Leveraging Different Types of Predictors for Online Optimization (Invited Paper)

Instruction prefetching is an effective technique to reduce the instruction cache miss latency for improving the average-case performance. For real-time systems, however, the use of instruction prefetching will only besuitable if a reasonably tight worst-case performance of programs using instruction prefetching can be predicted. This paper presents an approach to modeling and computing the worst-case instruction cache performance with prefetching. Our experimental results indicate that instruction prefetching can benefit both the average-case and worst-case performance; however, the degree of the worst-case performance improvement due to instruction prefetching is less than that of the average-case performance, thus leading to increased time variation for real-time computing. Also, we find that the prefetching distance can significantly impact the worst-case performance analysis with instruction prefetching. Particularly, when the prefetching distance is equal to the L1 miss penalty, the worst-case execution time with instruction prefetching is minimized (i.e., optimal).

Average-case performance analysis of scheduling random parallel tasks with precedence constraints on mesh connected multicomputer systems

In this paper we consider a one-dimensional bin packing problem where the bins do not have identical sizes, or a variable-sized bin packing problem, to minimize the bin consumption, i.e., the total size of the opened bins. The identical size problem has been extensively studied in the literature both for static and dynamic settings. The worst-case or average-case performance has been analyzed. Our problem setting particularly arises in metal cutting industries. Therefore, it presents a great practical relevance. Four greedy approximation algorithms based on a construction approach called largest object first with least absolute waste (LFLAW), largest object first with least relative waste (LFLRW), least absolute waste (LAW), and least relative waste (LRW) are examined. Their absolute worst-case performances are analyzed. The worst case bounds are 2 for LFLAW and LFLRW, 3 for LAW, and 2+ln 2 for LRW. We also show that these worst-case bounds are tight.

We study competitive online algorithms for EV (electrical vehicle) charging under the scenario of an aggregator serving a large number of EVs together with its background load, using both its own renewable energy (for free) and the energy procured from the external grid. The goal of the aggregator is to minimize its peak procurement from the grid, subject to the constraint that each EV has to be fully charged before its deadline. Further, the aggregator can predict the future demand and the renewable energy supply with some levels of uncertainty. The key challenge here is how to develop a model that captures the prior knowledge from such prediction, and how to best utilize this prior knowledge to reduce the peak under future uncertainty. In this paper, we first propose a 2-level increasing precision model (2-IPM), to capture the system uncertainty. We develop a powerful computation approach that can compute the optimal competitive ratio under 2-IPM over any online algorithm, and also online algorithms that can achieve the optimal competitive ratio. A dilemma for online algorithm design is that an online algorithm with good competitive ratio may exhibit poor average-case performance. We then propose a new Algorithm-Robustification procedure that can convert an online algorithm with reasonable average-case performance to one with both the optimal competitive ratio and good average-case performance. The robustified version of a well-known heuristic algorithm, Receding Horizon Control (RHC), is found to demonstrate superior performance via trace-based simulations.

The current study was an attempt to examine the new application of the Adaptive Exploration Artificial Bee Colony algorithm in evolutionary clustering. An optimized version of the traditional Artificial Bee Colony (ABC) algorithm, AEABC relies heavily on a probability parameter that is based on distance. In this regard, a balanced relationship of exploration and exploitation leads to the dynamic parameter adaption, resulting in the spatial distribution of solutions. As a result, AEABC provides an efficient performance for solving dynamic clustering tasks, involving consistent data changes and constant reformation of cluster structures. To conduct the present study, the performance of AEABC on evolutionary clustering was compared with that of other metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results confirmed that AEABC produced lower performance error rates than competing algorithms in all three scenarios representing best case, worst case and average case performance. This established both its reliability as well as robust efficiency. AEABC indicated convergence performance because the algorithm initiates with quick development in the first stages then reached stable points effectively, which shows its ability to position itself well in changing data environments. Moreover, the findings could confirm AEABC as an effective means for evolutionary clustering, which makes it a suitable candidate for applications involving dynamic systems, such as social network analysis, customer segmentation, and anomaly detection. By applying AEABC to evolutionary clustering, this paper contributes to expanding the range of applications for swarm intelligence algorithms in handling real-world, dynamic data challenges

