPAR), and requirement-aware knowledge-free algorithm with replication (RAKAR). RAPAR and RAKAR address the scheduling of multiple BoT applications with heterogeneous processing requirements on Grids. RAPAR works in scenarios where inaccurate performance prediction information is provided whereas RAKAR works without any prediction information. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches when compared to existing algorithms.

TESLA: an extended study of an energy-saving agent that leverages schedule flexibility

This paper presents transformative energy-saving schedule-leveraging agent (TESLA), an agent for optimizing energy usage in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. This paper provides four key contributions: (i) online scheduling algorithms, which are at the heart of TESLA, to solve a stochastic mixed integer linear program for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; (iii) an extensive analysis on energy savings achieved by TESLA; and (iv) surveys of real users which indicate that TESLA’s assumptions of user flexibility hold in practice. TESLA was evaluated on data gathered from over 110,000 meetings held at nine campus buildings during an 8-month period in 2011–2012 at the University of Southern California and Singapore Management University. These results and analysis show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.

Equilibria of Online Scheduling Algorithms

We describe a model for competitive online scheduling algorithms. Two servers, each with a single observable queue, compete for customers. Upon arrival, each customer strategically chooses the queue with minimal expected wait time. Each scheduler wishes to maximize its number of customers, and can strategically select which scheduling algorithm, such as First-Come-First-Served (FCFS), to use for its queue. This induces a game played by the servers and the customers. We consider a non-Bayesian setting, where servers and customers play to maximize worst-case payoffs. We show that there is a unique subgame perfect safety-level equilibrium and we describe the associated scheduling algorithm (which is not FCFS). The uniqueness result holds for both randomized and deterministic algorithms, with a different equilibrium algorithm in each case. When the goal of the servers is to minimize competitive ratio, we prove that it is an equilibrium for each server to apply FCFS: each server obtains the optimal competitive ratio of 2.

Exploring blind online scheduling for mobile cloud multimedia services

Mobile cloud is a new emerging technology which can be used to enable users to enjoy abundant multimedia applications in a pervasive computing environment. Therefore, the scheduling of massive multimedia flows with heterogeneous QoS guarantees becomes an important issue for the mobile cloud. Generally, the predominant popular cloud-based scheduling algorithms assume that the request rate and service time, are available for the system operator. However, this assumption can hardly be maintained in many practical scenarios, especially for the largescale mobile cloud. In this article, we consider the scheduling problem for a practical mobile cloud in which the above parameters are unavailable and unknown. Taking into account the performance of the users and the impartial free time among the servers, the highlight of this article lies in proposing a blind online scheduling algorithm (BOSA). Specifically, we assign available multimedia servers based on the last timeslot information of the users' requests, and route all the multimedia flows according to the first-come- first-served rule. Moreover, we design detailed steps to apply the BOSA to a content recommendation system, and show that the proposed BOSA can achieve asymptotic optimality.

Stochastic online appointment scheduling of multi-step sequential procedures in nuclear medicine

The increased demand for medical diagnosis procedures has been recognized as one of the contributors to the rise of health care costs in the U.S. in the last few years. Nuclear medicine is a subspecialty of radiology that uses advanced technology and radiopharmaceuticals for the diagnosis and treatment of medical conditions. Procedures in nuclear medicine require the use of radiopharmaceuticals, are multi-step, and have to be performed under strict time window constraints. These characteristics make the scheduling of patients and resources in nuclear medicine challenging. In this work, we derive a stochastic online scheduling algorithm for patient and resource scheduling in nuclear medicine departments which take into account the time constraints imposed by the decay of the radiopharmaceuticals and the stochastic nature of the system when scheduling patients. We report on a computational study of the new methodology applied to a real clinic. We use both patient and clinic performance measures in our study. The results show that the new method schedules about 600 more patients per year on average than a scheduling policy that was used in practice by improving the way limited resources are managed at the clinic. The new methodology finds the best start time and resources to be used for each appointment. Furthermore, the new method decreases patient waiting time for an appointment by about two days on average.

Network Coding-Based Mobile Video Streaming over Unreliable Wireless Links

Today's mobile video streaming performance on wireless networks is choppy due to the unpredictable wireless errors and mobility. In this Letter, we propose a practical online scheduling algorithm for mobile video streaming to multiple users with network coding capabilities. Implementation and analysis results show that our scheme closely approximates the optimal solution both mobile video quality and throughput.

