Serverless edge computing is an efficient way to execute event-driven, short-duration, and bursty IoT data processing tasks on resource-limited edge servers, using on-demand resource allocation and dynamic auto-scaling. In this paradigm, function requests are handled in virtualized environments,e.g., containers. When a function request arrives online, if there is no container in memory to execute it, the serverless platform will initialize such a container with non-negligible latency, known as cold start. Otherwise, it results in a warm start with no latency in previous studies. However, based on our experiments, we find there is a remarkable third case called Late-Warm,i.e., when a request arrives during the container initializing, its latency is less than a cold start but not zero. In this paper, we study online container caching in serverless edge computing to minimize the total latency with Late-Warm and other practical issues considered. We proposeOnCoLa, a novel$O(T_{c}K)$-competitive algorithm supporting request relaying on multiple edge servers. Here,$T_{c}$and$K$are the maximum container cold start latency and the memory size, respectively. Extensive simulations on two real-world traces demonstrate thatOnCoLaconsistently outperforms the state-of-the-art container caching algorithms and reduces the latency by$23.33\%$. Experiments on Raspberry Pi and Jetson Nano show thatOnCoLareduces latency by up to$21.38\%$compared with the representative lightweight policy.

Serverless edge computing is an efficient way to execute event-driven, short-duration, and bursty IoT data processing tasks on resource-limited edge servers, using on-demand resource allocation and dynamic auto-scaling. In this paradigm, function requests are handled in virtualized environments, e.g., containers. When a function request arrives online, if there is no container in memory to execute it, the serverless platform will initialize such a container with non-negligible latency, known as cold start. Otherwise, it results in a warm start with no latency in previous studies. However, based on our experiments, we find there is a remarkable third case called Late-Warm, i.e., when a request arrives during the container initializing, its latency is less than a cold start but not zero. In this paper, we study online container caching in serverless edge computing to minimize the total latency with Late-Warm and other practical issues considered. We propose OnCoLa, a novel$O(T_{c}^{3}/2K)$-competitive algorithm supporting request relaying on multiple edge servers. Here, Tc and$K$are the maximum container cold start latency and the memory size, respectively. Experiments on Raspberry Pi and Jetson Nano with OpenFaaS and faasd using common IoT data processing tasks show that OnCoLa reduces latency by up to 21.38% compared with representative lightweight policies. Extensive simulations on two real-world traces demonstrate that OnCoLa consistently outperforms the state-of-the-art container caching algorithms and reduces the latency by 27.8%.

In Mobile Edge Computing, edge servers have limited storage and computing resources that can only support a small number of functions. Meanwhile, mobile applications are becoming more complex, consisting of multiple dependent tasks, modeled as a Directed Acyclic Graph (DAG). When a request arrives, typically in an online manner with a deadline specified, we need to configure the servers and assign the dependent tasks for efficient processing. This work jointly considers the problem of dependent task placement and scheduling with on-demand function configuration on edge servers, aiming to meet as many deadlines as possible. For a single request, when the configuration on each edge server is fixed, we derive FixDoc to find the optimal task placement and scheduling. When the on-demand function configuration is allowed, we propose GenDoc , a novel approximation algorithm, and analyze its additive error from the optimal theoretically. For multiple requests, we derive OnDoc , an online algorithm easy to deploy in practice. Our extensive experiments show that GenDoc outperforms state-of-the-art baselines in processing 86.14% of these unique applications, and reduces their average completion time by at least 24%. The number of deadlines that OnDoc can satisfy is at least 1.9× that of the baselines.

Edge cloud computing is a new paradigm in which the computation and storage services of remote cloud data centers are moved to Edge Cloud Nodes (ECNs) in network edges. Compared to traditional cloud data centers, ECNs are geographically close to mobile users so the communication latency is significantly reduced. In this paper, we study the problem of allocating Virtual Machine (VM) resources in geo-distributed ECNs to mobile users by using the auction theory. First, we treat mobile users and ECNs as the buyers and sellers of the VM resource auction, respectively. Then, we model the VM resource allocation problem as an$n$-to-one weighted bipartite graph matching problem with 0-1 knapsack constraints. Since this problem is NP-hard, we design a greedy approximation algorithm to determine the winners of the auction, based on which we propose a truthful Auction-based VM resource Allocation (AVA) mechanism to solve the problem. Moreover, we prove that the AVA mechanism not only achieves an approximately optimal solution for winner selection, but also has the properties of truthfulness, individual rationality, and computational efficiency. Finally, we conduct extensive simulations on real traces to verify the significant performances of the proposed AVA mechanism.