Designed to adapt spectrum usage on-the-fly, frequency-agile radios can drastically improve performance of wireless networks. Such flexibility, however, comes with a cost of increased hardware complexity. This motivates us to understand when and why having higher degree of frequency-agility helps and how much improvement it can lead to. In this paper, we approach this question by comparing two types of agile radios in the context of dynamic spectrum sharing in any given spectrum chunk. We consider 1-agile radios that use a single frequency channel but can adjust the channel's width and central frequency, and k-agile radios that can combine up to k non-contiguously aligned frequency segments into one transmission. We show that, due to inherent demand dynamics and conflict heterogeneity, networks using 1-agile radios often face the problem of spectrum fragmentation. But k-agile radios can effectively suppress this problem directly at the physical layer. Using theoretical analysis and simulation experiments, we quantify the advantage of k- agile radios over 1-agile radios in their network spectrum usage. For a fair comparison, we abstract the impact of demand and topology configurations by evaluating the worst case and average case performance. Our results show that in worst cases, the improvement of using fully-agile radios is arbitrarily large, although the improvement of using k-agile radios is upper bounded by k. In average cases, the improvement reduces to 10-40% under typical network configurations. Interestingly, in the context of dynamic spectrum sharing, 2-agile radios realize the majority of the improvement brought by fully-agile radios.

This paper addresses the problem of assessing the effect of communication delays on the control performance of a platoon of two vehicles. We consider Bluetooth communication and develop a simple stochastic model for the Bluetooth delay distribution. It is shown that due to the long tails of this distribution, the computation time of a Monte Carlo approach would be prohibitive when assessing the effect of long communication delays on the control performance. Hence, we propose a novel and fast simulation approach that is computationally more efficient than pure Monte Carlo simulations. Simulations on a platoon of two vehicles illustrate the effectiveness of the approach for assessing average and worst case performance or for approximating the probability density function of the control performance.

In this article, we formalize the cyclic sequencing problem in the two-machine flow shop. When jobs are processed in a repetitive cycle, the size of a scheduling problem is significantly reduced, and the resulting schedule is easy to implement because of its simplicity. Two types of cyclic sequencing problems are considered: the no-wait problem and the minimum-wait problem. The no-wait problem maximizes the throughput rate subject to the condition that there is no buffer space between the two machines. The minimum-wait problem minimizes the average WIP level subject to the conditions that the maximum throughput rate is maintained and that the FIFO dispatching rule is used in the intermediate buffer space. The no-wait problem is a well-known special case of the traveling salesman problem (TSP) and is polynomially solvable. The minimum-wait problem is shown to be NP-hard; therefore, we develop a heuristic procedure along with the analysis of its worst-case and average-case performance. Here, the average-case analysis is based on the expected length of the Hamiltonian tour for this special case of the TSP. The average-case analysis indicates that when the number of jobs in a cycle is small, the derived cyclic schedule yields a low WIP level.

File designs suitable for retrieval from a file of k -field records when queries may be partially specified are examined. Storage redundancy is introduced to obtain improved worst-case and average-case performances. The resulting storage schemes are appropriate for replicated distributed database environments; it is possible to improve the overall average and worst-case behavior for query response as well as provide an environment with very high reliability. Within practical systems it will be possible to improve the query response time performance as well as reliability over comparable systems without replication.

We consider online convex optimization (OCO) problems with switching costs and noisy predictions. While the design of online algorithms for OCO problems has received considerable attention, the design of algorithms in the context of noisy predictions is largely open. To this point, two promising algorithms have been proposed: Receding Horizon Control (RHC) and Averaging Fixed Horizon Control (AFHC). The comparison of these policies is largely open. AFHC has been shown to provide better worst-case performance, while RHC outperforms AFHC in many realistic settings. In this paper, we introduce a new class of policies, Committed Horizon Control (CHC), that generalizes both RHC and AFHC. We provide average-case analysis and concentration results for CHC policies, yielding the first analysis of RHC for OCO problems with noisy predictions. Further, we provide explicit results characterizing the optimal CHC policy as a function of properties of the prediction noise, e.g., variance and correlation structure. Our results provide a characterization of when AFHC outperforms RHC and vice versa, as well as when other CHC policies outperform both RHC and AFHC.

Equitable and sufficient charging infrastructure is required for transport decarbonization to reach its goals. Despite increased electric vehicle infrastructure roll out rates, there is still considerable uncertainty regarding the charging market. For example, studies have evidenced disparities in electric vehicle charging placement, however, predictable as the market caters for early adopters. While there is an emerging discourse surrounding social equity in charging infrastructure, this is scattered across interdisciplinary research covering broader aspects of electric vehicle infrastructure provision with a lack of studies consolidating issues. This study aims to synthesize evidence on social equity in various aspects of electric vehicle charging infrastructure provision and set an agenda for centering social equity in the debate. Findings of this critical synthesis of research have helped to draw out the complexities involved in the equitable roll out of electric vehicle charging infrastructure, which are interlinked with an array of other dimensions including the affordability of electric vehicle purchase. Research into solutions and best practice has shown examples of local target setting, monetary incentives (grants, loans and rebates for electric vehicle purchase and charging infrastructure and smart energy tariffs) and other policy incentives (increased public overnight charging, electric car-clubs, extended battery warranties for second-hand vehicles) that can or have been employed to redress the balance. The outcomes could be utilized when developing and implementing electric vehicle strategies to support uptake across all people. Policy implications and further study suggested could ensure that communities and individuals are not locked out of the benefits of investment.