Practical Online Scheduling for Mobile Video Streaming on Smartphones

Mobile video streaming is the killer application for wireless networks. Mobile video traffic accounts for more than 50% of the total traffic on mobile networks and will grow 66-fold over a period of five years. In addition, watching videos over mobile devices such as smartphones and tablets has been attracting interest from users, and demand for mobile video streaming is increasing. However, existing wireless technologies (e.g., WiFi, WiMax, or LTE) cannot support this impending demand. And today’s mobile video streaming performance on wireless networks is choppy due to unpredictable wireless errors and mobility. To support seamless mobile video streaming, several approaches, such as new video codecs, multiple interfaces, metric techniques, novel multiplexing and network coding schemes, were developed. However, these approaches need client modification and did not consider multiple streams sharing the same medium, realistic scenarios and mobility. This paper proposes a practical online scheduling algorithm for mobile video streaming to multiple users with network coding capabilities. Implementation and analysis results show that this scheme closely approximates the optimal solution for both mobile video quality and throughput.

Design and evaluation of token-based reservation for a roadway system

Traffic congestion has become a major source of fuel waste, economic burden, environmental pollution, and commuter frustration. A lot of effort has been made to alleviate these problems, but traditional countermeasures, such as expanding transportation capacity, are no longer a sustainable solution to the rapidly growing levels of congestion. Both governments and transportation planners are actively engaged in the development of traffic management strategies and policies, but their endeavors are not sufficient either. Therefore, it is imperative to further improve the efficiency of the existing road infrastructure by leveraging the latest technological advances in computing and networking. Recent studies have shown that emerging technologies in vehicular networks have made the roadway reservation system a viable option for mitigating traffic congestion and improving transportation efficiency. In this paper, we present a roadway reservation system, where the drivers make reservations in advance in order to enter a high-priority lane. The drivers who successfully make the reservation are guaranteed certain quality of service, i.e. end-to-end travel delay. We propose a token-based reservation system which monitors and controls the traffic workload in real-time. We have also designed an on-line scheduling algorithm which maintains sufficient transportation efficiency by selecting which reservation requests should be accepted. The algorithm also handles any necessary user interaction for reservation. A comprehensive performance evaluation based on our simulation model demonstrates the practicality and soundness of the proposed reservation system.

Scheduling Periodic Continuous Queries in Real-Time Data Broadcast Environments

On-demand broadcast is a promising data dissemination approach in mobile computing environments thanks to its adaptability and scalability for large-scale and dynamic workload. An important class of emerging data broadcast applications needs to monitor multiple time-varying data items continuously to be kept aware of the up-to-date information. This paper investigates the broadcast schedule problem for disseminating timely data to periodic continuous queries, and a systematic and highly efficient solution for applications of this type is provided. In particular, we propose a novel measure, called Bandwidth Utilization, to quantify the minimum bandwidth demand of a periodic continuous query set. The timing predictability can be ensured if a set of periodic continuous queries passes a bandwidth utilization based schedulability test. The schedulability test techniques are also extended to deal with dynamic query arrival and departure. An efficient online scheduling algorithm, called RM-UO, is developed, which can fulfill the timing constraints combined with the proposed query release and deletion policies. To demonstrate the effectiveness of theoretical results, an illustrative algorithm implementation is presented along with comprehensive performance analysis. Simulation results show that our solution offers nice timing predictability whereas other comparable best effort scheduling algorithms such as SIN-Q and DTIU experience different deadline miss ratios at different query workloads.

A Simulation and Verification Tool for Online Scheduling Algorithms Based on Component Technology

In this paper, we design and develop an extensible tool which is used to simulate and verify online scheduling algorithms based on component technology. After generating jobs sequence randomly, this tool can simulate algorithms’ execution process and find the optimal solution, then compare the results. Users can understand algorithms’ rules better and then design new algorithms with the help of this tool.