Mobile crowdsensing, through which a requester can coordinate a crowd of workers to complete some sensing tasks, has attracted significant attention recently. In this paper, we focus on the unknown worker recruitment problem in mobile crowdsensing, where workers' sensing qualities are unknown a priori. We consider the scenario of recruiting workers to complete some continuous sensing tasks. The whole process is divided into multiple rounds. In each round, every task may be covered by more than one recruited workers, but its completion quality only depends on these workers' maximum sensing quality. Each recruited worker will incur a cost, and each task is attached a weight to indicate its importance. Our objective is to determine a recruiting strategy to maximize the total weighted completion quality under a limited budget. We model such an unknown worker recruitment process as a novel combinatorial multi-armed bandit problem, and propose an extended UCB based worker recruitment algorithm. Moreover, we extend the problem to the case where the workers' costs are also unknown and design the corresponding algorithm. We analyze the regrets of the two proposed algorithms and demonstrate their performance through extensive simulations on real-world traces.

Mobile crowdsensing, through which a requester can recruit a group of crowd workers via a platform and coordinate them to perform some sensing tasks, has attracted lots of attention recently. However, most of the existing mobile crowdsensing systems assume that the qualities of workers are known in advance. Based on this assumption, they study the task assignment and worker recruitment problems. Unfortunately, the qualities of workers are generally unknown in reality, so the platform must find the tradeoff between exploring and exploiting the qualities by using reinforcement learning. At the same time, all sensing tasks are required to be covered in each round (covering constraint), and the requester usually has a limited budget (budget constraint). In this paper, we study how to recruit unknown workers under the budget and covering constraints so that the total expected achieved qualities can be maximized. To this end, we model the problem as a combination of a maximum weight matching problem and a special multi-armed bandit problem. We first consider that the recruitment costs of workers are homogeneous and propose a recruitment algorithm with a performance guarantee. Then, we study the heterogenous case and devise a heuristic algorithm. Finally, we demonstrate the performances of our algorithms through extensive simulations.

Recently, influence maximization in social networks has attracted great attention. In this paper, we consider that a company intends to select some users to promote its multiple products (called influences) in online social network consisting of many communities, in which each user has different preferences for each influence. We focus on the Minimum Cost Seed Selection (MCSS) problem for multiple influences, that is, how to select some seeds with minimum cost so that the average influenced probability of all users in each community is not less than a threshold. To solve the MCSS problem, we design a submodular utility function, based on which we turn our problem into a non-trivial set cover problem with non-linear constraints. After proving the NP-hardness of MCSS, we propose a greedy algorithm, called G-MCSS, to solve it. We analyze the approximation ratio of G-MCSS. Additionally, we extend the MCSS problem to a complex case, where the number of acceptable influences for each user is limited, and the cost is proportional to the number of allocated influences. We further propose another greedy algorithm to solve the extended problem. Finally, we demonstrate the significant performances of our algorithms through extensive experiments based on real social network traces.

Population-based Algorithm Portfolios (PAP) is an appealing framework for integrating different Evolutionary Algorithms (EAs) to solve challenging numerical optimization problems. Particularly, PAP has shown significant advantages to single EAs when a number of problems need to be solved simultaneously. Previous investigation on PAP reveals that choosing appropriate constituent algorithms is crucial to the success of PAP. However, no method has been developed for this purpose. In this paper, an extended version of PAP, namely PAP based on Estimated Performance Matrix (EPM-PAP) is proposed. EPM-PAP is equipped with a novel constituent algorithms selection module, which is based on the EPM of each candidate EAs. Empirical studies demonstrate that the EPM-based selection method can successfully identify appropriate constituent EAs, and thus EPM-PAP outperformed all single EAs considered in this work.

The ever-intensifying time-to-market pressure imposes great challenges on the pre-silicon design phase of hardware. Before the tape-out, a pre-silicon design has to be thoroughly inspected by time-consuming functional verification and code review to exclude bugs. For functional verification and code review, a critical issue determining their efficiency is the allocation of resources (e.g., computational resources and manpower) to different modules of a design, which is conventionally guided by designers' experiences. Such practices, though simple and straightforward, may take high risks of wasting resources on bug-free modules or missing bugs in buggy modules, and thus could affect the success and timeline of the tape-out. In this paper, we propose a novel framework called pre-silicon bug forecast to predict the bug information of hardware designs. In this framework, bug models are built via machine learning techniques to characterize the relationship between design characteristics and the bug information, which can be leveraged to predict how bugs distribute in different modules of the current design. Such predicted bug information is adequate to regulate the resources among different modules to achieve efficient functional verification and code review. To evaluate the effectiveness of the proposed pre-silicon bug forecast framework, we conducted detailed experiments on several open-source hardware projects. Moreover, we also investigate the impacts of different learning techniques and different sets of characteristic on the performance of bug models. Experimental results show that with appropriate learning techniques and characteristics, about 90% modules could be correctly predicted as buggy or clean and the number of bugs of each module could also be accurately predicted.

