Jidong Ge

A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds

Security is increasingly critical for various scientific workflows that are big data applications and typically take quite amount of time being executed on large-scale distributed infrastructures. Cloud computing platform is such an infrastructure that can enable dynamic resource scaling on demand. Nevertheless, based on pay-per-use and hourly-based pricing model, users should pay attention to the cost incurred by renting virtual machines (VMs) from cloud data centers. Meanwhile, workflow tasks are generally heterogeneous and require different instance series (i.e., computing optimized, memory optimized, storage optimized, etc.). In this paper, we propose a security and cost aware scheduling (SCAS) algorithm for heterogeneous tasks of scientific workflow in clouds. Our proposed algorithm is based on the meta-heuristic optimization technique, particle swarm optimization (PSO), the coding strategy of which is devised to minimize the total workflow execution cost while meeting the deadline and risk rate constraints. Extensive experiments using three real-world scientific workflow applications, as well as CloudSim simulation framework, demonstrate the effectiveness and practicality of our algorithm. This algorithm allows users to trade off the security and cost of workflow.The heterogeneous tasks are taken into account in workflow scheduling.This algorithm based on PSO considers heterogeneous VM resources.A coding strategy is devised to solve the multi-constraint optimization problem.

Modeling Multi-View Software Process with Object Petri Nets

PSEE (Process-centered Software Engineering Environment) can manage and monitor software process. Software process modeling language is a core element in PSEE system. Due to the particularity and the complexity, software process model includes multi-views: activity view, product view and role view, which should be considered in modeling software process. Based on the similarity between multi-view software process modeling and object Petri nets, this paper proposes the MOPN-SP-net model which is a multi-view software process model based on multi-object Petri nets. The model includes twolevel models: system net and object net. A multi-view paradigm including activity views and product views is provided. Activity views are described by system nets and product views are described by object nets. MOPN-SP-net includes multi-views of software process model, which is characterized with clearer hierarchy, simpler structure, and more extendibility.

Applying the Value/Petri process to ERP software development in China

Commercial organizations increasingly need software processes sensitive to business value, quick to apply, and capable of early analysis for subprocess consistency and compatibility. This paper presents experience in applying a lightweight synthesis of a Value-Based Software Quality Achievement (VBSQA) process and an Object-Petri-Net-based process model (called VBSQA-OPN) to achieve a manager-satisfactory process for software quality achievement in an on-going ERP software project in China. The results confirmed that 1) the application of value-based approaches was inherently better than value-neutral approaches adopted by most ERP software projects; 2) the VBSQA-OPN model provided project managers with a synchronization and stabilization framework for process activities, success-critical stakeholders and their value propositions; 3) process visualization and simulation tools significantly increased management visibility and controllability for the success of software project.

Translation of nets within nets in cross-organizational software process modeling

Software process technology is very important to improve the software quality. Some process models have been developed for process management during the software development process. Today’s software corporations become so large and geographically distributed that modeling cross-organizational software process becomes an important topic. In this paper, we use nets within nets to model cross-organizational software processes based on mobile agent systems. For indirect analysis of the model of nets within nets, this paper presents translation rules from nets within nets to flat nets, which preserve the soundness property. For enhancing the flexibility of execution, we introduce weak synchronous concept into the model of nets within nets.

Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds

Cloud computing is a suitable platform to execute the deadline-constrained scientific workflows which are typical big data applications and often require many hours to finish. Moreover, the problem of energy consumption has become one of the major concerns in clouds. In this paper, we present a cost and energy aware scheduling (CEAS) algorithm for cloud scheduler to minimize the execution cost of workflow and reduce the energy consumption while meeting the deadline constraint. The CEAS algorithm consists of five sub-algorithms. First, we use the VM selection algorithm which applies the concept of cost utility to map tasks to their optimal virtual machine (VM) types by the sub-makespan constraint. Then, two tasks merging methods are employed to reduce execution cost and energy consumption of workflow. Further, In order to reuse the idle VM instances which have been leased, the VM reuse policy is also proposed. Finally, the scheme of slack time reclamation is utilized to save energy of leased VM instances. According to the time complexity analysis, we conclude that the time complexity of each sub-algorithm is polynomial. The CEAS algorithm is evaluated using Cloudsim and four real-world scientific workflow applications, which demonstrates that it outperforms the related well-known approaches.