In this paper, we study the problem of allocating a set of periodic tasks on a multiprocessor system such that tasks are scheduled to meet their deadlines on individual processors by the Rate-Monotonic scheduling algorithm. A new schedulability condition is developed for the Rate-Monotonic scheduling that allows us to develop more efficient on-line allocation algorithms. Two on-line allocation algorithms—RM-FF and RM-BF are presented, and shown that their worst-case performance, over the optimal allocation, is upper bounded by 2.33 and lower bounded by 2.28. Then RM-FF and RM-BF are further improved to form two new algorithms: Refined-RM-FF (RRM-FF) and Refined-RM-BF (RRM-BF), both of which have a worst-case performance bound of 2. We also show that when the maximum allowable utilization of a task is small, the worst-case performance of all the new algorithms can be significantly improved. The worst-case performance bounds of RRM-FF and RRM-BF are currently the best bounds in the class of on-line scheduling algorithms proposed to solve the same scheduling problem. Simulation studies show that the average-case performance of the newly proposed algorithms is significantly superior to those in the existing literature.

Battery charging and battery swapping are the two major techniques to re-energize an electric vehicle. This paper for the first time looks into the joint control problem in a battery charging and swapping station that adopts both re-energizing techniques. A cost minimization problem in the form of mixed integer linear programming is formulated to optimally find the amount charging power and/or the battery pack to swap in at the same time for each EV that requests service. The utility cost under a dynamic energy pricing scheme and the cost associated with battery aging are carefully modeled. Experimental results show that the proposed optimization framework consistently reduce the total energy cost by 80%, 11% and 63% comparing with three baseline algorithms.

We propose and evaluate the performance of communication algorithms for the NP-hard transmission time minimisation problem on multihop wireless networks modelled by directed graphs with multiple channels. We derive a lower bound for the minimum transmission time so that our heuristic solutions can be compared with optimal schedules. We present one randomised routing algorithm and nine heuristic transmission scheduling algorithms and demonstrate by extensive simulations on random multihop wireless networks that all these transmission scheduling algorithms have average-case performance reasonably close to that of optimal schedules. Furthermore, we show that the performance of our algorithms for undirected networks is significantly better than the pipelined breadth-first search tree algorithm by exploiting more transmission concurrency. We indicate that our transmission scheduling algorithms can be easily adapted to solve the related NP-hard throughput maximisation problem and present and compare their average-case performance by extensive simulations on random multihop wireless networks. We also demonstrate simulation data on the impact of the transmission power on transmission schedule length.

Network interdiction problems by upgading critical edges/nodes have important applications to reduce the infectivity of the COVID-19. A network of confirmed cases can be described as a rooted tree that has a weight of infectious intensity for each edge. Upgrading edges (nodes) can reduce the infectious intensity with contacts by taking prevention measures such as disinfection (treating the confirmed cases, isolating their close contacts or vaccinating the uninfected people). We take the sum of root-leaf distance on a rooted tree as the whole infectious intensity of the tree. Hence, we consider the sum of root-leaf distance interdiction problem by upgrading edges/nodes on trees (SDIPT-UE/N). The problem (SDIPT-UE) aims to minimize the sum of root-leaf distance by reducing the weights of some critical edges such that the upgrade cost under some measurement is upper-bounded by a given value. Different from the problem (SDIPT-UE), the problem (SDIPT-UN) aims to upgrade a set of critical nodes to reduce the weights of the edges adjacent to the nodes. The relevant minimum cost problem (MCSDIPT-UE/N) aims to minimize the upgrade cost on the premise that the sum of root-leaf distance is upper-bounded by a given value. We develop different norms to measure the upgrade cost. Under weighted Hamming distance, we show the problems (SDIPT-UE/N) and (MCSDIPT-UE/N) are NP-hard by showing the equivalence of the two problems and the 0–1 knapsack problem. Under weighted l_1 norm, we solve the problems (SDIPT-UE) and (MCSDIPT-UE) in O(n) time by transforimg them into continuous knapsack problems. We propose two linear time greedy algorithms to solve the problem (SDIPT-UE) under unit Hamming distance and the problem (SDIPT-UN) with unit cost, respectively. Furthermore, for the the minimum cost problem (MCSDIPT-UE) under unit Hamming distance and the problem (MCSDIPT-UN) with unit cost, we provide two O(nlog n) time algorithms by the binary search methods. Finally, we perform some numerical experiments to compare the results obtained by these algorithms.