The multiple longest common subsequence (MLCS) problem, related to the identification of sequence similarity, is an important problem in many fields. As an NP-hard problem, its exact algorithms have difficulty in handling large-scale data and time- and space-efficient algorithms are required in real-world applications. To deal with time constraints, anytime algorithms have been proposed to generate good solutions with a reasonable time. However, there exists little work on space-efficient MLCS algorithms. In this paper, we formulate the MLCS problem into a graph search problem and present two space-efficient anytime MLCS algorithms, SA-MLCS and SLA-MLCS. SA-MLCS uses an iterative beam widening search strategy to reduce space usage during the iterative process of finding better solutions. Based on SA-MLCS, SLA-MLCS, a space-bounded algorithm, is developed to avoid space usage from exceeding available memory. SLA-MLCS uses a replacing strategy when SA-MLCS reaches a given space bound. Experimental results show SA-MLCS and SLA-MLCS use an order of magnitude less space and time than the state-of-the-art approximate algorithm MLCS-APP while finding better solutions. Compared to the state-of-the-art anytime algorithm Pro-MLCS, SA-MLCS and SLA-MLCS can solve an order of magnitude larger size instances. Furthermore, SLA-MLCS can find much better solutions than SA-MLCS on large size instances.

In response to the high demand of big data analytics, several programming models on large and distributed cluster systems have been proposed and implemented, such as MapRe-duce, Dryad and Pregel. However, compared with high performance computing areas, the basis and principles of computation and communication behavior of big data analytics is not well studied. In this paper, we review the current big data computational model DOT and DOTA, and propose a more general and practical model p-DOT (p-phases DOT). p-DOT is not a simple extension, but with profound significance: for general aspects, any big data analytics job execution expressed in DOT model or BSP model can be represented by it; for practical aspects, it considers I/O behavior to evaluate performance overhead. Moreover, we provide a cost function implying that the optimal number of machines is near-linear to the square root of input size for a fixed algorithm and workload, and demonstrate the effectiveness of the function through several experiments.

A common practice for reducing synchronization overheads in parallel simulation of a large-scale cluster is to relax synchronization with lengthened synchronous steps. However, as a side effect, simulation accuracy degrades considerably. This paper proposes a novel mechanism that keeps the running speeds of different nodes consistent by synchronizing logical clocks with the wall clock periodically within each lax step. Because speed deviations of nodes are the main source of time causality errors, through aligning speeds our mechanism only causes modest precision loss while achieving a close performance to lax synchronization. The experimental results show that it improves the performance by 2 to 11 times relative to the baseline barrier synchronization with a high accuracy (e.g. 99% in most cases). Compared to the recently proposed adaptive mechanism, it also achieves nearly 30% performance improvement.

An enormous amount of sequence data has been generated with the development of new DNA sequencing technologies, which presents great challenges for computational biology problems such as haplotype phasing. Although arduous efforts have been made to address this problem, the current methods still cannot efficiently deal with the incoming flood of large-scale data. In this paper, we propose a flow network model to tackle haplotype phasing problem, and explain some classical haplotype phasing rules based on this model. By incorporating the heuristic knowledge obtained from these classical rules, we design an algorithm FNphasing based on the flow network model. Theoretically, the time complexity of our algorithm is (O(n(2)m+m(2)), which is better than that of 2SNP, one of the most efficient algorithms currently. After testing the performance of FNphasing with several simulated data sets, the experimental results show that when applied on large-scale data sets, our algorithm is significantly faster than the state-of-the-art Beagle algorithm. FNphasing also achieves an equal or superior accuracy compared with other approaches.

Complex features, such as temporal dependencies and numerical cost constraints, are hallmarks of real-world planning problems. In this article, we consider the challenging problem of cost-sensitive temporally expressive (CSTE) planning, which requires concurrency of durative actions and optimization of action costs. We first propose a scheme to translate a CSTE planning problem to a minimum cost (MinCost) satisfiability (SAT) problem and to integrate with a relaxed parallel planning semantics for handling true temporal expressiveness. Our scheme finds solution plans that optimize temporal makespan, and also minimize total action costs at the optimal makespan. We propose two approaches for solving MinCost SAT. The first is based on a transformation of a MinCost SAT problem to a weighted partial Max-SAT (WPMax-SAT), and the second, called BB-CDCL, is an integration of the branch-and-bound technique and the conflict driven clause learning (CDCL) method. We also develop a CSTE customized variable branching scheme for BB-CDCL which can significantly improve the search efficiency. Our experiments on the existing CSTE benchmark domains show that our planner compares favorably to the state-of-the-art temporally expressive planners in both efficiency and quality.