Energy cost minimization with job security guarantee in Internet data center

With the proliferation of various big data applications and resource demand from Internet data centers (IDCs), the energy cost has been skyrocketing, and it attracts a great deal of attention and brings many energy optimization management issues. However, the security problem for a wide range of applications, which has been overlooked, is another critical concern and even ranked as the greatest challenge in IDC. In this paper, we propose an energy cost minimization (ECM) algorithm with job security guarantee for IDC in deregulated electricity markets. Randomly arriving jobs are routed to a FIFO queue, and a heuristic algorithm is devised to select security levels for guaranteeing job risk probability constraint. Then, the energy optimization problem is formulated by taking the temporal diversity of electricity price into account. Finally, an online energy cost minimization algorithm is designed to solve the problem by Lyapunov optimization framework which offers provable energy cost optimization and delay guarantee. This algorithm can aggressively and adaptively seize the timing of low electricity price to process workloads and defer delay-tolerant workloads execution when the price is high. Based on the real-life electricity price, simulation results prove the feasibility and effectiveness of proposed algorithm. The energy cost optimization architecture is proposed for IDC operator.A heuristic algorithm is devised to select security services to guarantee the job security.The temporal diversity of electricity price is considered in minimizing the energy cost.The energy cost minimization algorithm is based on Lyapunov optimization technique.Extensive evaluation experiments demonstrate the effectiveness of our algorithms.

Discovering process models from event multiset

The aim of process mining is to discover the process model from the event log which is recorded by the information system. Typical steps of process mining algorithm can be described as: (1) generating event traces from event log, (2) analyzing event traces and obtaining ordering relations of tasks, (3) generating process model with ordering relations of tasks. The first two steps could be very time consuming involving millions of events and thousands of event traces. This paper presents a novel algorithm (@l-algorithm) which almost eliminates these two steps in generating event traces from event log and analyzing event traces so as to reduce the performance of process mining algorithm. Firstly, we retrieve the event multiset (input data of algorithm marked as MS) which records the frequency of each event but ignores their orders when extracted from event logs. The event in event multiset contains the information of post-activities. Secondly, we obtain ordering relations from event multiset. The ordering relations contain causal dependency, potential parallelism and non-potential parallelism. Finally, we discover a process models with ordering relations. The complexity of @l-algorithm is only bound up with the event classes (the set of events in event logs) that has significantly improved the performance of existing process mining algorithms and is expected to be more practical in real-world process mining based on event logs, as well as being able to detect SWF-nets, short-loops and most of implicit dependency (generated by non-free choice constructions).

Tailor the value-based software quality achievement process to project business cases

This paper proposes a risk-based process strategy decision-making approach. To improve the flexibility in applying the Value-Based Software Quality Achievement (VBSQA) process framework, we embed the risk-based process strategy decision-making approach into the VBSQA process framework. It facilitates project managers to tailor the VBSQA process framework to different project business cases (schedule-driven, product-driven, and market trend-driven). A real world ERP (Enterprise Resource Planning) software project (DIMS) in China is used as an example to illustrate different process strategies generated from process tailoring.

Personalized QoS Prediction via Matrix Factorization Integrated with Neighborhood Information

Currently, the number of Web services on the Internet is growing exponentially. Faced with a large number of functionally equivalent candidate services, users always hope to select the optimal one that can provide the best QoS values. However, users usually do not know the QoS values of all the candidate services as the limited historical service invocation records. Different QoS prediction methods are presented to predict QoS values of candidate services. Nevertheless, most of them do not take the physical features of Web services fully into consideration and thus the prediction accuracy is still not satisfying. To this end, we propose a novel Matrix Factorization method, integrating both user network neighborhood information and service neighborhood information with Matrix Factorization model, to predict personalized QoS values. To validate our method, experiments are conducted on a real-world Web service QoS dataset including 1,974,675 Web service invocation records. Experimental results show that our method performs better in prediction accuracy than the state-of-the-art methods.

Process mining with token carried data

Process mining is to discover, monitor and improve real processes by extracting the knowledge from logs which are available in todays information systems. The existing process mining algorithms are based on the event logs where only the executions of tasks are recorded. In order to reduce the pre-processing efforts and strengthen the mining ability of the existing process mining algorithms, we have proposed a novel perspective to employ the data carried by tokens recorded in token log which tracks the changes of process resources for process mining in this study. The feasibility of the token logs is proved and the results of pairwise t-tests show that there is no big difference between the efforts that are taken by the same workflow system to generate the token log and the event log. Besides, a process mining algorithm (?) based on the new log is proposed in this paper. With algorithm ?, the mining efficiency as well as the mining capability is improved compared to the traditional event-log-based mining algorithms. We have also developed three plug-ins on top of the existing workflow engine, process modeling and mining platforms (YAWL, PIPE and ProM) for proving the feasibility of token log and realizing the token log generation and algorithm ?.