arXiv subjects

Wenhong Tian

Publications and source records attributed to Wenhong Tian.

28 records · Page 2Linked to original sources

arXiv2016

On Polynomial Time Approximation Bounded Solution for TSP and NP Complete Problems

The question of whether all problems in NP class are also in P class is generally considered one of the most important open questions in mathematics and theoretical computer science as it has far-reaching consequences to other problems in mathematics, computer science, biology, philosophy and cryptography. There are intensive research on proving `NP not equal to P' and `NP equals to P'. However, none of the `proved' results is commonly accepted by the research community up to date. In this paper, motived by approximability of traveling salesman problem (TSP) in polynomial time, we aim to provide a new perspective: showing that NP=P from polynomial time approximation-bounded solutions of TSP in Euclidean space.

cs.DS↗

arXiv2016

Obtaining Quality-Proved Near Optimal Results for Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most challenging NP-hard problems. It has widely applications in various disciplines such as physics, biology, computer science and so forth. The best known approximation algorithm for Symmetric TSP (STSP) whose cost matrix satisfies the triangle inequality (called $\triangle$STSP) is Christofides algorithm which was proposed in 1976 and is a $\frac{3}{2}$-approximation. Since then no proved improvement is made and improving upon this bound is a fundamental open question in combinatorial optimization. In this paper, for the first time, we propose Truncated Generalized Beta distribution (TGB) for the probability distribution of optimal tour lengths in a TSP. We then introduce an iterative TGB approach to obtain quality-proved near optimal approximation, i.e., (1+$\frac{1}{2}(\frac{α+1}{α+2})^{K-1}$)-approximation where $K$ is the number of iterations in TGB and $α(>>1)$ is the shape parameters of TGB. The result can approach the true optimum as $K$ increases.

cs.DS↗

arXiv2016

On Equivalent Color Transform and Four Coloring Theorem

In this paper, we apply an equivalent color transform (ECT) for a minimal $k$-coloring of any graph $G$. It contracts each color class of the graph to a single vertex and produces a complete graph $K_k$ for $G$ by removing redundant edges between any two vertices. Based on ECT, a simple proof for four color theorem for planar graph is then proposed.

cs.DS↗

arXiv2016

On Dynamic Job Ordering and Slot Configurations for Minimizing the Makespan Of Multiple MapReduce Jobs

MapReduce is a popular parallel computing paradigm for Big Data processing in clusters and data centers. It is observed that different job execution orders and MapReduce slot configurations for a MapReduce workload have significantly different performance with regarding to the makespan, total completion time, system utilization and other performance metrics. There are quite a few algorithms on minimizing makespan of multiple MapReduce jobs. However, these algorithms are heuristic or suboptimal. The best known algorithm for minimizing the makespan is 3-approximation by applying Johnson rule. In this paper, we propose an approach called UAAS algorithm to meet the conditions of classical Johnson model. Then we can still use Johnson model for an optimal solution. We explain how to adapt to Johnson model and provide a few key features of our proposed method.

cs.DS↗

arXiv2016

Self-Adaptive Consolidation of Virtual Machines For Energy-Efficiency in the Cloud

In virtualized data centers, consolidation of Virtual Machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very efficient approach. This paper considers the energy-efficient consolidation of VMs in a Cloud Data center. Concentrating on CPU-intensive applications, the objective is to schedule all requests non-preemptively, subjecting to constraints of PM capacities and running time interval spans, such that the total energy consumption of all PMs is minimized (called MinTE for abbreviation). The MinTE problem is NP-complete in general. We propose a self-adaptive approached called SAVE. The approach makes decisions of the assignment and migration of VMs by probabilistic processes and is based exclusively on local information, therefore it is very simple to implement. Both simulation and real environment test show that our proposed method SAVE can reduce energy consumption about 30% against VMWare DRS and 10-20% against EcoCloud on average.

cs.DC↗

arXiv2015

CloudSimNFV: Modeling and Simulation of Energy-Efficient NFV in Cloud Data Centers