Cloud computing is emerging as a prominent computing model. It provides a low-cost, highly accessible alternative to other traditional high-performance computing platforms. It also has many other benefits such as high availability, scalability, elasticity, and free of maintenance. Given these attractive features, it is very desirable if automated planning can exploit the large, affordable computational power of cloud computing. However, the latency in inter-process communication in cloud computing makes most existing parallel planning algorithms unsuitable for cloud computing. In this paper, we propose a portfolio stochastic search framework that takes advantage of and is suitable for cloud computing. We first study the running time distribution of Monte-Carlo Random Walk (MRW) search, a stochastic planning algorithm, and show that the running time distribution usually has remarkable variability. Then, we propose a portfolio search algorithm that is suitable for cloud computing, which typically has abundant computing cores but high communication latency between cores. Further, we introduce an enhanced portfolio with multiple parameter settings to improve the efficiency of the algorithm. We implement the portfolio search algorithm in both a local cloud and the Windows Azure cloud. Experimental results show that our algorithm achieves good, in many cases super linear, speedup in the cloud platforms. Moreover, our algorithm greatly reduces the running time variance of the stochastic search and improves the solution quality. We also show that our scheme is economically sensible and robust under processor failures.

Single carrier frequency division multiple access (SC-FDMA) has been receiving much attention as the uplink multiple access technology in the next generation communication systems due to its lower peak-to-average power ratio (PAPR) compared to OFDMA. However, it was shown that PAPR is still an issue for SCFDMA, especially with the localized subcarrier allocation (SC-LFDMA) scheme. The precoding method has been shown to be effective in reducing the peak power. However, the construction of the codewords is a nondeterministic polynomial-time hard (NP-hard) problem. In this paper, we first formulate the problem of PAPR reduction by precoding as a combinatorial problem, and then propose the semidefinite relaxation approach with which the problem can then be solved in polynomial time. It will be shown that the proposed scheme can efficiently reduce the peak power with much lower complexity. By taking the transmit power limit into consideration, we further demonstrate the existence of a tradeoff between the transmit power increase and the peak power reduction. Specifically, less stringent power constraint will lead to more significant PAPR reduction.

Carrier frequency offset (CFO) damages the orthogonality between sub-carriers and thus causes multiuser interference in uplink OFDMA/SC-FDMA systems. For a given CFO, such multiuser interference is mainly dictated by channel (sub-carrier) allocation, which also specifies the diversity gain of one user over multi-path channels. In particular, the positions of one user's sub-channels will determine its diversity gain, while the distances between sub-channels of the concerned user and those of others will govern the CFO interference. Two popular channel allocation methods are the localized and interleaved (distributed) schemes where the former has less CFO interference but the latter achieves more diversity gain. In this paper, we will consider the channel allocation scheme for uplink LTE systems by investigating the effects of channel allocation on both of the diversity gain and CFO interference. By combining these two effects, we will propose a semi-interleaved scheme, which achieves full diversity gain with minimum CFO interference.

Due to its lower peak-to-average power ratio (PAPR) compared with orthogonal frequency division multiple access (OFDMA), single carrier frequency division multiple access (SC-FDMA) has been recently accepted as the uplink multiple access scheme in the Long Term Evolution (LTE) of cellular systems by the Third Generation Partnership Project (3GPP). However, similar to OFDMA, carrier frequency offset (CFO) can destroy the orthogonality among subcarriers and degrade the performance of SC-FDMA. To mitigate the effect of CFOs, we propose a combined minimum mean square error frequency-domain equalization (MMSE-FDE) and interference cancellation scheme. In this scheme, joint FDE with CFO compensation (JFC) is utilized to obtain the initial estimation for each user. In contrast to previous schemes, where the FDE and CFO compensation are done separately, in JFC, the MMSE FDE is designed to suppress the MUI after CFO compensation. To further eliminate the MUI, we combine JFC with parallel interference cancellation (PIC). In particular, we iteratively design the MMSE FDE equalizer to suppress the remaining MUI at each stage and obtain better estimation. Simulation results show that the proposed scheme can significantly improve the system performance.

Energy efficient asynchronous duty cycle MAC protocols are crucial to the success of wireless sensor networks (WSNs). In asynchronous protocols, each node operates its active/sleep schedule independently and enjoys a very low duty cycle when there is no traffic. However, when a node has data to send, it has to keep active and wait until the receiver wakes up due to the lack of schedule knowledge of the receiver. In low duty cycle networks, where nodes wake up infrequently, a sender usually suffers a long period of waiting, which consumes more energy than transmitting data packet itself. In this paper, we propose a new asynchronous MAC protocol, called OCMAC, which decreases the waiting time of sender by exploring opportunistic cooperation among senders. In OC-MAC, neighboring active senders are permitted to exchange data with each other aggressively when waiting for receivers to wake up. After delegating data to another sender, a sender can go to sleep before its receiver wakes up. Though this cooperation works only when there are multiple neighboring active senders, itself almost incurs no additional overhead. Simulation results and measurements on a testbed have shown that OC-MAC helps decrease idle listening, collision and end-to-end delay further.