Markov Decision Process Based Energy-Efficient On-Line Scheduling for Slice-Parallel Video Decoders on Multicore Systems

We consider the problem of energy-efficient on-line scheduling for slice-parallel video decoders on multicore systems. We assume that each of the processors are Dynamic Voltage Frequency Scaling (DVFS) enabled such that they can independently trade off performance for power, while taking the video decoding workload into account. In the past, scheduling and DVFS policies in multi-core systems have been formulated heuristically due to the inherent complexity of the on-line multicore scheduling problem. The key contribution of this report is that we rigorously formulate the problem as a Markov decision process (MDP), which simultaneously takes into account the on-line scheduling and per-core DVFS capabilities; the power consumption of the processor cores and caches; and the loss tolerant and dynamic nature of the video decoder's traffic. In particular, we model the video traffic using a Direct Acyclic Graph (DAG) to capture the precedence constraints among frames in a Group of Pictures (GOP) structure, while also accounting for the fact that frames have different display/decoding deadlines and non-deterministic decoding complexities. The objective of the MDP is to minimize long-term power consumption subject to a minimum Quality of Service (QoS) constraint related to the decoder's throughput. Although MDPs notoriously suffer from the curse of dimensionality, we show that, with appropriate simplifications and approximations, the complexity of the MDP can be mitigated. We implement a slice-parallel version of H.264 on a multiprocessor ARM (MPARM) virtual platform simulator, which provides cycle-accurate and bus signal-accurate simulation for different processors. We use this platform to generate realistic video decoding traces with which we evaluate the proposed on-line scheduling algorithm in Matlab.

Fitting Scheduling Timing–Elastic Weighted Granting (FST–EWG): An EPON DBA Algorithm

Online scheduling algorithms incur no idle time problem but with less bandwidth efficiency, while offline scheduling algorithms focus on bandwidth efficiency but with idle time overhead. Although a just-in-time (JIT) scheduling algorithm for multi-channel Ethernet passive optical networks (EPONs) has been proposed to increase the bandwidth efficiency without the idle time overhead, its applicability to single-channel EPONs is problematic. A new algorithm called fitting scheduling timing–elastic weighted granting (FST–EWG) is proposed and is suitable for single-channel EPONs. First, the FST determines the fitting timing for performing the dynamic bandwidth allocation scheduling algorithm in an attempt to consider as many optical network unit (ONU) demands as possible and to avoid the unnecessary idle time as much as possible. Then the EWG can efficiently re-allocate the excess bandwidth of light-load ONUs to all heavy-load ONUs. The computation complexity of the proposed FST–EWG is linear and is denoted as O(N), where N is the number of ONUs. Simulation results show that FST–EWG efficiently allocates bandwidth compared to previously proposed algorithms in terms of average packet delay and packet dropping probability, especially for higher load. In addition, by adding the non-strict priority intra-ONU scheduling, FST–EWG also outperforms the previously proposed DBAM method in terms of EF average delay and EF delay variation.

Online Speed Scaling Based on Active Job Count to Minimize Flow Plus Energy

This paper is concerned with online scheduling algorithms that aim at minimizing the total flow time plus energy usage. The results are divided into two parts. First, we consider the well-studied "simple" speed scaling model and show how to analyze a speed scaling algorithm (called AJC) that changes speed discretely. This is in contrast to the previous algorithms which change the speed continuously. More interestingly, AJC admits a better competitive ratio, and without using extra speed. In the second part, we extend the study to a more general speed scaling model where the processor can enter a sleep state to further save energy. A new sleep management algorithm called IdleLonger is presented. This algorithm, when coupled with AJC, gives the first competitive algorithm for minimizing total flow time plus energy in the general model.

On Wireless Scheduling With Partial Channel-State Information

A time-slotted queueing system for a wireless downlink with multiple flows and a single server is considered, with exogenous arrivals and time-varying channels. It is assumed that only one user can be serviced in a single time slot. Unlike much recent work on this problem, attention is drawn to the case where the server can obtain only partial information about the instantaneous state of the channel. In each time slot, the server is allowed to specify a single subset of flows from a collection of observable subsets, observe the current service rates for that subset, and subsequently pick a user to serve. The stability region for such a system is provided. An online scheduling algorithm is presented that uses information about marginal distributions to pick the subset and the Max-Weight rule to pick a flow within the subset, and which is provably throughput-optimal. In the case where the observable subsets are all disjoint, or where the subsets and channel statistics are symmetric, it is shown that a simple scheduling algorithm-Max-Sum-Queue-that essentially picks subsets having the largest squared-sum of queues, followed by picking a user using Max-Weight within the subset, is throughput-optimal.