Network Function Virtualization (NFV) takes advantage of hardware virtualization to undertake software processing for various functions, and complements the drawbacks of traditional network technology. To speed up NFV related research, we need a user friendly and easy to use research tool, which could support data center simulation, scheduling algorithms implementation and extension, and provide energy consumption simulation. As a cloud simulation toolkit, CloudSim has strong extendibility that could be extended to simulate NFV environment. This paper introduces a NFV cloud framework based on CloudSim and an energy consumption model based on multi-dimensional extension, implementing a toolkit named ClousimNFV to simulate the NFV scenario, proposing several scheduling algorithm based on for NFV applications. The toolkit validation and algorithm performance comparison are also given.

cs.DC↗

arXiv2015

Prepartition: Paradigm for the Load Balance of Virtual Machine Allocation in Data Centers

It is significant to apply load-balancing strategy to improve the performance and reliability of resource in data centers. One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines (VMs) as well as the integrated features of hosting physical machines (PMs) into consideration. In the reservation model, the workload of data centers has fixed process interval characteristics. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. Traditionally, for offline load balance without migration, one of the best approaches is LPT (Longest Process Time first), which is well known to have approximation ratio 4/3. With virtualization, reactive (post) migration of VMs after allocation is one popular way for load balance and traffic consolidation. However, reactive migration has difficulty to reach predefined load balance objectives, and may cause interruption and instability of service and other associated costs. In view of this, we propose a new paradigm, called Prepartition, it proactively sets process-time bound for each request on each PM and prepares in advance to migrate VMs to achieve the predefined balance goal. Prepartition can reduce process time by preparing VM migration in advance and therefore reduce instability and achieve better load balance as desired. We also apply the Prepartition to online (PrepartitionOn) load balance and compare it with existing online scheduling algorithms. Both theoretical and experimental results are provided.

cs.DC↗

arXiv2015

Open-Source Simulators for Cloud Computing: Comparative Study and Challenging Issues

Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be controlled or predicted. Performance evaluations of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations are difficult. Therefore, simulators are developed. To understand and apply better the state-of-the-art of cloud computing simulators, and to improve them, we study four known open-source simulators. They are compared in terms of architecture, modeling elements, simulation process, performance metrics and scalability in performance. Finally, a few challenging issues as future research trends are outlined.

cs.DC↗

arXiv2015

Transforming NP to P: An Approach to Solve NP Complete Problems

NP complete problem is one of the most challenging issues. The question of whether all problems in NP are also in P is generally considered one of the most important open questions in mathematics and theoretical computer science as it has far-reaching consequences to other problems in mathematics, computer science, biology, philosophy and cryptography. There are intensive research on proving `NP not equal to P' and `NP equals to P'. However, none of the `proved' results is commonly accepted by the research community up to now. In this paper, instead of proving either one, we aim to provide new perspective: transforming two typical NP complete problems to exactly solvable P problems in polynomial time. This approach helps to solve originally NP complete problems with practical applications. It may shine light on solving other NP complete problems in similar way.

cs.CC↗

arXiv2015

FlexCloud: A Flexible and Extendible Simulator for Performance Evaluation of Virtual Machine Allocation

Cloud Data centers aim to provide reliable, sustainable and scalable services for all kinds of applications. Resource scheduling is one of keys to cloud services. To model and evaluate different scheduling policies and algorithms, we propose FlexCloud, a flexible and scalable simulator that enables users to simulate the process of initializing cloud data centers, allocating virtual machine requests and providing performance evaluation for various scheduling algorithms. FlexCloud can be run on a single computer with JVM to simulate large scale cloud environments with focus on infrastructure as a service; adopts agile design patterns to assure the flexibility and extensibility; models virtual machine migrations which is lack in the existing tools; provides user-friendly interfaces for customized configurations and replaying. Comparing to existing simulators, FlexCloud has combining features for supporting public cloud providers, load-balance and energy-efficiency scheduling. FlexCloud has advantage in computing time and memory consumption to support large-scale simulations. The detailed design of FlexCloud is introduced and performance evaluation is provided.

cs.DC↗