A key problem of relational database is to aggregate different values of the same object and find the first k objects with highest overall values. Many sequential algorithms have been proposed to solve this problem. In this paper, we propose a new parallel algorithm using symmetry breaking strategy. New algorithm is proved to be instance optimal. Experiment results on both synthetic and real data also show that new algorithm costs fewer accesses, compared to previous algorithms.

With recent advances of processor technology, the LRU based shared last-level cache (LLC) has been widely employed in modern Chip Multi-processors (CMP). However, past research indicates that the cache performance of the LLC and further of the CMP processors may be degraded severely by LRU under the occurrence of the inter-thread interference or the excess of the working set size over the cache size. Existing approaches tackling this performance degradation problem have limited improvement of an overall cache performance because they usually focus on a single type of memory access behavior and thus lack full consideration of tradeoffs among different types of memory access behaviors. In this paper, we propose a unified cache management policy called Partitioning-Aware Eviction and Thread-aware Insertion/Promotion policy (PAE-TIP) that can effectively enhance capacity management, adaptive insertion/promotion, and further improve the overall cache performance. Specifically, PAE-TIP employs an adaptive mechanism to decide the position where to put the incoming lines or to move the hit lines, and chooses a victim line based on the target partitioning given by utility-based cache partitioning (UCP). In our study, we show that PAE-TIP can cover a variety of memory access behaviors simultaneously and provide a good tradeoff for overall cache performance improvement while retaining competitively low hardware and design overhead. The evaluation conducted on 4-way CMPs shows that the PAE-TIP-managed LLC can improve overall performance by19.3% on average over the LRU policy. Furthermore, the performance benefit of PAE-TIP is 1.09x compared to PIPP, 1.11x compared to TADIP and 1.12x compared to UCP.

Estimation of distribution algorithms (EDAs) are widely used in stochastic optimization. Impressive experimental results have been reported in the literature. However, little work has been done on analyzing the computation time of EDAs in relation to the problem size. It is still unclear how well EDAs (with a finite population size larger than two) will scale up when the dimension of the optimization problem (problem size) goes up. This paper studies the computational time complexity of a simple EDA, i.e., the univariate marginal distribution algorithm (UMDA), in order to gain more insight into EDAs complexity. First, we discuss how to measure the computational time complexity of EDAs. A classification of problem hardness based on our discussions is then given. Second, we prove a theorem related to problem hardness and the probability conditions of EDAs. Third, we propose a novel approach to analyzing the computational time complexity of UMDA using discrete dynamic systems and Chernoff bounds. Following this approach, we are able to derive a number of results on the first hitting time of UMDA on a well-known unimodal pseudo-boolean function, i.e., the LeadingOnes problem, and another problem derived from LeadingOnes, named BVLeadingOnes. Although both problems are unimodal, our analysis shows that LeadingOnes is easy for the UMDA, while BVLeadingOnes is hard for the UMDA. Finally, in order to address the key issue of what problem characteristics make a problem hard for UMDA, we discuss in depth the idea of ¿margins¿ (or relaxation). We prove theoretically that the UMDA with margins can solve the BVLeadingOnes problem efficiently.

In this paper, we consider the scenario that a population-based algorithm is applied to a numerical optimization problem and a solution needs to be presented within a given time budget. Although a wide range of population-based algorithms, such as evolutionary algorithms, particle swarm optimizers, and differential evolution, have been developed and studied under this scenario, the performance of an algorithm may vary significantly from problem to problem. This implies that there is an inherent risk associated with the selection of algorithms. We propose that, instead of choosing an existing algorithm and investing the entire time budget in it, it would be less risky to distribute the time among multiple different algorithms. A new approach named population-based algorithm portfolio (PAP), which takes multiple algorithms as its constituent algorithms, is proposed based upon this idea. PAP runs each constituent algorithm with a part of the given time budget and encourages interaction among the constituent algorithms with a migration scheme. As a general framework rather than a specific algorithm, PAP is easy to implement and can accommodate any existing population-based search algorithms. In addition, a metric is also proposed to compare the risks of any two algorithms on a problem set. We have comprehensively evaluated PAP via investigating 11 instantiations of it on 27 benchmark functions. Empirical results have shown that PAP outperforms its constituent algorithms in terms of solution quality, risk, and probability of finding the global optimum. Further analyses have revealed that the advantages of PAP are mostly credited to the synergy between constituent algorithms, which should complement each other either over a set of problems, or during different stages of an optimization process.

Univariate Marginal Distribution Algorithms (UMDAs) are a kind of Estimation of Distribution Algorithms (EDAs) which do not consider the dependencies among the variables. In this paper, on the basis of our proposed approach in [1], we present a rigorous proof for the result that the UMDA with margins (in [1] we merely showed the effectiveness of margins) cannot find the global optimum of the TRAPLEADINGONES problem [2] within polynomial number of generations with a probability that is super-polynomially close to 1. Such a theoretical result is significant in sheding light on the fundamental issues of what problem characteristics make an EDA hard/easy and when an EDA is expected to perform well/poorly for a given problem.

