Cost-aware real-time divisible loads scheduling in cloud computing

Cloud computing has become an important alternative for solving large scale resource intensive problems in science, engineering, and analytics. Resource management play an important role in improving the quality of service (QoS). This paper is concerned with the investigation of scheduling strategies for divisible loads with deadlines constraints upon heterogeneous processors in a cloud computing environment. The workload allocation approach presents in this paper is using Divisible Load Theory (DLT). It is based on the fact that the computation can be partitioned into some arbitrary sizes and each partition can be processed independently of each other. Through series of simulation against the baseline strategies, it can be found that the worker selection order in the service pool and the amount of fraction load assigned to each of them have significant effects on the total computation cost.Keywords: Cloud computing, Divisible Load Theory (DLT), Cost, Quality-of-service (QoS

Situational factors for modern code review to support software engineers' sustainability

Software engineers working in Modern Code Review (MCR) are confronted with the issue of lack of competency in the identification of situational factors. MCR is a software engineering activity for the identification and fixation of defects before the delivery of the software product. This issue can be a threat to the individual sustainability of software engineers and it can be addressed by situational awareness. Therefore, the objective of the study is to identify situational factors concerning the MCR process. Systematic Literature Review (SLR) has been used to identify situational factors. Data coding along with continuous comparison and memoing procedures of grounded theory and expert review has been used to produce an exclusive and validated list of situational factors grouped under categories. The study results conveyed 23 situational factors that are grouped into 5 broad categories i.e. People, Organization, Technology, Source Code and Project. The study is valuable for researchers to extend the research and for software engineers to identify situations and sustain for longer

Knowledge sharing factors for modern code review to minimize software engineering waste

Software engineering activities, for instance, Modern Code Review (MCR) produce quality software by identifying the defects from the code. It involves social coding and provides ample opportunities to share knowledge among MCR team members. However, the MCR team is confronted with the issue of waiting waste due to poor knowledge sharing among MCR team members. As a result, it delays the project delays and increases mental distress. To minimize the waiting waste, this study aims to identify knowledge sharing factors that impact knowledge sharing in MCR. The methodology employed for this study is a systematic literature review to identify knowledge sharing factors, data coding with continual comparison and memoing techniques of grounded theory to produce a unique and categorized list of factors influencing knowledge sharing. The identified factors were then assessed through expert panel for its naming, expressions, and categorization. The study finding reported 22 factors grouped into 5 broad categories i.e. Individual, Team, Social, Facility conditions, and Artifact. The study is useful for researchers to extend the research and for the MCR team to consider these factors to enhance knowledge sharing and to minimize waiting waste

Hybrid Real-Time Task Scheduling Algorithm in Overload Situation for Multiprocessor System

Real-time systems are reactive systems which should meet major constraints in scheduling tasks like time limitation and resources allocation for scheduling the task effectively when the system in overloaded condition. Failure of system in scheduling tasks when system is overloaded can result in catastrophic impacts. The goal of this research is to propose a task scheduling algorithm that able to perform better than traditional Earliest Deadline First (EDF) and minimize the overall completion time when the system in overloaded condition. The proposed scheduling algorithm is built based on three new improved scheduling algorithms namely: (1) Hybrid Particle Swarm Optimization (PSO) and Hybrid Invasive Weed Optimization (HPIO), (2) Enhanced Initial Swarm (EIS), and (3) Hybrid EDF, EIS and HPIO Optimization (HEDFPIO). The author proves that more successful tasks is scheduled by using HPIO in multiprocessor system in over loaded situation among PSO and ACO. The author uses EIS algorithm in order to improve local search in HPIO and have fair load balance among processors. Finally the author presents a new hybrid algorithm that combines HPIO, EIS and EDF which is called HEDFPIO, It is observed that we could achieve higher successful ratio in task scheduling and with shorter calculation time in overloaded situation

The challenges of extract, transform and load (ETL) for data integration in near real-time environment