JADE is a recent variant of differential evolution (DE) for numerical optimization, which has been reported to obtain some promising results in experimental study. However, we observed that the reliability, which is an important characteristic of stochastic algorithms, of JADE still needs to be improved. In this paper we apply two strategies together on the original JADE, to dedicatedly improve the reliability of it. We denote the new algorithm as rJADE. In rJADE, we first modify the control parameter adaptation strategy of JADE by adding a weighting strategy. Then, a ldquorestart with knowledge transferrdquo strategy is applied by utilizing the knowledge obtained from previous failures to guide the subsequent search. Experimental studies show that the proposed rJADE achieved significant improvements on a set of widely used benchmark functions.

In recent integrated MAC/routing solutions for wireless sensor networks (WSNs), hop-count is exploited to build a coarse-grained logical coordinates to help forward packets towards the direction of sink. This method can retain the merits of geographic routing at the absence of exact location knowledge. However, these solutions may present low energy-efficiency and unacceptable delays in real networks since they seldom consider the impacts of low duty-cycling and link unreliability on routing. Furthermore, geographic advancement of forwarding in hop- count based coordinates is very unreliable and even towards the reverse direction. In this work, average power cost to the sink of each node is considered together with hop-count to build a fine grained logical coordinates, which can help forward packets towards the direction of sink more accurately. Then we propose an energy-efficient integrated MAC/routing (EEMR) protocol for event-driven and time-critical applications based on new logical coordinates. The optimal relay is elected in each hop dynamically, where the objective is to optimize forwarding energy-efficiency on the premise that end-to-end delay is restricted under the predefined upper bound. Analysis and extensive simulations are given to demonstrate the superiority of EEMR by comparing its performance against existing solutions.

Technique advances have made image capture and storage very convenient, which results in an explosion of the amount of visual information. It becomes difficult to find useful information from these tremendous data. Content-based Visual Information Retrieval (CBVIR) is emerging as one of the best solutions to this problem. Unfortunately, CBVIR is a very compute-intensive task. Nowadays, with the boom of multi-core processors, CBVIR can be accelerated by exploiting multi-core processing capability. In this paper, we propose a parallelization implementation of a CBVIR system facing to server application and use some serial and parallel optimization techniques to improve its performance on an 8-core and on a 16-core systems. Experimental results show that optimized implementation can achieve very fast retrieval on the two multi-core systems.We also compare the performance of the application on the two multi-core systems and give an explanation of the performance difference between the two systems. Furthermore, we conduct detailed scalability and memory performance analysis to identify possible bottlenecks in the application. Based on these experimental results and performance analysis, we gain many insights into developing efficient applications on future multi-core architectures.

In the past decades, many theoretical results related to the time complexity of evolutionary algorithms (EAs) on different problems are obtained. However, there is not any general and easy-to-apply approach designed particularly for population-based EAs on unimodal problems. In this paper, we first generalize the concept of the takeover time to EAs with mutation, then we utilize the generalized takeover time to obtain the mean first hitting time of EAs and, thus, propose a general approach for analyzing EAs on unimodal problems. As examples, we consider the so-called (N + N) EAs and we show that, on two well-known unimodal problems, leadingones and onemax , the EAs with the bitwise mutation and two commonly used selection schemes both need O(n ln n + n(2)/N) and O(n ln ln n + n ln n/N) generations to find the global optimum, respectively. Except for the new results above, our approach can also be applied directly for obtaining results for some population-based EAs on some other unimodal problems. Moreover, we also discuss when the general approach is valid to provide us tight bounds of the mean first hitting times and when our approach should be combined with problem-specific knowledge to get the tight bounds. It is the first time a general idea for analyzing population-based EAs on unimodal problems is discussed theoretically.

MOTIVATION: Haplotype played an important role in the association studies of disease gene and drug responsivity over the past years, but the low throughput of expensive biological experiments largely limited its application. Alternatively, some efficient statistical methods were developed to deduce haplotypes from genotypes directly. Because these algorithms usually needed to estimate the frequencies of numerous possible haplotypes, the partition and ligation strategy was widely adopted to reduce the time complexity. The haplotypes were usually partitioned uniformly in the past, but recent studies showed that the haplotypes had their own block structure, which may be not uniform. More reasonable block partition and ligation strategy according to the haplotype structure may further improve the accuracy of individual haplotyping. RESULTS: In this article, we presented a simple algorithm for block partition and ligation, which provided better accuracy for individual haplotyping. The block partition and ligation could be completed within O(m(2) logm+m(2n)) time complexity, where m represented the length of genotypes and n represented the number of individuals. We tested the performance of our algorithm on both real and simulated dataset. The result showed that our algorithm yielded better accuracy with short running time. AVAILABILITY: The software is publicly available at http://mail.ustc.edu.cn/~zyzh.

Abstract The super connectivity κ1 of a connected graph G is the minimum number of vertices whose deletion results in a disconnected graph without isolated vertices; this is a more refined index than the connectivity parameter κ. This article provides bounds for the super connectivity κ1 of the Cartesian product of two connected graphs, and thus generalizes the main result of Shieh on the super connectedness of the Cartesian product of two regular graphs with maximum connectivity. Particularly, we determine that κ1(Km × Kn) = min{m + 2n − 4, 2m + n − 4} for m + n ≥ 6 and state sufficient conditions to guarantee κ1(K2 × G) = 2κ(G). As a consequence, we immediately obtain the super connectivity of the n‐cube for n ≥ 3. © 2008 Wiley Periodicals, Inc. NETWORKS, 2008

Estimation of Distribution Algorithm (EDA) is a well-known stochastic optimization technique. The average time complexity is a crucial criterion that measures the performance of the stochastic algorithms. In the past few years, various kinds of EDAs have been proposed, but the related theoretical study on the time complexity of these algorithms is relatively few. This paper analyzed the time complexity of two early versions of EDA, the Univariate Marginal Distribution Algorithm (UMDA) and the Incremental UMDA (IUMDA). We generalize the concept of convergence to convergence time, and manage to estimate the upper bound of the mean First Hitting Times (FHTs) of UMDA (IUMDA) on a well-known pseudo-modular function, which is frequently studied in the field of genetic algorithms. Our analysis shows that UMDA (IUMDA) has O(n) behaviors on the pseudo-modular function. In addition, we analyze the mean FHT of IUMDA on a hard problem. Our result shows that IUMDA may spend exponential generations to find the global optimum. This is the first time that the mean first hitting times of UMDA (IUMDA) are theoretically studied.

With the increasing popularity of shared-memory programming model, especially at the advent of multicore processors, applications need to become more concurrent to take advantage of the increased computational power provided by chip level multiprocessing. Traditionally locks are used to enforce data dependence and timing constraints between the various threads. However locks are error- prone, and often leading to unwanted race conditions, priority inversion, or deadlock. Therefore, recent waves of research projects are exploring transaction memory systems as an alternative synchronization mechanism to locks. This paper presents a software transactional memory execution model for parallel multithread programming without lock.

Abstract The super edge‐connectivity λ′ of a connected graph G is the minimum cardinality of an edge‐cut F in G such that every component of G − F contains at least two vertices. Let Gi be a connected graph with order ni, minimum degree δi and edge‐connectivity λi for i = 1, 2. This article shows that λ′(G1 × G2) ≥ min{n1λ2, n2λ1, λ1 + 2 λ2, 2 λ1 + λ 2} for n1, n2 ≥ 3 and λ′(K2 × G2) = min{ n2, 2λ2}, which generalizes the main result of Shieh on the super edge‐connectedness of the Cartesian product of two regular graphs with maximum edge‐connectivity. In particular, this article determines λ′(G1 × G2) = min{n1δ2, n2δ1, ξ{G1 × G2)} if λ′(Gi) = ξ(Gi), where ξ(G) is the minimum edge‐degree of a graph G. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 49(2), 152–157 2007

In the applications of global computing like SETI@home, the scheduling problem of computation components is an important issue to improve the performance. In this paper, we propose a theoretical model of global computing application concerning the scheduling of computation components. Based on the model, we evaluate the performance of different scheduling mechanisms to choose a proper scheduling strategy for the application according to the corresponding network environment and computing resources. Finally we make the comparisons among different scheduling strategies and suggestions to improve the performance of the application

Multi-core processors can easily provide benefits for multithreaded workloads, but many applications written for uniprocessors cannot automatically benefit from chip multiprocessors (CMP) designs. This paper presents a reverse compilation framework, which translates existing binary code without source code to the static single assignment (SSA) form, and then the internal SSA form is applied by the compilation phase to generate the speculative parallel threading (SPT) code. A profiler is applied to optimize the code dynamically during execution. The evaluation results show that these existing binary codes without source codes execute on CMP with performance improved, due to taking advantage of the speculative parallel threading support provided by the processor

BACKGROUND: Haplotype analysis has gained increasing attention in the context of association studies of disease genes and drug responsivities over the last years. The potential use of haplotypes has led to the initiation of the HapMap project which is to investigate haplotype patterns in the human genome in different populations. Haplotype inference and frequency estimation are essential components of this endeavour. RESULTS: We present a two-stage method to estimate haplotype frequencies in pedigrees, which includes haplotyping stage and estimation stage. In the haplotyping stage, we propose a linear time algorithm to determine all zero-recombinant haplotype configurations for each pedigree. In the estimation stage, we use the expectation-maximization (EM) algorithm to estimate haplotype frequencies based on these haplotype configurations. The experiments demonstrate that our method runs much faster and gives more credible estimates than other popular haplotype analysis software that discards the pedigree information. CONCLUSION: Our method suggests that pedigree information is of great importance in haplotype analysis. It can be used to speedup estimation process, and to improve estimation accuracy as well. The result also demonstrates that the whole haplotype configuration space can be substituted by the space of zero-recombinant haplotype configurations in haplotype frequency estimation, especially when the considered haplotype block is relatively short.