Organization with considerable investment into data warehousing, the influx of various data types and forms require certain ways of prepping data and staging platform that support fast, efficient and volatile data to reach its targeted audiences or users of different business needs. Extract, Transform and Load (ETL) system proved to be a choice standard for managing and sustaining the movement and transactional process of the valued big data assets. However, traditional ETL system can no longer accommodate and effectively handle streaming or near real-time data and stimulating environment which demands high availability, low latency and horizontal scalability features for functionality. This paper identifies the challenges of implementing ETL system for streaming or near real-time data which needs to evolve and streamline itself with the different requirements. Current efforts and solution approaches to address the challenges are presented. The classification of ETL system challenges are prepared based on near real-time environment features and ETL stages to encourage different perspectives for future research

Real-time divisible load theory : incorporating computation costs

We extend the current state of the art in real-time divisible load theory (RT-DLT), by considering the problems of scheduling a real-time divisible job on computing clusters in which different processing nodes have different computing capabilities, as well as different costs associated with executing on them. We seek to minimize the cost of executing a job while also meeting its deadline

Adapting market-oriented policies for scheduling divisible loads on clouds

Cloud computing has become an important alternative for solving big data processing. Nowadays, cloud service providers usually offer users a virtual machine with various combinations of prices. As each user has different circumstances, the problem of choosing the cost-minimized combination under a deadline constraint as well as user's preference is becoming more complex. This article is concerned with the investigation of adapting a user's preference policies for scheduling real-time divisible loads in a cloud computing environment. The workload allocation approach used in this research is using Divisible Load Theory. The proposed algorithm aggregates resources into groups and optimally distributes the fractions of load to the available resources according to user's preference. The proposed algorithm was evaluated by simulation experiments and compared with the baseline approach. The result obtained from the proposed algorithm reveals that a significant reduction in computation cost can be attained when the user's preferences are low priority

Data reconstruction through sequence based mapping in secured data partitioning

Recent researches have proposed data partitioning technique with secret sharing to enhance the security in cloud computing. However, its complexity in reconstructing while preserving confidentiality has a limitation of practical use, specifically when it involves a large amount of data. In this paper, we explored the existing mapping technique called partition based indexing that is being used to reconstruct the shares. Nevertheless, we found that its efficiency has decreased when the amount of data increased. Thus, this has motivated us to propose a sequence based mapping to increase the efficiency of data reconstruction in secured data partitioning with secret sharing. The proposed technique has been evaluated through a series of simulation using 10000 data. The performance was evaluated based on the time taken to achieve data reconstruction for different number of shares. As a result, we proved that our proposal, which is named as a sequence based mapping technique has successfully improved more than 40 percent of the performance of data reconstruction compared to indexing technique. As such, we conclude that our proposal on sequence based mapping is an ideal technique for improving performance of data reconstruction in data-partitioning with secret sharing and preserving confidentiality of big data in cloud computing

Utilization of mobile phone sensors for complex human activity recognition

Activity recognition is a significant part of pervasive computing as it can be employed in a wide range of fields which include eldercare and healthcare. While previous efforts have proven to be successful in identifying simple human activities, the means for identifying complex human activities remains an on-going effort. It has been established that more often than not, in an actual circumstance, human activities are conducted in an intricate mode. The objectives of this study are (a) to examine the utilization of solely the sensors of a mobile phone to distinguish complex human activities and (b) to enhance the complex activities recognition capacity of mobile phones through the application of multiple or other forms of sensors. This endeavour reassesses earlier studies on mobile phone utilization for complex activity recognition with the emphasis on schemes directed at smart home applications. An overall configuration for a human activity recognition (HAR) scheme, as well as an analysis of the latest investigations related to the use of mobile phones for complex activity recognition is also included in this paper. We conclude with a discussion on the results obtained and the forwarding of our proposals

Exploiting suitable color model for ripeness identification

Identify the ripeness of oil palm is essential for estimating the oil content. Therefore, choosing a right color model is extremely important to fit the purpose. Currently, manual system conducted by human grader at oil palm mills lead to misconduct and disputes. Since color is main indicator of ripeness, it is important to research a right technique to determine the fruit ripeness identification. Thus, this paper aims to propose a suitable color model for oil palm fruit ripeness identification. In this study, two color models are chosen and experimented; RGB and HSV color model. However based on the experiments, HSV color model proved to be the best color model for oil palm ripeness identification. This result is also in line with color definition set by Malaysian Palm Oil Board (MPOB) regarding color brightness or intensity as an indicator of ripeness for oil palm fruits