Computing reversal distance of two signed permutations has gained increasing attention over the last decade with the study of genome rearrangements in computational molecular biology. In this paper, we present a parallel algorithm to computing reversal distance of two signed permutations. Our algorithm consists three parts and runs in O(lg2(n)) time using O(n2) processors in SIMD-CREW model.

A hybrid P2P system, such as Napster, has a central directory where the peers publish information about the content they offer for sharing. We study the incentives for self-interested node’s participating in such P2P system with centralized directory, by developing the new cost model of P2P network. In our model, a node will participate the system if and only if it can get more than it gives. We discover several interesting conclusions on the character of the participating. There is probably a threshold value in such P2P systems, that is, the system is unstable when the number of its nodes is less than the threshold and the system is stable and self-developing when its size exceeding the thresh-old.

The fault-tolerant diameter, Dk, and wide diameter, dk, are two important parameters for measuring the reliability and efficiency of interconnection networks. It is well known that for any ω-connected graph G and any integer k, 1 ≤ k ≤ ω, we have Dk ≤ dk. However, what we are interested in is how large the difference between dk and Dk can be. For any 2-connected graph G with diameter d, Flandrin and Li proved that d2 ≤ D2 + 1 if d = 2 and d2 ≤ (d − 1)(D2 − 1) if d ≥ 3. In this article, we further prove that d2 ≤ max{D2 + 1, (d − 1)(D2 − d) + 2} for d ≤ ⌈(D2 − 1)/2⌉ and d2 ≤ max{D2 + 1,⌊(D2 − 1)2/4⌋ + 2} for d ≥ ⌈(D2 − 1)/2⌉ + 1, and we also show that this upper bound can be achieved. Moreover, for any ω(≥ 3)-connected graph G, we prove that dω ≤ Dω + 1 if Dω − 1 = 2 and dω ≤ max{Dω + 2,⌊(Dω)2/4⌋ + 2} if Dω − 1 = 2 and Dω − 1 ≥ 3. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 45(2), 88–94 2005

Memetic algorithms (MAs) have been shown to be very effective in finding near optimal solutions to hard combinatorial optimization problems. We propose a novel memetic algorithm (MsMA), in which a new local search scheme is introduced. We called this local search scheme as random multi-local-search (MLS). The MLS is composed of several local search schemes, each of which executes with a predefined probability to increase the diversity of the population. The combination of MsMA with the crossover operator edge assembly crossover (EAX) on the classic combinatorial optimization problem traveling salesman problem (TSP) is studied, and comparisons are also made with some best known MAs. We have found that it is significantly outperforming the known MAs on almost all of the selected instances. Furthermore, we have proposed a new crossover named M-EAX, which has more powerful local search ability than the EAX. The experimental results show that the MsMA with M-EAX has given a further improvement to the existing EAX.

With the proliferation of portable computing devices and ascending popularity of group-oriented computing, wireless ad hoc network multicasting remains a challenging research subject. While the virtual multicast backbone (VMB) structure is commonly used in current multicast protocols, this paper focuses on the construction of the optimal VMB with the fewest forwarding nodes to decrease overhead and cost, due to the scarce resource in ad hoc networks. Instead of the conventional Steiner tree model, the optimal shared VMB in ad hoc networks is modeled as the minimum Steiner dominating set (MSCDS) in unit-disk graphs (UDG), which is NP-hard. A performance evaluation of flooding for MSCDS is given and a one-hop algorithm is proposed with an approximation ratio of at most 10. To adapt various network scenarios, this paper further presents a fully distributed d-hop algorithm also with a constant approximation ratio, which organizes multicast nodes to form a hierarchical VMB. Based on the hierarchical structure, this paper proposes some approaches to maintain and update VMB, and gives a security framework to exclude malicious nodes from multicast groups. Simulation results show that the proposed algorithms perform very well.

The Computational Grid is an appealing high performance computational platform. Problem in implementing Computational Grid environment is how to effectively use various resources in the system, such as compute cycle, memory, communication network, and data repositories. A benefit function resource mapping heuristic for Computational Grid environments is presented to map a set of independent tasks (Meta-task) to resources. The algorithm considers the influence of input data repositories' location and QOS of tasks to result of mapping. This semi-dynamic algorithm is more suitable for the dynamic adaptability and domain autonomy in the grid, and the benefit function heuristic adopted in the algorithm can assure the QOS of tasks more effectively.